The First Indoor Pathloss Radio Map Prediction Challenge
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 4 of its 13 references that are in the map. First posted on arXiv 23 Jan 2025.
Builds on
4 of 13 references.
References · 4
- 2019RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networkscited by 126
- 2024Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Modelscited by 4
- 2024Vision Transformers for Efficient Indoor Pathloss Radio Map Predictioncited by 4
- 2025IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Predictioncited by 3
Built on by
Citing papers per year: 2025: 1; 2026: 2.
3 papers.
- 2026U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approachcited by 4
- 2025iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localizationcited by 2
- 2026Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approachescited by 0
- 2026Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches23 Apr 2026
- 2026U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach6 Mar 2026
- 2025iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization25 Nov 2025