RMSup: Physics-Informed Radio Map Super-Resolution for Compute-Enhanced Integrated Sensing and Communications

• URL: http://arxiv.org/abs/2512.10965v1
• Date: Sat, 29 Nov 2025 09:00:12 GMT
• Title: RMSup: Physics-Informed Radio Map Super-Resolution for Compute-Enhanced Integrated Sensing and Communications
• Authors: Qiming Zhang, Xiucheng Wang, Nan Cheng, Zhisheng Yin, Xiang Li,
• Abstract summary: We present RMSup, a physics-informed framework that functions with uniform sparse sampling and imperfect environment priors.<n> Experimental results show the proposed RMsup achieves state-of-the-art performance both in RM construction and ISAC-related environment sensing.
• Score: 28.003646295374022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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