Maximum Total Correntropy Diffusion Adaptation over Networks with Noisy Links

TL;DR

This paper introduces the diffusion maximum total correntropy (DMTC) algorithm, which enhances distributed estimation over networks by improving accuracy and robustness against noisy and impulsive link noise, outperforming traditional methods.

Contribution

The paper proposes the DMTC algorithm, which is unbiased in Gaussian noise and robust to impulsive noise, with stability analysis and adaptive combination for improved performance.

Findings

DMTC achieves unbiased estimation in Gaussian noise.

DMTC handles large outliers effectively.

Simulation results confirm superior performance in various noise environments.

Abstract

Distributed estimation over networks draws much attraction in recent years. In many situations, due to imperfect information communication among nodes, the performance of traditional diffusion adaptive algorithms such as the diffusion LMS (DLMS) may degrade. To deal with this problem, several modified DLMS algorithms have been proposed. However, these DLMS based algorithms still suffer from biased estimation and are not robust to impulsive link noise. In this paper, we focus on improving the performance of diffusion adaptation with noisy links from two aspects: accuracy and robustness. A new algorithm called diffusion maximum total correntropy (DMTC) is proposed. The new algorithm is theoretically unbiased in Gaussian noise, and can efficiently handle the link noises in the presence of large outliers. The adaptive combination rule is applied to further improve the performance. The stability analysis of the proposed algorithm is given. Simulation results show that the DMTC algorithm can achieve good performance in both Gaussian and non-Gaussian noise environments.