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 …

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 …

Due to quasi stochastic signal emission and multi parametric dependence, the accurate
assessment of electric field exposure levels in WLAN networks is proven to be a challenging …

One of the main challenges in optimally placing indoor Wi-Fi access points in a complex
indoor environment is the estimation of the received signal strength (RSS) given different …

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 …

The present work was performed to expand the knowledge on human RF-EMF exposure,
considering the use of mm-wave spectrum in mobile communication applications, due to the …

This study focused on the assessment of radio-frequency electromagnetic fields (RF-EMF)
exposure in a realistic apartment due to the presence of a WiFi source deployed in uncertain …

This study focused on the evaluation of the electric field 2D spatial distribution of the E-field
in a one-floor apartment when a WiFi source is placed in uncertain position. An innovative …

Wi-Fi and Bluetooth are two wireless technologies, available in every smart-phone, tablet,
and laptop. Wi-Fi Access Points (APs) and Bluetooth beacons are deployed in most indoor …

Spectrum occupancy prediction is a key enabling technology to facilitate a proactive
resource allocation for dynamic spectrum management systems. This work focuses on the …