Mobile Phone Emissions in 5G FR1: Using Statistic Inferences and Deep Learning for Empiric Features Extraction
Abstract
Overview
The study focuses on the quantitative analysis of electromagnetic emissions from 5G mobile phones across various applications, highlighting the statistical and time-frequency variations of exposure levels.
Findings
- Identification of amplitude probability densities and complementary cumulative density functions specific to mobile phone emissions.
- Review of time-frequency characteristics showing repeatable patterns in emissions.
- Analysis based on the modulation scheme and specific applications used.
- In-depth examination using devices like an Iphone 14pro connected to a 5G network, employing tools such as a signal analyzer and a planar antenna.
Conclusion
The investigations reveal significant variations in emissions, triggered by different mobile applications and modulation schemes used. Such variations could potentially affect biological systems, emphasizing the need for further research into non-thermal effects of these emissions.