RMDM: Radio Map Diffusion Model with Physics Informed

Abstract: With the rapid development of wireless communication technology, the efficient utilization of spectrum resources, optimization of communication quality, and intelligent communication have become critical. Radio map reconstruction is essential for enabling advanced applications, yet challenges such as complex signal propagation and sparse data hinder accurate reconstruction. To address these issues, we propose the Radio Map Diffusion Model (RMDM), a physics-informed framework that integrates Physics-Informed Neural Networks (PINNs) to incorporate constraints like the Helmholtz equation. RMDM employs a dual U-Net architecture: the first ensures physical consistency by minimizing PDE residuals, boundary conditions, and source constraints, while the second refines predictions via diffusion-based denoising. By leveraging physical laws, RMDM significantly enhances accuracy, robustness, and generalization. Experiments demonstrate that RMDM outperforms state-of-the-art methods, achieving NMSE of 0.0031 and RMSE of 0.0125 under the Static RM (SRM) setting, and NMSE of 0.0047 and RMSE of 0.0146 under the Dynamic RM (DRM) setting. These results establish a novel paradigm for integrating physics-informed and data-driven approaches in radio map reconstruction, particularly under sparse data conditions.