用物理约束提升无线地图分辨率，稀疏采样下也能精准还原环境细节。
RMSup: Physics-Informed Radio Map Super-Resolution for Compute-Enhanced Integrated Sensing and Communications
- 融合波动方程边界与奇点信息，实现物理引导的超分辨率重建。
- 在稀疏采样和不完整先验条件下，仍能保持高保真度与环境轮廓恢复。
- 适合需要高精度感知与通信一体化的智能城市、车联网等场景。
无线地图（RMs）提供无线传播的空间连续描述，支持跨层优化，并统一通信与感知，推动集成传感与通信（ISAC）的发展。然而，在实际运行尺度上构建高保真度的无线地图仍具挑战：基于物理的求解器耗时长且依赖精确场景模型；而学习方法在先验信息不全和测量稀疏时性能下降，常导致关键不连续性被平滑。本文提出RMSup，一种物理信息引导的超分辨率框架，可在均匀稀疏采样和不完整环境先验条件下工作。RMSup从测量数据中提取满足亥姆霍兹方程的边界与奇点提示，将其与基站侧信息及粗粒度场景描述融合为条件输入，并采用边界感知的双头网络，联合重建高保真无线地图并恢复环境轮廓。实验表明，RMSup在无线地图构建与相关环境感知任务上均达到当前最优性能。
原文摘要 · Abstract (English)
Radio maps (RMs) provide a spatially continuous description of wireless propagation, enabling cross-layer optimization and unifying communication and sensing for integrated sensing and communications (ISAC). However, constructing high-fidelity RMs at operational scales is difficult, since physics-based solvers are time-consuming and require precise scene models, while learning methods degrade under incomplete priors and sparse measurements, often smoothing away critical discontinuities. We present RMSup, a physics-informed super-resolution framework that functions with uniform sparse sampling and imperfect environment priors. RMSup extracts Helmholtz equation-informed boundary and singularity prompts from the measurements, fuses them with base-station side information and coarse scene descriptors as conditional inputs, and employs a boundary-aware dual-head network to reconstruct a high-fidelity RM and recover environmental contours jointly. Experimental results show the proposed RMsup achieves state-of-the-art performance both in RM construction and ISAC-related environment sensing.
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