We give an overview of recent developments in the modeling of radiowave propagation,
based on machine learning (ML) algorithms. We identify the input and output specification …

Accurate path loss (PL) prediction is essential for predicting transmitter coverage and
optimizing wireless network performance. Traditional PL models are difficult to cope with the …

Modern cellular communication networks are already being perturbed by large and steadily
increasing mobile subscribers in high demand for better service quality. To constantly and …

Tabular data and images have been used from machine learning models as two diverse
types of inputs, in order to perform path loss predictions in urban areas. Different types of …

Channel modeling of wireless communications from unmanned aerial vehicles (UAVs) is an
emerging research challenge. In this paper, we propose a solution to this issue by applying …

A fundamental challenge for machine learning (ML) models for electromagnetics is their
ability to predict output quantities of interest (such as fields and scattering parameters) in …

Radio propagation modeling and path loss prediction have been the subject of many
machine learning-based estimation attempts. Our current work uses deep learning for the …

The authors present a machine learning approach for the extraction of radiowave
propagation models in tunnels. To that end, they discuss three challenges related to the …

The performance of machine learning (ML)-based path loss models relies heavily on the
data they use at their inputs. Feature engineering is, therefore, essential for the model's …

Path loss prediction models are essential in the planning of wireless systems, particularly in
build-up environments. However, the efficacies of the models depend on the local ambient …