IPP-Net用深度学习预测室内信号衰减，表现优异且具备泛化能力。
IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction
- 基于UNet结构，融合射线追踪数据与3GPP模型训练
- 在三个任务上达到9.501 dB的加权均方根误差
- 适合需要高精度室内无线覆盖建模的研究与应用
本文提出一种可泛化的深度神经网络模型（称为IPP-Net），用于室内路径损耗无线图谱预测。IPP-Net基于UNet架构，利用大规模射线追踪仿真数据和改进的3GPP室内热点模型进行训练。其性能在ICASSP 2025首届室内路径损耗无线图谱预测挑战赛中得到评估，结果表明，IPP-Net在三个竞赛任务中取得9.501 dB的加权均方根误差，并获得总体第二名。
原文摘要 · Abstract (English)
In this paper, we propose a generalizable deep neural network model for indoor pathloss radio map prediction (termed as IPP-Net). IPP-Net is based on a UNet architecture and learned from both large-scale ray tracing simulation data and a modified 3GPP indoor hotspot model. The performance of IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025. The evaluation results show that IPP-Net achieves a weighted root mean square error of 9.501 dB on three competition tasks and obtains the second overall ranking.
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