Radio Map Estimation via Latent Domain Plug-and-Play Denoising
Le Xu, Lei Cheng, Junting Chen, Wenqiang Pu, Xiao Fu

TL;DR
This paper introduces a novel radio map estimation method using plug-and-play denoising in a latent domain, which leverages natural image denoisers to improve efficiency and robustness without extensive training.
Contribution
It proposes a latent domain PnP denoising approach for radio map estimation that avoids training on radio data and enhances computational efficiency and noise robustness.
Findings
Effective in synthetic and real data scenarios
Improves noise robustness and computational efficiency
Theoretically guarantees recoverability and convergence
Abstract
Radio map estimation (RME), also known as spectrum 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 training -- making it hard to quickly adapt to in situ sensing tasks. This work presents a spatio-spectral RME approach based on plug-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 similar -- despite the nontrivial differences of the signals themselves. Hence, sophisticated denoisers…
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Taxonomy
TopicsSpeech and Audio Processing · Speech Recognition and Synthesis · Advanced Data Compression Techniques
MethodsAlternating Direction Method of Multipliers · PnP
