The First Indoor Pathloss Radio Map Prediction Challenge
This addresses the need for standardized benchmarks in the niche area of indoor radio propagation modeling, though it is incremental as it builds on existing challenge frameworks.
The paper introduces the ICASSP 2025 First Indoor Pathloss Radio Map Prediction Challenge to foster research and enable fair comparisons in deep learning-based radio propagation models for directional indoor signals, presenting the problem, datasets, tasks, evaluation, and results.
To encourage further research and to facilitate fair comparisons in the development of deep learning-based radio propagation models, in the less explored case of directional radio signal emissions in indoor propagation environments, we have launched the ICASSP 2025 First Indoor Pathloss Radio Map Prediction Challenge. This overview paper describes the indoor path loss prediction problem, the datasets used, the Challenge tasks, and the evaluation methodology. Finally, the results of the Challenge and a summary of the submitted methods are presented.