Machine Learning for the Estimation of WiFi Field Exposure in Complex Indoor Multi-Source Scenario

This paper presents the preliminary results on the use of Machine Learning (ML) for the estimation of the electric-field exposure in indoor scenarios with multiple WiFi sources. Differently from similar previous approaches, the present approach aims to design a Neural Network (NN) capable to address complex indoor scenarios that include not only down-link transmission by access points (APs) but also up-link transmission by several clients (e.g., laptop, printers, tablets, and smartphones). The NN was trained and tested on the field generated by multiple WiFi sources (2400 MHz) in an office indoor setup; the ‘target’ exposure field in such a scenario was derived using a deterministic indoor network planner method. The median prediction accuracy of the ‘target’ field exposure by the proposed NN was 0.0 dB (1st quartile: −0.7 dB; 3rd quartile 0.9 dB), with a root mean square error of 2.1 dB. The proposed approach is fast (the NN training lasts about 30 minutes) and could be useful to assess radio-frequency (RF) exposure in complex indoor scenarios.