发表机构
Xidian University（西安电子科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有方法无法保留动态实体散射特征的问题，提出RadioVIL框架，通过DDPM与DMILO算法实现无线电地图补全，在零样本车辆定位任务中获75.20%召回率及3.31米平均误差，为6G边缘ISAC提供支撑。
AI 中文摘要
高精度无线电地图构建对于新兴的6G集成感知与通信（ISAC）应用（包括数字孪生和智能交通）至关重要。然而，现有深度学习方法大多将此视为纯粹的图像补全任务，导致重建结果过于平滑，从根本上抹去了隐藏车辆等动态物理实体的高频散射特征。为解决这一问题，我们提出RadioVIL，这是一种高效的两阶段框架，将联合无线电地图补全和零样本车辆定位重新表述为先验引导的物理逆问题。具体而言，我们首先训练一个去噪扩散概率模型（DDPM）以捕获环境的结构生成先验。在基于高度稀疏测量的推理过程中，我们采用基于扩散的中介中间层优化（DMILO）算法。通过优化L1正则化的稀疏偏差项，DMILO可逐层从数学上分离车辆散射异常，无需展开整个去噪链。大量实验表明，传统重建基线无法检测隐藏车辆，而零样本扩散基线因强制语义协调仅实现有限的检测能力；RadioVIL保留了真实的物理纹理，在我们的评估中获得了最佳的LPIPS（学习感知图像块相似度）值0.0587。尤为独特的是，它能直接从稀疏无线电地图实现准确的零样本车辆定位，达到75.20%的召回率和3.31米的平均误差，为6G边缘的ISAC应用开辟了一条可靠路径。
英文摘要
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.
Comments6 pages, 4 figures, 2 tables. Accepted to IEEE GLOBECOM 2026, Wireless Communications Symposium
