手机辐射的暴露电磁比较:5G与4G信号的统计和卷积神经网络分类分析

S. Miclaus, D. Deaconescu, D. Vatamanu, A. Buda
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引用次数: 0

摘要

为了更深入地了解微波的非热生物效应这一备受争议的话题,需要采用新的指标和方法。当前工作中提出的方向,包括峰值暴露分析,而不仅仅是时间平均分析,很好地符合这一目标。所提议的方法并非旨在促进4G和5G移动通信信号之间的一般特征的比较。相反,它的目的是提供一种方法来分析可能基于多个参数而变化的特定现实暴露条件。基于4G和5G信号的幅时特征进行区分,目的是描述当用户在两种移动网络中的任何一种移动电话上运行四种类型的移动应用程序时,其暴露的特性。为了实现这一目标,我们使用了具有足够实时分析带宽的信号和频谱分析仪,并通过幅度概率密度(APD)函数、互补累积分布函数(CCDF)、信道功率测量和记录的频谱图数据库提供了统计描述。基于特定的调制和编码方案,我们比较了4G和5G网络中文件下载、文件上传、互联网视频流和视频通话使用特定的排放暴露描述符。显示了在距离手机10厘米的空气中测量到的最高和最低电场强度。重点介绍了流行率最高的功率分布函数,并对其进行了评价。之后,属于单次发射探测器家族的卷积神经网络的能力被证明能够非常准确地识别和分类排放物,从而实现人体暴露动态的可追溯性。
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An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification
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.
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