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

Ryan G. Dempsey; Jonathan Ethier; Halim Yanikomeroglu

doi:10.1109/lawp.2025.3554357

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arXiv · 2411.17752

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

Ryan G. Dempsey·Jonathan Ethier·Halim Yanikomeroglu

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Abstract

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.

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Ryan G. Dempsey, Jonathan Ethier, Halim Yanikomeroglu. 2024-11-25. Map-Based Path Loss Prediction in Multiple Cities Using Convolutional Neural Networks. https://doi.org/10.1109/lawp.2025.3554357

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