The Sampling-Assisted Pathloss Radio Map Prediction Competition

Abstract

To encourage further research and facilitate fair comparisons of deep learning-based pathloss estimation methods in indoor environments, particularly in the less-explored case of having access to sparse ground truth pathloss samples in tandem with physical propagation environment information, we organized the MLSP 2025 Sampling-Assisted Pathloss Radio Map Prediction Data Competition. This overview paper describes the sampling-assisted indoor pathloss prediction problem, the datasets used, the competition tasks, and the evaluation methodology. Lastly, it provides an overview of the submitted methods and the results of the challenge.