LSK-RM: A Physics-Guided Large Selective Kernel U-Net for Radio Map Reconstruction

Abstract

Radio map construction aims to infer dense received-power fields from environmental layouts, sparse observations, and physical priors. While crucial for environment-aware wireless systems, it remains challenging in complex urban scenes with building-induced non-line-of-sight (NLOS) shadows and dynamic blockages. Non-iterative methods, such as interpolation techniques, RadioUNet, and RME-GAN, often struggle to accurately model these complex obstruction effects. Conversely, iterative generative methods tend to produce physically implausible hallucinations in shadowed or strongly obstructed areas. To address these issues, we propose LSK-RM, a physics-guided Large Selective Kernel U-Net for one-stage dense radio map reconstruction. Specifically, an LSK-based encoder-decoder is introduced to adaptively aggregate local shadow-boundary details and long-range attenuation context within a single forward pass. Furthermore, we develop a multi-source physical prior representation that fuses environmental geometry, sparse measurements, and fast ray-tracing visibility cues. To suppress physically implausible energy leakage, we design a novel logarithmic physics-guided objective combining pixel-wise supervision with Laplacian and obstacle-boundary consistency. Experiments on the RadioMapSeer dynamic blockage dataset demonstrate that LSK-RM outperforms representative baselines, including RadioUNet, RME-GAN, RMDM, and RadioFlow. Notably, it achieves higher accuracy across quantitative metrics such as NMSE, and exhibits significantly better modeling performance in diffraction transitions and shadow regions.