New Study Says 5G Phones Create Higher Radiation Exposures than 4G Phones

By B.N. Frank

Warnings about 5G radiation exposure has been ongoing since before widespread deployment and activation. Since 2017, doctors and scientists have been asking for moratoriums on Earth and in space due to biological and environmental health risks (see 1, 2, 3, 4) and the majority of scientists worldwide oppose deployment. Of course, research has also determined that 5G is indeed biologically and environmentally harmful. Adding insult to injury, in 2019 telecom executives gave U.S. congressional testimony that they had NO scientific evidence that 5G is safe.

Opposition to 5G has limited, slowed, and/or stopped deployment but obviously not everywhere, hence more people reporting symptoms and illnesses after activation. Case studies have been published to document this as well. If you’ve got a 5G phone, a new study reveals why you may want to swap it for a 4G phone (even though research says 4G is harmful too).

From Environmental Health Trust:

New study finds higher radiation exposures from 5G phones.

Dec 15, 2023

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New study finds higher radiation exposures from 5G phones.

An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks ClassificationMiclaus S, Deaconescu DB, Vatamanu D, Buda AM. An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification. Technologies. 2023; 11(5):113. doi: 10.3390/technologies11050113

Abstract

To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure.

Conclusions

In this present work, we aim to quantify the time variability of emissions in the proximity of a mobile phone connected to either a 4G or a 5G-FR1 network when using four different mobile applications. The central objective was to provide knowledge on human exposure dynamics that completes the dosimetric studies necessary to describe the potential biological effects.

The main contribution of this study to current knowledge belongs to the topics of the effects of EMF exposure on humans that are not limited to induced heating, while non-thermal effects remain subjects of debate and investigation. To gain a deeper understanding of this aspect, new metrics and methodologies need to be adopted. The direction proposed in this work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this goal.

A supplementary benefit is the possibility to discern between exposure dynamics corresponding to one specific mobile application based on the capability of a real-time detection algorithm to successfully classify the emission type.

The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters.

Synthetically, our results showed that:

Electric field strengths in the air at 10 cm from the phone were higher for 5G-FR1 emissions than for 4G, on average by 60%. None of the values exceeded human health and safety levels. The highest difference between technologies corresponded to Internet video streaming emissions, where 5G field strength was three times higher than 4G.
4G and 5G-FR1 amplitude probability density distributions differ; 4G traces depend much more on the type of mobile application used, while 5G traces are more similar one to another and more independent of the mobile application. The same probability range of power level distribution was covered by a larger window of power values in 5G than in 4G.
Crest factors were higher for 5G-FR1 emissions than for 4G emissions; the highest difference (almost double) evolved during file download applications, while the lowest difference was observed during Internet video streaming.
The prevalence of the highest power levels (superior tail emissions) appeared much more frequent for 5G-FR1 emissions than for 4G, and a difference of as much as 9.5 dB over mean power was encountered in 5G versus 4G emissions.
The recorded spectrograms emphasized peculiarities that have been excellently captured and valorized by the YOLO v7 deep learning algorithm. Practically, excellent recognition and classification rates were obtained for each technology and each category of mobile application with a minimum of training.

Overall, the contribution of the present approach consists in the provision of an exposimetric tool that underlines the differences in amplitude-time profiling of a user’s exposure when running various applications on the mobile phone in two different mobile communication technologies. Due to the limitations of the methodology employed, the data presented cannot be considered to be of total generality. However, realistic exposure and time-variability analysis need further investigation in varied situations.

Open access paper: https://www.mdpi.com/2227-7080/11/5/113