SAIPP-Net: A Sampling-Assisted Indoor Pathloss Prediction Method for Wireless Communication Systems

Bin Feng, Meng Zheng, Wei Liang, Lei Zhang

Keywords: Indoor pathloss prediction, sampling strategy, radio map, deep learning

TL;DR: We propose SAIPP-Net, a deep learning method that combines adaptive network design and intelligent sampling strategies to accurately predict indoor pathloss radio maps from sparse measurements.

Abstract: Accurate prediction of pathloss radio maps is essential for the design and optimization of next-generation indoor wireless communication systems. Incorporating sparse pathloss measurements as auxiliary information has demonstrated significant potential in improving prediction accuracy. In this paper, we propose a novel sampling-assisted indoor pathloss prediction method (SAIPP-Net). First, we design a UNet-based neural network with variable-channel inputs to adapt to different levels of sampling availability. Second, we introduce a sampling-aware training strategy that employs tailored training schemes for low and high sampling rates, respectively. Finally, we develop a prioritized hybrid sampling strategy that jointly considers the transmitter distance and signal gradient to guide the selection of informative sampling locations. SAIPP-Net was evaluated in the context of MLSP 2025 The Sampling-Assisted Pathloss Radio Map Prediction Data Competition, achieving a weighted root mean squared error of 4.67 dB on the test set and securing 1st place in the competition.

Submission Number: 7