Stochastic Dosimetry for Radio-Frequency Exposure Assessment in Realistic Scenarios. In: Canavero F, editor. Uncertainty Modeling for Engineering Applications. Cham: Springer International Publishing; 2019. pp. 89-102. [DOI: 10.1007/978-3-030-04870-9_6]

Application of Stochastic Dosimetry for assessing the Human RFEMF Exposure in a 5G indoor Scenario

In recent years the introduction of 5G networks is causing a drastically change of human exposure levels in the radio frequency range. The aim of this paper is on expanding the knowledge on this issue, assessing the exposure levels for a particular case of indoor 5G scenario, where the presence of an Access Point (AP) was simulated. Coupling the traditional deterministic computational method with an innovative stochastic approach, called Polynomial Chaos Kriging, allowed to evaluate the exposure variability of an user considering the 3D beamforming capability of the antenna. The exposure levels, expressed in terms of specific absorption rate (SAR) in specific tissues, showed low values compared to ICNIRP guidelines.

Use of Machine Learning for the Estimation of Down- and Up-Link Field Exposure in Multi-Source Indoor WiFi Scenarios

A novel Machine Learning (ML) method based on Neural Networks (NN) is proposed to assess radio‐frequency (RF) exposure generated by WiFi sources in indoor scenarios. The aim was to build an NN capable of addressing the complexity and variability of real‐life exposure setups, including the effects of not only down‐link transmission access points (APs) but also up‐link transmission by different sources (e.g. laptop, printers, tablets, and smartphones). The NN was fed with easy to be found data, such as the position and type of WiFi sources (APs, clients, and other users) and the position and material characteristics (e.g. penetration loss) of walls. The NN model was assessed using an additional new layout, distinct from that one used to build and optimize the NN coefficients. The NN model achieved a remarkable field prediction accuracy across exposure conditions in both layouts, with a median prediction error of −0.4 to 0.6 dB and a root mean square error of 2.5−5.1 dB, compared with the target electric field estimated by a deterministic indoor network planner. The proposed approach performs well for the different layouts and is thus generally used to assess RF exposure in indoor scenarios. © 2021 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.