20 citations · 34 across the 5 of their papers we have counts for
5 papers
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-…
Environmental Feature Engineering and Statistical Validation for ML-Based Path Loss Prediction
Jonathan Ethier, Mathieu Chateauvert, Ryan G. Dempsey +1
Wireless communications rely on path loss modeling, which is most effective when it includes the physical details of the propagation environment. Acquiring this data has historical…
Extending Machine Learning Based RF Coverage Predictions to 3D
Muyao Chen, Mathieu Châteauvert, Jonathan Ethier
This paper discusses recent advancements made in the fast prediction of signal power in mmWave communications environments. Using machine learning (ML) it is possible to train mode…
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…
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…