ICASSP 2025accepted0 citations
Generalizable Indoor Path Loss Prediction
Cheick Tidiani Cissé, Oumaya Baala, Valéry Guillet, François Spies, Alexandre Caminada
Abstract
This paper is presented in the context of the First Indoor Pathloss Radio Map Prediction Challenge at IEEE ICASSP 2025. We propose a deep learning approach using a customized ResUNet architecture with physics-informed features for predicting radio maps in indoor environments. Our architecture progressively adapts to handle increasing task complexity, incorporating dual-stream processing for frequency generalization and specialized antenna gain processing with dilated convolutions. Experimental results demonstrate effective generalization across unknown indoor scenes, frequencies, and antenna patterns while maintaining computational efficiency.
BibTeX
@inproceedings{icassp2025_generalizableind,
title = {Generalizable Indoor Path Loss Prediction},
author = {Cheick Tidiani Cissé and Oumaya Baala and Valéry Guillet and François Spies and Alexandre Caminada},
booktitle = {ICASSP 2025},
year = {2025}
}