RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization
2026-08-17 • Machine Learning
Machine Learning
AI summaryⓘ
The authors address the challenge of making detailed radio maps for future 6G networks, which current methods tend to blur and miss important details like hidden vehicles. They propose RadioVIL, a two-step approach that uses a special diffusion model to understand the environment and a new algorithm to spot vehicle signals in sparse data. Their method better keeps physical details and can accurately find vehicles without prior examples. Tests show it works much better than existing methods for both map quality and vehicle detection.
6GIntegrated Sensing and Communication (ISAC)radio map constructiondenoising diffusion probabilistic model (DDPM)zero-shot localizationinverse problemdiffusion-based intermediate optimizationLPIPSvehicle localizationsparse measurements
Authors
Ruixin Zhao, Xiucheng Wang, Qiming Zhang, Nan Cheng, Ruijin Sun, Conghao Zhou
Abstract
High-precision radio map construction is essential for emerging 6G Integrated Sensing and Communication (ISAC) applications, including digital twins and intelligent transportation. However, existing deep learning methods predominantly treat this as a pure image completion task, resulting in over-smoothed reconstructions that fundamentally erase high-frequency scattering signatures of dynamic physical entities such as hidden vehicles. To overcome this, we propose RadioVIL, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem. Specifically, we first train a Denoising Diffusion Probabilistic Model (DDPM) to capture the structural generative prior of the environment. During inference from highly sparse measurements, we employ a Diffusion-based Mediating Intermediate Layer Optimization (DMILO) algorithm. By optimizing an L1-regularized sparse deviation term, DMILO mathematically isolates vehicle scattering anomalies layer-by-layer without unfolding the entire denoising chain. Extensive experiments demonstrate that while conventional reconstruction baselines fail to detect hidden vehicles, and the zero-shot diffusion baseline achieves only limited detection ability due to forced semantic harmonization, RadioVIL preserves authentic physical textures, yielding the best LPIPS of 0.0587 in our evaluation. Uniquely, it unlocks accurate zero-shot vehicle localization directly from sparse radio maps, securing a 75.20% Recall and a 3.31-meter average error, paving a robust way for ISAC at the 6G edge.