most citedMachine Learning-Based Path Loss Modeling with Simplified Features

20 citations · 37 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2025

Estimating Rural Path Loss with ITU-R P.1812-7 : Impact of Geospatial Inputs

Mathieu Chateauvert, Jonathan Ethier, Adrian Florea

Accurate radio wave propagation modeling is essential for effective spectrum management by regulators and network deployment by operators. This paper investigates the ITU-R P.1812-…

eess.SP2024★ 13 cited

Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks

Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such…

cs.LG2024

Clutter Classification Using Deep Learning in Multiple Stages

Ryan Dempsey, Jonathan Ethier

Path loss prediction for wireless communications is highly dependent on the local environment. Propagation models including clutter information have been shown to significantly inc…

eess.SP2024★ 4 cited

Signal Attenuation through Foliage Estimator (SAFE)

Mathieu Châteauvert, Jonathan Ethier, Pierre Bouchard

The SAFE tool is an open-source Radio Frequency (RF) propagation model designed for path loss predictions in foliage-dominant environments. It utilizes the ITU-R P.1812-6 model as…

cs.LG2024★ 20 cited

Machine Learning-Based Path Loss Modeling with Simplified Features

Jonathan Ethier, Mathieu Chateauvert

Propagation modeling is a crucial tool for successful wireless deployments and spectrum planning with the demand for high modeling accuracy continuing to grow. Recognizing that det…