首个室内路径损耗地图预测挑战，推动深度学习在室内信号传播建模中的研究
The First Indoor Pathloss Radio Map Prediction Challenge
- 基于真实数据构建室内路径损耗预测任务
- 提供多场景数据集与标准化评估流程
- 适合从事无线通信与深度学习交叉研究者
为促进深度学习驱动的室内无线电波传播模型研究，并实现公平比较，我们发起了ICASSP 2025首届室内路径损耗无线电图预测挑战。本文介绍该挑战所针对的室内路径损耗预测问题、使用的数据集、具体任务及评估方法。最后，展示了挑战结果并总结了参赛方法。该挑战聚焦于方向性信号发射下的室内传播环境，填补了该领域研究空白。
原文摘要 · Abstract (English)
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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