Related papers: RMDM: Radio Map Diffusion Model with Physics Informed

RMDM: Radio Map Diffusion Model with Physics Informed

URL: http://arxiv.org/abs/2501.19160v2
Date: Wed, 19 Mar 2025 17:06:01 GMT
Title: RMDM: Radio Map Diffusion Model with Physics Informed
Authors: Haozhe Jia, Wenshuo Chen, Zhihui Huang, Hongru Xiao, Nanqian Jia, Keming Wu, Songning Lai, Yutao Yue,
Abstract summary: Radio map reconstruction is essential for enabling advanced applications.<n> challenges such as complex signal propagation and sparse data hinder accurate reconstruction.<n>We propose the **Radio Map Diffusion Model (RMDM)**, a physics-informed framework that incorporates constraints like the **Helmholtz equation**.
Score: 1.1796025053683545
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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