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.
Abstract (translated)
无线电图表示了某个区域内频谱资源的空间分布，支持高效的资源分配和干扰抑制。然而，在实际场景中由于可测量的样本数量有限，构建密集型无线电图非常困难。现有的研究工作使用深度学习从稀疏样本估算出密集型无线电图，但难以与无线电图的物理特性相结合。为了解决这一挑战，我们将无线电图估计视为稀疏信号恢复问题。通过进一步结合物理传播模型，将该问题分解成多个因子优化子问题，从而降低恢复复杂度。 受到现有的压缩感知方法的启发，我们提出了无线电深度递归网络（RadioDUN），以展开优化过程，在可学习的方式中实现自适应参数调整和先验拟合。为了考虑无线电传播特性，我们开发了一个动态重加权模块（DRM）来自适应地建模无线电图中每个因子的重要性。受到物理传播模型中的阴影因素的启发，我们将与障碍物相关的因素整合进来以表示由障碍物引起的信号随机衰减。 设计了阴影损失函数进一步约束因子预测，并作为补充监督目标，增强了RadioDUN的表现性能。我们进行了大量的实验来证明所提出的方法优于当前最先进的方法。我们的代码将在发表后公开发布。
URL
https://arxiv.org/abs/2506.08418
