Radio Map Estimation via Latent Domain Plug-and-Play Denoising

作者：Le Xu, Lei Cheng, Junting Chen, Wenqiang Pu, Xiao Fu · 发表于：IEEE Transactions on Signal Processing · 年份：2026 · DOI：10.1109/tsp.2025.3650699 · 被引用次数：1 · 研究领域：Sparse and Compressive Sensing Techniques、Advanced SAR Imaging Techniques、Synthetic Aperture Radar (SAR) Applications and Techniques

Radio map estimation (RME), also known asspectrum cartography, aims to reconstruct the strength of radio interference across different domains (e.g., space and frequency) from sparsely sampled measurements. To tackle this typical inverse problem, state-of-the-art RME methods rely on handcrafted or data-driven structural information of radio maps. However, the former often struggles to model complex radio frequency (RF) environments and the latter requires excessive trainingߞmaking it hard to quickly adapt toin situsensing tasks. This work presents an RME approach based onplug-and-play(PnP) denoising, a technique from computational imaging. The idea is to leverage the observation that the denoising operations of signals like natural images and radio maps are similarߞdespite the nontrivial differences of the signals themselves. Hence, sophisticated denoisers designed for or learned from natural images can be directly employed to assist RME, avoiding using radio map data for training. Unlike conventional PnP methods that operate directly in the data domain, the proposed method exploits the classical spatio-spectral decomposition model of radio maps and proposes an ADMM algorithm that denoises in the latent factor that only represents the spatial domain. This design significantly improves computational efficiency and enhances noise robustness. Theoretical aspects, e.g., recoverability of the complete radio map and convergence of the ADMM algorithm are analyzed. Synthetic and real...