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The impact of 5G on the evolution of intelligent automation and industry digitization

• Original Research
• Published: 21 February 2021
• Volume 14 , pages 5977–5993, ( 2023 )

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• Mohsen Attaran   ORCID: orcid.org/0000-0002-0358-4107 1  

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The mobile industry is developing and preparing to deploy the fifth-generation (5G) networks. The evolving 5G networks are becoming more readily available as a significant driver of the growth of IoT and other intelligent automation applications. 5G’s lightning-fast connection and low-latency are needed for advances in intelligent automation—the Internet of Things (IoT), Artificial Intelligence (AI), driverless cars, digital reality, blockchain, and future breakthroughs we haven’t even thought of yet. The advent of 5G is more than just a generational step; it opens a new world of possibilities for every tech industry. The purpose of this paper is to do a literature review and explore how 5G can enable or streamline intelligent automation in different industries. This paper reviews the evolution and development of various generations of mobile wireless technology underscores the importance of 5G revolutionary networks, reviews its key enabling technologies, examines its trends and challenges, explores its applications in different manufacturing industries, and highlights its role in shaping the age of unlimited connectivity, intelligent automation, and industry digitization.

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Purpose Claims about a supposed link between 5G and COVID-19 have been circulating the Internet, arguing that global elites were using 5G to spread the virus. It is needless to say that there’s no evidence to support the theory that 5G networks cause COVID-19 or contribute to its spread. The purpose of this research is to do a literature review and explore the practical implications of 5G revolutionary networks technology for growing industry digitization and intelligent automation.

Practical Implications 5G networks are at the very early stages of adoption. Based on the business applications presented in this paper, practitioners will learn 5G business potentials, challenges addressed by 5G, drivers for change, barriers to entry, and critical areas of concern regarding the adaptation of 5G technologies into their organizations. 

Originality/Value This paper examines the essential roles 5G plays in the success of different industries, including IoT, the auto industry and smart cars, manufacturing and smart factories, smart grids, and smart cities, and healthcare. It discusses how 5G will be critical for growing industry digitization and for addressing the numerous challenges different manufacturing industries will face in this rapidly changing landscape. Finally, this paper presents the crucial role that 5G will play in providing a competent platform to support the widespread adoption of critical communications services and driving the digitization and automation of industrial practices and processes of Industry 4.0.

Research Limitations Although the journey towards 5G networks has already begun, there have been very few reported examples of the business benefits realized by leading-edge manufacturing companies resulting from this new technology. This shortage of reporting has led to incomplete data with effects that are often anecdotal and notably, not thoroughly tested. There are only a few papers published in peer-reviewed academic journals or written as academic working papers exploring the advantages and limitations of firms implementing 5G technologies. This paper is a critical early academic contribution to a field dominated by the narratives and promises of consultants.

1 The evolution of cellular wireless networks

Cellular wireless networks have come a long way since the first 1G system was introduced in 1981, with a new mobile generation appearing approximately every 10 years (Pathak 2013 ; Mishra 2018 ). In the past 30 years, the mobile industry has transformed society through 4 or 5 generations of technology revolution and evolution, namely 1G, 2G, 3G, and 4G networking technologies (Fig.  1 ). 1G gave us a mass-market mobile telephony. 2G brought global interoperability and reliable mobile telephony and made SMS text messaging possible. 3G gave us high-speed data transfer capability for downloading information from the Internet. 4G provided a significant improvement in data capability and speed and made online platforms and high-speed mobile internet services available for the masses. 5G technology will be the most powerful cellular wireless networks with extraordinary data capabilities, unrestricted call volumes, and infinite data broadcast (Pathak 2013 ; GSMA 2017 ; Mishra 2018 ).

The evolution of mobile communications

The following section describes each cellular network generation in more detail.

1G -A nalog Cellular Networks The first commercially automated 1G cellular network was launched in Japan by NTT in 1979 and in the US by Bell Labs in 1984. 1G networks were based on analog protocols with the speed of only 2.4 Kbps (1 kilobit = 1000 bits) and were designed for voice only. 1G enabled the use of multiple cell sites, and the ability to transfer calls from one site to the next as the user traveled between cells during a conversation. 1G has several disadvantages, including low capacity, unreliable handoff, and weak voice links. The first phones, which were based on analog technology, were very large. Voice calls were played back in radio towers, making these calls susceptible to unwanted eavesdropping by third parties (Bhalla and Bhalla 2010 ; Mishra 2018 ).

2G - Digital Networks The second-generation (2G) wireless networks were launched in the early 1990 s and were based on digital standards instead of analog. 2G digital networks enabled rapid phone-to-network signaling and helped the advent of prepaid mobile phones. Additionally, 2G made SMS text messaging possible initially on GSM networks and eventually on all digital networks. Other advantages of 2G digital networks include reduced battery power consumption, voice clarity, and reduced noise in the line. Digital encryption provided secrecy and safety to the data and voice calls. Finally, digital signals are considered environment friendly (Bhalla and Bhalla 2010 ; Mishra 2018 ).

3G - High-Speed Data Networks The third-generation (3G) wireless networks were introduced in 1998 to provide high-speed data transfer capability for downloading information from the Internet and for sending videos with the speed of 2 Mbps (1Mbit = 1000 kbit). 3G technology uses a network of phone towers to pass signals, ensuring a stable connection over long distances. 3G systems provided a significant improvement in capability over the 2G networks by using packet switching rather than circuit switching for data transmission. The high connection speeds of 3G technology-enabled media streaming of radio and even television content to 3G handsets. The technology also provided Video-conferencing support and Web browsing at higher speeds (Pathak 2013 ; Bhalla and Bhalla 2010 ; Mishra 2018 ). According to some estimates, 3G offers a real-world maximum speed of 7.2 Mbps for downloads and 2 Mbps for uploads. In the mid-2000s, an enhanced 3G mobile telephony communications protocol in the High-Speed Packet Access (HSPA) family, also coined 3.5G, 3G + or turbo 3G was implemented. 3G + allows networks based on Universal Mobile Telecommunications System (UMTS) to have higher data transfer speeds and capacity (Mishra 2018 ).

4G — Growth of Mobile Broadband The fourth-generation (4G) wireless networks were commercially deployed in the United States by Verizon in 2011, with the promise of speed improvements up to 10-fold over existing 3G technologies. Standard 4G has download speeds of around 14 Mbps and can reach speeds as high as 150 Mbps. 4G networks are IP-based (Internet protocol). It uses IP even for voice data. It uses a standard communications protocol to send and receive data in packets. Using these standardized packets, 4G enables data to traverse all sorts of networks without being scrambled or corrupted. 4G networking technology is an extension of 3G technology with more bandwidth and services and with high-quality audio/video streaming capabilities. 4G provides a significant improvement in data capability and speed over the 3G systems with the data transfer speed of 100 Mbps. 4G systems eliminated circuit switching, and instead employed an all-IP network designed primarily for data. 4G enabled users to browse the web and stream HD videos on mobile devices. The 4G network allows users to download gigabytes of data in minutes or even seconds. The technology turned smartphones into the computers of the modern age (Pathak 2013 ; Bhalla and Bhalla 2010 ; Mishra 2018 ).

5G—Design Innovation Across Diverse Services The fifth-generation (5G) network, with the speed of 1–10 Gbps (1Gbit = 1000 Mbit), denotes the next major phase of mobile telecommunications standards beyond the current 4G Long Term Evolution (LTE). 5G systems are promised to be in the market by the end of 2019. 5G technology offers extraordinary data capabilities and unlimited data broadcast within the latest mobile operating systems. Other features of 5G networks are enhanced mobile broadband, dynamic low latency, wider bandwidths, device-centric mobility, simultaneous redundant, and reliable-device-to-device links (Bhalla and Bhalla 2010 ; Mishra 2018 ).

2 Key features of 5G networks

5G networks provide lower prices, lower battery consumption, and lower latency than 4G wireless networks. It is because 5G uses Ultra-Wide Band (UWB) networks with higher band breadth at low energy levels. Band breadth is 4000 Mbps, which is four hundred times faster than 4G wireless networks. 5G communication networks can also provide hundreds of billions of connections, massive machine communication, and extreme mobile broadband. Additionally, 5G offers ultra-low latency of 1 ms, 90% more energy efficiency, 99.9% ultra-reliability, 10 Gbps peak data rate transmission speeds, and mobile data volume of 10 Tb (Barreto et al. 2016 ; Hu 2016 ; Saha et al. 2016 ; Cero et al. 2017 ).

Following sections highlight key features of 5G networks in detail.

5G networking standards

The 5G networking technology standard is divided into two key parts:

Non-Standalone (NSA) The first 5G networks are based on NSA, which is the basis of commercial launches expected by the end of 2019. The NSA standard uses existing 4G LTE infrastructure to handle the Control Plane and the signal traffic. It can be thought of as just having an extra fast data pipe attached to existing 4G LTE infrastructure. NSA acts as an initial step that will allow carriers to offer commercial service throughout 2019 until the adoption of a 5G Standalone standard.

Standalone (SA) The 5G Standalone (SA) comes with entirely new core architecture. It moved the control plane transition over to the 5G Core and made significant changes for the way that networks operate. SA will be released in 2020—it will support more flexible network slicing and subcarrier encoding. It is designed to be more efficient than 4GLTE and NSA and will lead to lower costs for the carriers and improved performance for users (Cero et al. 2017 ; Saha et al. 2016 ).

Expanding the networking spectrum

According to a 2017 Cisco study, by 2021, wireless networks will increase in usage by a compounded annual growth rate of 47%. Speeds will reach peaks of 10 Gbps and deliver 1 Gbps at 500 km/h (Cisco 2019 ). 4G wireless networks lack enough spectrum bandwidth and network capacity to meet growing market demands. 5G is an evolving standard combining more spectrums and allowing for more bandwidth and much faster speeds for consumers. Consumers can connect to the 5G network and leverage the benefits of a wide range of spectrums.

The most used 5G technology is mmWave. Carriers will also be using a new spectrum in the sub-6 GHz WiFi region, low bands below 1 GHz, and existing 4G LTE bands, as shown in Fig.  2 . At present, there is a significant amount of unused high-frequency spectrum, and the higher the frequency, the more bandwidth is available (Mathias 2019 ; Kamel et al. 2016 ). 5G networking technology also relies on different wave spectrums. Wireless networks are composed of cell sites divided into sectors that send data through radio waves. Fourth-generation (4G) Long-Term Evolution (LTE) wireless technology requires high-power, large cell towers to radiate signals over long distances. 5G wireless signals, on the other hand, will be transmitted via large numbers of multiple small cell stations located in places like light poles or building roofs. The use of a large number of small cells is necessary since 5G relies on millimeter wave spectrum between 30 and 300 GHz which can only travel over short distances and is subject to interference from weather and physical obstacles (Liu and Jiang 2016 ; De Matos and Gondim 2016 ; Hossain 2013 ).

New technological innovations

Source: Robert Triggs, Online https://www.androidauthority.com/what-is-5g-explained-944868

Networking spectrum bands.

5G is using some key new technological innovations to greatly increase the amount of spectrum used to send and receive data compared to today’s 4G LTE networks. These technologies allow for more bandwidth and much faster speeds for consumers. They are shown in Fig.  3 and are explained below (Bogale and Le 2015 ; Cero et al. 2017 ; Hu 2016 ; 5G Forum 2016 ; Niu et al. 2016 ; Larsson et al. 2014 ):

mmWave It offers a very high frequency between 17 and 110 GHz and high bandwidth for fast data transfer. It is a short-range technology that will be used in densely populated areas. It is also the most referenced 5G technology.

Sub-6   GHz Most of the future 5G networks will likely operate in WiFi-like mid-band frequencies between 3 and 6 GHz. It will cover the medium range spectrum, and it will be useful for small cell hubs for indoor use or more powerful outdoor base stations.

Low-band Operates at a very low frequency below 800 MHz and covers very long distances. It also provides blanket backbone coverage.

Beamforming This key technology allows the beamformer (Router) to transmit signals in the direction of the consumer devices, thus creating stronger, faster, and more reliable wireless communications. Beamforming is a key technology in overcoming the range and direction limitations of the spectrum of high-frequency waveforms.

Massive MIMO Data is sent and received using multiple antennas on base stations to serve multiple end-users. The technology makes high-frequency networks much more efficient. It can also be combined with beamforming.

Sources: Barreto et al. ( 2016 ), Hu ( 2016 ), Saha et al. ( 2016 ), Cero et al. ( 2017 )

5G networks capabilities.

Unique features of 5G networks

5G networks provide improved support of machine to machine communication, aiming at lower prices, reduced battery consumption, and lower latency than 4G instrumentation. 5G uses Ultra-Wide Band (UWB) networks with higher band breadth at low energy levels. Band breadth is of 4000 Mbps, which is four hundred times quicker than today’s 4G wireless networks (Fig.  3 ). 5G communication networks can also provide hundreds of billions of connections, massive machine communication, and extreme mobile broadband. Additionally, 5G offers ultra-low latency of 1 ms, 90% more energy efficiency, 99.9% ultra-reliability, 10 Gbps peak data rate transmission speeds, and a mobile data volume of 10 Tb (Barreto et al. 2016 ; Hu 2016 ; Saha et al. 2016 ; Cero et al. 2017 ).

Impact on download times & streaming

The download speed measured by the rate at which data (e.g., web page, photo, application, or video) can be transferred from the internet to a computer or a smartphone. They are measured in “bits per second” (bps) where a “bit” is a one or zero in binary. More commonly, however, we measure download speeds in “megabits per second” (Mbps), where 1 Megabit is equal to one million bits. A faster download speed supports higher-quality streaming and makes content from the internet load faster and with less of a wait. (Ken’s Tech Tips 2018 ).Today, more and more applications make use of streaming, including voice over IP (e.g., calling via Skype or WhatsApp), online video apps (e.g., Netflix and YouTube), and online radio (Ken’s Tech Tips 2018 ). When the content is not downloaded at a sufficient speed, we will experience pauses during playback (also known as “buffering”). The actual download speeds will depend on several factors, including location (whether you are indoors or outdoors), the distance to nearby masts, and the amount of congestion on the network. The download times for 5G networks for a webpage, an e-mail, a photograph, and a music track are near-instantaneous (Ken’s Tech Tips 2018 ).

Another great advantage of 5G networks is its reduced latency. Latency, also known as the “lag” or “ping,” is an initial delay before the server on the other end starts to respond. The download will progress only once the server has responded. It is a critical concept that affects the experience of end-users on smartphones. High latency connections cause web pages to load slowly. It affects the experience in applications that require real-time connectivity such as voice calling, video calling, and gaming applications). The major benefits of 5G are reduced latency, increased capacity, and faster download speeds. Human reaction time is 200–300 ms. 5G will reduce that to 1 ms or less. That is almost real-time. It means that we can use 5G to replace real-time interactions. The reduction in latency from 5G technology will help overall response for some of the newer embedded applications of mobile technology such as autonomous cars (Ken’s Tech Tips 2018 ).

Wi-Fi 6 vs. 5G networks

Wi-Fi 6 is the latest wireless LAN technology and has been developed parallel with 5G and is expected to hit the market around the same time as 5G. Both technologies are designed to deliver similar services and have a core mission to bring gigabit-plus throughput to end-users.

Wi-Fi 6, like all other Wi-Fi technologies, operates in unlicensed bands where permission is not required (Mathias 2019 ). In the case of licensed bands, individual companies pay a licensing fee for the right to transmit on assigned channels within that band in each geographic area. Licensing ensures that wireless operators do not interfere with each other’s transmissions. Unlicensed wireless technologies are vulnerable to interference. When using an unlicensed technology like Wi-Fi, the end-users will have to adjust to avoid interference. Additionally, the radio environment is likely to continue to change over time (Phifer 2017 ).

5G, on the other hand, is a cellular, carrier-based technology. 5G carriers obtain an exclusive license to specific blocks of spectrum across specific geographies via an auction process. They can configure their specific network to meet their particular coverage, capacity, and business objectives. Therefore, interference shouldn’t be an issue. There are numerous ways that 5G and cellular are superior to Wi-Fi and Wi-Fi6, such as authentication—intercarrier roaming is transparent. Additionally, connecting to cellular is easy; simply turn on the mobile device, whereas Wi-Fi usually requires selecting an available service set identifier and providing a security key.

There is a hope that in the future, both technologies will be used by final consumers and move these customers closer to a superior mobile network. Business-class cell phones, for example, will likely support both technologies starting in 2020 (Mathias 2019 ).

3 Intelligent automation and economic contributions of 5G networks

Manufacturing industries are moving towards digitalization for several reasons, including increasing revenue by better serving their customers, increasing demand, beating the competition, decreasing costs by increasing productivity and efficiency, and decreasing risk by increasing safety and security. A recent study identified the key challenges and requirements in digitization industries digitization (Ericsson 2017 ). These requirements range from:

Ultra-reliable, resilient, instantaneous connectivity for millions of devices.

Low-cost devices with extended battery life.

Asset tracking throughout the ever-changing supply chains.

Performing remote medical procedures.

Using AR/VR to enhance the shopping experiences.

Using AI to enhance operations in multiple areas or enterprise-wide.

5G delivers a high-speed, reliable, and secure broadband experience, and will be a major technology for growing industry digitization. It will provide the networks and platforms to drive the digitization and automation of Industry 4.0. It will support the massive rollout of intelligent IoT and the widespread adoption of critical communications services (GSMA 2017 ).

In summary, 5G networks enable service providers to build virtual networks tailored to applications requirements such as:

Mobile broadband communication, media and entertainment, and the Internet

Machine-to-Machine (Massive IoT ) Retail, shopping, manufacturing

Reliable low latency Automobile, medical, smart cities

Critical communications

Others Industry-specific services, energy, etc.

4 5G for the Internet of Things (IoT)

Internet of Things Defined

The “Internet of things” (IoT) is an extension of the Internet and other network connections to different sensors and devices—or “things”. The concept is based on a general rule that ‘Anything that can be connected will be connected (Attaran 2017b ). This includes everything from industrial equipment such as car engines, jet engines, the drill of an oil rig, washing machines, coffee makers, cellphones, wearable devices, and much more. IoT provides a higher degree of computing and analytical capabilities to even single objects. IoT is a rapidly evolving technology that more and more industries are willing to adapt to improve their efficiency. Smart terminals, mobile broadband, and cloud computing enable widespread connectivity, transforming the way we perceive the world around us people (Attaran 2017b )

IOT architecture and working principle

Figure  4 shows major architectural layers of IoT architecture. Features of each of these layers are discussed below (Opentechdiary 2015 ):

Wireless sensors actuators, and network layer—this layer has sensors, RFID tags, and connectivity network. They form the essential “things” of IoT system and collects real-time information. Sensors convert the data obtained in the outer world into data for analysis. Actuators intervene in the physical reality—they can switch off the light and adjust the temperature in a room. Sensors and actuators cover and adjust everything needed in the physical world to gain the necessary insights for further analysis.

Internet Getaways and Data Acquisition Systems This stage makes data both digitalized and aggregated. Internet getaways work through Wi-Fi, embedded OS, Signal Processors, Micro-Controllers, and the Gateway Networks including LAN (Local Area Network), WAN (Wide Area Network), etc. The responsibility of Gateways is routing the data coming from the sensor, connectivity, and network layer and pass it to the next layer. Data acquisition systems (DAS) connect to the sensor network and aggregate output. This stage processes the enormous amount of information collected on the previous stage and squeeze it to the optimal size for further analysis.

Source: Opentechdiary ( 2015 )

IoT architecture layers.

Edge IT-Management Services This layer is responsible for data mining, text mining, analysis of IoT devices, analysis of information (stream analytics, data analytics) and device management. This stage provides analytics and pre-processing and prepares data before it is transferred to the data center or cloud for further analysis. Edge IT systems are located close to the sensors and actuators, creating a wiring closet.

Datacenter and cloud The main processes of analysis, management, and storage of data happen in the data center or cloud. This stage enables in-depth processing, along with a follow-up revision for feedback

The following sections review how the 5G network can improve processes in different layers of IoT architecture.

Mainstream adoptability

The IoT is a relatively new developing technology. Over the past few years, IoT-enabled devices have become broader, deeper, and more affordable. Sensors and tags are rapidly becoming cheaper. Readers and sensors are using less power, growing more intelligent, operating faster and at longer distances, and able to handle interference. This means better systems performance, greater capability to use sensors and tags with more data, and easier integration into existing systems without reprogramming. According to several recent research, IoT adoption over the next 10 years is on the rise. According to a Cisco estimate, devices connected to the Internet were 11 billion in 2013, 15 billion in 2014, 25 billion in 2016, and will be over 50 billion by 2020—that is seven Internet-connected “things” for every person on the planet (Evans 2011 ).

DBS Group Research has identified IoT technologies to reach the mass adoption stage in Asia over the next 5–10 years (DBS Asian Insights Insights 2018 ). According to this study, the IoT achieved a mainstream global consumer adoption rate of 14% in 2017. With growing uptake, the IoT is likely to reach an adoption rate of 18–20% by the end of 2019. By 2030, the global adoption of consumer IoT technology will reach 100% (DBS Asian Insights 2018 ).

Next stage in IoT development

In the past few years, technologies like Augmented Reality (AR), Industrial IoT (IIoT), edge computing, and Low Power Wide-Area (LPWA) were introduced that shape the next stages in IoT development. Over the next few years, more and more devices will become connected, increasing the application of IoT exponentially (Attaran 2017b ). Additionally, IoT technology is the driving force in our Industry 4.0 revolution. In Industry 4.0, industrial processes and the associated machines are becoming smarter and more modular. They could monitor, collect, exchange, analyze, and instantly act on information to intelligently change their behavior or their environment. Additionally, as the total cost of ownership of IoT devices and solutions decrease, the technology will be affordable for markets of asset tracking, agriculture, and environmental monitoring (ABI Research 2016 ).

The impact of 5G on IoT

A 2017 CEO survey of 5G potential applications revealed five different services that could be supported and would come to maturity when commercial 5G networks are widely deployed. They are highlighted in Fig.  5 (Obiodu and Giles 2017 ). IoT ranked second on the list, with 77% of the respondent of respondents believing that 5G provides broad enablement of IoT use cases. Gartner conducted another survey in 2018 to understand the growing demand and adoption plans for 5G. The results revealed that 65% of organizations had plans to deploy 5G networks to be mainly used for IoT and video communications by 2020. They identified operational efficiency as the key driver for their decision (Omale 2018 ).

A CEO survey of possible 5G applications

Leveraging cyber-physical systems and striving towards ever more automation and autonomous decisions in environments such as the smart factories, autonomous vehicles, smart buildings, smart cities and connected industrial applications, requires substantial resources to deal with the resulting amount of data that needs to be gathered, analyzed, and transferred. Today’s network technologies are not sufficient for the ultra-connectivity needed for the future. We often need to use a mix of fixed and wireless network technologies to realize massive IoT projects. 5G has the potential to bring the reliability, latency, scalability, mobility, and security that is required for mission-critical services in the IoT ecosystem (i-SCOOP 2018 ).

The existing IoT technology solutions are facing challenges such as a large number of connections of nodes and security issues. In order to meet widespread applications and different industry demands, IoT will require improved performance criteria in areas such as security, trustworthiness, wireless coverage, ultra-low latency, and mass connectivity. 5G can improve processes in different stages of IoT architecture (Fig.  2 ). 5G can contribute to the future of IoT through the connection of billions of smart devices to interact and share data independently. 5G is considered as a key enabling technology that will play an important role in the continued success and widespread applications of IoT. 5G will introduce new Radio Access technologies (RAT), smart antennas, and make use of higher frequencies while altering or re-architecting networks. The 5G enabled IoT will help the connection of an enormous number of these IoT devices and will also help to meet market demands for wireless services. The fifth- generation (5G) mobile network will meet the differing prerequisites of the IoT. To meet the growing requirements of IoT, the Long-Term Evolution (LTE) and 5G technologies must provide new connectivity interfaces for future IoT applications. To meet the differing prerequisites of the IoT, 5G mobile networks must guarantee that massive devices and new services such as enhanced Mobile Broadband (eMBB), massive Machine Type Communications, Critical Communications, and Network Operations are effectively upheld. 5G provides essential prerequisites and ubiquitous connectivity for end-clients, including high throughput, low latency, fast information conveyance, high versatility to empower a huge number of gadgets, productive energy utilization systems, etc. The fifth-generation (5G) mobile network will improve the range of IoT applications such as smart TVs, smart security cameras, smart dishwashers, smart thermostats, smart kitchen appliances, and so on.

The existing networks of 4G and 4G LTE cannot support the mobile telecommunications needs of IoT. 5G can also provide a solution to the issue and can provide the fastest network data rate with relatively low expectancy and better communication coverage when compared to present 4G LTE networking technologies. The fast speeds provided by 5G will bring new technological advancements. The next generation of 5G will handle hundreds of billions of connections and will provide transmission speeds of 10 Gbps and ultra-low latency of 1 ms. It also provides more reliable service in rural areas reducing the differences in service between rural and urban areas (Li et al. 2018 ). Although 5G is an extension of the 4G and 4G LTE networks, yet it comes with entirely new network architecture and functions such as virtualization, which offers more than just the impressive fast data rates. Network function virtualization offers the ability to split physical networks into multiple virtual networks where the devices can be reconfigured to create multiple networks. This feature will provide the 5G enabled IoT applications with an immediate processing ability that will allow for improved speed and coverage, and also provide the capacity to meet the demands of applications. Virtualization will also enhance the feasibility of radio access network (RAN) for next-generation voice, video, and data services.

5G networks will integrate mobile tech, big data, IoT, and cloud computing, and will generate a variety of new applications as the technology is rolled out. 5G will support smart devices, including self-driving cars, wearable, telemedicine, and Internet of Things (IoT). Autonomous cars and IoT devices are expected to be major revenue drivers for 5G networks (i-SCOOP 2018 ).

Big data, IoT, and 5G networks

Another area where 5G networking can be very helpful is “Big Data.” Data is flooding in at a rate never seen before—–doubling every 18 months (Rossi and Hirama 2015 ). The International Data Corporation report predicted that there could be an increase in digital data by 40X from 2012 to 2020 (Gantz and Reinsel 2012 ). Public customer data and new data gathered from IoT enabled devices are generating what is broadly known as “Big Data.” The amount of data that IoT devices might report back to a cloud server could easily overwhelm a relational database. Companies offering IoT enabled devices need to be prepared for storing, tracking, and analyzing the vast amounts of data that will be generated. The real value that IoT creates is at the intersection of gathering data and leveraging it. Additionally, the privacy and security of enormous data produced by millions of interconnected devices going to be challenged and private information may leak at any time (Zheng et al. 2019 ). Zheng et al. ( 2019 ). It is anticipated that IoT’s billions of connected objects will generate data volume far in excess of what can easily be processed and analyzed in the cloud, due to issues like limited bandwidth, network latency, etc. 5G has the potential to keep up with consumer and enterprise data demand while lowering carriers’ operating expenses.

IoT performance requirements for 5G networks

An important challenge for 5G networks is to support a variety of performance requirements for IoT applications in a reliable, flexible, and cost-effective way (Zhang and Fitzek 2015 ). Activity-based IoT applications pose many performance requirements, as described in several studies. Energy optimization of streaming applications in IoT has been analyzed, and energy-efficient task mapping and scheduling have been proposed (Ali et al. 2018a , b , 2019 ; Tariq et al. 2019 ). A recent study identified eight key performance indicators and requirements of activity-based IoT (5G Forum 2016 ). These performance requirements range from data rate, mobility, latency, connection density, reliability, positioning accuracy, coverage, and energy efficiency and are usually well described for specific IoT applications. A comprehensive understanding of the performance requirements of each activity based IoT application could facilitate the selection of 5G technologies needed to meet the growing demands of these applications.

Following is a more detailed description of these performance requirements:

Data Rate Data rate is an important evaluation factor for generations of wireless communication networks (Saha et al. 2016 ). 5G core network will support both peak data rate—the maximum achievable data rate by the user, and minimum guaranteed user data rate—the minimum experience data rate by the user (Oughton and Frias 2017 ). The high data rate is important in most activity-based classes of IoT applications. 5G networks support 10 Gbps for minimum peak data rate and 100 Mbps as the minimum guaranteed user data rate (5G Forum 2016 ).

Mobility IoT applications have very diverse requirements for mobility (relative velocity between the receiver and the transmitter) in 5G networks (Oughton and Frias 2017 ). Many IoT use cases require ultra-high mobility, ultrahigh traffic volume density, and ultra-high connection density. These needs may be quite challenging for 5G networks to provide on- demand mobility for all devices and services (Le et al. 2015 ).

Latency latency is perceived by the end-user and is usually expressed in terms of end-to-end (E2E) latency. 5G networks, through significant enhancements and new technology in architecture aspects, enable “zero latency” expressed by the millisecond level of E2E latency (Saha et al. 2016 ; Hu 2016 ; Ford et al. 2017 ). IoT application determines required latency levels. For example, the acceptable delay for use case mobile health and remote surgery application is in order of sub-milliseconds (Le et al. 2015 ; Blanco et al. 2017 ).

Connection Density Connection density is the number of connected and/or accessible devices per unit area, e.g., 1 million connections per square meter (Le et al. 2015 ; NGMN Alliance 2017 ). Connectivity in 5G networks is not limited to mobile devices. 5G networks can satisfy connection density and traffic density of various identified activity-based classes of IoT applications (Amaral et al. 2016 ; NGMN Alliance 2017 ).

Reliability is measured by the maximum tolerable packet loss rate at the application layer. For certain IoT uses cases such as driverless cars, 5G must bring the reliability of 99,999% or higher (Ford et al. 2017 ; Rappaport et al. 2014 ; Ge et al. 2016 ; Elayoubi et al. 2016 ). Similarly, reliability is the main characteristic of monitoring, managing, and controlling activities. Reliability will present many challenges in the future. High-speed trains are just one example of this challenge because of speed, load, and cell distance (Oughton and Frias 2017 ; Erman and Yiu 2016 ),

Position Accuracy Position accuracy is defined as the maximum positioning error tolerated by the IoT application. Accuracy positioning is very important in monitoring-based activities such as monitoring remote cameras and in controlling-based activities such as driving (Blanco et al. 2017 ). 5G networking technology should ensure accurate positioning of the outdoors device with accuracy from 10 m to less than 1 m on 80% of occasions and better than 1 m in indoor deployment (Elayoubi et al. 2016 ).

Coverage 5G core network shall be able to build the network based on the user’s need. It should provide connectivity anytime and anywhere with a minimum user experience data rate of 1 Gbps (Hossain 2013 ). Almost every activity based IoT application requires very high levels of coverage—99,999% availability (NGMN Alliance 2017 ).

Spectrum Efficiency Spectrum efficiency is defined as the aggregate data throughput of all users per unit of spectrum resource per cell or per unit area. The minimum peak spectrum efficiency is 30 bps/Hz for downlink and 15 bps/Hz for uplink (Liu and Jiang 2016 ). IoT enabled 5G networks to require 3–5 times improvement in spectrum efficiency to achieve network sustainability (Liu and Jiang 2016 ; De Matos and Gondim 2016 ; Hossain 2013 ).

Energy Efficiency Energy efficiency is the number of bits that can be transmitted per joule of energy, and it is measured in b/J (Liu and Jiang 2016 ). 5G wireless technology should aim for higher energy efficiency against increased device/network energy consumption required on wireless communications. That means the energy efficiency of the 5G network may need to be improved by a factor of 1000 (Kaur and Singh 2016 ; Akyildiz et al. 2014 ; Kamel et al. 2016 ; Bogale and Le 2015 ). Energy efficiency is a significant factor for the reduction of operating costs of telecom operators, as well as for minimizing the environmental impact of wireless technology (Bogale and Le 2015 ).

End-user willingness to Pay for 5G enabled IoT

In the summer of 2017, Gartner conducted a survey to gauge the willingness among end-user organizations to pay more for 5G networking technology (Gartner 2017 ). A vast majority of correspondents (57%) believed that 5G-capable networks would play an important role in IoT in their organizations and that their intention is to use 5G to drive IoT communication. The video was the next most popular use case, which was chosen by 53% of the respondents. The study also identified the willingness to pay for the 5G networks of surveyed organizations. 57% of surveyed organizations were willing to pay the same cost as 4G and up to 10% higher (Fig.  6 ).

Source: Gartner ( 2017 )

Willingness of Organizations to pay for 5G.

5 5G for automotive industry and smart cars

Rethinking transportation

Henry Ford introduced his first Model T car using interchangeable parts on an assembly line in 1908. This led to a more efficient manufacturing process—the price of cars dropped, and sales picked up. Nearly 7% of American families owned a car in 1918. The number of cars nearly tripled from 8 million to 23 million in the 1920s. By 1929, 80% of American families owned a car. At this time, the auto manufacturing industry was also growing quickly—by 1925, 10% of the U.S. workforce was employed by the auto industry. Cars were the most significant innovation of the twentieth century that shaped our modern lifestyle. The rise of the automobile industry disrupted almost every industry and every aspect of the economy. Affordable cars enabled people to move from cities to the suburbs, which led to economic growth in the construction industry. This new era of transportation remained in place for 100 years (Sears 1977 ). However, a revolution is arriving by way of self-driving vehicles. These autonomous cars are anticipated to disrupt critical areas of the economy and have an even bigger impact than the automobile did in the 1920s. More specifically, self-driving cars are labeled as the fastest, deepest, most consequential disruptions of transportation in history (Arbib and Seba 2017 ).

Consumer mobility behavior is one of the areas that is changing. Individuals are increasingly using multiple modes of transportation to complete their journey(s). The “state of delivery” is another area of customer concern. Consumers are showing an obvious preference for delivered goods and services. The clear result in this practice is a decline in individual shopping trips. In dense big cities like New York City or Los Angeles, car ownership is increasingly becoming more of a burden for many, and the prospect of shared mobility now presents a competitive value proposition (McKinsey & Company 2016 ). According to a 2017 study by RethinkX, an independent think tank and research company, within 10 years of government approval of autonomous vehicles, 95% of the U.S. passenger miles will be covered by fleets of autonomous electric vehicles (Arbib and Seba 2017 ). This will create a new business model called “Transport as-a-Service” (TaaS) and will have enormous implications across the transportation and oil industries, causing oil demand and prices to plummet, and creating trillions of dollars in new business opportunities and GDP growth (Arbib and Seba 2017 ). It is predicted that TaaS will reduce energy demand by 80% and tailpipe emissions by over 90%, thus bringing dramatic reductions or perhaps even the elimination of air pollution and greenhouse gases from the transport sector and improved public health. TaaS will not only dramatically lower transportation costs but increase mobility and access to jobs, education, and health care. It has the potential to create trillions of dollars in consumer surplus and contribute to a cleaner, safer, and more walkable communities (Arbib and Seba 2017 ). According to this study, by 2030, by using the TaaS model, the average American family could save nearly $5600 per year in transportation costs, and the United States will save an additional $1 trillion per year (Arbib and Seba 2017 ).

Autonomous cars disrupt the transportation industry in several ways. Driven by the exponential rise in electric vehicles, improved connectivity services provided by faster networking solutions, and technological breakthrough, consumer mobility behavior is changing. It is predicted that one out of ten cars sold in 2030 will potentially be a shared vehicle. Once regulatory issues have been resolved, up to 15% of new cars sold in 2030 could be fully autonomous (McKinsey & Company 2016 ). Auto production will suffer because autonomous fleets will need far fewer cars than are currently consumed. According to an estimate by RethinkX Sector Disruption Report, the number of U.S. vehicles will drop 82% from 247 to 44 million in the new age of autonomous vehicles. That will lead to a 70% reduction in automotive manufacturing. Moreover, nearly 100 million existing vehicles will be abandoned as they become economically unviable (Arbib and Seba 2017 ). This could result in total disruption and almost complete destruction of the auto industry—specifically car dealers, maintenance, and insurance companies. Automakers’ business models will shift from producing cars for public consumption to producing cars to deploy in their self-driving fleets. Traffic becomes a thing of the past, commute times will decline significantly, and workers can move even further from their place of employment. As a result, real estate will become more accessible, increasing urban sprawl (Arbib and Seba 2017 ). The primary challenges impeding faster market penetration for fully autonomous vehicles are pricing, consumer understanding, and safety/security issues. Fully self-driving vehicles are unlikely to be commercially available before 2020 (McKinsey & Company 2016 ). However, these driverless cars are already here to stay. Tesla recently announced the company’s aspiration to release a fully autonomous Robo taxi fleet next year. Lyft announced that self-driving cars are a central part of its vision for reducing individual car ownership, creating safer streets, and alleviating congestion. In 2018, Lyft partnered with vehicle technology firm Aptiv to begin its driverless car program in Las Vegas. Lyft’s fleet of 30 driverless cars has completed 50,000 rides in Las Vegas, up from 30,000 in January 2019. Passengers rated their trips an impressive average of 4.97 out of 5. Moreover, 92% of riders felt very safe or extremely safe during the ride. 95% of riders indicated it was their first time inside a self-driving vehicle (Lyft Blog 2019 ). Lyft is looking for partnerships to further its self-driving ambitions. It recently announced a deal with self-driving technology firm Waymo for a ridesharing service in Phoenix, Arizona (Mogg 2019 ).

The impact of 5G on automotive industry

According to a 2017 study by Qualcomm, by 2035, 5G networks will enable more than $2.4 trillion in total economic output in the automotive sector, including its supply chain and its customers. 5G economic impacts in this sector will represent about 20% of the total global 5G economic impact by 2035 (Condon 2017 ). According to the World Economic Forum, the digital transformation of the automotive industry will generate $67 billion in value for that sector over the 2015–2025 periods. Additionally, this transformation will generate $3.1 trillion in the societal benefit that includes autonomous vehicles improvement and the transportation enterprise ecosystem over the same period (World Economic Forum 2015 ).

Automakers are racing to improve the technology that will power self-driving cars. 5G networks enable the digital transformation of the automotive industry. Smart cars consume a lot of bandwidth, require quicker responses from the network, and demand continuous connectivity to the network. 5G supports higher bandwidth and lower latencies, which enables Smart Cars to function efficiently. 5G technology improves mobile wireless networks’ capacity and data speeds. It allows network providers to offer much more robust internet connections to devices. As such, 5G will play an important role in the proliferation of self-driving cars, which will produce enormous amounts of data. This technology makes intelligent driving safer and more efficient. As such, 5G networks will help enable the autonomous urban ride services and most self-driving car players. Additionally, 5G networks can offer many services to automakers, including navigation information, traffic information, e-tolling, hazard warning, collision warning, weather updates, and cybersecurity services to monitor vehicles for intrusions.

6 5G for manufacturing sector and smart factory

The constantly changing manufacturing industry

The manufacturing industry is going through a significant period of change driven by rapid technological advancements that have enabled manufacturers to meet consumer demands better. Technology will play a key role in empowering manufacturers to innovate and embrace the opportunities that will present themselves. Manufacturers must keep up with the technological evolution of the products and processes, as they are continually improved. As more and more ‘smart’ devices are integrated into manufacturing, industry 4.0 will continue to dominate the manufacturing process. Industry 4.0 combines artificial intelligence and data science to realize the potential of the Internet of Things (IoT) (Attaran 2017b ). Sensors and tags are attached to parts to track them throughout the manufacturing and assembly process. Sensors are also used to improve the performance of machines, to extend their lives, to predict when equipment is wearing down or in need of repair, and to learn how machines can be redesigned to be more efficient. This could reduce maintenance costs by 40% and cut unplanned downtime by 50% (Hale 2019 ). Furthermore, an increasing amount of data being created by Industry 4.0 provides the opportunity for the manufacturer to significantly enhance the customer experience.

Additionally, during the past years, the use of additive manufacturing (AM) technologies in different industries have increased substantially. AM is used to produce products that can be customized individually. The technology offers several benefits to the manufacturing industry, including shorter production lead times, reduced time to market for new product designs, and faster response to customer demand (Attaran 2017a ).

Finally, Artificial Intelligence (AI) is another technology that is set to have a profound impact on the manufacturing industry in several diverse ways. For example, AI can be used to make more sense of the mountains of data manufacturers are now collecting and storing. It can also be used to improve customer service and support.

5G and manufacturing industry

Manufacturing companies around the world are under extreme competitive pressure due to shorter business and product lifecycles. Margins are being squeezed more than ever, and workforces are aging and becoming costlier to maintain. To compete globally, manufacturing companies have to improve efficiency and reduce costs through new process innovations—technologies like robotics, warehouse automation, smart factories, and flexible manufacturing help. 5G networks and IoT will play crucial roles in enhancing and enabling these manufacturing advances. 5G networking technologies provide the network characteristics essential for manufacturing. 5G will give manufacturing companies a chance to build smart factories and truly take advantage of technologies such as automation, artificial intelligence, and augmented reality for troubleshooting. 5G is a significant technology for industry digitalization that directly enhances connectivity, quality, speed, latency, and bandwidth. 5G could help overcome manufacturing problems and pain points, including connectivity issues such as insufficient bandwidth, speed, and latency issues. 5G will also improve connectivity for a large network of sensors for predictive maintenance of factory floor machines and robots. 5G networks will allow for higher flexibility, lower cost, and shorter lead times for factory floor layout changes and alterations. 5G networks, services, and connectivity capabilities have the potential to transform production, business models, and sales in ways that will benefit manufacturing. Advanced 5G networks and information processing technology can streamline smart factories, improve internal and external communications, and unify full product life cycle management on a single network. Other important pain points and crucial manufacturing use cases 5G can overcome are summarized in Table  1 (Ericsson 2019 ).

7 5G for the healthcare industry

The ever-changing healthcare industry

Allied Market Research estimates that there are 3.7 million connected medical devices in use to enable healthcare decisions. According to its prediction, the worldwide IoT healthcare market will reach $136.8 billion by 2021 (Market Watch 2016 ). The applications of IoT in the healthcare industry are limitless. The concept is referred to as the Internet of Medical Things or “IoMT.” It is the collection of medical devices equipped with Wi-Fi and applications connected to healthcare IT systems through online computer networks. As hospitals struggle to lower operating costs and remain competitive, IoMT has the potential to reduce costs and improve a patient’s journey through a medical facility. The idea of telemedicine or the ability of a doctor with a webcam to diagnose a patient’s problems without an office visit is becoming popular. This is very useful when patients live in remote areas or when they need specialized care. Mobile health can help the healthcare industry improve efficiency and reduce costs in the areas of disease prevention, counseling, treatment, and rehabilitation (Marr 2018 ).

5G advantages for healthcare

5G networks and services provide mobile health platform advantages such as integrated mobility and advanced connectivity so doctors and nurses can achieve patient monitoring anywhere, anytime. 5G technology enables patients to use wearable devices to transmit their health symptoms and status. 5G enhanced mobile broadband with faster speed and more bandwidth can help doctors have access to patient’s information for remote monitoring and diagnosis.

5G networks enable factory robots to communicate their task and position, allowing them to do more tasks efficiently and wirelessly. Drones could fly over a field of crops, using sensors on the ground, to sort, pick, feed, and water individual plants. In April 2019, a Chinese neurosurgeon successfully operated on a patient suffering from Parkinson’s disease. The doctor used a pacemaker-like implant on a patient that was about 1864 miles away during the surgery. This surgery was only possible because of the lightning-fast connection of 5G networks that allows surgeons such as the one in China to control an off-site surgical robot and operate in real-time (China Daily 2019 ).

A recent study by Ericsson identified different ways the healthcare industry can derive value out of 5G networking technology (Ericsson 2018 ). They are summarized below:

Effective capture of the vast amount of patient data.

Real-time mobile delivery of rich medical data.

Improved availability of suitable infrastructure.

Improved security of patient data and superior data storage.

Ability to accurately control remote medical equipment without delay.

Ability to incorporate augmented and virtual reality for enhanced training of interns.

Facilitate the connectivity and operations of smart medical objects and instruments such as syringes, beds, and cabinets.

8 5G for smart grids and smart cities

5G for smart grids

The smart grid is one example of the application of IoT where components of the electric grid from transformers to power lines to home electric meters have sensors and are capable of two-way communication. The electric company can use the smart grid to manage distribution more efficiently, be proactive about maintenance, and respond to outages faster. Smart grids integrate traditional power systems with information, communication, and control technology to improve the power grid’s stability, security, and operating efficiency. Power generation facilities are digitizing form, scale, power management, and control to increase systems and operating efficiency. The communications systems for smart grids cover all nodes on the power system, including power generation, transformation, transmission, distribution, and usage. The new digitized power generation facility attempts to improve the efficiency of power systems by building a high capacity, high-speed, real-time, secure, and stable communications networks. 5G greatly enhances the amount of spectrum used to send and receive data. It can act as an integrator and support the diverse requirements of smart grids. 5G is more efficient and faster than fiber optic and short-range wireless communications technology, supports over-the-air wireless connectivity, and has excellent disaster recovery capabilities. Other advantages like ultra-high bandwidth, wide-area seamless coverage, and roaming make 5G an ideal technology for smart and digital grids.

A recent study by Ericsson identified different ways the Energy and Utilities industry can derive value out of 5G networking technology (Ericsson 2018 ). They are summarized below:

Improves the integration of new technologies within the existing infrastructure.

Improves capturing and handling of the large volume of data.

Facilitates automation across distribution, operations, and energy efficiencies.

Facilitates connecting and monitoring of remote sites such as wind farms.

Improves industrial control and automation systems.

Improves applications to gather and monitor data.

Improves management of distributed energy resources.

Improves integration of sensors in microgrid and distributed generation.

5G for smart cities

In addition, 5G is a critical element in providing better networking in our technological world. For example, a smart city integrates information and communication technology and 5G networking solutions in a secure fashion to manage a city’s different functions. Those functions include, but are not limited to, schools, libraries, transportation systems, hospitals, power plants, water supply networks, waste management, law enforcement, and other community services. There is a need for finding a way of aggregating multiple layers of data, spanning traffic flows, individual transactions, human movement, shifts in energy usage, security activity, and almost any major component of contemporary economies. 5G technology can facilitate this aggregation. 5G technology can facilitate this aggregation. The savings gained from Smart Cities is incredible. For example, smart water technology can save $12 billion annually. Sensors installed in individual vehicles can be linked to broader systems that help to manage traffic congestion across the city.

9 Obstacles to rapid adoption

There are numerous challenges in applying 5G networking technology in a way that would allow for its significant and rapid growth. Security and privacy is the primary concern among consumers and businesses as devices become more connected. The major challenges include technological maturity, global standardization, government regulations, and cost. A recent study conducted by Ericsson revealed that companies are still hobbled when it comes to overcoming barriers to actually using the 5G technology. The significant barriers were identified as data security and privacy, lack of standards, and challenges of end-to-end implementation (Ericsson 2018 ). 5G’s speed will expedite incidents of a breach, and as we add more small cells, there will also be more vulnerable hardware. 5G technology also brings an increase in open-source designs and technologies. Open source brings the speed of innovation and collaboration, but it can also bring security vulnerabilities.

Technology standard is non-consistent and remains fragmented in most areas. Technical and boundary limitations still exist in some areas of technology. Capturing the full potential of 5G networking potentials will require innovation in technologies and business models, as well as investment in new capabilities and talent. Most businesses have not equipped their teams with 5G capable smartphones, scanners, laptops, nor, in the case of manufacturing facilities, smart machines on the factory floor. These devices will need to be upgraded or replaced, which means added training and cost for businesses. Business infrastructures will require updating to reap the full interconnected benefits of 5G. Existing devices will need to be upgraded or replaced with new devices that are enabled for 5G technology.

10 Summary and Conclusions

5G networks and services will be deployed in stages over the next few years to provide a platform on which new digital services and business models can thrive. 5G will mark a turning point in the future of communications bringing high-powered connectivity to billions of devices. It will enable machines to communicate in an IoT environment capable of driving a near-endless array of services. As more devices become connected, and the IoT use cases grow exponentially, 5G networks facilitate the rapid increase of IoT and will bring significant benefits to corporations and consumers. 5G networks will revolutionize transportation and will reliably connect patients and doctors all over the globe providing improved access to medical treatment. As digital transformation is shifting user experience away from the text, image, and video into immersive VR and AR., 5G cellular technology will facilitate this new shift by offering high speed, superior reliability, extreme bandwidth capacity, and low latency.

This paper examined the essential roles 5G plays in the success of different industries, including IoT, the auto industry and smart cars, manufacturing and smart factories, smart grids, and smart cities, and healthcare. It discussed how 5G is critical for growing industry digitization and for addressing the numerous challenges different manufacturing industries face in this rapidly changing landscape. Finally, this paper presented the crucial role that 5G plays in providing a competent platform to support the widespread adoption of critical communications services and driving the digitization and automation of industrial practices and processes of Industry 4.0.

Future directions

5G will continue to evolve as companies work towards its next phase, though it will take some time before 5G networks are fully rolled out and utilized. It is expected that 5G will scale rapidly after launch in 2020, with coverage reaching just over a third of the global population in 5 years.

The implications of the rise of an autonomous electric fleet for the transportation industry, society, and the automotive industry are huge. 5G will play an important role in making electric vehicles and autonomous ride-sharing a reality. 5G will enable networks of self-driving cars with the ability to send data between each other, communicate with traffic lights, road sensors, aerial drones, and so on within a millisecond. Additionally, autonomous trains, delivery trucks, even airplanes could be on the horizon soon.

5G Wireless will also play a crucial role in a growing number of consumer electronics technologies and companies and will transform the fundamental ways industries conduct business. 5G wireless will enable companies to be on the growing side of the growth wave keeping their investors, customers, and workers happy. So, the very near future will be one of the most exciting times for business in our lifetimes, full of challenges, opportunities, and risks.

ABI Research (2016) Driving the IoT journey: 10 trends to watch. Retrieved April 25, 2018. from file:///E:/The%20Internet%20of%20Things/ABI%20Research%20Driving%20Your%20IoT%20ourney.pdf

Akyildiz IF, Nie S, Lin SC, Chandrasekaran M (2014) 5G roadmap: 10 key enabling technologies. Comput Netw 106:17–48

Article   Google Scholar  

Ali H, Zhai X, Tariq UU, Liu L (2018a) Energy efficient heuristic algorithm for task mapping on shared-memory heterogeneous MPSoCs. In: IEEE. 20th International Conference on high performance computing and communications. June 28–30. Exeter, United Kingdom, pp 1099–1104

Ali H, Tariq UU, Zhai X, Liu L (2018b) Energy efficient task mapping & scheduling on heterogeneous NoC-MPSoCs in IoT based Smart City. In: IEEE 20th International Conference on high performance computing and communications. June 28–30. Exeter, United Kingdom, pp 1305–1313

Ali H, Zhai X, Tariq UU, Panneerselvan J, Liu L (2019) Energy optimization of streaming applications in IoT on NoC based heterogeneous MPSoCs using re-timing and DVFS. In: IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. August 19–23. Leicester, United Kingdom

NGMN Alliance (2017) 5G ehite paper. Retrieved June 14, 2019, from https://www.ngmn.org/uploads/media/NGMN_5G_White_Paper_V1_0.pdf

Amaral LA, de Matos E, Tiburski RT, Hessel F, Lunardi WT, Marczak S (2016) Middleware technology for IoT systems: challenges and perspectives toward 5G. In: Mavromoustakis C, Mastorakis G, Batalla J (eds) Internet of Things (IoT) in 5G mobile technologies. Springer International Publishing, Cham, pp 333–367

Chapter   Google Scholar  

Arbib J, Seba T (2017) Rethinking Transportation 2020-2030. Retrieved May 14, 2019, https://static1.squarespace.com/static/585c3439be65942f022bbf9b/t/591a2e4be6f2e1c13df930c5/1509063152647/RethinkX+Report_051517.pdf

Attaran M (2017a) The rise of 3-D printing: the advantages of additive manufacturing over traditional manufacturing. Bus Horizons 60(5):677–688

Attaran M (2017b) The Internet of things: limitless opportunities for business and society. J Strat Innov Sustain 12(1):10–29

Google Scholar  

Barreto AN, Faria B, Almeida E, Rodriguez I, Lauridsen M, Amorim R, Vieira R (2016) 5G-wireless communications for 2020. J Commun Inf Syst 31:146–163

Bhalla MR, Bhalla AV (2010).Generations of mobile wireless technology: a survey. Int J Comput Appl 5(4): 26–32. Retrieved June 9, 2019, from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.206.5216&rep=rep1&type=pdf

Blanco B, Fajardo HO, Giannis I, Kafetzakis E, Pneg S, Perez-Romero J, Trajkovska I, Khodashenas PS, Goratti L, Paolino M (2017) Technology pillars in the architecture of future 5G mobile networks: NFV, MEC and SDN. Comput Stand Interfaces. 54:216–228

Bogale TE, Le LB (2015) Massive MIMO and mmWave for 5G wireless HetNet: potential benefits and challenges. IEEE Veh Technol Mag 11:64–75

Cero E, Baraković Husić J, Baraković S (2017) IoT’s tiny steps towards 5G: telco’s perspective. Symmetry 9:1–38

China Daily (2019) China performs the first 5G-based remote surgery on the human brain. March 18. Retrieved July 30, 2019, from http://www.chinadaily.com.cn/a/201903/18/WS5c8f0528a3106c65c34ef2b6.html

Cisco (2019) Cisco visual networking index: forecast and trends, 2017–2022 White paper. Retrieved June 10, 2019, from https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-741490.html

Condon S (2017) Report: By 2035, 20 percent of 5G’s economic impact will be in automotive. Between the Lines, May 3. Retrieved June 24, 2019, from https://www.zdnet.com/article/report-by-2035-20-percent-of-5gs-economic-impact-will-be-in-automotive/

De Matos WD, Gondim PRLM (2016) Health solutions using 5G networks and M2M communications. IT Prof. 18:24–29

Elayoubi SE, Fallgren M, Spapis P, Zimmermann G, Martín-Sacristán D, Yang C, Jeux S, Agyapong P, Campoy L, Qi Y (2016) 5G service requirements and operational use cases: analysis and METIS II vision. In: Proceedings of the 2016 European Conference on networks and communications (EuCNC), Athens, Greece, 27–30 June

Ericsson (2017) The 5G business potential. Second Edition. October. Retrieved May 24, 2019, from https://www.economiadehoy.es/adjuntos/19430/Ericsson-5G-business-potential-report.pdf

Ericsson (2018) The Industry impact of 5G. Retrieved June 10, 2019, from https://www.economiadehoy.es/adjuntos/19430/Ericsson-5G-business-potential-report.pdf

Ericsson (2019) 5G for manufacturing. Retrieved June 17, 2019, from https://www.ericsson.com/en/networks/trending/insights-and-reports/5g-for-manufacturing

Erman B, Yiu S (2016) Modeling 5G wireless network service reliability predictions with the Bayesian network. In: Proceedings of the 2016 IEEE International Workshop Technical Committee on communications quality and reliability, Stevenson, WA, USA, 10–12 May

Evans D (2011) The Internet of Things: how the next evolution of the Internet is changing everything. April. Cisco Internet Business Solutions Group. Retrieved May 24, 2019, from https://www.cisco.com/c/dam/en_us/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf

Ford R, Zhang M, Mezzavilla M, Duttam S, Rangap S, Zorzi M (2017) Achieving ultra-low latency in 5G millimeter wave cellular networks. IEEE Commun Manag 55:196–203

G Forum (2016) 5G vision, requirements, and rnabling technologies. Retrieved June 14, 2019, from http://kani.or.kr/5g/whitepaper/5G%20Vision,%20Requirements,%20and%20Enabling%20Technologies.pdf

Gantz J, Reinsel D (2012) The digital universe in 2020: Big data, bigger digital shadow s, and biggest growth in the far east. IDC Iview. Retrieved August 02, 2019, from https://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf

Gartner (2017) Gartner end-user survey finds three-quarters of respondents are willing to pay more for 5G. August 9. Retrieved May 14, 2019, from https://www.gartner.com/en/newsroom/press-releases/2017-08-09-gartner-end-user-survey-finds-three-quarters-of-respondents-are-willing-to-pay-more-for-5g

Ge X, Chen J, Ying S, Chen M (2016) Energy and coverage efficiency trade-off in 5G small cell network. IEEE Trans Green Commun Netw XX(Y):1–28

GSMA (2017) The5G era: age of boundless connectivity and intelligent automation. GSM Association. Retrieved May 14, 2019, from https://www.gsmaintelligence.com/research/?file=0efdd9e7b6eb1c4ad9aa5d4c0c971e62&download

Hale Z (2019) How ERP data analytics improve predictive maintenance. February 20. Retrieved May 14, 2019, from https://www.softwareadvice.com/resources/predictive-maintenance-data-analytics/

Hossain S (2013) 5G wireless communication systems. Am J Eng Res 2:344–353

Hu F (2016) 5G overview: key technologies. In: Hu F (ed) Opportunities in 5G Networks, 1st edn. CRC Press, Boca Raton, pp 1–557

DBS Asian Insights (2018) Internet of Things- The pillar of artificial intelligence. DBS Group Research. Retrieved May 22, 2018, from file:///E:/Blockchain-Book/180625_insights_internet_of_things_the_pillar_of_artificial_intelligence.pdf

i-SCOOP (2018) 5G and IoT: the mobile broadband future of IoT. Retrieved May 14, 2019. https://www.i-scoop.eu/internet-of-things-guide/5g-iot/

Kamel M, Hamouda W, Youssef A (2016) Ultra-dense networks: a survey. IEEE Commun Surv Tutor 18:2522–2545

Kaur S, Singh I (2016) A survey report on Internet of Things applications. Int J Comput Sci Trends Technol 4:330–335

Ken’s Tech Tips (2018) Download speeds: what do 2G, 3G, 4G & 5G actually mean? November 23. Retrieved May 24, 2019, from https://kenstechtips.com/index.php/download-speeds-2g-3g-and-4g-actual-meaning#2G_3G_4G_5G_Download_Speeds

Larsson EG, Edfors O, Tufvesson F, Marzetta TL (2014) A massive MIMO for next-generation wireless systems. IEEE Commun Mag 52:186–195

Le LB, Lau V, Jorswieck E, Dao ND, Haghighat A, Kim DI, Le-Ngoc T (2015) Enabling 5G mobile wireless technologies. J Wirel Com Netw. https://doi.org/10.1186/s13638-015-0452-9

Li S, Xu LD, Zhao S (2018) 5G Internet of Things: a survey. Journal of Industrial Information Integration. February 19. Retrieved May 24, 2019, from https://pdfs.semanticscholar.org/b305/d424a5d590ff7fff8e6d0bbf4f2767146423.pdf

Liu G, Jiang D (2016) 5G: vision and requirements for mobile communication system towards the year 2020. Chin J Eng. https://doi.org/10.1155/2016/5974586

Lyft Blog (2019) One year in, 50,000 self-driving rides later. May 31. Retrieved June 14, 2019, from https://blog.lyft.com/posts/2019/5/30/one-year-in-50000-self-driving-rides-later

Market Watch (2016) Internet of Things (IoT) healthcare market is expected to reach $136.8 billion worldwide by 2021. Retrieved June 14, 2019, from https://www.marketwatch.com/press-release/internet-of-things-iot-healthcare-market-is-expected-to-reach-1368-billion-worldwide-by-2021-2016-04-12-8203318

Marr B (2018) Why the Internet of Medical Things (IoMT) will start to transform healthcare In 2018. Forbs, January 25. Retrieved June 14, 2019, from https://www.forbes.com/sites/bernardmarr/2018/01/25/why-the-internet-of-medical-things-iomt-will-start-to-transform-healthcare-in-2018/#523c742c4a3c

Mathias C (2019) Wi-Fi 6 vs. 5G networks is more about cooperation than competition. TechTarget. April. Retrieved June 14, 2019, from https://searchnetworking.techtarget.com/tip/Wi-Fi-6-vs-5G-networks-is-more-about-cooperation-than-competition

McKinsey & Company (2016) Automotive revolution—perspective towards 2030. Retrieved June 24, 2019, from https://www.mckinsey.com/~/media/mckinsey/industries/high%20tech/our%20insights/disruptive%20trends%20that%20will%20transform%20the%20auto%20industry/auto%202030%20report%20jan%202016.ashx

Mishra AR (2018) Fundamentals of network planning and optimization 2G/3G/4G: evolution to 5G, 2nd edn. Wiley, New York (ISBN: 9781119331711)

Book   Google Scholar  

Mogg T (2019) Lyft’s Robo-taxis have made more than 50,000 rides in Las Vegas. June 06. Retrieved June 14, 2019, from https://www.digitaltrends.com/cars/lyfts-robo-taxis-have-made-more-than-50000-rides-in-las-vegas/

Niu Y, Li Y, Jin D, Su L, Vasilakos AV (2016) A survey of millimeter-wave communications (Mmwave) for 5G: opportunities and challenges. Wirel Netw 21:2657–2676

Obiodu E, Giles M (2017) The 5G era: age of boundless connectivity and intelligent automation. GSM Association. Retrieved May 24, 2019, from https://www.gsma.com/latinamerica/wp-content/uploads/2018/08/2017-02-27-0efdd9e7b6eb1c4ad9aa5d4c0c971e62.pdf

Omale G (2018) Gartner survey reveals two-thirds of organizations Intend to deploy 5G by 2020. Gartner. December 18, Retrieved May 14, 2019, from https://www.gartner.com/en/newsroom/press-releases/2018-12-18-gartner-survey-reveals-two-thirds-of-organizations-in

Opentechdiary (2015) Internet of Things world Europe. Retrieved from: https://opentechdiary.wordpress.com/2015/07/16/a-walk-through-internet-of-things-iot-basics-part-2/

Oughton EJ, Frias Z (2017) Exploring the cost, coverage, and rollout implications of 5G in Britain. Retrieved May 24, 2019, from http://www.itrc.org.uk/wp-content/uploads/Exploring-costs-of-5G.pdf

Pathak S (2013) Evolution in generations of cellular mobile communication. Master of Science in Cyber Law and Information Security. Project report on Telecommunication and network security on “Evolution in generations of cellular mobile communication.” Retrieved June 14, 2019, from https://www.academia.edu/5742206/Evolution_of_generations_from_0G_to_4G

Phifer L (2017) What’s the difference between licensed and unlicensed wireless? TechTarget. September. Retrieved June 14, 2019, from https://searchnetworking.techtarget.com/answer/Whats-the-difference-between-licensed-and-unlicensed-wireless

Rappaport TS, Daniels RC, Heath RW, Murdock JN (2014) Introduction. In: Millimeter wave wireless communication. Pearson Education, Upper Saddle River, NJ, USA ( ISBN-13: 978-0-13-217228-8 )

Rossi R, Hirama IL (2015) Characterizing big data management. Issues Inf Sci Inf Technol 12:165–180

Saha RK, Saengudomlert P, Aswakul C (2016) Evolution towards 5G mobile networks—a survey on enabling technologies. Eng J 20(1):87–112

Sears SW (1977) The American heritage history of the automobile in America, 1st edn. Scribner (Simon & Schuster), New York, NY (ISBN-13: 978-0671229863)

Tariq UU, Ali H, Liu L, Panneerselvan J, Zhai X (2019) Energy-efficient static task scheduling on VFI-based NoC-HMPSoCs for intelligent edge devices in cyber-physical systems. ACM Trans Intell Syst Technol 66:22

World Economic Forum (2015) Reinventing the wheel: digital transformation in the automotive industry. Retrieved June 14, 2019, from http://reports.weforum.org/digital-transformation/reinventing-the-wheel/

Zhang Q, Fitzek FHP (2015) Mission critical IoT communication in 5G. In: Future access enablers for ubiquitous and intelligent infrastructures, vol.159. Springer International Publishing. Cham, Switzerland, pp 35–41

Zheng Y Ali H, Tariq UU (2019) Chapter 13, Big data security in internet of things. In: Security and privacy for big data, cloud computing and applications, vol 28. The Institution of Engineering and Technology, London, UK, p 47

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5G mobile networks and health—a state-of-the-science review of the research into low-level RF fields above 6 GHz

• Ken Karipidis   ORCID: orcid.org/0000-0001-7538-7447 1 ,
• Rohan Mate 1 ,
• David Urban 1 ,
• Rick Tinker 1 &
• Andrew Wood 2  

Journal of Exposure Science & Environmental Epidemiology volume  31 ,  pages 585–605 ( 2021 ) Cite this article

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The increased use of radiofrequency (RF) fields above 6 GHz, particularly for the 5 G mobile phone network, has given rise to public concern about any possible adverse effects to human health. Public exposure to RF fields from 5 G and other sources is below the human exposure limits specified by the International Commission on Non-Ionizing Radiation Protection (ICNIRP). This state-of-the science review examined the research into the biological and health effects of RF fields above 6 GHz at exposure levels below the ICNIRP occupational limits. The review included 107 experimental studies that investigated various bioeffects including genotoxicity, cell proliferation, gene expression, cell signalling, membrane function and other effects. Reported bioeffects were generally not independently replicated and the majority of the studies employed low quality methods of exposure assessment and control. Effects due to heating from high RF energy deposition cannot be excluded from many of the results. The review also included 31 epidemiological studies that investigated exposure to radar, which uses RF fields above 6 GHz similar to 5 G. The epidemiological studies showed little evidence of health effects including cancer at different sites, effects on reproduction and other diseases. This review showed no confirmed evidence that low-level RF fields above 6 GHz such as those used by the 5 G network are hazardous to human health. Future experimental studies should improve the experimental design with particular attention to dosimetry and temperature control. Future epidemiological studies should continue to monitor long-term health effects in the population related to wireless telecommunications.

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Introduction.

There are continually emerging technologies that use radiofrequency (RF) electromagnetic fields particularly in telecommunications. Most telecommunication sources currently operate at frequencies below 6 GHz, including radio and TV broadcasting and wireless sources such as local area networks and mobile telephony. With the increasing demand for higher data rates, better quality of service and lower latency to users, future wireless telecommunication sources are planned to operate at frequencies above 6 GHz and into the ‘millimetre wave’ range (30–300 GHz) [ 1 ]. Frequencies above 6 GHz have been in use for many years in various applications such as radar, microwave links, airport security screening and in medicine for therapeutic applications. However, the planned use of millimetre waves by future wireless telecommunications, particularly the 5th generation (5 G) of mobile networks, has given rise to public concern about any possible adverse effects to human health.

The interaction mechanisms of RF fields with the human body have been extensively described and tissue heating is the main effect for RF fields above 100 kHz (e.g. HPA; SCENHIR) [ 2 , 3 ]. RF fields become less penetrating into body tissue with increasing frequency and for frequencies above 6 GHz the depth of penetration is relatively short with surface heating being the predominant effect [ 4 ].

International exposure guidelines for RF fields have been developed on the basis of current scientific knowledge to ensure that RF exposure is not harmful to human health [ 5 , 6 ]. The guidelines developed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) in particular form the basis for regulations in the majority of countries worldwide [ 7 ]. In the frequency range above 6 GHz and up to 300 GHz the ICNIRP guidelines prevent excessive heating at the surface of the skin and in the eye.

Although not as extensively studied as RF fields at lower frequencies, a number of studies have investigated the effects of RF fields at frequencies above 6 GHz. Previous reviews have reported studies investigating frequencies above 6 GHz that show effects although many of the reported effects occurred at levels greater than the ICNIRP guidelines [ 1 , 8 ]. Given the public concern over the planned roll-out of 5 G using millimetre waves, it is important to determine whether there are any related adverse health consequences at levels encountered in the environment. The aim of this paper is to present a state-of-the-science review of the bioeffects research into RF fields above 6 GHz at low levels of exposure (exposure below the occupational limits of the ICNIRP guidelines). A meta-analysis of in vitro and in vivo studies, providing quantitative effect estimates for each study, is presented separately in a companion paper [ 9 ].

The state-of-the-science review included a comprehensive search of all available literature and examined the extent, range and nature of evidence into the bioeffects of RF fields above 6 GHz, at levels below the ICNIRP occupational limits. The review consisted of biomedical studies on low-level RF electromagnetic fields from 6 GHz to 300 GHz published at any starting date up to December 2019. Studies were initially found by searching the databases PubMed, EMF-Portal, Google Scholar, Embase and Web of Science using the search terms “millimeter wave”, “millimetre wave”, “gigahertz”, “GHz” and “radar”. We further searched major reviews published by health authorities on RF and health [ 2 , 3 , 10 , 11 ]. Finally, we searched the reference list of all the studies included. Studies were only included if the full paper was available in English.

Although over 300 studies were considered, this review was limited to experimental studies (in vitro, in vivo, human) where the stated RF exposure level was at or below the occupational whole-body limits specified by the ICNIRP (2020) guidelines: power density (PD) reference level of 50 W/m 2 or specific absorption rate (SAR) basic restriction of 0.4 W/kg. Since the PD occupational limits for local exposure are more relevant to in vitro studies, and since these limits are higher, we have included those studies with PD up to 100–200 W/m 2 , depending on frequency. The review included studies below the ICNIRP general public limits that are lower than the occupational limits.

The review also included epidemiological studies (cohort, case-control, cross-sectional) investigating exposure to radar but excluded studies where the stated radar frequencies were below 6 GHz. Epidemiological studies on radar were included as they represent occupational exposure below the ICNIRP guidelines. Case reports or case series were excluded. Studies investigating therapeutical outcomes were also excluded unless they reported specific bio-effects.

The state-of-the-science review appraised the quality of the included studies, but unlike a systematic review it did not exclude any studies based on quality. The review also identified gaps in knowledge for future investigation and research. The reporting of results in this paper is narrative with tabular accompaniment showing study characteristics. In this paper, the acronym “MMWs” (or millimetre waves) is used to denote RF fields above 6 GHz.

The review included 107 experimental studies (91 in vitro, 15 in vivo, and 1 human) that investigated various bioeffects, including genotoxicity, cell proliferation, gene expression, cell signalling, membrane function and other effects. The exposure characteristics and biological system investigated in experimental studies for the various bioeffects are shown in Tables  1 – 6 . The results of the meta-analysis of the in vitro and in vivo studies are presented separately in Wood et al. [ 9 ].

Genotoxicity

Studies have examined the effects of exposing whole human or mouse blood samples or lymphocytes and leucocytes to low-level MMWs to determine possible genotoxicity. Some of the genotoxicity studies have looked at the possible effects of MMWs on chromosome aberrations [ 12 , 13 , 14 ]. At exposure levels below the ICNIRP limits, the results have been inconsistent, with either a statistically significant increase [ 14 ] or no significant increase [ 12 , 13 ] in chromosome aberrations.

MMWs do not penetrate past the skin therefore epithelial and skin cells have been a common model of examination for possible genotoxic effects. DNA damage in a number of epithelial and skin cell types and at varied exposure parameters both below and above the ICNIRP limits have been examined using comet assays [ 15 , 16 , 17 , 18 , 19 ]. Despite the varied exposure models and methods used, no statistically significant evidence of DNA damage was identified in these studies. Evidence of genotoxic damage was further assessed in skin cells by the occurrence of micro-nucleation. De Amicis et al. [ 18 ] and Franchini et al. [ 19 ] reported a statistically significant increase in micro-nucleation, however, Hintzsche et al. [ 15 ] and Koyama et al. [ 16 , 17 ] did not find an effect. Two of the studies also examined telomere length and found no statistically significant difference between exposed and unexposed cells [ 15 , 19 ]. Last, a Ukrainian research group examined different skin cell types in three studies and reported an increase in chromosome condensation in the nucleus [ 20 , 21 , 22 ]; these results have not been independently verified. Overall, there was no confirmed evidence of MMWs causing genotoxic damage in epithelial and skin cells.

Three studies from an Indian research group have examined indicators of DNA damage and reactive oxygen species (ROS) production in rats exposed in vivo to MMWs. The studies reported DNA strand breaks based on evidence from comet assays [ 23 , 24 ] and changes in enzymes that control the build-up of ROS [ 24 ]. Kumar et al. also reported an increase in ROS production [ 25 ]. All the studies from this research group had low animal numbers (six animals exposed) and their results have not been independently replicated. An in vitro study that investigated ROS production in yeast cultures reported an increase in free radicals exposed to high-level but not low-level MMWs [ 26 ].

Other studies have looked at the effect of low-level MMWs on DNA in a range of different ways. Two studies reported that MMWs induce colicin synthesis and prophage induction in bacterial cells, both of which are suggested as indicative of DNA damage [ 27 , 28 ]. Another study suggested that DNA exposed to MMWs undergoes polymerase chain reaction synthesis differently than unexposed DNA [ 29 ], although no statistical analysis was presented. Hintzsche et al. reported statistically significant occurrence of spindle disturbance in hybrid cells exposed to MMWs [ 30 ]. Zeni et al. found no evidence of DNA damage or alteration of cell cycle kinetics in blood cells exposed to MMWs [ 31 ]. Last, two studies from a Russian research group examined the protective effects of MMWs where mouse blood leukocytes were pre-exposed to low-level MMWs and then to X-rays [ 32 , 33 ]. The studies reported that there was statistically significant less DNA damage in the leucocytes that were pre-exposed to MMWs than those exposed to X-rays alone. Overall, these studies had no independent replication.

Cell proliferation

A number of studies have examined the effects of low-level MMWs on cell proliferation and they have used a variety of cellular models and methods of investigation. Studies have exposed bacterial cells to low-level MMWs alone or in conjunction with other agents. Two early studies reported changes in the growth rate of E. coli cultures exposed to low-level MMWs; however, both of these studies were preliminary in nature without appropriate dosimetry or statistical analysis [ 34 , 35 ]. Two studies exposed E. coli cultures and one study exposed yeast cell cultures to MMWs alone, and before and after UVC exposure [ 36 , 37 , 38 ]. All three studies reported that MMWs alone had no significant effect on bacterial cell proliferation or survival. Rojavin et al., however, did report that when E. coli bacteria were exposed to MMWs after UVC sterilisation treatment, there was an increase in their survival rate [ 36 ]. The authors suggested this could be due to the MMW activation of bacterial DNA repair mechanisms. Other studies by an Armenian research group reported a reduction in E. coli cell growth when exposed to MMWs [ 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. These studies reported that when E.coli cultures were exposed to MMWs in the presence of antibiotics, there was a greater reduction in the bacterial growth rate and an increase in the time between bacterial cell division compared with antibiotics exposure alone. Two of these studies investigated if these effects could be due to a reduction in the activity of the E. coli ATPase when exposed to MMWs. The studies reported exposure to MMWs in combination with particular antibiotics changed the concentration of H + and K + ions in the E.coli cells, which the authors linked to changes in ATPase activity [ 43 , 44 ]. Overall, the results from studies on cell proliferation of bacterial cells have been inconsistent with different research groups reporting conflicting results.

Studies have also examined how exposure to low-level MMWs could affect cell proliferation in yeast. Two early studies by a German research group reported changes in yeast cell growth [ 46 , 47 ]. However, another two independent studies did not report any changes in the growth rate of exposed yeast [ 48 , 49 ]. Furia et al. [ 48 ] noted that the Grundler and Keilmann studies [ 46 , 47 ] had a number of methodical issues, which may have skewed their results, such as poor exposure control and analysis of results. Another study exposed yeast to MMWs before and after UVC exposure and reported that MMWs did not change the rates of cell survival [ 37 ].

Studies have also examined the possible effect of low-level MMWs on tumour cells with some studies reporting a possible anti-proliferative effect. Chidichimo et al. reported a reduction in the growth of a variety of tumour cells exposed to MMWs; however, the results of the study did not support this conclusion [ 50 ]. An Italian research group published a number of studies investigating proliferation effects on human melanoma cell lines with conflicting results. Two of the studies reported reduced growth rate [ 51 , 52 ] and a third study showed no change in proliferation or in the cell cycle [ 53 ]. Beneduci et al. also reported changes in the morphology of MMW exposed cells; however, the authors did not present quantitative data for these reported changes [ 51 , 52 ]. In another study by the same Italian group, Beneduci et al. reported that exposure to low-level MMWs had a greater than 40% reduction in the number of viable erythromyeloid leukaemia cells compared with controls; however, there was no significant change in the number of dead cells [ 54 ]. More recently, Yaekashiwa et al. reported no statistically significant effect in proliferation or cellular activity in glioblastoma cells exposed to low-level MMWs [ 55 ].

Other studies did not report statistically significant effects on proliferation in chicken embryo cell cultures, rat nerve cells or human skin fibroblasts exposed to low-level MMWs [ 55 , 56 , 57 ].

Gene expression

Some studies have investigated whether low-level MMWs can influence gene expression. Le Queument et al. examined a multitude of genes using microarray analyses and reported transient expression changes in five of them. However, the authors concluded that these results were extremely minor, especially when compared with studies using microarrays to study known pollutants [ 58 ]. Studies by a French research group have examined the effect of MMWs on stress sensitive genes, stress sensitive gene promotors and chaperone proteins in human glial cell lines. In two studies, glial cells were exposed to low-level MMWs and there was no observed modification in the expression of stress sensitive gene promotors when compared with sham exposed cells [ 59 , 60 , 61 ]. Further, glial cells were examined for the expression of the chaperone protein clusterin (CLU) and heat shock protein HSP70. These proteins are activated in times of cellular stress to maintain protein functions and help with the repair process [ 60 ]. There was no observed modification in gene expression of the chaperone proteins. Other studies have examined the endoplasmic reticulum of glial cells exposed to MMWs [ 62 , 63 ]. The endoplasmic reticulum is the site of synthesis and folding of secreted proteins and has been shown to be sensitive to environmental insults [ 62 ]. The authors reported that there was no elevation in mRNA expression levels of endoplasmic reticulum specific chaperone proteins. Studies of stress sensitive genes in glial cells have consistently shown no modification due to low-level MMW exposure [ 59 , 60 , 61 , 62 , 63 ].

Belyaev and co-authors have studied a possible resonance effect of low-level MMWs primarily on Escherichia Coli (E. coli) cells and cultures. The Belyaev research group reported that the resonance effect of MMWs can change the conformation state of chromosomal DNA complexes [ 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]; however, most of these experiments were not temperature controlled. This resonance effect was not supported by earlier experiments on a number of different cell types conducted by Gandhi et al. and Bush et al. [ 75 , 76 ].

The results of Belyaev and co-workers have primarily been based on evidence from the anomalous viscosity time dependence (AVTD) method [ 77 ]. The research group argued that changes in the AVTD curve can indicate changes to the DNA conformation state and DNA-protein bonds. Belyaev and co-workers have reported in a number of studies that differences in the AVTD curve were dependent on several parameter including MMW characteristics (frequency, exposure level, and polarisation), cellular concentration and cell growth rate [ 69 , 71 , 72 , 73 , 74 ]. In some of the Belyaev studies E. coli were pre-exposed to X-rays, which was reported to change the AVTD curve; however, if the cells were then exposed to MMWs there was no longer a change in the AVTD curve [ 64 , 65 , 66 , 67 ]. The authors suggested that exposure to MMWs increased the rate of recovery in bacterial cells previously exposed to ionising radiation. The Belyaev group also used rat thymocytes in another study and they concluded that the results closely paralleled those found in E. coli cells [ 67 ]. The studies on the DNA conformation state change relied heavily on the AVTD method that has only been used by the Balyaev group and has not been independently validated [ 78 ].

Cell signalling and electrical activity

Studies examining effects of low-level MMWs on cell signalling have mainly involved MMW exposure to nervous system tissue of various animals. An in vivo study on rats recorded extracellular background electrical spike activity from neurons in the supraoptic nucleus of the hypothalamus after MMW exposure [ 79 ]. The study reported that there were changes in inter-spike interval and spike activity in the cells of exposed animals when compared with controls. There was also a mixture of significant shifts in neuron population proportions and spike frequency. The effect on the regularity of neuron spike activity was greater at higher frequencies. An in vitro study on rat cortical tissue slices reported that neuron firing rates decreased in half of the samples exposed to low-level MMWs [ 80 ]. The width of the signals was also decreased but all effects were short lived. The observed changes were not consistent between the two studies, but this could be a consequence of different brain regions being studied.

In vitro experiments by a Japanese research group conducted on crayfish exposed the dissected optical components and brain to MMWs [ 81 , 82 ]. Munemori and Ikeda reported that there was no significant change in the inter-spike intervals or amplitude of spontaneous discharges [ 81 ]. However, there was a change in the distribution of inter-spike intervals where the initial standard deviation decreased and then restored in a short time to a rhythm comparable to the control. A follow-up study on the same tissues and a wide range of exposure levels (many above the ICNIRP limits) reported similar results with the distribution of spike intervals decreasing with increasing exposure level [ 82 ]. These results on action potentials in crayfish tissue have not been independently investigated.

Mixed results were reported in experiments conducted by a US research group on sciatic frog nerve preparations. These studies applied electrical stimulation to the nerve and examined the effect of MMWs on the compound action potentials (CAPs) conductivity through the neurological tissue fibre. Pakhomov et al. found a reduction in CAP latency accompanied by an amplitude increase for MMWs above the ICNIRP limits but not for low-level MMWs [ 83 ]. However, in two follow-up studies, Pakhomov et al. reported that the attenuation in amplitude of test CAPs caused by high-rate stimulus was significantly reduced to the same magnitude at various MMW exposure levels [ 84 , 85 ]. In all of these studies, the observed effect on the CAPs was temporal and reversible, but there were implications of a frequency specific resonance interaction with the nervous tissue. These results on action potentials in frog sciatic nerves have not been investigated by others.

Other common experimental systems involved low-level MMW exposure to isolated ganglia of leeches. Pikov and Siegel reported that there was a decrease in the firing rate in one of the tested neurons and, through the measurement of input resistance in an inserted electrode, there was a transient dose-dependent change in membrane permeability [ 86 ]. However, Romanenko et al. found that low-level MMWs did not cause suppression of neuron firing rate [ 87 ]. Further experiments by Romanenko et al. reported that MMWs at the ICNIRP public exposure limit and above reported similar action potential firing rate suppression [ 88 ]. Significant differences were reported between MMW effects and effects due to an equivalent rise in temperature caused by heating the bathing solution by conventional means.

Membrane effects

Studies examining membrane interactions with low-level MMWs have all been conducted at frequencies above 40 GHz in in vitro experiments. A number of studies investigated membrane phase transitions involving exposure to a range of phospholipid vesicles prepared to mimic biological cell membranes. One group of studies by an Italian research group reported effects on membrane hydration dynamics and phase transition [ 89 , 90 , 91 ]. Observations included transition delays from the gel to liquid phase or vice versa when compared with sham exposures maintained at the same temperature; the effect was reversed after exposure. These reported changes remain unconfirmed by independent groups.

A number of studies investigated membrane permeability. One study focussed on Ca 2+ activated K + channels on the membrane surface of cultured kidney cells of African Green Marmosets [ 92 ]. The study reported modifications to the Hill coefficient and apparent affinity of the Ca 2+ by the K + channels. Another study reported that the effectiveness of a chemical to supress membrane permeability in the gap junction was transiently reduced when the cells were exposed to MMWs [ 93 , 94 ]. Two studies by one research group reported increases in the movement of molecules into skin cells during MMW exposure and suggested this indicates increased cell membrane permeability [ 21 , 91 ]. Permeability changes based on membrane pressure differences were also investigated in relation to phospholipid organisation [ 95 ]. Although there was no evidence of effects on phospholipid organisation on exposed model membranes, the authors reported a measurable difference in membrane pressure at low exposure levels. Another study reported neuron shrinkage and dehydration of brain tissues [ 96 ]. The study reported this was due to influences of low-level MMWs on the cellular bathing medium and intracellular water. Further, the authors suggested this influence of MMWs may have led to formation of unknown messengers, which are able to modulate brain cell hydration. A study using an artificial axon system consisting of a network of cells containing aqueous phospholipid vesicles reported permeability changes with exposure to MMWs by measuring K + efflux [ 97 ]. In this case, the authors emphasised limitations in applying this model to processes within a living organism. The varied effects of low-level MMWs on membrane permeability lack replication.

Other studies have examined the shape or size of vesicles to determine possible effects on membrane permeability. Ramundo-Orlando et al., reported effects on the shape of giant unilamellar vesicles (GUVs), specifically elongation, attributed to permeability changes [ 98 ]. However, another study reported that only smaller diameter vesicles demonstrated a statistically significant change when exposed to MMWs [ 99 ]. A study by Cosentino et al. examined the effect of MMWs on the size distributions of both large unilamellar vesicles (LUVs) and GUVs in in vitro preparations [ 100 ]. It was reported that size distribution was only affected when the vesicles were under osmotic stress, resulting in a statistically significant reduction in their size. In this case, the effect was attributed to dehydration as a result of membrane permeability changes. There is, generally, lack of replication on physical changes to phospholipid vesicles due to low-level MMWs.

Studies on E. coli and E. hirae cultures have reported resonance effects on membrane proteins and phospholipid constituents or within the media suspension [ 39 , 40 , 41 , 42 ]. These studies observed cell proliferation effects such as changes to cell growth rate, viability and lag phase duration. These effects were reported to be more pronounced at specific MMW frequencies. The authors suggested this could be due to a resonance effect on the cell membrane or the suspension medium. Torgomyan et al. and Hovnanyan et al. reported similar changes to proliferation that they attributed to changes in membrane permeability from MMW exposure [ 43 , 45 ]. These experiments were all conducted by an Armenian research group and have not been replicated by others.

Other effects

A number of studies have reported on the experimental results of other effects. Reproductive effects were examined in three studies on mice, rats and human spermatozoa. An in vivo study on mice exposed to low-level MMWs reported that spermatogonial cells had significantly more metaphase translocation disturbances than controls and an increased number of cells with unpaired chromosomes [ 101 ]. Another in vivo study on rats reported increased morphological abnormalities to spermatozoa following exposure, however, there was no statistical analysis presented [ 102 ]. Conversely, an in vitro study on human spermatozoa reported that there was an increase in motility after a short time of exposure to MMWs with no changes in membrane integrity and no generation of apoptosis [ 103 ]. All three of these studies looked at different effects on spermatozoa making it difficult to make an overall conclusion. A further two studies exposed rats to MMWs and examined their sperm for indicators of ROS production. One study reported both increases and decreases in enzymes that control the build-up of ROS [ 104 ]. The other study reported a decrease in the activity of histone kinase and an increase in ROS [ 105 ]. Both studies had low animal numbers (six animals exposed) and these results have not been independently replicated.

Immune function was also examined in a limited number of studies focussing on the effects of low-level MMWs on antigens and antibody systems. Three studies by a Russian research group that exposed neutrophils to MMWs reported frequency dependant changes in ROS production [ 106 , 107 , 108 ]. Another study reported a statistically significant decrease in antigen binding to antibodies when exposed to MMWs [ 109 ]; the study also reported that exposure decreased the stability of previously formed antigen–antibody complexes.

The effect on fatty acid composition in mice exposed to MMWs has been examined by a Russian research group using a number of experimental methods [ 110 , 111 , 112 ]. One study that exposed mice afflicted with an inflammatory condition to low-level MMWs reported no change in the fatty acid concentrations in the blood plasma. However, there was a significant increase in the omega-3 and omega-6 polyunsaturated fatty acid content of the thymus [ 110 ]. Another study exposed tumour-bearing mice and reported that monounsaturated fatty acids decreased and polyunsaturated fatty acids increased in both the thymus and tumour tissue. These changes resulted in fatty acid composition of the thymus tissue more closely resembling that of the healthy control animals [ 111 ]. The authors also examined the effect of exposure to X-rays of healthy mice, which was reported to reduce the total weight of the thymus. However, when the thymus was exposed to MMWs before or after exposure to X-rays, the fatty acid content was restored and was no longer significantly different from controls [ 112 ]. Overall, the authors reported a potential protective effect of MMWs on the recovery of fatty acids, however, all the results came from the same research group with a lack of replication from others.

Physiological effects were examined by a study conducted on mice exposed to WWMs to assess the safety of police radar [ 113 ]. The authors reported no statistically significant changes in the physiological parameters tested, which included body mass and temperature, peripheral blood and the mass and cellular composition, and number of cells in several important organs. Another study exposing human volunteers to low-level MMWs specifically examined cardiovascular function of exposed and sham exposed groups by electrocardiogram (ECG) and atrioventricular conduction velocity derivation [ 114 ]. This study reported that there were no significant differences in the physiological indicators assessed in test subjects.

Other individual studies have looked at various other effects. An early study reported differences in the attenuation of MMWs at specific frequencies in healthy and tumour cells [ 115 ]. Another early study reported no effect in the morphology of BHK-21/C13 cell cultures when exposed to low-level MMWs; the study did report morphological changes at higher levels, which were related to heating [ 116 ]. One study examined whether low-level MMWs induced cancer promotion in leukaemia and Lewis tumour cell grafted mice. The study reported no statistically significant growth promotion in either of the grafted cancer cell types [ 117 ]. Another study looked at the activity of gamma-glutamyl transpeptidase enzyme in rats after treatment with hydrocortisone and exposure to MMWs [ 118 ]. The study reported no effects at exposures below the ICNIRP limit, however, at levels above authors reported a range of effects. Another study exposed saline liquid solutions to continuous low and high level MMWs and reported temperature oscillations within the liquid medium but lacked a statistical analysis [ 119 ]. Another study reported that low-level MMWs decrease the mobility of the protozoa S. ambiguum offspring [ 120 ]. None of the reported effects in all of these other studies have been investigated elsewhere.

Epidemiological studies

There are no epidemiological studies that have directly investigated 5 G and potential health effects. There are however epidemiological studies that have looked at occupational exposure to radar, which could potentially include the frequency range from 6 to 300 GHz. Epidemiological studies on radar were included as they represent occupational exposure below the ICNIRP guidelines. The review included 31 epidemiological studies (8 cohort, 13 case-control, 9 cross-sectional and 1 meta-analysis) that investigated exposure to radar and various health outcomes including cancer at different sites, effects on reproduction and other diseases. The risk estimates as well as limitations of the epidemiological studies are shown in Table  7 .

Three large cohort studies investigated mortality in military personnel with potential exposure to MMWs from radar. Studies reporting on over 40-year follow-up of US navy veterans of the Korean War found that radar exposure had little effect on all-cause or cancer mortality with the second study reporting risk estimates below unity [ 121 , 122 ]. Similarly, in a 40-year follow-up of Belgian military radar operators, there was no statistically significant increase in all-cause mortality [ 123 , 124 ]; the study did, however, find a small increase in cancer mortality. More recently in a 25-year follow-up of military personnel who served in the French Navy, there was no increase in all-cause or cancer mortality for personnel exposed to radar [ 125 ]. The main limitation in the cohort studies was the lack of individual levels of RF exposure with most studies based on job-title. Comparisons were made between occupations with presumed high exposure to RF fields and other occupations with presumed lower exposure. This type of non-differential misclassification in dichotomous exposure assessment is associated mostly with an effect measure biased towards a null effect if there is a true effect of RF fields. If there is no true effect of RF fields, non-differential exposure misclassification will not bias the effect estimate (which will be close to the null value, but may vary because of random error). The military personnel in these studies were compared with the general population and this ‘healthy worker effect’ presents possible bias since military personnel are on average in better health than the general population; the healthy worker effect tends to underestimate the risk. The cohort studies also lacked information on possible confounding factors including other occupational exposures such as chemicals and lifestyle factors such as smoking.

Several epidemiological studies have specifically investigated radar exposure and testicular cancer. In a case-control study where most of the subjects were selected from military hospitals in Washington DC, USA, Hayes et al. found no increased risk between exposure to radar and testicular cancer [ 126 ]; exposure to radar was self-reported and thus subject to misclassification. In this study, the misclassification was likely non-differential, biasing the result towards the null. Davis and Mostofi reported a cluster of testicular cancer within a small cohort of 340 police officers in Washington State (USA) where the cases routinely used handheld traffic radar guns [ 127 ]; however, exposure was not assessed for the full cohort, which may have overestimated the risk. In a population-based case-control study conducted in Sweden, Hardell et al. did not find a statistically significant association between radar work and testicular cancer; however, the result was based on only five radar workers questioning the validity of this result [ 128 ]. In a larger population-based case control study in Germany, Baumgardt-Elms et al. also reported no association between working near radar units (both self-reported and expert assessed) and testicular cancer [ 129 ]; a limitation of this study was the low participation of identified controls (57%), however, there was no difference compared with the characteristics of the cases so selection bias was unlikely. In the cohort study of US navy veterans previously mentioned exposure to radar was not associated with testicular cancer [ 122 ]; the limitations of this cohort study mentioned earlier may have underestimated the risk. Finally, in a hospital-based case-control study in France, radar workers were also not associated with risk of testicular cancer [ 130 ]; a limitation was the low participation of controls (37%) with a difference in education level between participating and non-participating controls, which may have underestimated this result.

A limited number of studies have investigated radar exposure and brain cancer. In a nested case-control study within a cohort of male US Air Force personnel, Grayson reported a small association between brain cancer and RF exposure, which included radar [ 131 ]; no potential confounders were included in the analysis, which may have overestimated the result. However, in a case-control study of personnel in the Brazilian Navy, Santana et al. reported no association between naval occupations likely to be exposed to radar and brain cancer [ 132 ]; the small number of cases and lack of diagnosis confirmation may have biased the results towards the null. All of the cohort studies on military personnel previously mentioned also examined brain cancer mortality and found no association with exposure to radar [ 122 , 124 , 125 ].

A limited number of studies have investigated radar exposure and ocular cancer. Holly et al. in a population-based case-control study in the US reported an association between self-reported exposure to radar or microwaves and uveal melanoma [ 133 ]; the study investigated many different exposures and the result is prone to multiple testing. In another case-control study, which used both hospital and population controls, Stang et al. did not find an association between self-reported exposure to radar and uveal melanoma [ 134 ]; a high non-response in the population controls (52%) and exposure misclassification may have underestimated this result. The cohort studies of the Belgian military and French navy also found no association between exposure to radar and ocular cancer [ 124 , 125 ].

A few other studies have examined the potential association between radar and other cancers. In a hospital-based case-control study in Italy, La Vecchia investigated 14 occupational agents and risk of bladder cancer and found no association with radar, although no risk estimate was reported [ 135 ]; non-differential self-reporting of exposure may have underestimated this finding if there is a true effect. Finkelstein found an increased risk for melanoma in a large cohort of Ontario police officers exposed to traffic radar and followed for 31 years [ 136 ]; there was significant loss to follow up which may have biased this result in either direction. Finkelstein found no statistically significant associations with other types of cancer and the study reported a statistically significant risk estimate just below unity for all cancers, which is reflective of the healthy worker effect [ 136 ]. In a large population-based case-control study in France, Fabbro-Peray et al. investigated a large number of occupational and environmental risk factors in relation to non-Hodgkin lymphoma and found no association with radar operators based on job-title; however, the result was based on a small number of radar operators [ 137 ]. The cohort studies on military personnel did not find statistically significant associations between exposure to radar and other cancers [ 122 , 124 , 125 ].

Variani et al. conducted a recent systematic review and meta-analysis investigating occupational exposure to radar and cancer risk [ 138 ]. The meta-analysis included three cohort studies [ 122 , 124 , 125 ] and three case-control studies [ 129 , 130 , 131 ] for a total sample size of 53,000 subjects. The meta-analysis reported a decrease in cancer risk for workers exposed to radar but noted the small number of studies included with significant heterogeneity between the studies.

Apart from cancer, a number of epidemiological studies have investigated radar exposure and reproductive outcomes. Two early studies on military personnel in the US [ 139 ] and Denmark [ 140 ] reported differences in semen parameters between personnel using radar and personnel on other duty assignments; these studies included only volunteers with potential fertility concerns and are prone to bias. A further volunteer study on US military personnel did not find a difference in semen parameters in a similar comparison [ 141 ]; in general these type of cross-sectional investigations on volunteers provide limited evidence on possible risk. In a case-control study of personnel in the French military, Velez de la Calle et al. reported no association between exposure to radar and male infertility [ 142 ]; non-differential self-reporting of exposure may have underestimated this finding if there is a true effect. In two separate cross-sectional studies of personnel in the Norwegian navy, Baste et al. and Møllerløkken et al. reported an association between exposure to radar and male infertility, but there has been no follow up cohort or case control studies to confirm these results [ 143 , 144 ].

Again considering reproduction, a number of studies investigated pregnancy and offspring outcomes. In a population-based case-control study conducted in the US and Canada, De Roos et al. found no statistically significant association between parental occupational exposure to radar and neuroblastoma in offspring; however, the result was based on a small number of cases and controls exposed to radar [ 145 ]. In another cross-sectional study of the Norwegian navy, Mageroy et al. reported a higher risk of congenital anomalies in the offspring of personnel who were exposed to radar; the study found positive associations with a large number of other chemical and physical exposures, but the study involved multiple comparisons so is prone to over-interpretation [ 146 ]. Finally, a number of pregnancy outcomes were investigated in a cohort study of Norwegian navy personnel enlisted between 1950 and 2004 [ 147 ]. The study reported an increase in perinatal mortality for parental service aboard fast patrol boats during a short period (3 months); exposure to radar was one of many possible exposures when serving on fast patrol boats and the result is prone to multiple testing. No associations were found between long-term exposure and any pregnancy outcomes.

There is limited research investigating exposure to radar and other diseases. In a large case-control study of US military veterans investigating a range of risk factors and amyotrophic lateral sclerosis, Beard et al. did not find a statistically significant association with radar [ 148 ]; the study reported a likely under-ascertainment of non-exposed cases, which may have biased the result away from the null. The cohort studies on military personnel did not find statistically significant associations between exposure to radar and other diseases [ 122 , 124 , 125 ].

A number of observational studies have investigated outcomes measured on volunteers in the laboratory. They are categorised as epidemiological studies because exposure to radar was not based on provocation. These studies investigated genotoxicity [ 149 ], oxidative stress [ 149 ], cognitive effects [ 150 ] and endocrine function [ 151 ]; the studies generally reported positive associations with radar. These volunteer studies did not sample from a defined population and are prone to bias [ 152 ].

The experimental studies investigating exposure to MMWs at levels below the ICNIRP occupational limits have looked at a variety of biological effects. Genotoxicity was mainly examined by using comet assays of exposed cells. This approach has consistently found no evidence of DNA damage in skin cells in well-designed studies. However, animal studies conducted by one research group reported DNA strand breaks and changes in enzymes that control the build-up of ROS, noting that these studies had low animal numbers (six animals exposed); these results have not been independently replicated. Studies have also investigated other indications of genotoxicity including chromosome aberrations, micro-nucleation and spindle disturbances. The methods used to investigate these indicators have generally been rigorous; however, the studies have reported contradictory results. Two studies by a Russian research group have also reported indicators of DNA damage in bacteria, however, these results have not been verified by other investigators.

The studies of the effect of MMWs on cell proliferation primarily focused on bacteria, yeast cells and tumour cells. Studies of bacteria were mainly from an Armenian research group that reported a reduction in the bacterial growth rate of exposed E. coli cells at different MMW frequencies; however, the studies suffered from inadequate dosimetry and temperature control and heating due to high RF energy deposition may have contributed to the results. Other authors have reported no effect of MMWs on E. coli cell growth rate. The results on cell proliferation of yeast exposed to MMWs were also contradictory. An Italian research group that has conducted the majority of the studies on tumour cells reported either a reduction or no change in the proliferation of exposed cells; however, these studies also suffered from inadequate dosimetry and temperature control.

The studies on gene expression mainly examined two different indicators, expression of stress sensitive genes and chaperone proteins and the occurrence of a resonance effect in cells to explain DNA conformation state changes. Most studies reported no effect of low-level MMWs on the expression of stress sensitive genes or chaperone proteins using a range of experimental methods to confirm these results; noting that these studies did not use blinding so experimental bias cannot be excluded from the results. A number of studies from a Russian research group reported a resonance effect of MMWs, which they propose can change the conformation state of chromosomal DNA complexes. Their results relied heavily on the AVTD method for testing changes in the DNA conformation state, however, the biological relevance of results obtained through the AVTD method has not been independently validated.

Studies on cell signalling and electrical activity reported a range of different outcomes including increases or decreases in signal amplitude and changes in signal rhythm, with no consistent effect noting the lack of blinding in most of the studies. Further, temperature contributions could not be eliminated from the studies and in some cases thermal interactions by conventional heating were studied and found to differ from the MMW effects. The results from some studies were based on small sample sizes, some being confined to a single specimen, or by observed effects only occurring in a small number of the samples tested. Overall, the reported electrical activity effects could not be dismissed as being within normal variability. This is indicated by studies reporting the restoration of normal function within a short time during ongoing exposure. In this case there is no implication of an expected negative health outcome.

Studies on membrane effects examined changes in membrane properties and permeability. Some studies observed changes in transitions from liquid to gel phase or vice versa and the authors implied that MMWs influenced cell hydration, however the statistical methods used in these studies were not described so it is difficult to examine the validity of these results. Other studies observing membrane properties in artificial cell suspensions and dissected tissue reported changes in vesicle shape, reduced cell volume and morphological changes although most of these studies suffered from various methodological problems including poor temperature control and no blinding. Experiments on bacteria and yeast were conducted by the same research group reporting changes in membrane permeability, which was attributed to cell proliferation effects, however, the studies suffered from inadequate dosimetry and temperature control. Overall, although there were a variety of membrane bioeffects reported, these have not been independently replicated.

The limited number of studies on a number of other effects from exposure to MMWs below the ICNIRP limits generally reported little to no consistent effects. The single in vivo study on cancer promotion did not find an effect although the study did not include sham controls. Effects on reproduction were contradictory that may have been influenced by opposing objectives of examining adverse health effects or infertility treatment. Further, the only study on human sperm found no effects of low-level MMWs. The studies on reproduction suffered from inadequate dosimetry and temperature control, and since sperm is sensitive to temperature, the effect of heating due to high RF energy deposition may have contributed to the studies showing an effect. A number of studies from two research groups reported effects on ROS production in relation to reproduction and immune function; the in vivo studies had low animal numbers (six animals per exposure) and the in vitro studies generally had inadequate dosimetry and temperature control. Studies on fatty acid composition and physiological indicators did not generally show any effects; poor temperature control was also a problem in the majority of these studies. A number of other studies investigating various other biological effects reported mixed results.

Although a range of bioeffects have been reported in many of the experimental studies, the results were generally not independently reproduced. Approximately half of the studies were from just five laboratories and several studies represented a collaboration between one or more laboratories. The exposure characteristics varied considerably among the different studies with studies showing the highest effect size clustered around a PD of approximately 1 W/m 2 . The meta-analysis of the experimental studies in our companion paper [ 9 ] showed that there was no dose-response relationship between the exposure (either PD or SAR) and the effect size. In fact, studies with a higher exposure tended to show a lower effect size, which is counterfactual. Most of the studies showing a large effect size were conducted in the frequency range around 40–55 GHz, representing investigations into the use of MMWs for therapeutic purposes, rather than deleterious health consequences. Future experimental research would benefit from investigating bioeffects at the specific frequency range of the next stage of the 5 G network roll-out in the range 26–28 GHz. Mobile communications beyond the 5 G network plan to use frequencies higher than 30 GHz so research across the MMW band is relevant.

An investigation into the methods of the experimental studies showed that the majority of studies were lacking in a number of quality criteria including proper attention to dosimetry, incorporating positive controls, using blind evaluation or accurately measuring or controlling the temperature of the biological system being tested. Our meta-analysis showed that the bulk of the studies had a quality score lower than 2 out of a possible 5, with only one study achieving a maximum quality score of 5 [ 9 ]. The meta-analysis further showed that studies with a low quality score were more likely to show a greater effect. Future research should pay careful attention to the experimental design to reduce possible sources of artefact.

The experimental studies included in this review reported PDs below the ICNIRP exposure limits. Many of the authors suggested that the resulting biological effects may be related to non-thermal mechanisms. However, as is shown in our meta-analysis, data from these studies should be treated with caution because the estimated SAR values in many of the studies were much higher than the ICNIRP SAR limits [ 9 ]. SAR values much higher than the ICNIRP guidelines are certainly capable of producing significant temperature rise and are far beyond the levels expected for 5 G telecommunication devices [ 1 ]. Future research into the low-level effects of MMWs should pay particular attention to appropriate temperature control in order to avoid possible heating effects.

Although a systematic review of experimental studies was not conducted, this paper presents a critical appraisal of study design and quality of all available studies into the bioeffects of low level MMWs. The conclusions from the review of experimental studies are supported by a meta-analysis in our companion paper [ 9 ]. Given the low-quality methods of the majority of the experimental studies we infer that a systematic review of different bioeffects is not possible at present. Our review includes recommendations for future experimental research. A search of the available literature showed a further 44 non-English papers that were not included in our review. Although the non-English papers may have some important results it is noted that the majority are from research groups that have published English papers that are included in our review.

The epidemiological studies on MMW exposure from radar that has a similar frequency range to that of 5 G and exposure levels below the ICNIRP occupational limits in most situations, provided little evidence of an association with any adverse health effects. Only a small number of studies reported positive associations with various methodological issues such as risk of bias, confounding and multiple testing questioning the result. The three large cohort studies of military personnel exposed to radar in particular did not generally show an association with cancer or other diseases. A key concern across all the epidemiological studies was the quality of exposure assessment. Various challenges such as variability in complex occupational environments that also include other co-exposures, retrospective estimation of exposure and an appropriate exposure metric remain central in studies of this nature [ 153 ]. Exposure in most of the epidemiological studies was self-reported or based on job-title, which may not necessarily be an adequate proxy for exposure to RF fields above 6 GHz. Some studies improved on exposure assessment by using expert assessment and job-exposure matrices, however, the possibility of exposure misclassification is not eliminated. Another limitation in many of the studies was the poor assessment of possible confounding including other occupational exposures and lifestyle factors. It should also be noted that close proximity to certain very powerful radar units could have exceeded the ICNIRP occupational limits, therefore the reported effects especially related to reproductive outcomes could potentially be related to heating.

Given that wireless communications have only recently started to use RF frequencies above 6 GHz there are no epidemiological studies investigating 5 G directly as yet. Some previous epidemiological studies have reported a possible weak association between mobile phone use (from older networks using frequencies below 6 GHz) and brain cancer [ 11 ]. However, methodological limitations in these studies prevent conclusions of causality being drawn from the observations [ 152 ]. Recent investigations have not shown an increase in the incidence of brain cancer in the population that can be attributed to mobile phone use [ 154 , 155 ]. Future epidemiological research should continue to monitor long-term health effects in the population related to wireless telecommunications.

The review of experimental studies provided no confirmed evidence that low-level MMWs are associated with biological effects relevant to human health. Many of the studies reporting effects came from the same research groups and the results have not been independently reproduced. The majority of the studies employed low quality methods of exposure assessment and control so the possibility of experimental artefact cannot be excluded. Further, many of the effects reported may have been related to heating from high RF energy deposition so the assertion of a ‘low-level’ effect is questionable in many of the studies. Future studies into the low-level effects of MMWs should improve the experimental design with particular attention to dosimetry and temperature control. The results from epidemiological studies presented little evidence of an association between low-level MMWs and any adverse health effects. Future epidemiological research would benefit from specific investigation on the impact of 5 G and future telecommunication technologies.

Wu T, Rappaport TS, Collins CM. Safe for generations to come: considerations of safety for millimeter waves in wireless communications. IEEE Micro Mag. 2015;16:65–84.

Article   Google Scholar  

Health protection agency (HPA). Health effects from radiofrequency electromagnetic fields: the report of the independent advisory group on non-ionising radiation (AGNIR). HPA. 2012; RCE 20.

Scientific committee on emerging and newly identified health risks (SCENHIR). Potential health effects of exposure to electromagnetic fields (EMF). Euro Comm. 2015; 1831-4783.

Australian radiation protection and nuclear safety agency (ARPANSA). Radiation protection standard for maximum exposure levels to radiofrequency fields—3 kHz to 300 GHz. Radiation Protection Series 3. ARPANSA; 2002.

International Commission on Non-Ionizing Radiation Protection (ICNIRP). ICNIRP guidelines for limiting exposure to electromagnetic fields (100 KHz to 300 GHz). Health Phys. 2020;118:483–524.

Article   CAS   PubMed   Google Scholar  

Institute of electrical and electronics engineers (IEEE). IEEE standard for safety levels with respect to human exposure to electric, magnetic, and electromagnetic fields, 0 Hz to 300 GHz. IEEE 2019; C95.1.

Stam R. Comparison of international policies on electromagnetic fields (power frequency and radiofrequency fields). National institute for public health and the environment, RIVM 2018.

Simkó M, Mattsson MO. 5G Wireless communication and health effects—a pragmatic review based on available studies regarding 6 to 100 GHz. Int J Environ Res Public Health. 2019;16:3406.

Article   PubMed Central   CAS   Google Scholar  

Wood A, Mate R, Karipidis K. Meta-analysis of in vitro and in vivo studies of the biological effects of low-level millimetre waves. 2020. https://doi.org/10.1038/s41370-021-00307-7 .

International commission on non-Ionizing radiation protection (ICNIRP). Exposure to high frequency electromagnetic fields, biological effects and health consequences (100 kHz-300 GHz). ICNIRP 2009; 978-3-934994-10-2.

International agency for research on cancer (IARC). IARC monographs: non-ionizing radiation, part 2: radiofrequency electromagnetic fields. IARC 2013;102:1–460.

Google Scholar  

Garaj-Vrhovac V, Horvat D, Koren Z. The relationship between colony-forming ability, chromosome aberrations and incidence of micronuclei in V79 Chinese hamster cells exposed to microwave radiation. Mutat Res Lett. 1991;263:143–9.

Article   CAS   Google Scholar  

Garaj-Vrhovac V, Fučić A, Horvat D. The correlation between the frequency of micronuclei and specific chromosome aberrations in human lymphocytes exposed to microwave radiation in vitro. Mutat Res Lett. 1992;281:181–6.

Korenstein-Ilan A, Barbul A, Hasin P, Eliran A, Gover A, Korenstein R. Terahertz radiation increases genomic instability in human lymphocytes. Radiat Res. 2008;170:224–34.

Hintzsche H, Jastrow C, Kleine-Ostmann T, Kärst U, Schrader T, Stopper H. Terahertz electromagnetic fields (0.106 THz) do not induce manifest genomic damage in vitro. PloS One. 2012;7:e46397.

Koyama S, Narita E, Shimizu Y, Suzuki Y, Shiina T, Taki M, et al. Effects of long-term exposure to 60 GHz millimeter-wavelength radiation on the genotoxicity and heat shock protein (Hsp) expression of cells derived from human eye. Int J Environ Res Public Health. 2016;13:802.

Koyama S, Narita E, Suzuki Y, Shiina T, Taki M, Shinohara N, et al. Long-term exposure to a 40-GHz electromagnetic field does not affect genotoxicity or heat shock protein expression in HCE-T or SRA01/04 cells. J Radiat Res. 2019;60:417–23.

Article   CAS   PubMed   PubMed Central   Google Scholar  

De Amicis A, De Sanctis S, Di Cristofaro S, Franchini V, Lista F, Regalbuto E, et al. Biological effects of in vitro THz radiation exposure in human foetal fibroblasts. Mutat Res Genet Toxicol Environ Mutagen. 2015;793:150–60.

Franchini V, Regalbuto E, De Amicis A, De Sanctis S, Di Cristofaro S, Coluzzi E, et al. Genotoxic effects in human fibroblasts exposed to microwave radiation. Health Phys. 2018;115:126–39.

Shckorbatov YG, Grigoryeva NN, Shakhbazov VG, Grabina VA, Bogoslavsky AM. Microwave irradiation influences on the state of human cell nuclei. Bioelectromagnetics. 1998;19:414–9.

Shckorbatov YG, Pasiuga VN, Kolchigin NN, Grabina VA, Batrakov DO, Kalashnikov VV. The influence of differently polarised microwave radiation on chromatin in human cells. Int J Radiat Biol. 2009;85:322–9.

Shckorbatov YG, Pasiuga VN, Goncharuk EI, Petrenko TP, Grabina VA, Kolchigin NN, et al. Effects of differently polarized microwave radiation on the microscopic structure of the nuclei in human fibroblasts. J Zhejiang Univ Sci B. 2010;11:801–5.

Article   PubMed   PubMed Central   Google Scholar  

Paulraj R, Behari J. Single strand DNA breaks in rat brain cells exposed to microwave radiation. Mutat Res. 2006;596:76–80.

Kesari KK, Behari J. Fifty-gigahertz microwave exposure effect of radiations on rat brain. Appl Biochem Biotechnol. 2009;158:126.

Kumar S, Kesari KK, Behari J. Evaluation of genotoxic effects in male Wistar rats following microwave exposure. Indian J Exp Biol. 2010;48:586–92.

PubMed   Google Scholar  

Crouzier D, Perrin A, Torres G, Dabouis V, Debouzy JC. Pulsed electromagnetic field at 9.71 GHz increase free radical production in yeast (Saccharomyces cerevisiae). Patho Biol. 2009;57:245–51.

Smolyanskaya AZ, Vilenskaya RL. Effects of millimeter-band electromagnetic radiation on the functional activity of certain genetic elements of bacterial cells. Sov Phys. 1974;16:571. USPEKHI

Lukashevsky KV, Belyaev IY. Switching of prophage lambda genes in Escherichia coli by millimetre waves. Med Sci Res. 1990;18:955–7.

Kalantaryan VP, Vardevanyan PO, Babayan YS, Gevorgyan ES, Hakobyan SN, Antonyan AP. Influence of low intensity coherent electromagnetic millimeter radiation (EMR) on aqua solution of DNA. Prog Electromag Res. 2010;13:1–9.

Hintzsche H, Jastrow C, Kleine-Ostmann T. Terahertz radiation induces spindle disturbances in human-hamster hybrid cells. Radiat Res. 2011;175:569–74.

Zeni O, Gallerano GP, Perrotta A, Romano M, Sannino A, Sarti M, et al. Cytogenetic observations in human peripheral blood leukocytes following in vitro exposure to THz radiation: a pilot study. Health Phys. 2007;92:349–57.

Gapeyev A, Lukyanova N, Gudkov S. Hydrogen peroxide induced by modulated electromagnetic radiation protects the cells from DNA damage. Open Life Sci. 2014;9:915–21.

Gapeyev AB, Lukyanova NA. Pulse-modulated extremely high-frequency electromagnetic radiation protects cellular DNA from the damaging effects of physical and chemical factors in vitro. Biophys. 2015;60:732–8.

Webb SJ, Dodds DD. Inhibition of bacterial cell growth by 136 GC microwaves. Nature. 1968;218:374–5.

Webb SJ, Booth AD. Absorption of microwaves by microorganisms. Nature. 1969;222:1199–200.

Rojavin MA, Ziskin MC. Effect of millimeter waves on survival of UVC‐exposed Escherichia coli. Bioelectromagnetics. 1995;16:188–96.

Pakhomova ON, Pakhomov AG, Akyel Y. Effect of millimeter waves on UV-induced recombination and mutagenesis in yeast. Bioelectrochem Bioenerg. 1997;43:227–32.

Cohen I, Cahan R, Shani G, Cohen E, Abramovich A. Effect of 99 GHz continuous millimeter wave electro-magnetic radiation on E. coli viability and metabolic activity. Int J Radiat Biol. 2010;86:390–9.

Tadevosyan H, Kalantaryan V, Trchounian A. Extremely high frequency electromagnetic radiation enforces bacterial effects of inhibitors and antibiotics. Cell Biochem Biophys. 2008;51:97–103.

Torgomyan H, Trchounian A. Low-intensity electromagnetic irradiation of 70.6 and 73 GHz frequencies enhances the effects of disulfide bonds reducer on Escherichia coli growth and affects the bacterial surface oxidation–reduction state. Biochem Biophys Res Commun. 2011;414:265–9.

Torgomyan H, Kalantaryan V, Trchounian A. Low intensity electromagnetic irradiation with 70.6 and 73 GHz frequencies affects Escherichia coli growth and changes water properties. Cell Biochem Biophys. 2011;60:275–81.

Torgomyan H, Hovnanyan K, Trchounian A. Escherichia coli growth changes by the mediated effects after low-intensity electromagnetic irradiation of extremely high frequencies. Cell Biochem Biophys. 2012;65:445–54.

Torgomyan H, Ohanyan V, Blbulyan S, Kalantaryan V, Trchounian A. Electromagnetic irradiation of Enterococcus hirae at low-intensity 51.8-and 53.0-GHz frequencies: changes in bacterial cell membrane properties and enhanced antibiotics effects. FEMS microbiol Lett. 2012;329:131–7.

Soghomonyan D, Trchounian A. Comparable effects of low-intensity electromagnetic irradiation at the frequency of 51.8 and 53 GHz and antibiotic ceftazidime on Lactobacillus acidophilus growth and survival. Cell Biochem Biophys. 2013;67:829–35.

Hovnanyan K, Kalantaryan V, Trchounian A. The distinguishing effects of low‐intensity electromagnetic radiation of different extremely high frequencies on Enterococcus hirae: growth rate inhibition and scanning electron microscopy analysis. Lett Appl microbiol. 2017;65:220–5.

Grundler W, Keilmann F. Nonthermal effects of millimeter microwaves on yeast growth. Z Naturforsch. 1977;33:15–22.

Grundler W, Keilmann F. Sharp resonances in yeast growth prove nonthermal sensitivity to microwaves. Phys Rev Lett. 1983;51:1214.

Furia L, Hill DW, Gandhi OMP. Effect of millimeter-wave irradiation on growth of Saccharomyces cerevisiae. IEEE Trans Biom Eng. 1986;33:993–9.

Gos P, Eicher B, Kohli J, Heyer WD. Extremely high frequency electromagnetic fields at low power density do not affect the division of exponential phase Saccharomyces cerevisiae cells. Bioelectromagnetics. 1997;18:142–55.

Chidichimo G, Beneduci A, Nicoletta M, Critelli M, De RR, Tkatchenko Y, et al. Selective inhibition of tumoral cells growth by low power millimeter waves. Anticancer Res. 2002;22:1681–8.

Beneduci A, Chidichimo G, Tripepi S, Perrotte E. Frequency and irradiation time-dependant antiproliferative effect of low-power millimeter waves on RPMI 7932 human melanoma cell line. Anticancer Res. 2005;25(2A):1023–8.

Beneduci A, Chidichimo G, Tripepi S, Perrotte E. Transmission electron microscopy study of the effects produced by wide-band low-power millimeter waves on MCF-7 human breast cancer cells in culture. Anticancer Res. 2005;25(2A):1009–13.

Beneduci A. Evaluation of the potential in vitro antiproliferative effects of millimeter waves at some therapeutic frequencies on RPMI 7932 human skin malignant melanoma cells. Cell Biochem Biophys. 2009;1:25–32.

Beneduci A, Chidichimo G, Tripepi S, Perrotta E, Cufone F. Antiproliferative effect of millimeter radiation on human erythromyeloid leukemia cell line K562 in culture: ultrastructural-and metabolic-induced changes. Bioelectrochemistry. 2007;70:214–20.

Yaekashiwa N, Otsuki S, Hayashi SI, Kawase K. Investigation of the non-thermal effects of exposing cells to 70–300 GHz irradiation using a widely tunable source. J Radiat Res. 2017;59:116–21.

Badzhinyan SA, Sayadyan AB, Sarkisyan NK, Grigoryan RM, Gasparyan GG. Lethal effect of electromagnetic radiation of the millimeter wavelength range on cell cultures of chicken embryo. Dokl Biochem Biophys. 2001;377:94–5.

Shiina T, Suzuki Y, Kasai Y, Inami Y, Taki M, Wake K. Effect of two-times 24 h exposures to 60 GHz millimeter-waves on neurite outgrowth in PC12VG cells in consideration of polarization. IEEE Int Sympo Electromag Compat. 2014;13:166–9.

Le Quément C, Nicolas Nicolaz C, Zhadobov M, Desmots F, Sauleau R, Aubry M, et al. Whole‐genome expression analysis in primary human keratinocyte cell cultures exposed to 60 GHz radiation. Bioelectromagnetics. 2012;33:147–58.

Article   PubMed   CAS   Google Scholar  

Zhadobov M, Sauleau R, Le Coq L, Thouroude D, Orlov I, Michel D et al. 60 GHz electromagnetic fields do not activate stress-sensitive gene expression. IEEE 11th Int Sympo on Antenna Technol and appl electromag. 2005;11:1–4.

Zhadobov M, Sauleau R, Le Coq L, Debure L, Thouroude D, Michel D, et al. Low‐power millimeter wave radiations do not alter stress‐sensitive gene expression of chaperone proteins. Bioelectromagnetics. 2007;28:188–96.

Zhadobov M, Nicolaz CN, Sauleau R, Desmots F, Thouroude D, Michel D, et al. Evaluation of the potential biological effects of the 60-GHz millimeter waves upon human cells. IEEE Trans Antennas Propag. 2009;57:2949–56.

Nicolaz CN, Zhadobov M, Desmots F, Ansart A, Sauleau R, Thouroude D, et al. Study of narrow band millimeter‐wave potential interactions with endoplasmic reticulum stress sensor genes. Bioelectromagnetics. 2008;30:365–73.

Nicolaz CN, Zhadobov M, Desmots F, Sauleau R, Thouroude D, Michel D, et al. Absence of direct effect of low-power millimeter-wave radiation at 60.4 GHz on endoplasmic reticulum stress. Cell Biol Toxicol. 2009;25:471–8.

Belyaev IY, Alipov YD, Shcheglov VS, Lystsov VN. Resonance effect of microwaves on the genome conformational state of E. coli cells. Z Naturforsch C. 1992;47:621–7.

Belyaev IY, Shcheglov VS, Alipov YD. Existence of selection rules on helicity during discrete transitions of the genome conformational state of E. coli cells exposed to low-level millimetre radiation. Bioelectrochem Bioenerg. 1992;27:405–11.

Belyaev IY, Shcheglov VS, Alipov YD. Selection rules on helicity during discrete transitions of the genome conformational state in intact and X-rayed cells of E. coli in millimeter range of electromagnetic field. Charg Field Eff Biosyst. 1992;3:115–26.

Belyaev I, Alipov YD, Shcheglov VS, Chromosome DNA. as a target of resonant interaction between Escherichia coli cells and low–intensity millimeter waves. Electro Magnetobiol. 1992;11:97–108.

Belyaev IY, Alipov YD, Polunin VA, Shcheglov VS. Evidence for dependence of resonant frequency of millimeter wave interaction with Escherichia coli K12 cells on haploid genome length. Electro Magnetobiol. 1993;12:39–49.

Belyaev IY, Shcheglov VS, Alipov YD, Radko SP. Regularities of separate and combined effects of circularly polarized millimeter waves on E. coli cells at different phases of culture growth. Bioelectrochem Bioenerg. 1993;31:49–63.

Belyaev IY, Alipov YD, Shcheglov VS, Polunin VA, Aizenberg OA. Cooperative response of Escherichia coli cells to the resonance effect of millimeter waves at super low intensity. Electro Magnetobiol. 1994;13:53–66.

Belyaev IY, Kravchenko VG. Resonance effect of low-intensity millimeter waves on the chromatin conformational state of rat thymocytes. Z Naturforsch. 1994;49:352–8.

Belyaev IY, Shcheglov VS, Alipov YD, Polunin VA. Resonance effect of millimeter waves in the power range from 10‐19 to 3× 10‐3 W/cm2 on Escherichia coli cells at different concentrations. Bioelectromagnetics. 1996;17:312–21.

Shcheglov VS, Belyaev I, Alipov YD, Ushakov VL. Power-dependent rearrangement in the spectrum of resonance effect of millimeter waves on the genome conformational state of Escherichia Coli cells. Electro Magnetobiol. 1997;16:69–82.

Shcheglov VS, Alipov ED, Belyaev I. Cell-to-cell communication in response of E. coli cells at different phases of growth to low-intensity microwaves. Biochim biophys Acta. 2002;1572:101–6.

Gandhi OP, Hagmann MJ, Hill DW, Partlow LM, Bush L. Millimeter wave absorption spectra of biological samples. Bioelectromagnetics. 1980;1:285–98.

Bush LG, Hill DW, Riazi A, Stensaas LJ, Partlow LM, Gandhi OP. Effects of millimeter‐wave radiation on monolayer cell cultures. III. A search for frequency‐specific athermal biological effects on protein synthesis. Bioelectromagnetics. 1981;2:151–9.

Belyaev IY, Shcheglov VS, Alipov ED, Ushakov VD. Nonthermal effects of extremely high-frequency microwaves on chromatin conformation in cells in vitro—dependence on physical, physiological, and genetic factors. IEEE Trans Micro Theory Tech. 2000;48:2172–9.

Pakhomov AG, Akyel Y, Pakhomova ON, Stuck BE, Murphy MR. Current state and implications of research on biological effects of millimeter waves: a review of the literature. Bioelectromagnetics. 1998;19:393–413.

Minasyan SM, Grigoryan GY, Saakyan SG, Akhumyan AA, Kalantaryan VP. Effects of the action of microwave-frequency electromagnetic radiation on the spike activity of neurons in the supraoptic nucleus of the hypothalamus in rats. Neurosci Behav Physiol. 2007;37:175–80.

Pikov V, Arakaki X, Harrington M, Fraser SE, Siegel PH. Modulation of neuronal activity and plasma membrane properties with low-power millimeter waves in organotypic cortical slices. J Neural Eng. 2010;7:045003.

Article   PubMed   Google Scholar  

Munemori J, Ikeda T. Effects of low-level microwave radiation on the eye of the crayfish. Med Biol Eng Comput. 1982;20:84–8.

Munemori J, Ikeda T. Biological effects of X-band microwave radiation on the eye of the crayfish. Med Biol Eng Comput. 1984;22:263–7.

Pakhomov AG, Prol HK, Mathur SP, Akyel Y, Campbell CB. Frequency-specific effects of millimeter-wavelength electromagnetic radiation in isolated nerve. Electro Magnetobiol. 1997;16:43–57.

Pakhomov AG, Prol HK, Mathur SP, Akyel Y, Campbell CB. Search for frequency‐specific effects of millimeter‐wave radiation on isolated nerve function. Bioelectromagnetics. 1997;18:324–34.

Pakhomov AG, Prol HK, Mathur SP, Akyel Y, Campbell CB. Role of field intensity in the biological effectiveness of millimeter waves at a resonance frequency. Bioelectrochem Bioenerg. 1997;43:27–33.

Pikov V, Siegel PH. Millimeter wave-induced changes in membrane properties of leech Retzius neurons. Photonic Therapeutics Diagnostics. 2011;7883:56–1.

Romanenko S, Siegel PH, Pikov V. Microdosimetry and physiological effects of millimeter wave irradiation in isolated neural ganglion preparation. IEEE 2013 International kharkov symposium on physics and engineering of microwaves, millimeter and submillimeter waves. IEEE. 2013;13:512–6.

Romanenko S, Siegel PH, Wagenaar DA, Pikov V. Effects of millimeter wave irradiation and equivalent thermal heating on the activity of individual neurons in the leech ganglion. J Neurophysiol. 2014;112:2423–31.

Beneduci A, Filippelli L, Cosentino K, Calabrese ML, Massa R, Chidichimo G. Microwave induced shift of the main phase transition in phosphatidylcholine membranes. Bioelectrochemistry. 2012;1:18–24.

Beneduci A, Cosentino K, Chidichimo G. Millimeter wave radiations affect membrane hydration in phosphatidylcholine vesicles. Materials. 2013;6:2701–12.

Beneduci A, Cosentino K, Romeo S, Massa R, Chidichimo G. Effect of millimetre waves on phosphatidylcholine membrane models: a non-thermal mechanism of interaction. Soft Matter. 2014;10:5559–67.

Geletyuk VI, Kazachenko VN, Chemeris NK, Fesenko EE. Dual effects of microwaves on single Ca2+-activated K+ channels in cultured kidney cells Vero. FEBS Lett. 1995;359:85–8.

Chen Q, Zeng QL, Lu DQ, Chiang H. Millimeter wave exposure reverses TPA suppression of gap junction intercellular communication in HaCaT human keratinocytes. Bioelectromagnetics. 2004;25:1–4.

Shckorbatov YG, Shakhbazov VG, Navrotskaya VV, Grabina VA, Sirenko SP, Fisun AI, et al. Application of intracellular microelectrophoresis to analysis of the influence of the low‐level microwave radiation on electrokinetic properties of nuclei in human epithelial cells. Electrophoresis. 2002;23:2074–9.

Zhadobov M, Sauleau R, Vié V, Himdi M, Le Coq L, Thouroude D. Interactions between 60-GHz millimeter waves and artificial biological membranes: dependence on radiation parameters. IEEE Trans Micro Theory Tech. 2006;54:2534–42.

Deghoyan A, Heqimyan A, Nikoghosyan A, Dadasyan E, Ayrapetyan S. Cell bathing medium as a target for non thermal effect of millimeter waves. Electromag Biol Med. 2012;31:132–42.

D’Agostino S, Della Monica C, Palizzi E, Di Pietrantonio F, Benetti M, Cannatà D, et al. Extremely high frequency electromagnetic fields facilitate electrical signal propagation by increasing transmembrane potassium efflux in an artificial axon model. Sci Rep. 2018;8:9299.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Ramundo-Orlando A, Longo G, Cappelli M, Girasole M, Tarricone L, Beneduci A, et al. The response of giant phospholipid vesicles to millimeter waves radiation. Biochem Biophys Acta. 2009;1788:1497–507.

Di Donato L, Cataldo M, Stano P, Massa R, Ramundo-Orlando A. Permeability changes of cationic liposomes loaded with carbonic anhydrase induced by millimeter waves radiation. Radiat Res. 2012;178:437–46.

Cosentino K, Beneduci A, Ramundo-Orlando A, Chidichimo G. The influence of millimeter waves on the physical properties of large and giant unilamellar vesicles. J Biol Phys. 2013;39:395–410.

Manikowska E, Luciani JM, Servantie B, Czerski P, Obrenovitch J, Stahl A. Effects of 9.4 GHz microwave exposure on meiosis in mice. Experientia. 1979;35:388–90.

Subbotina TI, Tereshkina OV, Khadartsev AA, Yashin AA. Effect of low-intensity extremely high frequency radiation on reproductive function in Wistar rats. Bull Exp Biol Med. 2006;142:189–90.

Volkova NA, Pavlovich EV, Gapon AA, Nikolov OT. Effects of millimeter-wave electromagnetic exposure on the morphology and function of human cryopreserved spermatozoa. Bull Exp Biol Med. 2014;157:574–6.

Kesari KK, Behari J. Microwave exposure affecting reproductive system in male rats. Appl Biochem Biotechnol. 2010;162:416–28.

Kumar S, Kesari KK, Behari J. Influence of microwave exposure on fertility of male rats. Fertil Steril. 2011;95:1500–2.

Gapeyev AB, Safronova VG, Chemeris NK, Fesenko EE. Inhibition of the production of reactive oxygen species in mouse peritoneal neutrophils by millimeter wave radiation in the near and far field zones of the radiator. Bioelectrochem Bioenerg. 1997;43:217–20.

Gapeyev AB, Yakushina VS, Chemeris NK, Fesenko EE. Modification of production of reactive oxygen species in mouse peritoneal neutrophils on exposure to low-intensity modulated millimeter wave radiation. Bioelectrochem Bioenerg. 1998;46:267–72.

Safronova VG, Gabdoulkhakova AG, Santalov BF. Immunomodulating action of low intensity millimeter waves on primed neutrophils. Bioelectromagnetics. 2002;23:599–606.

Homenko A, Kapilevich B, Kornstein R, Firer MA. Effects of 100 GHz radiation on alkaline phosphatase activity and antigen–antibody interaction. Bioelectromagnetics. 2009;30:167–75.

Gapeyev AB, Kulagina TP, Aripovsky AV, Chemeris NK. The role of fatty acids in anti‐inflammatory effects of low‐intensity extremely high‐frequency electromagnetic radiation. Bioelectromagnetics. 2011;32:388–95.

Gapeyev AB, Kulagina TP, Aripovsky AV. Exposure of tumor-bearing mice to extremely high-frequency electromagnetic radiation modifies the composition of fatty acids in thymocytes and tumor tissue. Int J Radiat Biol. 2013;89:602–10.

Gapeyev AB, Aripovsky AV, Kulagina TP. Modifying effects of low-intensity extremely high-frequency electromagnetic radiation on content and composition of fatty acids in thymus of mice exposed to X-rays. Int J Radiat Biol. 2015;91:277–85.

Rotkovská D, Moc J, Kautská J, Bartonícková A, Keprtová J, Hofer M. Evaluation of the biological effects of police radar RAMER 7F. Environ Health Perspect. 1993;101:134–6.

PubMed   PubMed Central   Google Scholar  

Müller J, Hadeler KP, Müller V, Waldmann J, Landstorfer FM, Wisniewski R, et al. Influence of low power cm-/mm-microwaves on cardiovascular function. Int J Environ Health Res. 2004;14:331–41.

Webb SJ, Booth AD. Microwave absorption by normal and tumor cells. Science. 1971;1:72–4. 174

Stensaas LJ, Partlow LM, Bush LG, Iversen PL, Hill DW, Hagmann MJ, et al. Effects of millimeter‐wave radiation on monolayer cell cultures. II. Scanning and transmission electron microscopy. Bioelectromagnetics. 1981;2:141–50.

Bellossi A, Dubost G, Moulinoux JP, Himdi M, Ruelloux M, Rocher C. Biological effects of millimeter wave irradiation on mice-preliminary results. IEEE Trans Micro Theory Tech. 2000;48:2104–10.

Olchowik G, Maj JG. Inhibitory action of microwave radiation on gamma-glutamyl transpeptidase activity in liver of rats treated with hydrocortisone. Folia Histochemica Et Cytobiologica. 2000;38:189–91.

CAS   PubMed   Google Scholar  

Khizhnyak EP, Ziskin MC. Temperature oscillations in liquid media caused by continuous (nonmodulated) millimeter wavelength electromagnetic irradiation. Bioelectromagnetics. 1996;17:223–9.

Sarapultseva EI, Igolkina JV, Tikhonov VN, Dubrova YE. The in vivo effects of low-intensity radiofrequency fields on the motor activity of protozoa. Int J Radiat Biol. 2014;90:262–7.

Robinette CD, Silverman C, Jablon S. Effects upon health of occupational exposure to microwave radiation (radar). Am J Epidemiol. 1980;112:39–53.

Groves FD, Page WF, Gridley G, Lisimaque L, Stewart PA, Tarone RE, et al. Cancer in Korean war navy technicians: mortality survey after 40 years. Am J Epidemiol. 2002;155:810–8.

Degrave E, Autier P, Grivegnée AR, Zizi M. All-cause mortality among Belgian military radar operators: a 40-year controlled longitudinal study. Eur J Epidemiol. 2005;20:677–81.

Degrave E, Meeusen B, Grivegnée AR, Boniol M, Autier P. Causes of death among Belgian professional military radar operators: a 37‐year retrospective cohort study. Int J Cancer. 2009;124:945–51.

Dabouis V, Arvers P, Debouzy JC, Sebbah C, Crouzier D, Perrin A. First epidemiological study on occupational radar exposure in the French Navy: a 26-year cohort study. Int J Environ Health Res. 2016;26:131–44.

Hayes RB, Brown LM, Pottern LM, Gomez M, Kardaun JW, Hoover RN, et al. Occupation and risk for testicular cancer: a case-control study. Int J Epidemiol. 1990;19:825–31.

Davis RL, Mostofi FK. Cluster of testicular cancer in police officers exposed to hand‐held radar. Am J Ind Med. 1993;24:231–3.

Hardell LE, Näsman A, Ohlson CG, Fredrikson MA. Case-control study on risk factors for testicular cancer. Int J Oncol. 1998;13:1299–602.

Baumgardt-Elms C, Ahrens W, Bromen K, Boikat U, Stang A, Jahn I, et al. Testicular cancer and electromagnetic fields (EMF) in the workplace: results of a population-based case–control study in Germany. Cancer Causes Control 2002;13:895–902.

Walschaerts M, Muller A, Auger J, Bujan L, Guérin JF, Lannou DL, et al. Environmental, occupational and familial risks for testicular cancer: a hospital‐based case‐control study. Int J Androl. 2007;30:222–9.

Grayson JK. Radiation exposure, socioeconomic status, and brain tumor risk in the US Air Force: a nested case-control study. Am J Epidemiol. 1996;143:480–6.

Santana VS, Silva M, Loomis D. Brain neoplasms among naval military men. Int J Occup Environ health. 1999;5:88–94.

Holly EA, Aston DA, Ahn DK, Smith AH. Intraocular melanoma linked to occupations and chemical exposures. Epidemiology. 1996;1:55–61.

Stang A, Anastassiou G, Ahrens W, Bromen K, Bornfeld N, Jöckel KH. The possible role of radiofrequency radiation in the development of uveal melanoma. Epidemiology. 2001;1:7–12.

La Vecchia CA, Negri E, D’avanzo BA, Franceschi S. Occupation and the risk of bladder cancer. Int J Epidemiol. 1990;19:264–8.

Finkelstein MM. Cancer incidence among Ontario police officers. Am J Ind Med. 1998;34:157–62.

Fabbro-Peray P, Daures JP, Rossi JF. Environmental risk factors for non-Hodgkin’s lymphoma: a population-based case–control study in Languedoc-Roussillon, France. Cancer Causes Control. 2001;12:201–12.

Variani AS, Saboori S, Shahsavari S, Yari S, Zaroushani V. Effect of occupational exposure to radar radiation on cancer risk: a systematic review and meta-analysis. Asian Pac J cancer prev. 2019;20:3211–9.

Weyandt TB, Schrader SM, Turner TW, Simon SD. Semen analysis of military personnel associated with military duty assignments. Reprod Toxicol. 1996;10:521–8.

Hjollund NH, Bonde JP, Skotte J. Semen analysis of personnel operating military radar equipment. Reprod Toxicol. 1997;11:897

Schrader SM, Langford RE, Turner TW, Breitenstein MJ, Clark JC, Jenkins BL. Reproductive function in relation to duty assignments among military personnel. Reprod Toxicol. 1998;12:465–8.

Velez De La Calle JF, Rachou E, le Martelot MT, Ducot B, Multigner L, Thonneau PF. Male infertility risk factors in a French military population. Hum reprod. 2001;16:481–6.

Baste V, Riise T, Moen BE. Radiofrequency electromagnetic fields; male infertility and sex ratio of offspring. Eur J Epidemiol. 2008;23:369–77.

Møllerløkken OJ, Moen BE. Is fertility reduced among men exposed to radiofrequency fields in the Norwegian Navy? Bioelectromagnetics. 2008;29:345–52.

De Roos AJ, Teschke K, Savitz DA, Poole C, Grufferman S, Pollock BH, et al. Parental occupational exposures to electromagnetic fields and radiation and the incidence of neuroblastoma in offspring. Epidemiology. 2001;1:508–17.

Mageroy N, Mollerlokken OJ, Riise T, Koefoed V, Moen BE. A higher risk of congenital anomalies in the offspring of personnel who served aboard a Norwegian missile torpedo boat. Occup Environ Med. 2006;63:92–7.

Baste V, Moen BE, Oftedal G, Strand LA, Bjørge L, Mild KH. Pregnancy outcomes after paternal radiofrequency field exposure aboard fast patrol boats. J Occup Environ Med. 2012;54:431–8.

Beard JD, Kamel F. Military service, deployments, and exposures in relation to amyotrophic lateral sclerosis etiology and survival. Epidemiol Rev. 2015;37:55–70.

Garaj-Vrhovac V, Gajski G, Pažanin S, Šarolić A, Domijan AM, Flajs D, et al. Assessment of cytogenetic damage and oxidative stress in personnel occupationally exposed to the pulsed microwave radiation of marine radar equipment. Int J Hyg Environ Health. 2011;214:59–65.

Mortazavi SM, Shahram TA, Dehghan N. Alterations of visual reaction time and short term memory in military radar personnel. Iran J Public Health. 2013;42:428.

Singh S, Mani KV, Kapoor N. Effect of occupational EMF exposure from radar at two different frequency bands on plasma melatonin and serotonin levels. Int J Radiat Biol. 2015;91:426–34.

Ahlbom A, Green A, Kheifets L, Savitz D, Swerdlow A. ICNIRP standing committee on epidemiology: epidemiology of health effects of radiofrequency exposure. Environ Health Perspect. 2004;112:1741–54.

Savitz DA. Exposure assessment strategies in epidemiological studies of health effects of electric and magnetic fields. Sci Total Environ. 1995;168:143–53.

J‐H Kim S, Ioannides SJ, Elwood JM. Trends in incidence of primary brain cancer in New Zealand, 1995 to 2010. Aust NZ J Public Health. 2015;39:148–52.

Karipidis K, Elwood M, Benke G, Sanagou M, Tjong L, Croft RJ. Mobile phone use and incidence of brain tumour histological types, grading or anatomical location: a population-based ecological study. BMJ Open. 2018;8:e024489.

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This work was supported by the Australian Government’s Electromagnetic Energy Program. This work was also partly supported by National Health and Medical Research Council grant no. 1042464. 

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Karipidis, K., Mate, R., Urban, D. et al. 5G mobile networks and health—a state-of-the-science review of the research into low-level RF fields above 6 GHz. J Expo Sci Environ Epidemiol 31 , 585–605 (2021). https://doi.org/10.1038/s41370-021-00297-6

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Study and investigation on 5g technology: a systematic review.

1. Introduction

1.1. evolution from 1g to 5g, 1.2. key contributions.

• This survey focused on the recent trends and development in the era of 5G and novel contributions by the researcher community and discussed technical details on essential aspects of the 5G advancement.
• In this paper, the evolution of the mobile network from 1G to 5G is presented. In addition, the growth of mobile communication under different attributes is also discussed.
• This paper covers the emerging applications and research groups working on 5G & different research areas in 5G wireless communication network with a descriptive taxonomy.
• This survey discusses the current vision of the 5G networks, advantages, applications, key technologies, and key features. Furthermore, machine learning prospects are also explored with the emerging requirements in the 5G era. The article also focused on technical aspects of 5G IoT Based approaches and optimization techniques for 5G.
• we provide an extensive overview and recent advancement of emerging technologies of 5G mobile network, namely, MIMO, Non-Orthogonal Multiple Access (NOMA), mmWave, Internet of Things (IoT), Machine Learning (ML), and optimization. Also, a technical summary is discussed by highlighting the context of current approaches and corresponding challenges.
• Security challenges and considerations while developing 5G technology are discussed.
• Finally, the paper concludes with the future directives.

2. Existing Surveys and Their Applicability

2.1. limitations of existing surveys, 2.2. article organization, 3. preliminary section, 3.1. emerging 5g paradigms and its features, 3.2. commercial service providers of 5g, 3.3. 5g research groups, 3.4. 5g applications.

• High-speed mobile network: 5G is an advancement on all the previous mobile network technologies, which offers very high speed downloading speeds 0 of up to 10 to 20 Gbps. The 5G wireless network works as a fiber optic internet connection. 5G is different from all the conventional mobile transmission technologies, and it offers both voice and high-speed data connectivity efficiently. 5G offers very low latency communication of less than a millisecond, useful for autonomous driving and mission-critical applications. 5G will use millimeter waves for data transmission, providing higher bandwidth and a massive data rate than lower LTE bands. As 5 Gis a fast mobile network technology, it will enable virtual access to high processing power and secure and safe access to cloud services and enterprise applications. Small cell is one of the best features of 5G, which brings lots of advantages like high coverage, high-speed data transfer, power saving, easy and fast cloud access, etc. [ 40 ].
• Entertainment and multimedia: In one analysis in 2015, it was found that more than 50 percent of mobile internet traffic was used for video downloading. This trend will surely increase in the future, which will make video streaming more common. 5G will offer High-speed streaming of 4K videos with crystal clear audio, and it will make a high definition virtual world on your mobile. 5G will benefit the entertainment industry as it offers 120 frames per second with high resolution and higher dynamic range video streaming, and HD TV channels can also be accessed on mobile devices without any interruptions. 5G provides low latency high definition communication so augmented reality (AR), and virtual reality (VR) will be very easily implemented in the future. Virtual reality games are trendy these days, and many companies are investing in HD virtual reality games. The 5G network will offer high-speed internet connectivity with a better gaming experience [ 41 ].
• Smart homes : smart home appliances and products are in demand these days. The 5G network makes smart homes more real as it offers high-speed connectivity and monitoring of smart appliances. Smart home appliances are easily accessed and configured from remote locations using the 5G network as it offers very high-speed low latency communication.
• Smart cities: 5G wireless network also helps develop smart cities applications such as automatic traffic management, weather update, local area broadcasting, energy-saving, efficient power supply, smart lighting system, water resource management, crowd management, emergency control, etc.
• Industrial IoT: 5G wireless technology will provide lots of features for future industries such as safety, process tracking, smart packing, shipping, energy efficiency, automation of equipment, predictive maintenance, and logistics. 5G smart sensor technology also offers smarter, safer, cost-effective, and energy-saving industrial IoT operations.
• Smart Farming: 5G technology will play a crucial role in agriculture and smart farming. 5G sensors and GPS technology will help farmers track live attacks on crops and manage them quickly. These smart sensors can also be used for irrigation, pest, insect, and electricity control.
• Autonomous Driving: The 5G wireless network offers very low latency high-speed communication, significant for autonomous driving. It means self-driving cars will come to real life soon with 5G wireless networks. Using 5G autonomous cars can easily communicate with smart traffic signs, objects, and other vehicles running on the road. 5G’s low latency feature makes self-driving more real as every millisecond is essential for autonomous vehicles, decision-making is done in microseconds to avoid accidents.
• Healthcare and mission-critical applications: 5G technology will bring modernization in medicine where doctors and practitioners can perform advanced medical procedures. The 5G network will provide connectivity between all classrooms, so attending seminars and lectures will be easier. Through 5G technology, patients can connect with doctors and take their advice. Scientists are building smart medical devices which can help people with chronic medical conditions. The 5G network will boost the healthcare industry with smart devices, the internet of medical things, smart sensors, HD medical imaging technologies, and smart analytics systems. 5G will help access cloud storage, so accessing healthcare data will be very easy from any location worldwide. Doctors and medical practitioners can easily store and share large files like MRI reports within seconds using the 5G network.
• Satellite Internet: In many remote areas, ground base stations are not available, so 5G will play a crucial role in providing connectivity in such areas. The 5G network will provide connectivity using satellite systems, and the satellite system uses a constellation of multiple small satellites to provide connectivity in urban and rural areas across the world.

4. 5G Technologies

4.1. 5g massive mimo.

• Data rate: Massive MIMO is advised as the one of the dominant technologies to provide wireless high speed and high data rate in the gigabits per seconds.
• The relationship between wave frequency and antenna size: Both are inversely proportional to each other. It means lower frequency signals need a bigger antenna and vise versa.
• Number of user: From 1G to 4G technology one cell consists of 10 antennas. But, in 5G technologies one cell consist of more than 100 antennas. Hence, one small cell at the same time can handle multiple users [ 45 ]. As shown in Figure 2 .
• MIMO role in 5G: Massive MIMO will play a crucial role in the deployment of future 5G mobile communication as greater spectral and energy efficiency could be enabled.

State-of-the-Art Approaches

4.2. 5g non-orthogonal multiple access (noma).

• NOMA is different than all the previous orthogonal access techniques such as TDMA, FDMA and CDMA. In NOMA, multiple users work simultaneously in the same band with different power levels. As shown in Figure 3 .
• NOMA provides higher data rates and resolves all the loop holes of OMA that makes 5G mobile network more scalable and reliable.
• As multiple users use same frequency band simultaneously it increases the performance of whole network.
• To setup intracell and intercell interference NOMA provides nonorthogonal transmission on the transmitter end.
• The primary fundamental of NOMA is to improve the spectrum efficiency by strengthening the ramification of receiver.

State-of-the-Art of Approaches

4.3. 5g millimeter wave (mmwave).

• In the technological world, everyone uses WiMax, GPS, wifi, 4G, 3G, L-Band, S-Band, C- Band Satellite, etc., for communication. The radio frequency spectrum of these technologies is minimal, which lies between 1 GHz to 6 GHz. Hence, it is very crowded. The spectrum range from 30 GHz to 300 GHz, known as mmWave, is less utilized and still not allocated to other communication technologies. After a long time, the range from 24 GHz to 100 GHz is allocated to 5G. As shown in Figure 4 .
• The 5G mmWave offer three advantages: (1) MmWave is very less used new Band, (2) MmWave signals carry more data than lower frequency wave, and (3) MmWave can be incorporated with MIMO antenna with the potential to offer a higher magnitude capacity compared to current communication systems.

4.4. 5G IoT Based Approaches

• IoT is termed as “Internet of Things.” It provides machine-to-machine (M2M) communication and shares information between heterogeneous devices without human interference. As shown in the Figure 5 .
• 5G with IoT is a new feature of next-generation mobile communication, which provides a high-speed internet connection between moderated devices. 5G IoT also offers smart homes, smart devices, sensors, smart transportation systems, smart industries, etc., for end-users to make them smarter.
• IoT deals with moderate devices which connect through the internet. The approach of the IoT has made the consideration of the research associated with the outcome of providing wearable, smart-phones, sensors, smart transportation systems, smart devices, washing machines, tablets, etc., and these diverse systems are associated to a common interface with the intelligence to connect.
• Significant IoT applications include private healthcare systems, traffic management, industrial management, and tactile internet, etc.

4.5. Machine Learning Techniques for 5G

• Machine learning (ML) is a part of artificial intelligence. It processes and analyses the data that automates a systematic model that finds patterns and carries out decisions with minimum human interference. As shown in the Figure 6 .
• In ML, a model will be defined which fulfills the desired requirements through which desired results are obtained. In the later stage, it examines accuracy from obtained results.
• ML plays a vital role in 5G network analysis for threat detection, network load prediction, final arrangement, and network formation. Searching for a better balance between power, length of antennas, area, and network thickness crossed with the spontaneous use of services in the universe of individual users and types of devices.

4.6. Optimization Techniques for 5G

5. description of novel 5g features over 4g, 5.1. small cell, 5.2. beamforming, 5.3. mobile edge computing, 6. 5g security, 7. summary of 5g technology based on above-stated challenges, 8. conclusions, 9. future findings, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

• Bhalla, M.R.; Bhalla, A.V. Generations of mobile wireless technology: Asurvey. Int. J. Comput. Appl. 2010 , 5 , 26–32. [ Google Scholar ] [ CrossRef ]
• Mehta, H.; Patel, D.; Joshi, B.; Modi, H. 0G to 5G mobile technology: A survey. J. Basic Appl. Eng. Res. 2014 , 5 , 56–60. [ Google Scholar ]
• Sharma, V.; Choudhary, G.; You, I.; Lim, J.D.; Kim, J.N. Self-enforcing Game Theory-based Resource Allocation for LoRaWAN Assisted Public Safety Communications. J. Internet Technol. 2018 , 2 , 515–530. [ Google Scholar ]
• Al-Namari, M.A.; Mansoor, A.M.; Idris, M.Y.I. A brief survey on 5G wireless mobile network. Int. J. Adv. Comput. Sci. Appl. 2017 , 8 , 52–59. [ Google Scholar ]
• Agiwal, M.; Roy, A.; Saxena, N. Next generation 5G wireless networks: A comprehensive survey. IEEE Commun. Surv. 2016 , 18 , 1617–1655. [ Google Scholar ] [ CrossRef ]
• Buzzi, S.; Chih-Lin, I.; Klein, T.E.; Poor, H.V.; Yang, C.; Zappone, A. A survey of energy-efficient techniques for 5G networks and challenges ahead. IEEE J. Sel. Areas Commun. 2016 , 34 , 697–709. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chataut, R.; Akl, R. Massive MIMO systems for 5G and beyond networks—Overview, recent trends, challenges, and future research direction. Sensors 2020 , 20 , 2753. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Prasad, K.S.V.; Hossain, E.; Bhargava, V.K. Energy efficiency in massive MIMO-based 5G networks: Opportunities and challenges. IEEE Wirel. Commun. 2017 , 24 , 86–94. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kiani, A.; Ansari, N. Edge computing aware NOMA for 5G networks. IEEE Internet Things J. 2018 , 5 , 1299–1306. [ Google Scholar ] [ CrossRef ]
• Timotheou, S.; Krikidis, I. Fairness for non-orthogonal multiple access in 5G systems. IEEE Signal Process. Lett. 2015 , 22 , 1647–1651. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Niu, Y.; Li, Y.; Jin, D.; Su, L.; Vasilakos, A.V. A survey of millimeter wave communications (mmWave) for 5G: Opportunities and challenges. Wirel. Netw. 2015 , 21 , 2657–2676. [ Google Scholar ] [ CrossRef ]
• Qiao, J.; Shen, X.S.; Mark, J.W.; Shen, Q.; He, Y.; Lei, L. Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Commun. Mag. 2015 , 53 , 209–215. [ Google Scholar ] [ CrossRef ]
• Ramesh, M.; Priya, C.G.; Ananthakirupa, V.A.A. Design of efficient massive MIMO for 5G systems—Present and past: A review. In Proceedings of the International Conference on Intelligent Computing and Control (I2C2), Coimbatore, India, 23–24 June 2017; pp. 1–4. [ Google Scholar ]
• Khurpade, J.M.; Rao, D.; Sanghavi, P.D. A survey on IOT and 5G network. In Proceedings of the 2018 International Conference on Smart City and Emerging Technology (ICSCET), Mumbai, India, 5 January 2018; pp. 1–3. [ Google Scholar ]
• Bega, D.; Gramaglia, M.; Banchs, A.; Sciancalepore, V.; Costa-Pérez, X. A machine learning approach to 5G infrastructure market optimization. IEEE Commun. Mag. 2019 , 19 , 498–512. [ Google Scholar ] [ CrossRef ]
• Abrol, A.; Jha, R. K Power optimization in 5G networks: A step towards GrEEn communication. IEEE Commun. Mag. 2016 , 4 , 1355–1374. [ Google Scholar ] [ CrossRef ]
• Wei, Z.; Yuan, J.; Ng, D.W.K.; Elkashlan, M.; Ding, Z. A survey of downlink non-orthogonal multiple access for 5G wireless communication networks. arXiv 2016 , arXiv:1609.01856. [ Google Scholar ]
• Hoydis, J.; Kobayashi, M.; Debbah, M. Green small-cell networks. IEEE Veh. Technol. Mag. 2011 , 6 , 37–43. [ Google Scholar ] [ CrossRef ]
• Papadopoulos, H.; Wang, C.; Bursalioglu, O.; Hou, X.; Kishiyama, Y. Massive MIMO technologies and challenges towards 5G. IEICE Trans. Commun. 2016 , 99 , 602–621. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rajoria, S.; Trivedi, A.; Godfrey, W.W. A comprehensive survey: Small cell meets massive MIMO. Phys. Commun. 2018 , 26 , 40–49. [ Google Scholar ] [ CrossRef ]
• Vouyioukas, D. A survey on beamforming techniques for wireless MIMO relay networks. Int. J. Antennas Propag. 2013 , 2013 , 745018. [ Google Scholar ] [ CrossRef ]
• Al-Imari, M.; Xiao, P.; Imran, M.A. Receiver and resource allocation optimization for uplink NOMA in 5G wireless networks. In Proceedings of the International Symposium on Wireless Communication Systems (ISWCS), Brussels, Belgium, 25–28 August 2015; pp. 151–155. [ Google Scholar ]
• Beck, M.T.; Werner, M.; Feld, S.; Schimper, S. Mobile edge computing: A taxonomy. In Proceedings of the Sixth International Conference on Advances in Future Internet, Lisbon, Portugal, 16–20 November 2014; pp. 48–55. [ Google Scholar ]
• Wang, S.; Zhang, X.; Zhang, Y.; Wang, L.; Yang, J.; Wang, W. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access 2017 , 5 , 6757–6779. [ Google Scholar ] [ CrossRef ]
• Gupta, A.; Jha, R.K. A survey of 5G network: Architecture and emerging technologies. IEEE Access 2015 , 3 , 1206–1232. [ Google Scholar ] [ CrossRef ]
• 5G Explained—How 5G Works. Available online: https://vinit.com.vn/en/5g-explained-how-5g-works (accessed on 10 May 2019).
• Kang, M. The Study on the Effect of the Internet and Mobile-Cellular on Trade in Services: Using the Modified Gravity Model. J. Internet Serv. Inf. Secur. 2020 , 10 , 90–100. [ Google Scholar ]
• Chen, S.; Ma, R.; Chen, H.H.; Zhang, H.; Meng, W.; Liu, J. Machine-to-machine communications in ultra-dense networks—A survey. IEEE Commun. Surv. 2017 , 19 , 1478–1503. [ Google Scholar ] [ CrossRef ]
• Li, S.; Da Xu, L.; Zhao, S. 5G Internet of Things: A survey. J. Ind. Inf. Integr. 2018 , 10 , 1–9. [ Google Scholar ] [ CrossRef ]
• Magsi, H.; Sodhro, A.H.; Chachar, F.A.; Abro, S.A.K.; Sodhro, G.H.; Pirbhulal, S. Evolution of 5G in Internet of medical things. In Proceedings of the International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan, 3–4 March 2018; pp. 1–7. [ Google Scholar ]
• Parvez, I.; Rahmati, A.; Guvenc, I.; Sarwat, A.I.; Dai, H. A survey on low latency towards 5G: RAN, core network and caching solutions. IEEE Commun. Surv. Tutor. 2018 , 20 , 3098–3130. [ Google Scholar ] [ CrossRef ]
• Liu, G.; Hou, X.; Jin, J.; Wang, F.; Wang, Q.; Hao, Y.; Huang, Y.; Wang, X.; Deng, A. 3-D-MIMO with massive antennas paves the way to 5G enhanced mobile broadband: From system design to field trials. IEEE J. Sel. Areas Commun. 2017 , 35 , 1222–1233. [ Google Scholar ] [ CrossRef ]
• Noura, M.; Nordin, R. A survey on interference management for device-to-device (D2D) communication and its challenges in 5G networks. J. Netw. Comput. Appl. 2016 , 71 , 130–150. [ Google Scholar ] [ CrossRef ]
• Zhang, P.; Lu, J.; Wang, Y.; Wang, Q. Cooperative localization in 5G networks: A survey. Ict Express 2017 , 3 , 27–32. [ Google Scholar ] [ CrossRef ]
• Meunier, B.; Cosmas, J. 5G Internet of Radio Light Virtual Reality System. In Proceedings of the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Valencia, Spain, 6–8 June 2018; pp. 1–5. [ Google Scholar ]
• Service Providers of 5G. Available online: https://technosports.co.in/2020/12/20/the-top-5-service-providers-of-5g-network-in-2020/ (accessed on 5 September 2021).
• Baldi, G.; Diaz-Tellez, Y.; Dimitrakos, T.; Martinelli, F.; Michailidou, C.; Mori, P.; Osliak, O.; Saracino, A. Session-dependent Usage Control for Big Data. J. Internet Serv. Inf. Secur. 2020 , 10 , 76–92. [ Google Scholar ]
• Pirinen, P. A brief overview of 5G research activities. In Proceedings of the 1st International Conference on 5G for Ubiquitous Connectivity, Levi, Finland, 26–27 November 2014; pp. 17–22. [ Google Scholar ]
• Mitra, R.N.; Agrawal, D.P. 5G mobile technology: A survey. Ict Express 2015 , 1 , 132–137. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Dash, L.; Khuntia, M. Energy efficient techniques for 5G mobile networks in WSN: A Survey. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 13 March 2020; pp. 1–5. [ Google Scholar ]
• Milovanovic, D.A.; Bojkovic, Z.S. An Evolution of 5G Multimedia Communication: New Ecosystem. In 5G Multimedia Communication ; CRC Press: Boca Raton, FL, USA, 2020; pp. 129–156. [ Google Scholar ] [ CrossRef ]
• Hui, H.; Ding, Y.; Shi, Q.; Li, F.; Song, Y.; Yan, J. 5G network-based Internet of Things for demand response in smart grid: A survey on application potential. Appl. Energy 2020 , 257 , 113972. [ Google Scholar ] [ CrossRef ]
• Abdullah, M.; Altaf, A.; Anjum, M.R.; Arain, Z.A.; Jamali, A.A.; Alibakhshikenari, M.; Falcone, F.; Limiti, E. Future smartphone: MIMO antenna system for 5G mobile terminals. IEEE Access 2021 , 9 , 91593–91603. [ Google Scholar ] [ CrossRef ]
• Larsson, E.G.; Van der Perre, L. Massive MIMO for 5G ; IEEE: Piscataway, NJ, USA, 2017; Volume 1. [ Google Scholar ]
• Wang, C.X.; Wu, S.; Bai, L.; You, X.; Wang, J.; Chih-Lin, I. Recent advances and future challenges for massive MIMO channel measurements and models. Sci. China Inf. Sci. 2016 , 59 , 1–16. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Mae, M.; Ohnishi, W.; Fujimoto, H. MIMO multirate feedforward controller design with selection of input multiplicities and intersample behavior analysis. Mechatronics 2020 , 71 , 102442. [ Google Scholar ] [ CrossRef ]
• Panzner, B.; Zirwas, W.; Dierks, S.; Lauridsen, M.; Mogensen, P.; Pajukoski, K.; Miao, D. Deployment and implementation strategies for massive MIMO in 5G. In Proceedings of the IEEE Globecom Workshops (GC Wkshps), Austin, TX, USA, 8–12 December 2014; Volume 59, pp. 346–351. [ Google Scholar ]
• He, C.; Gitlin, R.D. System performance of cooperative massive MIMO downlink 5G cellular systems. In Proceedings of the IEEE 17th Annual Wireless and Microwave Technology Conference (WAMICON), Clearwater, FL, USA, 11–13 April 2016; pp. 1–5. [ Google Scholar ]
• Sheikh, T.A.; Bora, J.; Hussain, A. A survey of antenna and user scheduling techniques for massive MIMO-5G wireless system. In Proceedings of the International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India, 8–9 September 2017; pp. 578–583. [ Google Scholar ]
• Elijah, O.; Leow, C.Y.; Rahman, T.A.; Nunoo, S.; Iliya, S.Z. A comprehensive survey of pilot contamination in massive MIMO—5G system. IEEE Commun. Surv. Tutor. 2015 , 18 , 905–923. [ Google Scholar ] [ CrossRef ]
• Zhou, Y.; Li, D.; Wang, H.; Yang, A.; Guo, S. QoS-aware energy-efficient optimization for massive MIMO systems in 5G. In Proceedings of the Sixth International Conference on Wireless Communications and Signal Processing (WCSP), Hefei, China, 23–25 October 2014; pp. 1–5. [ Google Scholar ]
• Shental, O.; Zaidel, B.M.; Shitz, S.S. Low-density code-domain NOMA: Better be regular. In Proceedings of the 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 25–30 June 2017; pp. 2628–2632. [ Google Scholar ]
• Islam, S.R.; Avazov, N.; Dobre, O.A.; Kwak, K.S. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: Potentials and challenges. IEEE Commun. Surv. Tutor. 2016 , 19 , 721–742. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Wu, Z.; Lu, K.; Jiang, C.; Shao, X. Comprehensive study and comparison on 5G NOMA schemes. IEEE Access 2018 , 6 , 18511–18519. [ Google Scholar ] [ CrossRef ]
• Deka, K.; Sharma, S. Hybrid NOMA for Future Radio Access: Design, Potentials and Limitations. arXiv 2020 , arXiv:2012.08106. [ Google Scholar ] [ CrossRef ]
• Tao, Y.; Liu, L.; Liu, S.; Zhang, Z. A survey: Several technologies of non-orthogonal transmission for 5G. China Commun. 2015 , 12 , 1–15. [ Google Scholar ] [ CrossRef ]
• Dai, L.; Wang, B.; Ding, Z.; Wang, Z.; Chen, S.; Hanzo, L. A survey of non-orthogonal multiple access for 5G. IEEE Commun. Surv. Tutor. 2018 , 20 , 2294–2323. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Hong, T.; Zheng, S.; Liu, R.; Zhao, W. Design of mmWave Directional Antenna for Enhanced 5G Broadcasting Coverage. Sensors 2021 , 21 , 746. [ Google Scholar ] [ CrossRef ]
• Pi, Z.; Khan, F. An introduction to millimeter-wave mobile broadband systems. IEEE Commun. Mag. 2011 , 49 , 101–107. [ Google Scholar ] [ CrossRef ]
• Busari, S.A.; Mumtaz, S.; Al-Rubaye, S.; Rodriguez, J. 5G millimeter-wave mobile broadband: Performance and challenges. IEEE Commun. Mag. 2018 , 56 , 137–143. [ Google Scholar ] [ CrossRef ]
• Hong, W.; Baek, K.H.; Ko, S. Millimeter-wave 5G antennas for smartphones: Overview and experimental demonstration. IEEE Trans. Antennas Propag. 2017 , 65 , 6250–6261. [ Google Scholar ] [ CrossRef ]
• Wei, L.; Hu, R.Q.; Qian, Y.; Wu, G. Key elements to enable millimeter wave communications for 5G wireless systems. IEEE Wirel. Commun. 2014 , 21 , 136–143. [ Google Scholar ]
• Goudos, S.K.; Dallas, P.I.; Chatziefthymiou, S.; Kyriazakos, S. A survey of IoT key enabling and future technologies: 5G, mobile IoT, sematic web and applications. Wirel. Pers. Commun. 2017 , 97 , 1645–1675. [ Google Scholar ] [ CrossRef ]
• Slalmi, A.; Chaibi, H.; Saadane, R.; Chehri, A.; Jeon, G. 5G NB-IoT: Efficient network call admission control in cellular networks. Concurr. Comput. Pract. Exp. 2021 , 33 , e6047. [ Google Scholar ] [ CrossRef ]
• Akpakwu, G.A.; Silva, B.J.; Hancke, G.P.; Abu-Mahfouz, A.M. A survey on 5G networks for the Internet of Things: Communication technologies and challenges. IEEE Access 2017 , 6 , 3619–3647. [ Google Scholar ] [ CrossRef ]
• Ni, J.; Lin, X.; Shen, X.S. Efficient and secure service-oriented authentication supporting network slicing for 5G-enabled IoT. IEEE J. Sel. Areas Commun. 2018 , 36 , 644–657. [ Google Scholar ] [ CrossRef ]
• Kaur, J.; Khan, M.A.; Iftikhar, M.; Imran, M.; Haq, Q.E.U. Machine learning techniques for 5g and beyond. IEEE Access 2021 , 9 , 23472–23488. [ Google Scholar ] [ CrossRef ]
• Alawe, I.; Ksentini, A.; Hadjadj-Aoul, Y.; Bertin, P. Improving traffic forecasting for 5G core network scalability: A Machine Learning approach. IEEE Wirel. Commun. 2018 , 32 , 42–49. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Suomalainen, J.; Juhola, A.; Shahabuddin, S.; Mämmelä, A.; Ahmad, I. Machine learning threatens 5G security. IEEE Access 2020 , 8 , 190822–190842. [ Google Scholar ] [ CrossRef ]
• Bashir, A.K.; Arul, R.; Basheer, S.; Raja, G.; Jayaraman, R.; Qureshi, N.M.F. An optimal multitier resource allocation of cloud RAN in 5G using machine learning. Trans. Emerg. Telecommun. Technol. 2020 , 30 , e3627. [ Google Scholar ] [ CrossRef ]
• Balevi, E.; Gitlin, R.D. Unsupervised machine learning in 5G networks for low latency communications. In Proceedings of the 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC), San Diego, CA, USA, 10–12 December 2017; pp. 1–2. [ Google Scholar ]
• Tayyaba, S.K.; Khattak, H.A.; Almogren, A.; Shah, M.A.; Din, I.U.; Alkhalifa, I.; Guizani, M. 5G vehicular network resource management for improving radio access through machine learning. IEEE Access 2020 , 8 , 6792–6800. [ Google Scholar ]
• Sim, G.H.; Klos, S.; Asadi, A.; Klein, A.; Hollick, M. An online context-aware machine learning algorithm for 5G mmWave vehicular communications. IEEE/ACM Trans. Netw. 2018 , 26 , 2487–2500. [ Google Scholar ] [ CrossRef ]
• Li, J.; Zhao, Z.; Li, R. Machine learning-based IDS for software-defined 5G network. IET Netw. 2018 , 7 , 53–60. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Kafle, V.P.; Fukushima, Y.; Martinez-Julia, P.; Miyazawa, T. Consideration on automation of 5G network slicing with machine learning. In Proceedings of the 2018 ITU Kaleidoscope: Machine Learning for a 5G Future (ITU K), Santa Fe, Argentina, 26–28 November 2018; pp. 1–8. [ Google Scholar ]
• Chen, S.; Wen, H.; Wu, J.; Chen, J.; Liu, W.; Hu, L.; Chen, Y. Physical-layer channel authentication for 5G via machine learning algorithm. Wirel. Commun. Mob. Comput. 2018 , 2018 , 6039878. [ Google Scholar ] [ CrossRef ]
• Sevgican, S.; Turan, M.; Gökarslan, K.; Yilmaz, H.B.; Tugcu, T. Intelligent network data analytics function in 5g cellular networks using machine learning. J. Commun. Netw. 2020 , 22 , 269–280. [ Google Scholar ] [ CrossRef ]
• Abidi, M.H.; Alkhalefah, H.; Moiduddin, K.; Alazab, M.; Mohammed, M.K.; Ameen, W.; Gadekallu, T.R. Optimal 5G network slicing using machine learning and deep learning concepts. Comput. Stand. Interfaces 2021 , 76 , 103518. [ Google Scholar ] [ CrossRef ]
• Fang, H.; Wang, X.; Tomasin, S. Machine Learning for Intelligent Authentication in 5G and Beyond Wireless Networks. IEEE Wirel. Commun. 2019 , 26 , 55–61. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zi, R.; Ge, X.; Thompson, J.; Wang, C.X.; Wang, H.; Han, T. Energy efficiency optimization of 5G radio frequency chain systems. IEEE Wirel. Commun. 2016 , 34 , 758–771. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Pérez-Romero, J.; Sallent, O.; Ferrús, R.; Agustí, R. Knowledge-based 5G radio access network planning and optimization. In Proceedings of the IEEE International Conference on Communications Workshops (ICC), Poznan, Poland, 20–23 September 2016; pp. 1–6. [ Google Scholar ]
• Lähetkangas, E.; Pajukoski, K.; Vihriälä, J.; Berardinelli, G.; Lauridsen, M.; Tiirola, E.; Mogensen, P. Achieving low latency and energy consumption by 5G TDD mode optimization. In Proceedings of the IEEE International Conference on Communications Workshops (ICC), Sydney, NSW, Australia, 10–14 June 2014; pp. 1–6. [ Google Scholar ]
• Mowla, M.M.; Ahmad, I.; Habibi, D.; Phung, Q.V. Energy efficient backhauling for 5G small cell networks. IEEE Trans. Sustain. Comput. 2018 , 4 , 279–292. [ Google Scholar ] [ CrossRef ]
• Vook, F.W.; Ghosh, A.; Thomas, T.A. MIMO and beamforming solutions for 5G technology. In Proceedings of the IEEE MTT-S International Microwave Symposium (IMS2014), Tampa, FL, USA, 1–6 June 2014; pp. 1–4. [ Google Scholar ]
• Bhatt, C.; Sihag, V.; Choudhary, G.; Astillo, P.V.; You, I. A Multi-Controller Authentication approach for SDN. In Proceedings of the 2021 International Conference on Electronics, Information, and Communication (ICEIC), Jeju, Korea, 31 January–3 February 2021; pp. 1–4. [ Google Scholar ]
• Pavlenko, A.; Askarbekuly, N.; Megha, S.; Mazzara, M. Micro-frontends: Application of microservices to web front-ends. J. Internet Serv. Inf. Secur. 2020 , 10 , 49–66. [ Google Scholar ]
• Kholod, I.; Shorov, A.; Gorlatch, S. Efficient Distribution and Processing of Data for Parallelizing Data Mining in Mobile Clouds. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 2–17. [ Google Scholar ]
• Kim, H. 5G core network security issues and attack classification from network protocol perspective. J. Internet Serv. Inf. Secur. 2020 , 10 , 1–15. [ Google Scholar ]
• Kasturi, G.S.; Jain, A.; Singh, J. Detection and Classification of Radio Frequency Jamming Attacks using Machine learning. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 49–62. [ Google Scholar ]
• Lal, N.; Tiwari, S.M.; Khare, D.; Saxena, M. Prospects for Handling 5G Network Security: Challenges, Recommendations and Future Directions. J. Phys. Conf. Ser. 2021 , 1714 , 012052. [ Google Scholar ] [ CrossRef ]
• Ahmad, I.; Kumar, T.; Liyanage, M.; Okwuibe, J.; Ylianttila, M.; Gurtov, A. Overview of 5G security challenges and solutions. IEEE Commun. Stand. Mag. 2018 , 2 , 36–43. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Park, J.H.; Rathore, S.; Singh, S.K.; Salim, M.M.; Azzaoui, A.E.; Kim, T.W.; Pan, Y.; Park, J.H. A Comprehensive Survey on Core Technologies and Services for 5G Security: Taxonomies, Issues, and Solutions. Hum.-Centric Comput. Inf. Sci. 2021 , 11 , 3. [ Google Scholar ]
• Geller, M.; Nair, P. 5G security innovation with Cisco. Whitepaper Cisco Public 2018 , 1–29. Available online: https://infotech.report/Resources/Whitepapers/eed3eb22-b342-443c-99c1-41a96eb43512_5g-security-innovation-with-cisco-wp.pdf (accessed on 19 December 2021).
• Choudhary, G.; Kim, J.; Sharma, V. Security of 5G-mobile backhaul networks: A survey. arXiv 2019 , arXiv:1906.11427. [ Google Scholar ]
• Choudhary, G.; Sharma, V. A Survey on the Security and the Evolution of Osmotic and Catalytic Computing for 5G Network ; Springer: Berlin/Heidelberg, Germany, 2019; pp. 69–102. [ Google Scholar ]
• Nowaczewski, S.; Mazurczyk, W. Securing Future Internet and 5G using Customer Edge Switching using DNSCrypt and DNSSEC. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 87–106. [ Google Scholar ]
• Abhishta, A.; van Heeswijk, W.; Junger, M.; Nieuwenhuis, L.J.M.; Joosten, R. Why would we get attacked? An analysis of attacker’s aims behind DDoS attacks. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 3–22. [ Google Scholar ]
• Wong, S.K.; Yiu, S.M. Location spoofing attack detection with pre-installed sensors in mobile devices. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 16–30. [ Google Scholar ]
• Jasim, A.H.H.; Ögren, N.; Minovski, D.; Andersson, K. Packet probing study to assess sustainability in available bandwidth measurements: Case of high-speed cellular networks. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 2020 , 11 , 106–125. [ Google Scholar ]
• Kim, J.; Choudhary, G.; Heo, J.; Duguma, D.G.; You, I. 5G wireless P2MP backhaul security protocol: An adaptive approach. EURASIP J. Wirel. Commun. Netw. 2019 , 2019 , 265. [ Google Scholar ] [ CrossRef ]
• Chin, W.H.; Fan, Z.; Haines, R. Emerging technologies and research challenges for 5G wireless networks. IEEE Wirel. Commun. 2014 , 21 , 106–112. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Rashid-Farrokhi, F.; Liu, K.R.; Tassiulas, L. Transmit beamforming and power control for cellular wireless systems. IEEE J. Sel. Areas Commun. 1998 , 16 , 1437–1450. [ Google Scholar ] [ CrossRef ] [ Green Version ]

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Dangi, R.; Lalwani, P.; Choudhary, G.; You, I.; Pau, G. Study and Investigation on 5G Technology: A Systematic Review. Sensors 2022 , 22 , 26. https://doi.org/10.3390/s22010026

Dangi R, Lalwani P, Choudhary G, You I, Pau G. Study and Investigation on 5G Technology: A Systematic Review. Sensors . 2022; 22(1):26. https://doi.org/10.3390/s22010026

Dangi, Ramraj, Praveen Lalwani, Gaurav Choudhary, Ilsun You, and Giovanni Pau. 2022. "Study and Investigation on 5G Technology: A Systematic Review" Sensors 22, no. 1: 26. https://doi.org/10.3390/s22010026

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Research areas in 5G Technology

We are currently on the cusp of 5G rollout. As industry experts predict , 5G deployments will gain momentum, and the accessibility of 5G devices will grow in 2020 and beyond. But as the general public waits for mass-market 5G devices, our understanding of this new technology is continuing to develop. Public and private organizations are exploring several research areas in 5G technology, helping to create more awareness of breakthroughs in this technology, its potential applications and implications, and the challenges surrounding it. 

What is especially clear at this point is that 5G technology offers a transformative experience for mobile communications around the globe. Its benefits, which include higher data rates, faster connectivity, and potentially lower power consumption, promise to benefit industry, professional users, casual consumers, and everyone in between. As this article highlights, researchers have not yet solved or surmounted all of the challenges and obstacles surrounding the wide scale deployment of 5G technology. But the potential impact that it will have on the entire matrix of how we communicate is limited only by the imagination of the experts currently at its frontier. 

New developments and applications in 5G technologies

Much of the transformative impact of 5G stems from the higher data transmission speeds and lower latency that this fifth generation of cellular technology enables. Currently, when you click on a link or start streaming a video, the lag time between your request to the network and its delivery to your device is about twenty milliseconds. 

That may not seem like a long time. But for the expert mobile robotics surgeon, that lag might be the difference between a successful or failed procedure. With 5G, latency can be as low as one millisecond. 

5G will greatly increase bandwidth capacity and transmission speeds. Wireless carriers like Verizon and AT&T have recorded speeds of one gigabyte per second. That’s anywhere from ten to one hundred times faster than an average cellular connection and even faster than a fiber-optic cable connection. Such speeds offer exciting possibilities for new developments and applications in numerous industries and economic sectors.

E-health services

For example, 5G speeds allow telemedicine services to enhance their doctor-patient relationships by decreasing troublesome lag times in calls. This helps patients return to the experience of intimacy they are used to from in-person meetings with health-care professionals. 

As 5G technology continues to advance its deployment, telemedicine specialists find that they can live anywhere in the world, be licensed in numerous states, and have faster access to cloud data storage and retrieval. This is especially important during the COVID-19 pandemic , which is spurring new developments in telemedicine as a delivery platform for medical services. 

Energy infrastructure

In addition to transforming e-health services, the speed and reliability of 5G network connectivity can improve the infrastructure of America’s energy sector with smart power grids. Such grids bring automation to the legacy power arrangement, optimizing the storage and delivery of energy. With smart power grids, the energy sector can more effectively manage power consumption and distribution based on need and integrate off-grid energy sources such as windmills and solar panels.

Another specific area to see increased advancement due to 5G technology is artificial intelligence (AI). One of the main barriers to successful integration of AI is processing speeds. With 5G, data transfer speeds are ten times faster than those possible with 4G. This makes it possible to receive and analyze information much more efficiently. And it puts AI on a faster track in numerous industries in both urban and rural settings. 

In rural settings, for example, 5G is helping improve cattle farming efficiency . By placing sensors on cows, farmers capture data that AI and machine learning can process to predict when cows are likely to give birth. This helps both farmers and veterinarians better predict and prepare for cow pregnancies.

However, it’s heavily populated cities across the country that are likely to witness the most change as mobile networks create access to heretofore unexperienced connectivity. 

Smart cities

Increased connectivity is key to the emergence of smart cities . These cities conceive of improving the living standards of residents by increasing the connectivity infrastructure of the city. This affects numerous aspects of city life, from traffic management and safety and security to governance, education, and more. 

Smart cities become “smarter” when services and applications become remotely accessible. Hence, innovative smartphone applications are key to smart city infrastructure. But the potential of these applications is seriously limited in cities with spotty connectivity and wide variations in data transmission speed. This is why 5G technology is crucial to continued developments in smart cities.

Other applications

Many other industries and economic sectors will benefit from 5G. Additional examples include automotive communication, smart retail and manufacturing. 

Wave spectrum challenges with 5G

While the potential applications of 5G technology are exciting, realizing the technology’s potential is not without its challenges. Notably, 5G global upgrades and changes are producing wave spectrum challenges.

A number of companies, such as Samsung, Huawei Technologies, ZTE Corporation, Nokia Networks, Qualcomm, Verizon, AT&T, and Cisco Systems are competing to make 5G technology available across the globe. But while in competition with each other, they all share the same goal and face the same dilemma.

Common goal

The goal for 5G is to provide the requisite bandwidth to every user with a device capable of higher data rates. Networks can provide this bandwidth by using a frequency spectrum above six gigahertz . 

Though the military has already been using frequencies above six gigahertz, commercial consumer-based networks are now doing so for the first time. All over the globe, researchers are exploring the new possibilities of spectrum and frequency channels for 5G communications. And they are focusing on the frequency range between twenty-five and eighty-six gigahertz.

Common dilemma

While researchers see great potential with a high-frequency version of 5G, it comes with a key challenge. It is very short range. Objects such as trees and buildings cause significant signal obstruction, necessitating numerous cell towers to avoid signal path loss. 

However, multiple-input, multiple-output (MIMO) technology is proving to be an effective technique for expanding the capacity of 5G connectivity and addressing signal path challenges. Researchers are keying into MIMO deployment due to its design simplicity and multiple offered features. 

A massive MIMO network can provide service to an increased multiplicity of mobile devices in a condensed area at a single frequency simultaneously. And by facilitating a greater number of antennas, a massive MIMO network is more resistant to signal interference and jamming.

Even with MIMO technology, however, line of sight will still be important for high-frequency 5G. Base stations on top of most buildings are likely to remain a necessity. As such, a complete 5G rollout is potentially still years away. 

Current solutions and the way forward

In the interim, telecommunication providers have come up with an alternative to high-frequency 5G— “midband spectrum.” This is what T-Mobile uses. But this compromise does not offer significant performance benefits in comparison to 4G and thus is unlikely to satisfy user expectations. 

Despite the frequency challenges currently surrounding 5G, it is important to keep in mind that there is a common evolution with new technological developments. Initial efforts to develop new technology are often complex and proprietary at the outset. But over time, innovation and advancements provide a clear, unified pathway forward.

This is the path that 5G is bound to follow. Currently, however, MIMO technological advancements notwithstanding, 5G rollout is still in its early, complex phase.

Battery life and energy storage for 5G equipment

For users to enjoy the full potential of 5G technology, longer battery life and better energy storage is essential. So this is what the industry is aiming for.

Currently, researchers are looking to lithium battery technology to boost battery life and optimize 5G equipment for user expectations. However, the verdict is mixed when it comes to the utility of lithium batteries in a 5G world. 

Questions about battery demands and performance

In theory, 5G smartphones will be less taxed than current smartphones. This is because a 5G network with local 5G base stations will dramatically increase computation speeds and enable the transfer of the bulk of computation from your smartphone to the cloud. This means less battery usage for daily tasks and longer life for your battery. Or does it?

A competing theory focuses on the 5G phones themselves. Unlike 4G chips, the chips that power 5G phones are incredibly draining to lithium batteries. 

Early experiments indicate that the state-of-the-art radio frequency switches running in smartphones are continually jumping from 3G to 4G to Wi-Fi. As a smartphone stays connected to these different sources, its battery drains faster.

The present limited infrastructure of 5G exacerbates this problem. Current 5G smartphones need to maintain a connection to multiple networks in order to ensure consistent phone call, text message, and data delivery. And this multiplicity of connections contributes to battery drain.

Until the technology improves and becomes more widely available, consumers are left with a choice: the regular draining expectations that come with 4G devices or access to the speeds and convenience of 5G Internet. 

Possibilities for improvement on the horizon

Fortunately, what can be expected with continuous 5G rollout is continuous improvements in battery performance. As 5G continues to expand across the globe, increasing the energy density and extending the lifetime of batteries will be vital. So market competition for problem-solving battery solutions promises to be fierce and drive innovation to meet user expectations. 

Additional research areas in 5G technology

While research in battery technology remains important, researchers are also focusing their attention on a number of other areas of concern. This research is likewise aimed at meeting user expectations and realizing the full potential of 5G technology as it gains more footing in public and private sectors. 

Small cell research

For example, researchers are focusing on small cells to meet the much higher data capacity demands of 5G networks. As mobile carriers look to densify their networks, small cell research is leading the way toward a solution.

Small cells are low-powered radio access points that take the place of traditional wireless transmission systems or base stations. By making use of low-power and short-range transmissions in small geographic areas, small cells are particularly well suited for the rollout of high-frequency 5G. As such, small cells are likely to appear by the hundreds of thousands across the United States as cellular companies work to improve mobile communication for their subscribers. The faster small cell technology advances, the sooner consumers will have specific 5G devices connected to 5G-only Internet. 

Security-oriented research

Security is also quickly becoming a major area of focus amid the push for a global 5G rollout. Earlier iterations of cellular technology were based primarily on hardware. When voice and text were routed to separate physical devices, each device managed its own network security. There was network security for voice calls, network security for short message system (SMS), and so forth.

5G moves away from this by making everything more software based. In theory, this makes things less secure, as there are now more ways to attack the network. Originally, 5G did have some security layers built in at the federal level. Under the Obama administration, legislation mandating clearly defined security at the network stage passed. However, the Trump administration is looking to replace these security layers with its own “national spectrum strategy.”

With uncertainty about existing safeguards, the cybersecurity protections available to citizens and governments amid 5G rollout is a matter of critical importance. This is creating a market for new cybersecurity research and solutions—solutions that will be key to safely and securely realizing the true value of 5G wireless technology going forward.

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Study and Investigation on 5G Technology: A Systematic Review

Affiliations.

• 1 School of Computing Science and Engineering, VIT University Bhopal, Bhopal 466114, India.
• 2 Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Lyngby, Denmark.
• 3 Department of Information Security Engineering, Soonchunhyang University, Asan-si 31538, Korea.
• 4 Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy.
• PMID: 35009569
• PMCID: PMC8747744
• DOI: 10.3390/s22010026

In wireless communication, Fifth Generation (5G) Technology is a recent generation of mobile networks. In this paper, evaluations in the field of mobile communication technology are presented. In each evolution, multiple challenges were faced that were captured with the help of next-generation mobile networks. Among all the previously existing mobile networks, 5G provides a high-speed internet facility, anytime, anywhere, for everyone. 5G is slightly different due to its novel features such as interconnecting people, controlling devices, objects, and machines. 5G mobile system will bring diverse levels of performance and capability, which will serve as new user experiences and connect new enterprises. Therefore, it is essential to know where the enterprise can utilize the benefits of 5G. In this research article, it was observed that extensive research and analysis unfolds different aspects, namely, millimeter wave (mmWave), massive multiple-input and multiple-output (Massive-MIMO), small cell, mobile edge computing (MEC), beamforming, different antenna technology, etc. This article's main aim is to highlight some of the most recent enhancements made towards the 5G mobile system and discuss its future research objectives.

Keywords: 5G; beamforming; machine learning; massive multiple input and multiple output (MIMO); millimeter wave (mmW); mobile edge computing (MEC); small cell.

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Conflict of interest statement

The authors declare no conflict of interest.

Systematic layout representation of survey.

Pictorial representation of multi-input and…

Pictorial representation of multi-input and multi-output (MIMO).

Pictorial representation of orthogonal and…

Pictorial representation of orthogonal and Non-Orthogonal Multiple Access (NOMA).

Pictorial representation of millimeter wave.

Pictorial representation of IoT with…

Pictorial representation of IoT with 5G.

Pictorial representation of machine learning…

Pictorial representation of machine learning (ML) in 5G.

Pictorial representation of communication with…

Pictorial representation of communication with and without small cells.

Pictorial Representation of communication with…

Pictorial Representation of communication with and without using beamforming.

Pictorial representation of cloud computing…

Pictorial representation of cloud computing vs. mobile edge computing.

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5G Wireless Communication and Health Effects—A Pragmatic Review Based on Available Studies Regarding 6 to 100 GHz

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The introduction of the fifth generation (5G) of wireless communication will increase the number of high-frequency-powered base stations and other devices. The question is if such higher frequencies (in this review, 6–100 GHz, millimeter waves, MMW) can have a health impact. This review analyzed 94 relevant publications performing in vivo or in vitro investigations. Each study was characterized for: study type (in vivo, in vitro), biological material (species, cell type, etc.), biological endpoint, exposure (frequency, exposure duration, power density), results, and certain quality criteria. Eighty percent of the in vivo studies showed responses to exposure, while 58% of the in vitro studies demonstrated effects. The responses affected all biological endpoints studied. There was no consistent relationship between power density, exposure duration, or frequency, and exposure effects. The available studies do not provide adequate and sufficient information for a meaningful safety assessment, or for the question about non-thermal effects. There is a need for research regarding local heat developments on small surfaces, e.g., skin or the eye, and on any environmental impact. Our quality analysis shows that for future studies to be useful for safety assessment, design and implementation need to be significantly improved.

1. Introduction

Recent decades have experienced an unparalleled development of technologies that are categorized as information and communication technologies (ICT), which include wireless communication used for mobile telephony (MP) and e.g., Wi-Fi by using electromagnetic fields (EMF). The first generation of handheld mobile phones were available for individual, private, customers in a few countries in the late 1980’s. Subsequently, the second (2G), third (3G), and fourth (4G, LTE) generations increased their penetration rates in the society in a dramatic way, so that today there are more devices than inhabitants of the Earth. In addition, Wi-Fi and other forms of wireless data transfer have become ubiquitous, and are globally available. At present we are starting to introduce the next generation, 5G, of mobile networks. Importantly, 5G is not a new technology, but an evolution of already existing G1 to G4 technologies.

With the upcoming deployment of 5G mobile networks, significantly faster mobile broadband speeds and increasingly extensive mobile data usage will be ensured. This is made possible by the use of additional higher frequency bands. 5G is intended to be the intersection of communications, from virtual reality to autonomous vehicles to the industrial Internet and smart cities. In addition, 5G is considered the base technology for the Internet of Things (IoT), where machines communicate with machines (M2M communication). At the same time, a change in the exposure to electromagnetic fields (EMF) of humans and the environment is expected (see, for example [ 1 , 2 ]).

The 5G networks will work with within several different frequency bands ( Table 1 ), of which the lower frequencies are being proposed for the first phase of the 5G networks. Several of these frequencies (principally below 1 GHz; Ultra-high frequencies, UHF) have actually been or are presently used for earlier mobile communication generations. Furthermore, much higher radio frequencies (RF) are also planned to be used at later stages of technology evolutions. The new bands are well above the UHF ranges, having wavelengths in the centimeter (3–30 GHz) or the millimeter ranges (30–300 GHz; millimeter waves, MMW). These latter bands have traditionally been used for radars and microwave links.

Subdivision of the 5G frequency spectrum.

The introduction of wireless communication devices that operate in the high frequency parts of the electromagnetic spectrum has attracted considerable amounts of studies that focus on health concerns. These studies encompass studies on humans (epidemiology as well as experimental studies), on animals, and on in vitro systems. Summaries and conclusions from such studies are regularly published by both national and international committees containing relevant experts (see e.g., [ 3 , 4 , 5 ]. The conclusions from these agencies and committees are that low level RF exposure does not cause symptoms (“Idiopathic Environmental Intolerance attributed to Electromagnetic Fields”, IEI-EMF), but that a “nocebo” effect (expectation of a negative outcome) can be at hand. Some studies suggest that RF exposure can cause cancer, and thus the International Agency for Research on Cancer classified RF EMF as a “possibly carcinogenic to humans” (Group 2B) [ 3 ]. In a recent recommendation of a periodically working Advisory Group for IARC “to ensure that the Monographs evaluations reflect the current state of scientific evidence relevant to carcinogenicity” the group recommended radiofrequency exposure (among others) for re-evaluation “with high priority” [ 6 ]. There is further no scientific support for that effects on other health parameters occur at exposure levels that are below exposure guideline levels, even though some research groups have published non-carcinogen related findings after RF exposure at such levels (see [ 4 , 5 ]). Environmental aspects of this technological development are much less investigated.

Frequencies in the MMW range are used in applications such as radar, and for some medical uses. Occupational exposure to radars have been investigated in some epidemiological studies, and the overall conclusion is that this exposure does not constitute a health hazard for the exposed personnel [ 7 ]. This is due to that exposures for all practical purposes are below the guideline levels and thus not causing tissue heating. However, further studies are considered necessary concerning the possible cancer risk in exposed workers. Medical use of MMW has been recently reviewed [ 8 , 9 ] suggesting a possibility for certain therapeutic applications, although the action mechanisms are unclear.

The 5G networks and the associated IoT will greatly increase the number of wireless devices compared to the present situation, necessitating a high density of infrastructure. Thus, a much higher mobile data volume per geographic area is to be created. Consequently, it is necessary to build a higher network density because the higher frequencies have shorter ranges. The question that arises, is whether using the higher frequencies can cause health effects?

Exposure limits for both the general public and occupational exposure are available and recommended by the WHO in most countries, based on recommendations from ICNIRP [ 10 ] or IEEE [ 11 ] guidelines. These limits, which have considerable safety factors included, are set so that exposure will not cause thermal damage to the biological material (thermal effects). Thus, for 10 GHz to 300 GHz, 10 W/m 2 is recommended as the basic restriction (no thermal effects), with reference values for 400 MHz to 2 GHz (2–10 W/m 2 ) and >2 GHz (10 W/m 2 ). It should be pointed out that the present ICNIRP guidelines [ 10 ] are currently being revised, and new versions are to be expected in the near future. In addition, ICNIRP proposes two categories of recommendations: (1) the basic restriction values based on proven biological effects from the exposure and (2) the reference levels given for the purpose of comparison with physical value measurements. ICNIRP guidelines present no reference values above 10 GHz, only considering the basic restriction values. This is due to that only surface heating occurs since the penetration depth is so small at these frequencies. Therefore any calculations of the Specific Absorption Rate (SAR) values, that take larger volumes into consideration, are not reasonable to perform.

The SAR is the measure of the absorption of electromagnetic fields in a material and is expressed as power per mass/volume (W/kg), where the penetration depth of the electromagnetic fields depends on the wavelength of the radiation and the type of matter. The penetration depth of MMW is very shallow, hence the exposed surface area and not the volume is considered. The appropriate exposure metric for MMW is therefore the power density, power per area (W/m 2 ).

It is of course too early to forecast the actual exposures to 5G networks. However, the antennas planned for 5G will have narrow antenna beams with direct alignment [ 12 ] to the receiving device. This could possibly significantly reduce environmental exposure compared to the present exposure situation. However, it is also argued that the addition of a very high number of 5G network components will increase the total EMF exposure in the environment, and that higher exposures to the higher frequencies can lead to adverse health effects.

Therefore, the question arises, what do we know so far about the effects on biological structures and on health due to exposure to the higher frequency bands (in this review we consider 6–100 GHz, since lower frequencies have been extensively investigated due to their use in already existing wireless communication networks)? Do so-called “non-thermal” effects (effects that occur below the thermal effect threshold) occur, that can lead to health effects? Is there relevant health-oriented research using the 5G technology relevant frequencies? Is there relevant research that can make a significant contribution to improving the risk assessment of exposure to the general population? Answers to these questions are necessary for a rapid and safe implementation of a technology with great potential.

2. Materials and Methods

This review takes into account scientific studies that used frequencies from 6 GHz to 100 GHz as the source of exposure. The review is based on available data in the field of public literature, papers written in English until the end of 2018 (PubMed database: www.ncbi.nlm.nih.gov/pubmed ), EMF-Portal ( www.emf-portal.org ), and other relevant literature such as documents from ICNIRP, SCENIHR, WHO, IARC, IEEE, etc.). In addition, more refined research was conducted when necessary from sources that were not included in the above-mentioned databases (relevant abstracts from conferences, abstract books, and archives of journals). The resulting studies were examined for technical and scientific data and presented in the supplementary Table S1 .

As a pragmatic approach, we interpreted the results as a “response” when the authors themselves reported the result as an “effect/response” based on a statistical analysis and the p -value < 0.05.

Next we defined necessary criteria for study quality, both from a biomedical and physical point of view (see [ 13 ]). The results of the studies were (if possible) analysed for correlations with study quality according to the correlation approach done by Simkó et al. [ 14 ]. The studies were analysed with reference to a minimum of criteria in terms of experimental design and implementation. The following criteria were considered: were the experiments performed in the presence of an appropriate sham/exposure control, temperature control, positive control, were the samples blinded, and was a comprehensive dosimetry presented.

The study is divided into a descriptive part, which covers the description of all selected studies, their exposure conditions, frequency ranges (6 GHz to 100 GHz), dose levels, etc., as well as the biological results, presented in a Master-Table ( Table S1 ). Review articles were not considered. The outcomes of the studies were furthermore analyzed and discussed according to frequency domains, and power density and exposure duration. If appropriate, we include an evidence-based interpretative part regarding risk from exposures according to the criteria of SCHEER [ 15 ].

In the following, health-related published scientific papers dealing with frequencies from 6 GHz to 100 GHz (using the term MMW for all the frequencies) are described in detail. It should be noted that there are no epidemiological studies dealing with wireless communication for this frequency range, thus, this review will cover studies performed in vivo and in vitro.

Thermal biological effects of radiofrequency electromagnetic fields occur when the SAR values exceed a certain limit, namely 4 W/kg (general population exposure limit: SAR 0.08 W/kg), which causes a tissue heating of 1 °C. However, in the literature, biological effects below 4 W/kg SAR values have been described. Since such effects are considered to be not due to warming, they are termed non-thermal effects. In the present review, in some individual studies, the authors interpreted thermal effects as “no effect”. Those ones and studies without response/effect of MMW exposure were considered as “no response/effect” in our present analysis.

3.1. Grouping of Selected Parameters

For analysis, 94 publications were identified and selected from the accessible databases (in vivo and in vitro) [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 ]. It should be noted that the total number of individual examinations is larger than the number of publications, since some authors investigated several physical and/or biological conditions in the same publication.

Various biological endpoints have been identified, which are referred to as “response” or effects when appropriate. Since the list of these endpoints is relatively long, we have not mentioned them in detail, but summarized them in groups: Physiological, neurological, histological changes, or in in vitro studies gene or protein expression, cytotoxic effects, genotoxic changes, and also temperature-related reactions.

For a detailed analysis, a “Master-table” ( Table S1 ) was prepared in which all parameters considered in the studies were included. The table contains the following information: frequency, in vivo or in vitro study (the latter distinguishes between primary cells and cell lines), power density, exposure duration, biological endpoints, and response. Some studies lack information on individual parameters. For example, a publication had to be excluded completely because there was no information about the frequency. In nine studies the power density data were absent and in seven studies the calculated SAR values were provided instead of the power density. In ten studies, the exposure time was not given.

The 45 in vivo studies were mainly conducted on mammals (mouse, rat, rabbit) and a few on humans. In some studies, bacteria, fungi, and other living material were also used for the experiments. 80% of all in vivo studies showed exposure-related reactions.

Primary cells (n = 24) or cell lines (n = 29) were used in the 53 in vitro studies, with approximately 70% of the primary cell studies and 40% of the cell line investigations showing exposure-related responses ( Table 2 ).

Overview of the total number of publications examinations.

All identified studies were analyzed as a function of frequency. For this purpose, frequency domains (groups) have been created ( Figure 1 ) to analyze and illustrate the results. The frequency groups from 30 to 60 GHz were grouped in ten-GHz increments (up to 30, 30.1–40, 40.1–50, 50.1–60 GHz). The frequency range 60–65 GHz was extra analyzed as in this group a larger number of publications was identified (in comparison to the other groups). Due to the low number of publications above 65.0 GHz, data was merged into the groups of “65.1–90” and “above 90 GHz”. As shown in Figure 1 , the majority of studies show a frequency-independent response after MMW exposure.

The number of publications as a function of frequency domains. The black line represents the total number of publications, and bars represent the in vivo (dark blue) and in vitro (light blue) studies with biological responses.

3.1.1. Frequency Ranges

All data regarding the individual papers are found in Table S1 .

Up to 30 GHz

The first group “up to 30 GHz” was introduced since some of the 5G frequencies fall within this frequency range. Unfortunately, there are only two publications in this group, both showing responses to the MMW exposure. A study that was conducted on bacteria and fungi showed an increase in cell growth [ 58 ]. The other in vitro study was performed on fibroblasts (25 GHz, 0.80 mW/cm 2 , 20 min), with genotoxic effects observed at high SAR levels (20 W/kg) [ 24 ]. A graphical presentation of the outcomes is presented in Figure 1 for this and all other frequency domains.

Frequency Group 30.1–40 GHz

As shown in Figure 1 , responses were detected in approximately 95% of the 19 studies. In all in vivo studies responses were described after exposure [ 25 , 27 , 36 , 37 , 55 , 56 , 78 , 79 , 87 , 91 , 103 , 104 ]. Endpoints ranged from recorded footpad edema, which is a frequent endpoint for the measurement of inflammatory responses, to morphological changes, changes in skin temperature, blood pressure, heart rate, body temperature, neuronal electrical activity, and EEG analyses. Protein expression studies, oxidative stress marker measurements, histological investigations, and induction of cell death (apoptosis) were performed. Only one study used lower power densities (0.01 mW/cm 2 , 0.1 mW/cm 2 ; SAR: 0.15, 1.5 W/kg; 20 min, 40 min) to study inflammatory responses [ 27 ]. The authors determined the frequency-dependent anti-inflammatory effect as a function of power density and exposure duration and did not rule out temperature-related effects. The power densities of the other in vivo studies were extremely high (10, 75, 500–5000 mW/cm 2 ), so the induced effects were likely temperature dependent.

Eight in vitro studies were performed [ 18 , 20 , 47 , 91 , 97 , 99 , 101 , 102 ] of which seven reported responses. In one study [ 99 ], human blood cells ( ex vivo ) were exposed to MMW for 5, 15 and 30 min (32.9–39.6 GHz, 10 mW/cm 2 ). The activation of the cells was examined in the presence or absence of bacteria. It was shown that in the presence of bacterial activation and after 15 min of exposure, the cells were activated to release free radicals. These results were similar to the heated samples (positive controls), so a temperature effect is plausible. The induction of differentiation of bone marrow cells in to neuronal phenotype cells was also demonstrated (36.11 GHz, 10 mW/cm 2 , 3 × 10 min every 2 h for 24 h) [ 97 ]. In two studies, temperature-related reactions were described at the protein level [ 18 , 91 ]. When the cell cultures were cooled during exposure to prevent the induced temperature increase, no responses were detected.

In three publications, a research group described cell cycle changes, induction of cell death and activation of differentiation processes in primary cells (rat bone cells and mesenchymal stem cells) after exposure to 30–40 GHz (4 mW/cm 2 , different exposure durations) [ 47 , 101 , 102 ]. Unfortunately, the minimum quality criteria were not fulfilled in any of the three studies, mainly because there were no temperature controls.

Frequency Group 40.1–50 GHz

In the 40.1–50 GHz frequency group, 26 studies were identified, 13 in vivo [ 16 , 17 , 26 , 48 , 49 , 51 , 53 , 65 , 69 , 74 , 80 , 84 , 98 ] and 13 in vitro [ 29 , 30 , 31 , 62 , 64 , 86 , 89 , 92 , 93 , 100 , 105 , 107 ] with nine studies showing responses. A large number of studies have tested cell biology endpoints such as cell proliferation, gene or protein expression, and changes in oxidative stress. In addition, immunological, neurological, morphological and genotoxic effects were investigated. The power densities used vary enormously, from 0.02 to 450 mW/cm 2 , and one publication gave no information.

In healthy volunteers, a double-blind study was performed to investigate the effects of MMW on experimentally induced cold pain (42.25 GHz, <17.2 mW/cm 2 , 30 min) [ 74 ]. The authors found no difference from the placebo effect. This study was a repeat of a previous study with volunteers and the results of the older study could not be confirmed. The other four in vivo studies with no detectable effects were investigating genotoxic effects or oxidative stress [ 17 , 48 , 49 , 98 ].

Five in vivo publications addressed the effects of MMW on the immune system of mice or rats, finding activation of the immune system at both the cellular and molecular levels (41.95 or 42.2 GHz, 19.5 μW/cm 2 , 0, 1, 31.5 mW/cm 2 , 20 min or intermittently over 3 days) [ 26 , 48 , 51 , 53 , 84 ].

MMW exposure of frog isolated nerve cells, (41.34 GHz, 0.02, 0.1, 0.5, 2.6 mW/cm 2 , 10–23 min) lead to a reduction of the action potential frequency. Interestingly, the effects at higher power density (2.6 mW/cm 2 ) were similar to conventional heating [ 49 ].

One study detected an increase in the motility of human spermatozoa after 15 min of exposure (42.25 GHz, 0.03 mW/cm 2 ) [ 100 ]. Additional in vitro tests have identified the formation of free radicals, the activation of calcium-dependent potassium ion channels (around 42 GHz, 100, 150, 240 μW/cm 2 , 20–40 min) as well as changes at the cell membrane in exposed cells [ 29 , 30 , 100 ].

No responses on cell biological endpoints (cell cycle changes, cell death, heat shock proteins) were detected in four additional in vitro studies.

Frequency Group 50.1–60 GHz

We identified 16 studies in the frequency group 50.1-60 GHz (six in vivo, ten in vitro) and 60% of the studies showed responses to MMW exposures [ 21 , 23 , 35 , 38 , 43 , 46 , 59 , 61 , 72 , 77 , 81 , 83 , 85 , 94 , 109 ].

In five of the in vivo studies very different responses were shown. In a study on healthy volunteers, the authors wanted to find out whether the human skin at a so-called acupuncture point has different dielectric properties during exposure to MMW. They found that these properties change during exposure to 50–61 GHz from the surrounding skin [ 23 ].

A pilot study on mice (60 GHz, 0.5 mW/cm 2 , lifelong exposure for 30 min/5 days a week) showed that MMW exposure affects cancer-induced cells and increases in motor activity of healthy mice [ 61 ].

In rats, the influence of 54 GHz, 150 mW/cm 2 , on an area of approximately 2 cm 2 on the head was examined [ 81 ]. This transcranial electromagnetic brain stimulation induced pain prevention and prevented the conditioned avoidance response to a pain stimulus in 50% of the animals. However, no changes were detected when serotonin inhibitors were previously administered. Therefore, the authors concluded that transcranial electromagnetic brain stimulation promotes the synthesis of serotonin, a transmitter that changes the animals’ pain threshold.

The effects of MMW were also tested (60 GHz, 475 mW/cm 2 , 1.898 mW/cm 2 , 6, 30 min) on rabbit eyes, describing acute thermal injuries of various types [ 38 ]. The authors also pointed out that the higher temperature just below the eye surface could induce injury.

Neurological investigations were performed on leeches (60 GHz, 1 min, 1, 2, 4 mW/cm 2 ) [ 77 ] and electrophysiological studies were performed on frog oocytes (60 GHz, up to 5 min) [ 85 ]. In both experimental systems effects were described, which were induced by the temperature rise.

Cell biological and morphological changes after exposure to 0.7–1.0 μW/cm 2 (intermittent) were reported in three in vitro studies [ 72 , 83 , 94 ], with two publications providing no information regarding power density or exposure duration. At the level of protein analysis and total genome analysis no changes were identified in four in vitro studies [ 35 , 46 , 59 , 109 ].

Frequency Group 60.1–65 GHz

The number of studies in the 60.1–65 GHz frequency group is 27. Of these, twelve reported effects from exposure to MMW, and no responses were found in 15 studies.

The in vivo studies investigated different topics [ 23 , 27 , 44 , 52 , 67 , 68 , 70 , 71 , 73 , 75 , 76 ]. Thus, two studies examined the effects on tumor development in mice injected with tumor cells [ 52 , 70 ]. In one of the studies it was reported that exposure to 61.22 GHz, 13.3 mW/cm 2 , inhibited the growth of melanoma cells (exposure 15 days after tumor cell injection, 15 min/day) [ 70 ].

Other publications from one research group investigated the potential of MMW for pain relief and the associated biological mechanisms of action [ 67 , 71 , 73 , 75 , 76 ]. Several of the studies were performed on mice skin exposed to 61.22 GHz for 15 min. The most commonly used power density was 15 mW/cm 2 . Another study addressed the dose issue with no effect below 1.5 mW/cm 2 . The authors’ conclusion is that MMW can lower the hypoalgesia threshold, which is likely mediated by the release of opioids.

The effects of 61.22 GHz exposure of mice were examined also with respect to the immune system [ 52 ]. The animals were exposed on three consecutive days for 30 min per day. The exposure caused peak SAR values of 885 W/kg on the nose of the animals where the exposure took place. The power density was 31 mW/cm 2 and the measured temperature rise reached 1 °C. It was found that MMW modulates the effects of the cancer drug cyclophosamide. In particular, the T-cell system of the immune system was activated and various other immune system relevant parameters affected.

The similar exposure condition was used in a study on gastrointestinal function, however no effects were identified [ 68 ].

A single exposure for eight hours (61 GHz, 10 mW/cm 2 ), or five times four hours, did not cause eye damage to rabbits and rhesus monkeys [ 44 ]. It should be emphasized that several of the mentioned studies come from the same laboratory, and all criteria for the study quality are met. However, the authors were able to replicate their own findings on pain relief whereas other laboratories have not replicated this work. In the in vitro studies, various biological endpoints were examined [ 28 , 32 , 33 , 34 , 42 , 45 , 50 , 59 , 60 , 66 , 83 , 88 , 94 , 95 , 108 ].

In one study, neurons of snails ( Lymnea ) were exposed at 60.22–62.22 GHz and no non-thermal responses on the ion currents were identified [ 28 ].

In a series of investigations with nerve cell-relevant cell lines, the dopamine transmission properties, stress, pain and membrane protein expression were investigated (60.4 GHz, 10 mW/cm 2 , 24 h) and no responses were detected [ 32 , 33 , 34 , 59 , 60 , 108 ].

The same exposure setup has also been used in studies examining different stress response related genes (0.14–20 mW/cm 2 ) [ 59 ]. No effects were found at the gene expression level. Interestingly, the overall genome impact was influenced when the exposure (60.4 GHz, 20 mW/cm 2 , 3 h) of the primary human keratinocytes was combined with 2-deoxyglucose, a glucose-6- phosphatase inhibitor. This co-exposure caused a change in the amount of six different transcription factors, the effect differing from the effect of 2-deoxyglucose alone and 60.4 GHz alone (both factors alone induced no changes).

Other studies also examined human keratinocytes and astrocytoma glial cells after exposure to 60 GHz (0.54, 1 and 5.4 mW/cm 2 ) [ 60 , 108 ]. Various parameters such as cell survival, intracellular protein homeostasis, and stress-sensitive gene expression were investigated. Also, in these studies, no effects were observed. In contrast, in one publication, the elevation of an inflammatory marker (IL1-β) was observed in human keratinocytes after exposure (61.2 GHz, 29 mW/cm 2 , 15, 30 min), while other inflammatory markers (chemotaxis, adhesion and proliferation) have remained unchanged [ 95 ].

Another type of study was performed on rat brain cortical slices [ 66 ]. The brain slices were exposed to a field of 60.125 GHz (1 μW/cm 2 ) for 1 min, and then specific electrophysiological parameters were measured. In many slices, transient responses on membrane characteristics and action potential amplitude and duration were observed. The exposure caused a temperature rise of the medium (of 3 °C) in which the sections were stored. Interestingly, a chronically induced Ca 2+ blockade did not affect the MMW response.

Frequency Group 65.1–90 GHz

The studies in the frequency group of 65.1 to 90 GHz were performed both in vivo and in vitro in a total of 14 articles (four in vivo and 11 in vitro investigations). The studies vary widely, based on different hypotheses, biological endpoints, power densities, and exposure durations. In addition, some studies have used biological materials to identify physical properties such as dielectric properties and skin reflection coefficient. The latter studies are discussed in Section 4.2 .

Four in vivo studies reported responses after MMW exposure. One study examined the dose of eye damage (especially damage to the corneal epithelium) [ 40 ]. The dose was calculated as DD 50 (based on the results for which the probability of eye damage was 50%). The experiments were carried out on rats with an exposure of 75 GHz, the DD 50 value being 143 mW/cm 2 .

Other in vivo studies were performed on rats and mice as well as on insects [ 27 , 42 , 57 ]. The study on mice used different frequencies of 37.5 to 70 GHz, with power densities of 0.01 and 0.3 mW/cm 2 for 20 to 40 min. A single whole-body exposure of the animals reduced both the footpad edema and local hyperthermia on average by 20% at the frequencies of 42.2, 51.8, and 65 GHz. Other frequencies had no influence.

The study on insects ( Chironomidae ) focused on DNA effects of giant chromosomes of the salivary glands of the animals with different frequencies (64.1–69.1, 67.2, 68.2 GHz) [ 42 ]. All frequencies, using power densities <6 mW/cm 2 , caused a reduction in the size of a particular area of the chromosome. This in turn led to the expression of certain secretory proteins of the salivary gland.

Different aspects were studied in the in vitro studies [ 18 , 28 , 39 , 50 , 64 , 72 , 83 , 89 , 94 , 106 ], where nerve cell function was investigated in three studies. Two studies used nerve cells from the snail Lymnea that were exposed at 75 GHz for a few minutes at very high SAR levels (up to 4200 W/kg, power density was not reported) [ 28 , 39 ]. The authors observed thermal effects on the ion currents and the firing rate of the action potentials. Another study also described thermal effects on transmembrane currents and ionic conductivity of the cell membrane. Again, the exposure was at very high SAR levels (2000 W/kg), and the authors emphasized the temperature dependence of the reaction.

Broadband frequencies (52–78 GHz) have been used in several publications, mainly investigating the effects on cell growth and cell morphology as well as the ultrastructure of different cell lines [ 50 , 72 , 83 , 94 ]. The values for the power densities were not given consistently but appear to have been very low (not higher than 1 μW/cm 2 ). The results indicated the inhibition of cell growth, accompanied by changes in cell morphology.

Another group of studies used hamster fibroblasts, BHK cells, and exposed the cells at 65 to 75 GHz, with the power density reaching 450 mW/cm 2 [ 18 , 64 , 89 ]. The authors noted the inhibition of protein synthesis and cell proliferation as well as cell death at higher power densities. In a study using human dermal fibroblasts and human glioblastoma cells, no effects at the protein level (proliferation or cytotoxicity markers) were detected (70 GHz and higher, in 1 GHz increments; 3, 70 or 94 h) [ 106 ]. Power densities varied across frequencies, ranging from 1.27 μW/cm 2 in the lower frequency range to 0.38 μW/cm 2 at higher frequencies.

The in vitro studies in this group are similar to the in vivo studies in their diversity. The majority of studies in which responses were reported are thermal-effects due to MMW exposure. In three studies, responses at low power densities were described, but all results were from the same laboratory, and were not replicated by others. Moreover, the quality of these studies is questionable, as the quality criteria were not met.

Frequency Group 90.1–100 GHz

Eight out of eleven studies in the 90.1–100 GHz frequency group are in vitro studies [ 22 , 41 , 57 , 82 , 90 , 96 , 106 ]. The three in vivo investigations addressed a variety of issues including acute effects on muscle contraction, skin-reflection properties (which are more of a dose-related than health-related issue), and skin cancer [ 19 , 54 , 57 ]. The rat skin cancer study (one to two weekly, short-term exposures at 94 GHz, 1 W/kg; DMBA-initiated animals) did not show any positive outcome [ 54 ]. Another study examined the muscle contraction of mice and described some responses [ 19 ]. Again, 94 GHz was used, but power density or SAR values were not reported.

Seven of the eight in vitro studies showed responses after MMW exposure. In some studies, primary neurons were used to study the cytoskeleton (94 GHz, 31 mW/cm 2 ) [ 82 ] or specific electrophysiological parameters (90–160 GHz) [ 22 ]. In the latter study it was found that the observed responses were more likely due to interactions with the cell culture medium than with the cells, although the mechanisms of action were not clear. Other studies identified responses on the DNA integrity (100 GHz and higher) [ 41 ] or described changes in intracellular signaling pathways (94 GHz, 90–160 GHz) using different cell types [ 57 , 96 ]. The exposure time ranged from minutes to 24 h for partially unknown exposure values. In one study no cytotoxic influence at power density levels of a few μW/cm 2 was detected in either normal or in tumor cells.

3.1.2. Power Densities

All identified studies were analyzed as a function of the used power densities. The studies were grouped depending on the power density as follows: below 1; 1.1–10; 10.1 to 50; 50.1–100, and 100.1 mW/cm 2 or higher. Studies that do not provide information on power density or SAR values are not displayed in these groups. As shown in Figure 2 , the vast majority of studies show responses regardless of the power density used.

The number of publications as a function of power density. The black line represent the total number of publications, and bars represent the in vivo (dark blue) and in vitro (light blue) studies with biological responses.

3.1.3. Exposure Duration

Exposure duration of the studies was also grouped for data analysis ( Figure 3 ). The time groups were selected as seconds to 10 min; 10–30 min; 30–60 min; over 60 min-days and alternately/intermittently. The groups were selected so that the used exposure times and the number of studies are meaningfully summarized. Here, too, it becomes clear that the majority of all studies show responses regardless of the exposure time. Interestingly, longer exposure times (over 60 min—days) seemingly lead to fewer reactions than in the other groups.

The number of publications as a function of exposure duration. The black line represent the total number of publications, and bars represent the in vivo (dark blue) and in vitro (light blue) studies with biological responses.

3.2. Studies without Responses

Table 3 shows the number of studies in which no responses were detected after or during MMW exposure. As “no response” also such investigations were referred to, which were considered by the authors themselves as such. This means that in some cases the observed effects were described as temperature-related and not as a non-thermal MMW effect.

Studies without responses.

Few in vivo studies have shown no response at all. Noticeable is the frequency group 40.1–50 GHz, in which 6 studies were identified. These studies investigated immunosuppression, genotoxic effects, changes in pain sensitivity, and changes in enzyme activity. One study was carried out on bacteria and fungi.

There are a variety of in vitro studies in which no responses were detected. Interestingly, studies on protein or gene expression levels often failed to detect any changes after MMW exposure. This could be due to the fact that in in vitro studies the possibility of non-thermal effects were specifically investigated, where cooling was used to counteract the temperature increase.

3.3. Quality Analysis

We analyzed the quality of the selected studies according to specific criteria [ 14 ]. The studies were categorized by the presence of sham/control, dosimetry, positive control, temperature control, and whether the study was blinded. The presence of these five criteria while performing an MMW study is the minimum requirement for qualifying as a study with sufficient technical quality.

Of the 45 in vivo studies, 78% (35) demonstrated biological responses after exposure to MMW. Of all studies, 73% were performed with sham/controls, 76% employed appropriate dosimetry, 44% used positive control, and 67% were done under temperature control conditions ( Figure 4 ). Unfortunately, only 16% of the studies were performed according to protocols that ensured blinding and only three publications were identified that met all five criteria [ 26 , 51 , 53 ]. If the blinding criterion was excluded, 13 studies could be identified that met the remaining four criteria. Considering three criteria only, namely sham, dosimetry, and temperature control, 40% (20 papers) were identified. Thus, the quality of the in vivo studies is unsatisfactory.

The quality of all publications: The number of in vivo (top) and in vitro (bottom) experiments (blue: no reaction, red: reaction) using the listed quality features (y-axis). The spider web shows the percentage of the quality characteristics in all examinations.

Out of the 53 in vitro studies, 31 showed biological responses. Only in 13 studies (42%) were three of the five quality criteria satisfied, namely the presence of sham/control, dosimetry, and temperature control ( Figure 4 ). Positive controls were used in 47% and only one study was performed with blinded protocol (2%).

These results show that the number of examinations and the quality criteria are insufficient for a statistical analysis. It should be stressed that this quality analysis covers all publications dealing with the responses/effects of exposure to 6 to 100 GHz MMW, irrespective of the endpoints tested. To perform a correlation analysis, a larger number of comparable studies (e.g., identical endpoints in a frequency group) would be required.

4. Discussion

The first relevant observation during the analysis of the studies is that in most publications the aim of the investigations has been to determine the effects of MMW exposure for medical purposes. This means that the exposure devices used primarily come from medical applications (therapy or diagnostics). Very few publications dealt with health-related issues after MMW exposure in general, or with the specific topic of 5G. Therefore, the 94 publications are very heterogeneous.

We divided the frequency bands into seven ranges and placed the studies in the relevant groups. All available information on physical and experimental parameters was collected, but the exact number of experiments in each study was not taken into account. (One publication can contain more than one experiment.) Therefore, it is the provided numbers of studies/publications that constitute the data set, not the exact numbers of experiments performed, which is significantly higher.

This report does not provide a statistical analysis of the correlation between the exposure conditions and the results, which was our original ambition. In the correlation study according to Simkó et al. [ 14 ] a frequency group was selected, with only one group of biological endpoints considered. About one hundred, exclusively in vitro, studies were identified and broken down into individual experiments in that paper. In this way, the number of experiments was sufficient to perform a correlation analysis. In the present review, the spread of biological endpoints in the individual frequency groups and the models used (in vivo and in vitro) is large and the number of studies is very low. Therefore, it was not possible to group the studies by specific endpoints and perform a statistical analysis.

Interestingly, more than half of the studies (53 publications) were conducted in the frequency bands 40.1–50 and 60.1–65 GHz (with different models and endpoints). One possible reason for this is that medical use of MMW has a long tradition in Eastern Europe. These applications use specific frequencies that fall in these two frequency groups. The studies were conducted with the aim of testing specific effects with medical relevance. In these two frequency groups, the “with responses” percentage was generally lower than in the other frequency bands (see Figure 1 ), where a majority of studies showed responses to exposure.

With regard to the power densities used, about half of the studies were carried out in the range up to 10 mW/cm 2 ( Figure 2 ). This value is ten times higher than the current ICNIRP exposure guideline [ 10 ] for the general population. Based on available data, there is no indication that higher power densities cause more frequent responses, since the percentage of responses in all groups is already at 70% ( Figure 2 ). One exception from this high response rate is the group 50.1–100 mW/cm 2 , where the proportion of studies with reactions is slightly lower (54%). However, the total number of examinations (11) is relatively small in this group.

The results of some of the studies may suggest that exposure to power densities at or below the guideline recommendations induce biological effects. There are, however, some arguments against it. One of these is the apparent heterogeneity of the study design and the outcomes studied. There are very few (if any) independent replication studies that confirm the reported results. It is also noteworthy that there is no trend towards a classic dose-response pattern where stronger or more frequent effects would be caused by higher exposure levels. Since the studies with conditions promoting tissue warming show no greater effect than below the guideline values (1 mW/cm 2 ), this would either mean that the same interactions are present at all power densities tested, or that experimental artifacts unknown to the scientists are present.

The most important physical experimental parameter is the temperature during exposure, therefore, the temperature must be consistently controlled. The need for stringent temperature control is not an insignificant or trivial matter and has been neglected or at least undervalued in many studies. Although some authors report that they performed specific temperature measurements during the experiments, this does not necessarily mean that this represents the actual temperature in the biological material. Measurements can be made, for example, in the surrounding medium but not in the exposed tissue or in the cell. It also has to be considered that the “bulk” heating (from outside to inside with a certain time course) can differ from a heating that occurs at a rather limited point (“hot spot”). In addition, the intensity of a short burst can be lost if the measurements are based on average exposure times. Such errors and problems are possible factors that have contributed to the questionable interpretation of “non-thermal effects” in some studies.

Effects after MMW exposure were shown at all exposure times with no clear time dependency. The data presented shows one exception, namely in the group “>60 min to days”, where fewer reactions were detected ( Figure 3 ). It has to be taken into account that 27 examinations were carried out in this group, 23 of which were in vitro studies. In vitro experiments can be carried out under cooling, therefore the results can be different (see further below).

Two research groups together provide 30 of the 94 publications in the data set, and could thus possibly have a large impact on the analysis of the outcomes. One group presented at least 21 publications (42.25 and 61.82 GHz; 10 to 30 mW/cm 2 ; with different exposure durations), with a variety of in vivo and in vitro studies, which mostly reported responses to exposure. The other group mainly studied gene and protein expressions (60 GHz; 5.4 to 20 mW/cm 2 ; exposure durations from minutes to days) and found mainly no responses. Studies from both groups adhered well to the quality criteria in our analysis.

4.1. Temperature Controls in In Vitro Studies

In vivo studies that are performed within or directly on the living organism have shown both thermal and purportedly non-thermal effects after or during MMW exposure. In vitro studies are carried out on cells and most experimental parameters can be accurately set and observed. Cell cultures can thus be very carefully controlled, e.g., an induced temperature increase can be counter-cooled. Many in vitro studies considered in this review were performed using cooling of the cell culture vessels and the authors did not detect any non-thermal effects in these studies. In in vivo studies counter-cooling is not possible, thus it is very difficult to differentiate between thermal and non-thermal reactions. Therefore, in vivo and in vitro studies regarding the induced effects cannot be directly compared. An accurate dosimetry could solve this problem.

4.2. Dosimetry

It is important to know what the exposure of the MMW will be due to the expected introduction of a large number of 5G wireless communication devices. Given the novelty of the technology, it is currently unlikely that a large number of relevant exposure assessment studies will be available. However, an example from a recent study [ 110 ] shows that a “typical” office environment with wireless communication transmitters (5.50 GHz) leads to power densities well below the exposure guideline limits. Thus, the maximum power density was measured at 0.89 μW/cm 2 .

Partly (n = 25) the experimental studies on biological and health effects of MMW exposure are at or below the ICNIRP exposure guidelines. The power densities were often chosen so that the exposure caused no or very moderate tissue warming (<1 °C), namely in the range of 1 to 10 mW/cm 2 . Since the penetration into the tissue of these frequencies are on the order of millimeters and below, it is important to study biological effects directly or indirectly related to skin and eyes exposure. As mentioned previously, the number of available studies in the 6–100 GHz frequency range is relatively low, which is in contrast to the number of studies for lower radio frequencies. Similarly, the number of tissue dosimetry studies (especially for the skin) is very limited. However, such studies are very relevant because they show how certain exposure parameters can influence the energy input and thus the thermal behavior of the skin.

Currently, both the ICNIRP guidelines and the IEEE standards are being revised to replace the SAR values with power density above 6 GHz. However, it has already been recognized that there is a reactive near field close to the transmitter (around the antennas). Here, the energy is not radiated, but the energy envelopes the antennas. The question is whether these “reactive near fields” are important for the energy delivery to a human body near the transmitter? If this is not the case, it is sufficient to comply with the existing exposure limits based on free space power density measurements. On the other hand, a strong reactive near field would considerably complicate the exposure situation [ 111 ]. Therefore, for dosimetry modeling of distances (from the antenna) below the wavelength of the MMW (mm), temperature measurements should rather be performed in suitable phantoms rather than direct measurements of the power densities in the free space [ 111 ].

The question is how reliably the power density (in free space) can be extrapolated to possible temperature increases in human tissue? For example, Neufeld et al. [ 112 ] found that 10 GHz “bursts” (considered “safe” by ICNIRP and IEEE) can cause temperature increases of >1 °C if the burst duration is long enough. It was also discussed whether the average values of the power densities for the safety assessment are the right ones. In addition, the temperature increase by the MMW also depends on the size of the area. Thus, the factors such as the amplitude of the burst, the “averaging area” and the “averaging time” for the dosimetry would have to be considered.

Foster et al. [ 113 ] reviewed and modelled data on MMW-induced temperature increases in human skin. The model takes into account the frequencies of 3–100 GHz and smaller skin areas with the diameter of 1–2 cm. Available data on exposures lasting more than a few minutes, as well as areas of skin larger than 2 cm in diameter, were limited and made modeling difficult, but consistent with existing data. This means that this model, after appropriate evaluation for dosimetry, could use smaller areas of the skin. The authors also commented on the exposure guidelines for frequencies from 3 to 300 GHz in a separate article [ 114 ]. Based on “thermal modeling,” the authors considered the current guidelines to be conservative in terms of protection against temperature increases in the tissue. They also pointed out that the averaging time and average area provisions require further refinement and that the effects of short high intensity bursts may not be protected by the guidelines.

Zhadobov et al. [ 115 ] addressed the problem of accurate temperature measurement in in vitro MMW studies. They found that the type of thermal probe (thermocouples are better than fiber optic probes) and the size of the probe (smaller probes are more accurate) are relevant. In addition, they were able to show that the initial temperature rise during exposure is rapid (within seconds until a plateau is reached) and that the cells absorb very small amounts of energy, since most of the energy is already absorbed in the cell culture medium. Nevertheless, the authors have calculated that the exposure of 58.4 GHz with 10 mW/cm 2 leads to SAR values of more than 100 W/kg in a cell monolayer. This value is a fraction of the SAR values of the fluid surrounding the cells.

Several studies focused on the distribution of power density and the change in skin temperature as a result of exposure to MMW in the 6 to 100 GHz frequency range. The studies are experimental and/or modeling studies using previously published data. Alekseev et al. [ 116 , 117 ] investigated the absorption of the skin of mice and humans at frequencies between 30 and 82 GHz (10 mW/cm 2 ). They found that in both species absorption into both the epidermis and the dermis occurs with a concomitant loss of power density in the deeper regions. An extended study from the same group [ 118 ] on human forearm skin showed that both temperature increase and SAR values depend on frequency (in the interval of 25 to 75 GHz; 25, 73.3 and 128 mW/cm 2 ).

Frequency dependence for temperature increases was also observed in a modeling study with human facial skin [ 119 ]. Pulsed MMWs were used (6–100 GHz, 100 mW/cm 2 , 200–10,000 ms pulse length) and the skin temperatures were modeled as the function of both pulse length and frequency. Peak skin temperature increased as a function of frequency up to 20 GHz, while above 20 GHz it proved to be dependent on “absorption hotspots”. In deeper regions (>2 mm), the temperature increases were very low and highest around 10 GHz.

In addition, certain skin constituents have been shown to affect energy absorption. It has been shown that the presence of sweat glands [ 120 , 121 ] and also capillaries in the dermis can cause locally elevated SAR levels [ 122 ]. The latter study showed that SAR levels in vessels could be up to 30 times higher than in the surrounding skin, depending on the diameter of the vessels.

Both [ 23 ] and [ 123 ] have reported that the dielectric properties of different areas of the skin differ. The first study found that so-called acupuncture points in healthy volunteers show different dielectric properties when exposed to MMW (50–75 GHz, 14 mW/cm 2 ), while the second study even found differences between the epidermis and dermis (0–110 GHz).

These studies suggest that both the frequency and the specific condition and composition of the skin are relevant for tissue dosimetry. However, too few and very different studies are available to give a conclusive picture on dosimetry of 5G-relevant MMW exposures.

4.3. ICNIRP and other Exposure Recommendations

The guidelines for exposure limits for radiofrequency electromagnetic fields from 3 to 300 GHz in many countries are based on the recommendations of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) [ 10 ]. However, there are also other organizations dealing with limit values such as the Institute of Electrical and Electronics Engineers, IEEE [ 11 ] or the US Federal Communications Commission, FCC [ 124 ].

The guidelines contain basic exposure limits that are indicated as SAR or power density. The limits for a given frequency differ only slightly, if at all, between the different guidelines. However, an important difference between the guidelines concerns frequency, as the SAR basic restriction values change to power density. This frequency (range) is currently set by ICNIRP at 10 GHz, while IEEE and FCC see this between 3–6 GHz. The current revision of these guidelines aims to harmonize these frequencies.

The exposure limits specified in the guidelines should protect against warming of tissue above 1 °C. The reason is that the perceived dangers of MMW energy are associated with excessive heating, called thermal effects. However, it must be considered that the guidelines mean a temperature increase of 1 °C relative to the starting temperature, regardless of the starting temperature. Elevations in temperature may cause pain in the skin when moderately increased, whereas at temperatures of 43–44 °C it may even induce burns [ 124 , 125 ].

At present, only thermal effects due to high-frequency electromagnetic fields are recognized as effects. This means that effects have a thermal component even if it is obviously not due to tissue that has been damaged by excessive heating. On the other hand, it has been suggested that the MMW exposure may also cause non-thermal effects. So far, however, no recognized expert committee has supported such an assertion.

4.4. Knowledge Gaps and Research Recommendations

Exposure of humans can occur through 5G devices with frequencies above 6 GHz, and may be primarily on the skin and, to a lesser extent, on the eyes. This is due to the very low penetration depth of this MMW. Therefore, it is important to investigate whether there are any health-related effects on the skin and/or effects associated with the skin. These include acute skin damage from tissue heating (burns), but possibly also less acute effects (such as inflammation, tumor development, etc.). Such effects could appear after prolonged and repeated heating of superficial structures (the skin). This would mean that thermal effects occur that are not due to acute but to chronic damage.

It may also be that local exposure causes energy deposition in the dermis of the skin, which may be so great as to affect nerve endings and peripheral blood vessels through warming mechanisms. Such scenarios were proposed by Ziskin [ 9 ] based on a series of studies by his group. These studies typically used exposures around 60 GHz at a power density of 10 mW/cm 2 on the skin in the sternum area to produce systemic effects. The aim was to treat certain diseases and complaints. The idea was that the treatment induces the release of the body’s own opioids and additionally stimulates the peripheral nerves. The stimulation would depend on a local thermal effect, which, due to the frequencies, induces locally high SAR values, even at low power densities, thus warming the tissue.

Due to the contradictory information from various lines of evidence that cannot be scientifically explained, and given the large gaps in knowledge regarding the health impact of MMW in the 6–100 GHz frequency range at relevant power densities for 5G, research is needed at many levels. It is important to define exact frequency ranges and power densities for possible research projects. There is an urgent need for research in the areas of dosimetry, in vivo dose-response studies and the question of non-thermal effects. It is therefore recommended that the following knowledge gaps should be closed by appropriate research (the list of research recommendations is not prioritized):

• Exact dosimetry with consideration of the skin for relevant frequency ranges, including the consideration of short intense pulses (bursts)
• Studies on inflammatory reactions starting from the skin and the associated tissues
• In vivo studies on the influence of a possible tissue temperature increase (e.g., nude mouse or hairless mouse model)
• In vivo dose-response studies of heat development
• Use of in vitro models (3D models) of the skin for molecular and cellular endpoints
• Clarification of the question about non-thermal effects (in vitro)

There are also questions about the environmental impact, with potential consequences for human health. Since many MMW devices will be installed in the environment, the impact of MMW on insects, plants, bacteria, and fungi is relevant to investigate. Particularly relevant is the question of temperature increase in very small organisms, as the depth of penetration of the MMW could warm the whole organism.

An unrealistic scenario, however, is that MMW exposures at realistic power densities could cause systemic body warming in humans. Any local heat exposure would be dissipated by the body’s normal heat regulation system. This is mainly due to convection caused by blood flow adjacent to the superficial skin areas where the actual exposure takes place.

In summary, it should be noted that there are knowledge gaps with respect to local heat developments on small living surfaces, e.g., on the skin or on the eye, which can lead to specific health effects. In addition, the question of any possibility of non-thermal effects needs to be answered.

5. Conclusions

Since the ranges up to 30 GHz and over 90 GHz are sparingly represented, this review mainly covers studies done in the frequency range from 30.1 to 65 GHz.

In summary, the majority of studies with MMW exposures show biological responses. From this observation, however, no in-depth conclusions can be drawn regarding the biological and health effects of MMW exposures in the 6–100 GHz frequency range. The studies are very different and the total number of studies is surprisingly low. The reactions occur both in vivo and in vitro and affect all biological endpoints studied.

There does not seem to be a consistent relationship between intensity (power density), exposure time, or frequency, and the effects of exposure. On the contrary, and strikingly, higher power densities do not cause more frequent responses, since the percentage of responses in most frequency groups is already at 70%. Some authors refer to their study results as having “non-thermal” causes, but few have applied appropriate temperature controls. The question therefore remains whether warming is the main cause of any observed MMW effects?

In order to evaluate and summarize the 6–100 GHz data in this review, we draw the following conclusions:

• Regarding the health effects of MMW in the 6–100 GHz frequency range at power densities not exceeding the exposure guidelines the studies provide no clear evidence, due to contradictory information from the in vivo and in vitro investigations.
• Regarding the possibility of “non-thermal” effects, the available studies provide no clear explanation of any mode of action of observed effects.
• Regarding the quality of the presented studies, too few studies fulfill the minimal quality criteria to allow any further conclusions.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/16/18/3406/s1 , Table S1: Master-table of the selected (in vivo and in vitro) studies and the extracted physical, biological, and quality parameters.

Author Contributions

M.S. and M.-O.M. have contributed equally to conceptualization, structuring, data collection and analysis, interpretation of data, and all aspects of writing of the manuscript.

This research was funded by Deutsche Telekom Technik GmbH, Bonn, Germany, PO number 4806344812.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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