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

• URL: http://arxiv.org/abs/2501.06414v1
• Date: Sat, 11 Jan 2025 02:53:14 GMT
• Title: IPP-Net: A Generalizable Deep Neural Network Model for Indoor Pathloss Radio Map Prediction
• Authors: Bin Feng, Meng Zheng, Wei Liang, Lei Zhang,
• Abstract summary: IPP-Net is a generalizable deep neural network model for indoor pathloss radio map prediction. IPP-Net is evaluated in the First Indoor Pathloss Radio Map Prediction Challenge in ICASSP 2025.
• Score: 14.114311899326836
• License:
• 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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