Paving the path toward the fifth generation (5G) of wireless networks with a huge increase in
the number of user equipment has strengthened public concerns on human exposure to …

In this article, artificial neural networks are applied to data measured on a wireless indoor
mobile communications scenario for 768 MHz transmission. Three multilayer perceptron …

The estimation of Electromagnetic Field (EMF) exposure is critical for evaluating the
potential health risks associated with wireless network implementation. With the advent of …

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 …

Background The general population is exposed to Radio-Frequency Electromagnetic Fields
(RF-EMFs) used by telecommunication networks. Previous studies developed methods to …

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 …

3GPP LTE (Long Term Evolution) mobile networks, the fourth generation wireless access
technology, have been rolled out by many operators worldwide starting 2010. Since LTE …

Accurate received power prediction is important to wireless network planning and
optimization, and appropriate channel modeling approach is highly demanded. The existing …

The traditional wireless communication systems deployment models require expensive and
time-consuming procedures, including environment selection (rural, urban, and suburban) …

Despite the increasing usage of mobile communications, strengthened by the growing
penetration of smartphones, end users seem to be concerned about the potential health …