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

2025-12-24

AI Summary

Existing RSRP prediction methods struggle to jointly model large-scale (LS) and small-scale (SS) fading, while lacking physical priors—leading to low accuracy, poor interpretability, and weak cross-scenario generalizability. To address this, we propose a physics-informed conditional diffusion model that, for the first time, integrates multi-scale propagation mechanisms—including path loss, shadowing, and multipath scattering. We design a two-stage training paradigm guided by physics-based priors and introduce a noise prior mechanism to enable interpretable, joint LS/SS characterization. Additionally, we incorporate multimodal conditional inputs and physics-constrained loss functions. Experiments demonstrate that our method improves RSRP prediction accuracy by 25.15%–37.19% over state-of-the-art baselines, while significantly enhancing generalization capability and training efficiency.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

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.

Problem

• Predict RSRP accurately by modeling large-scale and small-scale signal attenuation
• Incorporate physical knowledge to improve accuracy, interpretability, and transferability
• Fuse multi-scale physical priors with diffusion models for wireless network optimization

Innovation

• Physics-informed diffusion models for multi-scale RSRP prediction
• Multimodal conditions model large and small-scale attenuation
• Two-stage training fuses physical priors with diffusion models