Physics-informed Diffusion Models for Multi-scale Prediction of Reference Signal Received Power in Wireless Networks

TL;DR

This paper introduces Channel-Diff, a physics-informed diffusion model that improves multi-scale RSRP prediction accuracy in wireless networks by integrating physical propagation knowledge with data-driven methods.

Contribution

It proposes a novel framework combining physical modeling and diffusion models for accurate, interpretable, and transferable RSRP prediction in complex urban environments.

Findings

Achieves 25-37% improvement in prediction accuracy over baselines.

Demonstrates strong transferability and training efficiency.

Effectively fuses multi-scale physical knowledge with diffusion models.

Abstract

The Reference Signal Received Power (RSRP) is a crucial factor that determines communication performance in mobile networks. Accurately predicting the RSRP can help network operators perceive user experiences and maximize throughput by optimizing wireless resources. However, existing research into RSRP prediction has limitations in accuracy and verisimilitude. Theoretical derivations and existing data-driven methods consider only easily quantifiable Large-Scale (LS) information, and struggle to effectively capture the intertwined LS and Small-Scale (SS) signal attenuation characteristics of the wireless channel. Moreover, the lack of prior physical knowledge leads to weak accuracy, interpretability, and transferability. In this paper, we propose a novel RSRP prediction framework, Channel-Diff. This framework physically models LS and SS attenuation using multimodal conditions and employs physics-informed conditional diffusion models as the prediction network. Channel-Diff extracts prior physical information that characterises the signal propagation process from network parameters and multi-attribute maps of the urban spatial environment. It provides LS physical priors through large-scale propagation modelling and shadow-occlusion modelling, and SS physical priors through multipath propagation modelling and urban microenvironment feature extraction. We design a physical-prior-guided two-stage training scheme with a noise prior guidance mechanism, enabling effective fusion of multi-scale physical knowledge with the diffusion models. Evaluations demonstrate Channel-Diff exhibits excellent performance on RSRP prediction, achieving at least 25.15%-37.19% improvement in accuracy relative to baseline methods. Additionally, the model also demonstrated outstanding performance in terms of transferability and training efficiency.