The first review was published in February 2018 by Di Ciaula ( 4 ) and was based on a systematic search of epidemiological, in vivo , and in vitro studies identified in the PubMed database. Di Ciaula reported no funding or conflict of interest (CoI), but an internet search identified membership of the International Society of Doctors for Environment (ISDE), which published a 5G appeal for a moratorium on the development of 5G ( https://www.isde.org/5G_appeal.pdf ). Di Ciaula discussed the evidence for cancer, reproductive effects, neurologic effects, and microbiological effects and specifically addressed evidence in relation to MMWs. No formal assessment of the quality of the studies was included, and the author concluded that “[the evidence] clearly point to the existence of multi-level interactions between high-frequency EMF and biological systems, and to the possibility of oncologic and non-oncologic (mainly reproductive, metabolic, neurologic, microbiologic) effects” and further raises concerns regarding the increased susceptibility of children. The main aim of the review was to provide the rationale to invoke the precautionary principle, which is mentioned both in the Conclusion section and Abstract.
Russell published a similar review in April 2018 ( 5 ). Despite being the Executive Director of Physicians for Safe Technology, the author reported no affiliation, funding, or CoI. Russell does acknowledge support from Smernoff and Moskowitz; an internet search identifies the latter as being on the Advisory Board of Physicians for Safe Technology as well as being an advisor to the International EMF Scientist Appeal (and its spokesperson for the United States). The review reported effects on cancer, dermal effects, ocular effects, effects on reproduction and neurology, microbiological effects, and effects on the immune system. It further reports specific effects from MMWs, electrohypersensitivity [or, more accurately, idiopathic environmental intolerance attributed to electromagnetic fields (IEI-EMF)], and effects on children, and discusses how industry bias has obscured these facts. Scientific uncertainty is only mentioned in passing and is largely attributed to industry distortion. Russell concludes that “current radiofrequency radiation wavelengths we are exposed to appear to act as a toxin to biological systems” and “although 5G technology may have many unimagined uses and benefits, it is also increasingly clear that significant negative consequences to human health and ecosystems could occur if it is widely adopted.” It further makes specific policy recommendations that “public health regulations need to be updated to match appropriate independent science with the adoption of biologically based exposure standards prior to further deployment of 4G or 5G technology” and that “a moratorium on the deployment of 5G is warranted, along with the development of independent health and environmental advisory boards that include independent scientists who research biological effects and exposure levels of radiofrequency radiation.”
McClelland and Jaboin, who do not seem to have published on the topic of mobile phones and health before, published a commentary in August 2018 ( 6 ). They reported no CoIs, the commentary was supported by a few references to in vivo studies, and the sole aim of the commentary was to bring a 5G moratorium to the attention of the journal's readership.
Miller et al. published their review on August 2019 ( 7 ). The manuscript was initially developed as a Position Statement of the International Network for Epidemiology in Policy (INEP), but after its board voted to abandon its involvement, the authors decided to publish it regardless. They reported affiliations to universities as well as the campaigning organizations the Environmental Health Trust and the Environment and Cancer Research Foundation, but did not, for example, report their involvement in the Physician's Health Initiative for Radiation and Environment (PHIRE) (Miller, Hardell, Davis) and Oceania Radiofrequency Scientific Advisory Association (ORSAA) (Hardell, Morgan, Davis). No information is provided on the methodology of this narrative review, and no quality assessment of included references is conducted, but scientific uncertainty is discussed. Carcinogenic and reproductive effects are reported as a specific susceptibility of children to RF. Particularly in relation to 5G, skin effects, oxidative stress, altered gene expression, immune function, and other biological endpoints are mentioned. The authors make several policy recommendations, but not specifically in relation to 5G.
In September 2019, Simkó and Mattsson published a pragmatic review of in vivo and in vitro evidence for health and biological effects in relation to 6 to 100 GHz frequency range ( 8 ). Both authors were from SciProof International and reported that their review was funded by Deutsche Telekom Technik GmbH. Although described in opaque language, the review seems to be based on a systematic approach to evidence synthesis and includes an assessment of study quality. Scientific uncertainty is discussed in detail, and the authors conclude that “regarding the health effects of 6–100 GHz 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.” They further highlight that “regarding the quality of the presented studies, a few studies fulfill the minimal quality criteria to allow any further conclusions.”
Hardell and Nyberg published a commentary in January 2020 ( 9 ). Both reported university affiliations and reported that neither funding was received for the work nor do they report any CoIs. However, in addition to unreported associations already mentioned above, it has also been documented that Hardell has previously received direct industry funding as well as funding from pressure groups, while he has also acted as an expert witness for the plaintiff in hearings around brain tumors and mobile phones ( 10 ). He is the spokesperson for the International EMF Scientist Appeal for Sweden and also runs a charity, the Environment and Cancer Research Foundation, which accepts direct donations and is heavily involved in appeals. The commentary includes several strong claims, including that “RF radiation may now be classified as a human carcinogen, Group 1” and that “experience with the EU, and the governments of the Nordic countries suggest that the majority of decision-makers are scientifically uninformed on health risks from RF radiation”, and interestingly and without basis that “they [the EU and governments of Nordic countries] seem to be uninterested to being informed by scientists representing the majority of the scientific community.”
In January 2020, there was also the publication of a review of health effects of 5G under real-life conditions by Kostoff et al. ( 11 ). They reported university affiliations and declared that neither external funding was received for the work nor any CoIs. However, an internet search identified that Héroux is the spokesperson for the International EMF Scientists Appeal for Canada. There is no assessment of study quality or scientific uncertainty. They mentioned that industry influence is the cause of the lack of consensus on health effects of mobile phones. The authors claimed that “there is a large body of data from laboratory and epidemiological studies showing that previous and present generations of wireless networking technology have significant adverse health impacts”, and that, with respect to 5G specifically, “superimposing 5G radiation on an already imbedded toxic wireless radiation environment will exacerbate the adverse health effects shown to exist.”
An information statement from the IEEE Committee on Man and Radiation (COMAR) was published in relation to health and safety issues concerning the exposure of the general public to electromagnetic energy from 5G wireless communication networks in June 2020 ( 1 ). All authors report industry CoIs. The main focus of the review relates to RF exposures from 5G, but some discussion specifically on potential biological and health effects of MMWs is included. Study quality is discussed in detail, including the varying quality of narrative reviews [including ( 4 )], and research gaps regarding the bioeffects of MMWs are highlighted. The authors refer back to ( 8 ) for a discussion on bioeffects and conclude that “… while we acknowledge gaps in the scientific literature, particularly for exposures at MMW frequencies, the likelihood of yet unknown health hazards at exposure levels within current exposure limits is considered to be very low, if they exist at all.”
Hardell contributed a second commentary in this period, with Carlberg as co-author ( 12 ). In this commentary, they reported the Environmental and Cancer Research Foundation as their affiliation, but declared neither CoI nor any external funding for the work. Also, the authors discussed the involvement of certain experts in various committees related to RF health and safety in the EU and internationally and the influence of industry. In addition, they mentioned effects of RF exposure, including 5G, on cancer, reproduction, and neurology; effects on the immune system; and microbiological effects, and also mentioned the susceptibility of children to RF. The claim that “the IARC Category should be upgraded from Group 2B to Group 1, a human carcinogen” is re-iterated, referencing Hardell's earlier contribution as the basis for this claim ( 9 ). Hardell and Carlberg highlighted the appeal for a 5G moratorium sent to the EU in 2017.
Leszczynski published a review on the physiological effects of MMWs on the skin and skin cells in August 2020 ( 13 ). He reports a university affiliation, neither external funding for the work nor CoI. Leszczynski conducted a systematic review of several databases for studies of >6 GHz. The quality and uncertainty of the available evidence are specifically discussed, and he concludes that “this evidence is currently insufficient to claim that any effects have been proven or disproven”. Leszczynski addresses policy and argues that “deployment for industrial use should be the first, but the further broader deployment for the non-industrial use should preferably await for the results of the biomedical research”.
Frank published an essay on 5G and the precautionary principle in January 2021 ( 14 ). He declares neither external funding nor CoI. He is, however, a member of the PHIRE team. Frank has no previous track record in radiation epidemiology, but he has reviewed the evidence and provided support for the work by Miller et al. ( 7 ). He concluded that the precautionary principle should be applied and recommended a moratorium on 5G development.
A team from the Swinburne University of Technology and the Australian Radiation Protection and Nuclear Safety Agency (ARPANSA) published two studies in March 2021: a comprehensive review of the literature for experimental studies of bioeffects of RF fields between 6 and 300 GHz and a complementary meta-analysis ( 15 , 16 ). The authors reported Australian government and National Health and Medical Research Council funding, but no CoIs. Of relevance is that Karipidis is a member of the International Commission on Non-Ionizing Radiation Protection (ICRNIRP). The included studies in these publications were identified in a systematic literature search, and the authors have explicitly discussed study quality. They concluded that many studies have low-quality methods and that experimental data do not provide evidence that low-level MMWs are associated with biological effects relevant to human health.
Jargin published a letter to the editor in March 2021 ( 17 ) in which he has argued that various publications claiming there are health harms related to 5G published by interest groups overestimate any health risks from RF-EMF to hamper the technological advancement of developed nations. He further argued that excessive restrictions would only be unfavorable for the economy and add difficulties to daily life. As such, it advocates a policy recommendation of no action. He has reported neither external funding for the work nor any CoI.
Hardell also contributed a third publication ( 18 ). In this opinion piece/review, Hardell argued that evaluations by the Health Council of the Netherlands, the WHO, ICNIRP, and the Swedish Radiation Safety Authority are not impartial and that a moratorium on the implementation of 5G is urgently required. He has reported both university and foundation affiliations, but has reported neither external funding nor any of the above identified CoI.
This chronological overview of the publications published during the initial critical phase of discussions around 5G and health leads to the interesting observation that publications by authors with links to anti-5G campaigning organizations dominated the early phase in which adverse effects related to 5G were discussed. Over half of the 15 publications had links to such organizations in the initial 3-year period covered here. Such patterns of efforts to control the narrative during critical periods have been studied elsewhere, for example, in the sugar-sweetened beverage research ( 19 ); although in this example, the opposite pattern was observed in which the contribution of industry-related studies was high at the start and decreased significantly with time.
With the increasing contribution from independent and industry-linked authors over the covered time period, the narrative shifts from the exclusive reporting of increased risks of all biological or health effects covered to predominantly descriptions of mixed results and conclusions not supporting increased risks. This difference in the interpretation of the same evidence depending on the affiliation in RF research has been mentioned previously, specifically in relation to the funding source of primary studies ( 20 , 21 ), but the current overview is indicative of a similar pattern in other types of peer-reviewed publications. Reviews from independent and industry-linked authors were systematic-style reviews, rather than narrative reviews, and were of higher methodological quality because they based their inferences on a more systematic approach to the identification of relevant literature and also explicitly included some forms of assessment of the quality of these studies. They also had a narrower aim in terms of exposures or health outcomes, which will have facilitated a more systematic approach. There is evidence from various industries, including the telecommunications industry ( 20 , 21 ), of a correlation between industry funding of research and null findings. However, there is much less discussion of its mirror image: the phenomenon that independently funded studies may be biased if the authors have strong a priori beliefs about the question under study. This “white hat bias” is observable in the literature as selective referencing and the acceptance of a lower standard of scientific evidence for studies supporting the authors' beliefs ( 22 ), and was first explored in obesity research ( 23 , 24 ). The non-systematic inclusion of references (or “cherry picking”) and lack of explicit assessment of study quality observed in the publications in the current work were most prominent in the narrative reviews by authors with links to campaigning organizations and likely will have resulted in biased inferences. Importantly, since these publications made up most of the earliest publications during the critical window, these inferences will have disproportionally influenced the narrative. Given that all of these articles had the specific aim to influence policy and, in most cases, advocated for a moratorium on 5G, this provides further support for the presence of “white hat bias” influencing the initial peer-reviewed and, through that, lay literature.
Given the observed differences between publications by authors with links to campaigning organizations and those with industry-linked or independent authors, the reporting of CoI becomes more important. Direct industry funding and other financial CoIs are generally considered the main sources of potential bias, and these were reported by the publications with links to industry (either as a CoI or as a funding source) and by one of the papers with links to activism. However, no other financial CoIs were reported; for example, it is recorded that Hardell, who has contributed three publications in this critical time period, has previously received direct industry funding as well as funding from pressure groups, while he has also acted as an expert witness for the plaintiff in hearings around brain tumors and mobile phones ( 10 ). Importantly, industry and other financial CoIs are not the only potential source of CoI bias ( 25 ), and a variety of non-financial CoIs have been described, for instance, originating from particular concerns, ideals, and predilections ( 26 ). Membership of campaigning organizations or their advisory or expert boards would, presumably, constitute such non-financial CoIs and, therefore, should have been reported. Despite internet searches by the authors identifying quite a number of such CoIs, only a few of these were reported by the authors (or could be inferred from affiliations). Likewise, the membership of national or international expert organizations constitutes non-financial CoIs that ideally should have been reported, and Karipidis' membership of ICNIRP is relevant in the context of these publications.
Although the discussed timeline of publications highlights some interesting trends and areas of concern, this work has a number of limitations. Although the selected manuscripts were identified through a systematic search, it was not a systematic review of the literature, and publications that did not specifically mention 5G in the title, abstract, or keywords might have been missed. Furthermore, the search was also limited to publications in English language. Although the wider debate about health effects of 5G is much larger and also includes gray literature, popular, and social media, these were not included in this overview. It would be an interesting future exercise to evaluate similar trends in these media. Although several non-reported CoIs were identified, these were identified following cursory internet searches only and do not constitute an exhaustive list. It is likely that a more thorough systematic search would reveal additional links not reported here. It is also possible that some such CoIs did not exist yet at the time of publication.
In conclusion, the discussion around 5G as a significant human health risk in the peer-reviewed literature was initially largely driven by authors from, or with links to, various campaigning organizations and linked publications directly to appeals for a moratorium on 5G. Commentaries and letters are personal opinions and are rarely based upon a methodological appraisal of the evidence, but the narrative of the initial period covered in the current review, relied mostly on reviews of lower methodological quality compared, with the subsequently published reviews by independent researchers and researchers with links to industry. It is likely that articles in the popular media, therefore, were influenced more heavily by the initial advocacy publications than by the later higher quality contributions. Importantly, there is no clear answer (yet) whether the resulting narrative from the peer-reviewed literature describes an overestimation of risks as a result of articles with links to campaigning organizations, or whether later contributions from authors with links to industry, and possibly most independent authors, at the latter stages of the critical window describe an underestimation of true causal associations, or whether their combined evaluation will inform future evidence synthesis closer to “the truth”. It is, however, well established that not including explicit evaluation of the quality of studies included in evidence synthesis, and which was most evident in publications classified as “activism”, makes such reviews more susceptible to biased inferences. In addition to issues related to controlling the narrative and the impact of “white hat bias”, the current work further describes undisclosed non-financial CoIs that are likely to have influenced the interpretation of evidence. This was also observed particularly for those publications associated with campaigning organizations. The narrative around 5G and potential human health effects should be interpreted through this lens, in particular because many of the authors with links to various campaigning organizations in this article (Hardell, Héroux, Miller, and Moskowitz) as well as others who published works after the covered period have recently joined up formally in a new advocacy group ICBE-EMF ( 27 ).
FdV conceived of the study and wrote the first version of the manuscript. FdV and PA conducted the analyses. All authors contributed to the article and approved the submitted version.
The authors would like to thank Tabitha Pring, whose MSc dissertation partly informed the current work.
FdV is a member of the Committee on Medical Aspects of Radiation in the Environment COMARE, IRPA NIR Task Group, SRP EMFOR, and EMF Group of the Health Council of the Netherlands. FdV consulted for EPRI not directly related to this work. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Ey study: companies are increasingly investing in 5g technology. europe leads the way.
Multidisciplinary professional services organization
The third edition of the EY Reimagining Industry Futures Study reveals that enterprises are looking to 5G to help alleviate immediate business pressures brought by the COVID-19 pandemic and related global events. Forty-nine percent of respondents are prioritizing process optimization as a key application, compared with 28% who favor advanced 5G use cases featuring virtual or augmented reality. The findings indicate enterprises are now focused on bolstering business resilience, meeting corporate priorities and responding to stakeholder demands.
A range of external factors underpin this trend. Eighty-five percent of respondents say the impact of the global health crisis is driving their interest in 5G, up from 52% in last year’s study. Eighty percent say supply chain disruption has galvanized their 5G pursuit, while 71% cite the focus on environmental, social and governance (ESG) issues. However, there is some way to go in realizing these ambitions: 37% are concerned that 5G and internet of things (IoT) vendors’ current use cases do not meet their business resilience and continuity needs, and 47% do not think their sustainability goals are met by today’s use cases.
It is fundamental to the wider acceptance of 5G investments to clarify the converge between technology, ecosystem of players, devices and services that could be offered. Technology maturity, financial viability of planned investments, technology and business skills to design and operate, manufacturing capacity and supply chain availability are all aspects to consider in the near future.
Europe leads 5G investment, but global confidence stalls
5G leads all other emerging technologies tracked in the study in terms of future spending intentions, with 56% of enterprise respondents planning to invest within three years. Current and future spending intentions for 5G over this period are highest in Europe (up 5% to 76%), in contrast to last year when Europe lagged other regions. However, the findings caution that investment should not be taken for granted, with intentions falling by 8% year-on-year to 70% in Asia-Pacific and the Middle East.
This caution is indicative not only of a more defensive approach toward 5G, but of stalling confidence generally, with only 24% of enterprise respondents stating that they are very confident they can successfully implement 5G (down by 1% year-on-year). This is compounded by enterprises’ poor understanding of 5G’s relationship to other emerging technologies, now cited as the biggest internal challenge to 5G perception – up from fifth position in last year’s ranking.
There are still fundamental anxieties around how 5G works alongside other emerging technologies. 5G providers should take this on board and adapt their customer discussions accordingly. By educating enterprises on how 5G can be harnessed by other emerging technologies, service providers can boost enterprise confidence in their 5G deployments.
Growing appeal of private networks as telcos battle credibility gap
The study further finds that enterprises are becoming increasingly receptive to 5G solutions delivered through disruptive business models. Seventy-seven percent of enterprise respondents are interested in using private networks to support the implementation of 5G and IoT use cases, and 71% are interested in buying 5G through an intermediary rather than directly from a telco.
Meanwhile telcos face a significant credibility gap with regards their perception as digital transformation experts, with only 19% of enterprises considering them as such (unchanged from last year’s study findings). Conversely, 30% trust network equipment vendors as favored digital transformation experts – up from 19% last year.
Disruptive customer signals suggest that telcos’ traditional relationships with enterprise customers are under pressure and more agile go-to-market strategies are essential in a 5G-IoT world. Telcos should take steps now to help ensure that they can meet enterprise demand for private network deployments.
Ecosystem collaboration continues to be central to the enterprise growth agenda
Sixty-nine percent of respondents state that they already collaborate with other organizations as part of a business ecosystem – unchanged from last year’s study. However, the findings indicate that businesses are being bolder in their approach to partnerships, with 36% seeking vertical partnerships with companies in other sectors (up from 24% last year), and 73% are prioritizing suppliers that can offer ecosystem relationships as part of their 5G capabilities.
At MWC Barcelona 2022, the EY organization will be exploring how connecting industry ecosystems can help support technology-based transformation, build resilience and create long-term value. Find out more at ey.com/en_gl/mwc .
About EY Romania
EY is one of the world's leading professional services firms with 312,250 employees in more than 700 offices across 150 countries, and revenues of approx. $40 billion in the financial year that ended on 30 June 2021. Our network is the most integrated worldwide, and its resources help us provide our clients with services allowing them to take advantage of opportunities anywhere in the world. With a presence in Romania ever since 1992, EY is the leading company on the market of professional services. Our more than 800 employees in Romania and Moldova provide integrated services in assurance, tax, strategy and transactions, and consulting to clients ranging from multinationals to local companies.
Our offices are based in Bucharest, Cluj-Napoca, Timisoara, Iasi and Chisinau. In 2014, EY Romania joined the only global competition dedicated to entrepreneurship, EY Entrepreneur Of The Year. The winner of the national award represents Romania at the world final taking place every year in June, at Monte Carlo. The title of World Entrepreneur Of The Year is awarded in the world final. For more information, please visit: www.ey.com
About the survey
The third EY Reimagining Industry Futures Study is based on an online survey of 5G and internet of things (IoT) perceptions among 1,018 enterprises worldwide conducted between November and December 2021. Respondents were drawn from multiple industry verticals and geographies, with only the responses from those who self-selected as “moderately knowledgeable” or above about IoT or 5G initiatives within their organizations included in the results.
The survey explored executives’ attitudes and intentions toward emerging technologies, with a specific focus on IoT and 5G-based IoT. Themes examined include emerging technology spending intentions and Industry 4.0 use cases delivered by 5G, as well as business’ attitudes to suppliers and collaborative ecosystems. Building on the survey results, this report provides additional insights and recommendations based on enterprises’ usage of 5G-IoT and their evolving relationships with 5G-IoT providers.
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With 185M new 5G connections in Q1 2024, they are projected to hit 7.7B in 2028
BELLEVUE, Wash. —The 5G industry continues to see strong growth according to new data from 5G Americas and Omdia that shows 185 million 5G connections were added in Q1 2024, pushing the global total to nearly two billion.
5G Americas and Omdia project that 5G connections will hit 7.7 billion by 2028.
“5G keeps accelerating as the increasing number of operators offering the technology continue to expand the population coverage of their networks in urban and suburban areas. While 4G continue its expanding presence in rural and remote areas helping governments fulfill their national connectivity goals,” said Jose Otero, vice president of Latin America and the Caribbean for 5G Americas, an industry trade group.
“The wireless technology sector continues to demonstrate its strength and significance through rapid adoption and sustained robust growth globally,” added Chris Pearson, president of 5G Americas. “North America remains at the forefront of 5G implementation.”
The new data shows that North America leads the charge in 5G adoption, with 5G connections in the region comprising 32% of all wireless cellular connections. Notably, the region experienced healthy growth in the first quarter, adding 22 million new connections to operator networks. In the first quarter of 2024, North American 5G connections totaled 220 million.
Last quarter, Latin America also witnessed solid growth in 4G LTE and 5G connections, adding eight million new LTE connections for a total of 591 million across the region, the researchers said. Additionally, the region continues to embrace the 5G revolution with nine million new 5G connections added to reach a total of 48 million 5G connections. 4G LTE subscriptions continue to remain strong throughout the region, even as the availability of 5G handsets and spectrum continue to grow.
Looking ahead, Omdia forecasts paint a picture of the telecommunications landscape we can expect to see throughout this decade. Global 5G connections are projected to reach 7.7 billion by 2028, with North America forecast to boast an impressive 700 million 5G connections by the same year.
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“North America is swiftly moving to a region of essentially only LTE and 5G, with most operators having closed their legacy networks already,” explained Omdia principal analyst Kristin Paulin. “On top of that, vast 5G coverage and devices at a range of price points helps drive 5G adoption. IoT is also expected to have more of a role in driving 5G adoption later in the forecast.”
The researchers added that the Internet of Things (IoT) ecosystem will continue to remain a fundamental component of the digital revolution. Currently, global IoT subscriptions stand at 3.3 billion, complemented by 6.7 billion smartphone subscriptions. Forecasts suggest that IoT subscriptions will reach 5 billion, while smartphone subscriptions will surge to 8 billion by 2028, highlighting the evolving nature of connectivity and the interconnectedness of our digital world.
Globally, the number of deployed 5G networks have exceeded the pace of 4G LTE network deployments at the equivalent time in the technology cycle, the study also found. There are nearly as many 5G North American deployments as 4G LTE networks. Currently, there are 316 commercial 5G networks worldwide, a number that is expected to grow alongside continued significant investments in 5G infrastructure worldwide. Visit www.5GAmericas.org for more information, statistical charts, and a list of LTE and 5G deployments by operator and region.
George Winslow is the senior content producer for TV Tech . He has written about the television, media and technology industries for nearly 30 years for such publications as Broadcasting & Cable , Multichannel News and TV Tech . Over the years, he has edited a number of magazines, including Multichannel News International and World Screen , and moderated panels at such major industry events as NAB and MIP TV. He has published two books and dozens of encyclopedia articles on such subjects as the media, New York City history and economics.
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This article identifies adverse effects of non-ionizing non-visible radiation (hereafter called wireless radiation) reported in the premier biomedical literature. It emphasizes that most of the laboratory experiments conducted to date are not designed to identify the more severe adverse effects reflective of the real-life operating environment in which wireless radiation systems operate. Many experiments do not include pulsing and modulation of the carrier signal. The vast majority do not account for synergistic adverse effects of other toxic stimuli (such as chemical and biological) acting in concert with the wireless radiation. This article also presents evidence that the nascent 5G mobile networking technology will affect not only the skin and eyes, as commonly believed, but will have adverse systemic effects as well.
Keywords: 5G; Adverse health effects; Combined effects; Electromagnetic fields; Mobile networking technology; Non-ionizing radiation; Real-life simulation; Synergistic effects; Systemic effects; Toxic stimuli combinations; Toxicology; Wireless radiation.
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June 25, 2024
T-Mobile for Business recognized groundbreaking 5G technology applications with the Unconventional Awards, highlighting industry disruptors and innovative uses across various sectors.
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Identifying signal modulation types using deep convolutional neural networks.
Encora Technology Practices
As we all expected, the 5th generation of the mobile network standard (5G, defined by the 3GPP ) provides much faster data rates than previous generations. However, 5G brings much more than that. Developed as an SBA (Service-Based-Architecture) and defining an NG-RAN (Next Generation — Radio Access Network), the new 5G standard is indeed faster (100x compared to 4G), addresses extremely low-latency scenarios (few milliseconds), and supports thousands of user connections in the same cell range.
But how is it possible? With lots of research, effort, smart people, and investment from great minds and companies.
Technologies have been created or enhanced to make the three primary use cases possible — eMBB (enhanced mobile broadband), mMTC (massive machine type of communications), and URLLC (Ultra-reliable and low latency communications). Moreover, recent advances on CUPS (Control and User Plane Separation), Edge Computing, and use of millimeter waves (which seemed unfeasible a few years ago), associated with massiveMIMO (Spatial diversity, spatial multiplexing, and beamforming) and Network Slicing were fundamental to achieve the results we can already witness in live scenarios today.
Another important aspect of 5G systems is the possibility to have AI/ML (Artificial Intelligence and Machine Learning) at pre-defined points on the network. Considering that UE (User Equipment, or Smartphones if you prefer) and 5G core network functions (especially the NWDAF — Network Data Analytics Function) within the SBA already provides computational resources to run ML algorithms, the big news is that, within 5G standardization, it intelligently leverages the usage of MEC (Multi-access Edge Computing) to push Machine Learning to the edge of the Telecom network (O-RAN specified RIC — RAN Intelligent Controller for Non-real-time and Near-real-time ), creating a multitude of possibilities.
In this article, we will look at how the NG-RAN can use an end-to-end deep learning system to recognize and classify modulated RF signals and adjust channel parameters appropriately, so both ends of the communication achieve optimal, effective resource usage.
In telecommunications systems, a common approach to transmitting information is to vary some properties of a periodic waveform (the carrier signal ) with a separate signal called the modulation signal . The modulation signal is what matters here — it contains information to be transmitted. In this context, the modulation process can be thought of as embedding the signal we care about (the modulation signal) onto a higher frequency waveform (the carrier signal) that will carry the information to a desired destination. For example, the modulation signal might be sounds from a microphone, a video signal representing moving images from a camera, or a digital signal representing a sequence of binary digits.
In non-cooperative communication systems, when sending signals, a transmitter may choose any modulation type for a particular signal. The modulation classification is an intermediate process that occurs between signal detection and demodulation at the receiver. To demodulate the received signal, intelligent radio receivers need knowledge of the modulation type to ensure a successful transmission. The problem is that, with no knowledge of the transmitted data, such as the amplitude of the signal, the phase offsets, or the carrier frequency, recognizing the modulation becomes much more challenging. In this context, automatic modulation classification (AMC) is an approach to solve this problem. The idea is to make intelligent receivers able to figure out the signal’s modulation by only observing it, without additional information about the modulation. This “blind” approach to modulation recognition is very efficient because it reduces information overload and increases transmission performance.
With the advances in 5G technology, signal modulation recognition/classification has become a key topic.
In fact, the ability to automatically identify a modulation type of a received signal has many civil and military applications, such as cognitive radio and adaptive communication.
Many algorithms for solving the automatic modulation classification (AMC) task have been proposed in the last two decades. There has been a substantial effort in developing feature-based (FB) methods for modulation classification among such solutions. Feature-based solutions for AMC have two steps: (1) feature extraction and (2) classifier training. The process of extracting features from the signals is fundamental. For instance, previous work has proposed to mine features from the signal’s frequency and phase in the time domain.
After devising some features, we need to train a classifier to perform the task of modulation classification. Here, any classifier can be used, from a simple linear model to non-linear methods such as decision trees and support vector machines. Nevertheless, for FB methods, the performance of the modulation classifier is limited to how good these features can be.
However, with the recent developments of deep neural networks, current approaches employ deep learning models to create modulation classifiers that improve classification performance over classic solutions.
In addition, one of the benefits of training deep learning models for AMC is that we can deploy models that can classify the received signal from the raw data — without a feature extraction module. In other words, deep learning models can learn the most relevant features for the task by themselves, which has been shown to produce much better classifiers. Moreover, operating in the raw signal (instead of using manually crafted features) means that the performance of deep learning models is limited by how much these models can learn patterns from the raw signal.
Here, we present our findings from creating a modulation classifier using PyTorch Lightning to build an end-to-end deep learning system capable of recognizing modulation signals. For this task, we used the GNU radio ML RML2016.10a dataset . This synthetic data contains 220000 input examples from a total of 11 (8 digital and 3 analog) modulation schemes at varying signal-to-noise (SNR) ratios. The digital modulations are BPSK, QPSK, 8PSK, 16QAM, 64QAM, BFSK, CPFSK, and PAM4, and the analog modulations consist of WB-FM, AM-SSB, and AM-DSB. These 11 modulations are widely used in wireless communications systems.
Since we are most interested in 5G use cases, we are going to discard the 3 analog modulations and work with the 8 following digital modulations: BPSK, QPSK, 8PSK, 16QAM, 64QAM, BFSK, CPFSK, and PAM4. Thus, our task is posed as an 8-way classification problem where we need to learn the probability distribution over the 8 modulations (classes) from a complex time-series representation of the received signal. To have an idea, according to the recommendations in the 3GPP R15 protocol , five commonly used 5G modulated signal models are: π/2-BPSK, QPSK, 16QAM, 64QAM, and 256QAM.
Our ConvNet model follows the following architecture. The input signal has a shape of (Batch Size, 1, 2, 128) and is processed by a sequence of convolutions and dense layers. The model has 3 convolutional blocks and one dense block. Each convolutional block contains a 2D convolutional layer followed by ReLU non-linearity, batch normalization, and Dropout. The three convolutional blocks transform the input data into respective feature volumes with channels 64, 128, and 256. We then flatten the output representation, resulting in a feature vector of shape (Batch Size, 10240, 256), and pass it to a dense block containing 256 neurons with ReLU, batch normalization, and Dropout regularization. Finally, a linear layer maps the output representation to a probability distribution over the 8 modulation classes. The deep modulation classifier is trained with Stochastic Gradient Descent optimization. For more details, you can check out our Jupyter notebook.
Since the data is well-balanced across signal-to-noise-ratio (SNR) and throughout the classes, the classification accuracy score is an acceptable metric to assess our classifier’s performance. To train our deep modulation classifier, we created:
Each record in the datasets has 128 samples in length. The datasets were stratified so that each subset contains an equal number of observations across different signal-to-noise ratios (SNR) from -10dB to +20dB. Note that the validation dataset is only used for tuning the model hyperparameters (learning rate and dropout probabilities). After finding suitable values for these hyperparameters, we incorporate the validation set into the training data, which gives a training data size of 136000.
After training a relatively light model, we achieved a test accuracy of approximately 62.8% for the 8 modulations. We can see accuracy scores for different modulations in the confusion matrix below. It is clear that, even with a relatively small convolutional neural network (approximately 8.9 Million training parameters), the ConvNet could learn useful features to discriminate between the 8 types of modulations. To have an idea, a classic ConvNet architecture, like the AlexNet, used by previous work for modulation classification, contains 62.3 million training parameters.
If we break down the evaluation protocol across different SNR values, we can see that for low-SNR, the performance of our classifier is significantly low. And in fact, it follows the same finding from research publications such as “Convolutional Radio Modulation Recognition Networks” [1].
You can see a more detailed assessment of our classifier over different SNR values below. Note how the confusion tables for low-SNRs are messy while the confusion matrices for SNR>=-4.0dB are much cleaner indicating higher performance.
Deep learning-based models have a strong case for 5G applications. As discussed in this article, we can build machine learning models using an established architecture such as convolutional neural networks to implement a modulation classifier that can identify many types of modulation schemes at varying signal-to-noise (SNR) ratios by only looking at the raw signal. In this use case, we tackled the problem of classifying 8 different modulations commonly used in 5G technology. We showed that we could learn representations that can tell the modulations apart with reasonable accuracy, even with a relatively light model. In cases of high SNR, our classifier achieves very high accuracy scores.
This piece was written by Thalles Silva with the Innovation Team at Daitan. Thanks to João Caleffi, Mario Zimmer and Kathleen McCabe for reviews and insights.
[1] O’Shea, Timothy J., Johnathan Corgan, and T. Charles Clancy. “Convolutional radio modulation recognition networks.” International conference on engineering applications of neural networks . Springer, Cham, 2016.
[2] O’Shea, Timothy J., and Nathan West. “Radio machine learning dataset generation with gnu radio.” Proceedings of the GNU Radio Conference . Vol. 1. №1. 2016.
[3] Zhang, Qing, et al. “Modulation recognition of 5G signals based on AlexNet convolutional neural network.” Journal of Physics: Conference Series . Vol. 1453. №1. IOP Publishing, 2020.
[4] Zhou, Siyang, et al. “A robust modulation classification method using convolutional neural networks.” EURASIP Journal on Advances in Signal Processing 2019.1 (2019): 1–15.
[5] Flowers, Bryse, R. Michael Buehrer, and William C. Headley. “Evaluating adversarial evasion attacks in the context of wireless communications.” IEEE Transactions on Information Forensics and Security 15 (2019): 1102–1113.
[6] Usama, Muhammad, et al. “Examining machine learning for 5g and beyond through an adversarial lens.” IEEE Internet Computing 25.2 (2021): 26–34.
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A summary of the recent 3gpp sa1 imt-2030 use cases workshop.
Last month, 3GPP held a Stage 1 workshop on IMT-2030 use cases in Rotterdam, Netherlands. A diverse group of vertical organizations and regional research alliances tasked to drive the technology advancement towards 6G were invited to present their views on system design priorities, enabling technologies and target use cases.
We had the honor and opportunity to attend the workshop in person, and for this blog post, let us provide a high-level summary of the 3-day workshop, aligning these fruitful discussions with our vision of the 6G technology platform . 6G is the next cellular generation building on 5G learning and technology foundation. It will fuel innovations that can enable new and enhanced use cases in the decade to come.
Full house at the 3GPP SA1 workshop.
Each day, various vertical and regional organizations presented their views on 6G, focusing on different goals, drivers, capabilities and use cases. Here is an overview of the three-day workshop and key presentations:
Wanshi Chen, 3GPP RAN Chair, on the “ITU & 3GPP synergies for 6G” panel.
The presentations from regional research alliances, vertical organizations and mobile operators highlighted the most exciting opportunities 6G can bring from their perspectives. They focus on how 6G, as a single global standard, can build upon the 5G wireless system foundation to enable societal transformations. At a high-level, here are the key goals and drivers for the 6G innovation platform to achieve in the next decade and beyond:
6G vision from ITU-R — capabilities and usage scenarios of IMT-2030.
The 6G technology platform is expected to take a significant leap forward, supporting enhanced system capabilities that go beyond communication. The IMT-2030 framework defined by ITU-R sets the initial vision for this advancement, outlining key usage scenarios and performance metrics that should shape the target for future wireless technologies.
As these technologies develop, 6G is anticipated to integrate AI, advanced computing, and system resilience features, alongside innovative green technologies and integrated sensing and communications (ISAC), marking a new converging era of the physical-digital-virtual worlds.
While different contributing presentations highlighted different sets of use cases targeting 6G, we observed some clear trends of what key 6G use cases are being envisioned. The following is an illustrative (i.e., non-exhaustive) list highlighting the main use cases discussed in the workshop:
Path to 6G includes the continued 5G Advanced technology evolution.
In 3GPP, the next steps will happen in SA WG1, preparing for the service requirements study. This will be followed by the work in RAN and SA, which will include technology workshop and studies starting in 2025.
Qualcomm Technologies’ vision for the future of wireless technology is continuous and expansive. With the evolution of 5G Advanced, the foundation for 6G is being laid, promising to bring a new era of technological advancements. The focus is not only on enhancing wireless designs but also on integrating a broader range of technologies to enable intelligent computing everywhere. The anticipation of 6G includes the potential for groundbreaking developments in wireless communication, AI, computing, RF sensing and network resiliency, setting the stage for a smarter, more sustainable wireless platform of our future.
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