The First Indoor Pathloss Radio Map Prediction Challenge

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.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10889381
Citations
12
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
8.77
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article

The First Indoor Pathloss Radio Map Prediction Challenge

Stefanos Bakirtzis, Kehai Qiu, Çağkan Yapar, Ian Wassell et al.
12 citations
Indoor and Outdoor Localization Technologies
8.77
article

The First Indoor Pathloss Radio Map Prediction Challenge

Stefanos Bakirtzis, Kehai Qiu, Çağkan Yapar, Ian Wassell, Jie Zhang
article en
12 citations

Abstract

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.

University of Cambridge (GB), Technische Universität Berlin (DE)
Climate action
Openalex Percentile: Top 2%
Indoor and Outdoor Localization Technologies
8.77
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