Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

2026-07-02

AI Summary

This work addresses the challenge of efficiently constructing high-fidelity, physically consistent multipath radio frequency (RF) maps under sparse observations. It presents the first integration of physics-informed neural networks (PINNs) with graph neural networks (GNNs) to jointly model key multipath parameters—including path gain, time of arrival, and angle of arrival—in both 2D and 2.5D environments. By incorporating electromagnetic propagation priors and spatial correlations among receivers, the proposed approach ensures physical plausibility and structural coherence. A novel peak-weighted dynamic time warping metric is introduced to simultaneously account for amplitude errors and temporal alignment of dominant peaks. Experimental results demonstrate that the method significantly outperforms baseline approaches—including image-based models, diffusion models, and interpolation techniques—at both map-level and multipath-level evaluations, achieving strong cross-scenario generalization and high-accuracy sparse RF map completion.

Abstract

Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage analysis, and environment-aware wireless optimization. This paper proposes a unified RF map construction framework based on a physics-informed neural network (PINN) and a graph neural network (GNN), supporting both cross-scene generation and in-scene completion with 2D and 2.5D environmental representations. The PINN embeds electromagnetic propagation constraints to establish a physically consistent mapping from receiver locations to multipath parameters, including path gain, time of arrival, and angles, while the GNN enforces spatial consistency by modeling correlations among neighboring receivers. To comprehensively evaluate multipath reconstruction quality, we propose a peak-weighted dynamic time warping metric that jointly accounts for amplitude errors and peak delay misalignment in channel impulse responses. Extensive experiments demonstrate that the proposed method consistently outperforms image-based, diffusion-based, and interpolation baselines across both map-level and multipath-level metrics, achieving robust generalization and high-fidelity RF map construction under sparse observations.

Problem

• RF maps
• multipath propagation
• cross-scene generation
• in-scene completion
• sparse observations

Innovation

• Physics-Informed Neural Network
• Graph Neural Network
• Multipath RF Maps
• Cross-Scene Generation
• In-Scene Completion