Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from 2-D obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.
@article{arxiv.2411.17752,
title = {Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks},
author = {Ryan G. Dempsey and Jonathan Ethier and Halim Yanikomeroglu},
journal= {arXiv preprint arXiv:2411.17752},
year = {2026}
}