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

Bin Feng; Meng Zheng; Wei Liang; Lei Zhang

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arXiv · 2501.06414

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

Bin Feng·Meng Zheng·Wei Liang·Lei Zhang

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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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BibTeX RIS

Bin Feng, Meng Zheng, Wei Liang, Lei Zhang. 2025-01-11. IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction. https://arxiv.org/abs/2501.06414

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