IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction

Bin Feng, Meng Zheng, Wei Liang, Lei Zhang
1/11/2025
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Abstract

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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Cite this paper

@article{feng2025ippnet,
  title  = {IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction},
  author = {Bin Feng and Meng Zheng and Wei Liang and Lei Zhang},
  year   = {2025},
  eprint = {2501.06414},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.06414v1}
}

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