# The First Indoor Pathloss Radio Map Prediction Challenge

arXiv: https://arxiv.org/abs/2501.13698 · HTML: https://mlsanity.com/paper/2501.13698/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 4 of 4)

- [RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks](https://mlsanity.com/paper/1911.09002/) (2019)
- [Vision Transformers for Efficient Indoor Pathloss Radio Map Prediction](https://mlsanity.com/paper/2412.09507/) (2024)
- [Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models](https://mlsanity.com/paper/2408.12193/) (2024)
- [IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction](https://mlsanity.com/paper/2501.06414/) (2025)

## Built on by (most cited first, 4 of 4)

- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization](https://mlsanity.com/paper/2511.20015/) (2025)
- [Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approaches](https://mlsanity.com/paper/2604.22056/) (2026)
- [Scaling Cheap Synthetic Pretraining Improves Indoor Radio-Map Prediction](https://mlsanity.com/paper/2610.19015/) (2026)

## 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.