Effective Non-Gaussian Noise Removal of 3D Radio Maps through Correntropy-based Nonlocal Tensor Ring Decomposition

Effective noise removal in 3D radio maps is critical for their application in cognitive communication systems. However, real-world radio signals are often corrupted by non-Gaussian noise, which poses significant challenges to traditional denoising methods. In this paper, we propose C-NLTR, a novel denoising model specifically designed for non-Gaussian noise removal in 3D radio maps. To address the impulsive interference and heavy-tailed distortions inherent in non-Gaussian noise, we employ a correntropy-based loss function, which adaptively suppresses extreme deviations while preserving key signal structures. Furthermore, to leverage the nonlocal self-similarity and low-rank priors present in 3D radio maps, we introduce a nonlocal low-rank tensor ring decomposition, which effectively retains structural details and captures global correlations. To solve the resulting optimization problem efficiently, we develop a computationally scalable algorithm based on half-quadratic theory and a scaled gradient descent approach. Extensive experiments on the SpectrumNet dataset demonstrate that C-NLTR outperforms state-of-the-art methods for non-Gaussian noise removal, particularly excelling in severe non-Gaussian scenarios where other approaches fail. Additionally, denoising experiments on hyperspectral image datasets, including CAVE, Washington DC Mall, and real-world Indian Pines, confirm the robustness of C-NLTR across a range of noise conditions.