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

2025-01-23

AI Summary

Radio map reconstruction under sparse sampling in the spatial-frequency domain remains challenging, with existing radio map estimation (RME) methods exhibiting limited adaptability to complex environments, poor training efficiency, and weak deployability in real-world scenarios. Method: We propose a training-free, plug-and-play (PnP) denoising framework operating in the latent space of generative models—marking the first application of PnP within latent rather than data space. Our approach integrates RF physics priors with the alternating direction method of multipliers (ADMM) for end-to-end unsupervised reconstruction and leverages state-of-the-art natural-image denoisers (e.g., DnCNN, FFDNet) via cross-domain transfer. Contribution/Results: We provide theoretical guarantees on convergence and signal recoverability. Experiments on both synthetic and real-world measurements demonstrate >30% improvement in noise robustness over conventional RME, 2.1× faster inference, and superior accuracy, computational efficiency, and generalization.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Machine Learning: Matrix & Tensor Methods

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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 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 underlying physical structure of radio maps and proposes an ADMM algorithm that denoises in a latent 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 data experiments are conducted to demonstrate the effectiveness of our approach.

Problem

• Radio Map Estimation
• Complex Radio Environment
• Interference Strength Estimation

Innovation

• Denoising Techniques
• Radio Map Estimation
• Image Analogous Processing