RadioDUN: A Physics-Inspired Deep Unfolding Network for Radio Map Estimation

TL;DR

RadioDUN is a physics-inspired deep unfolding network that effectively estimates dense radio maps from sparse samples by integrating physical models, adaptive optimization, and obstacle-aware features, outperforming existing methods.

Contribution

The paper introduces RadioDUN, a novel deep unfolding network that incorporates physical propagation models and obstacle factors for improved radio map estimation from sparse data.

Findings

RadioDUN outperforms state-of-the-art methods in radio map estimation.

The dynamic reweighting module adaptively models factor importance.

Shadowing loss enhances the accuracy of obstacle-related signal modeling.

Abstract

The radio map represents the spatial distribution of spectrum resources within a region, supporting efficient resource allocation and interference mitigation. However, it is difficult to construct a dense radio map as a limited number of samples can be measured in practical scenarios. While existing works have used deep learning to estimate dense radio maps from sparse samples, they are hard to integrate with the physical characteristics of the radio map. To address this challenge, we cast radio map estimation as the sparse signal recovery problem. A physical propagation model is further incorporated to decompose the problem into multiple factor optimization sub-problems, thereby reducing recovery complexity. Inspired by the existing compressive sensing methods, we propose the Radio Deep Unfolding Network (RadioDUN) to unfold the optimization process, achieving adaptive parameter adjusting and prior fitting in a learnable manner. To account for the radio propagation characteristics, we develop a dynamic reweighting module (DRM) to adaptively model the importance of each factor for the radio map. Inspired by the shadowing factor in the physical propagation model, we integrate obstacle-related factors to express the obstacle-induced signal stochastic decay. The shadowing loss is further designed to constrain the factor prediction and act as a supplementary supervised objective, which enhances the performance of RadioDUN. Extensive experiments have been conducted to demonstrate that the proposed method outperforms the state-of-the-art methods. Our code will be made publicly available upon publication.