A Multi-Scale Dictionary Learning Deep Unfolding Network for Radio Map Reconstruction
Abstract
The radio map characterizes the spatial distribution of spectrum resources within a region of interest and plays an important role in wireless network planning and spectrum management. In practice, observations are often sparse, making accurate radio map reconstruction challenging. Although deep learning–based methods can recover a radio map from sparse samples, they often suffer from two major limitations: a strong dependence on large amounts of training data and reconstructed results that may deviate from the actual physical distribution. To address this issue, this paper introduces dictionary factors to characterize radio propagation properties at different spatial scales and formulates radio map reconstruction as a multi-scale dictionary factor learning problem. Based on this formulation, we propose RadioMSDL-Net, a radio multi-scale dictionary learning network. The network consists of multiple layers with identical structures and progressively refines the radio map estimate in an iterative manner. In each layer, the dictionary factor update module learns propagation characteristics at different spatial scales, while the multi-level reconstruction module integrates cross-scale features to improve both the global structure and local details of the radio map. Extensive experiments demonstrate that RadioMSDL-Net consistently outperforms existing methods in reconstruction accuracy, computational efficiency, and cross-environment generalization.