Diffusion Least Mean P-Power Algorithms for Distributed Estimation in Alpha-Stable Noise Environments

TL;DR

This paper introduces a diffusion least mean p-power algorithm tailored for distributed estimation in alpha-stable noise environments, outperforming traditional LMS methods in such settings.

Contribution

The paper presents a novel diffusion LMP algorithm specifically designed for alpha-stable noise, enhancing distributed estimation performance over existing LMS-based approaches.

Findings

Diffusion LMP outperforms diffusion LMS in alpha-stable noise environments.

The proposed algorithm demonstrates improved estimation accuracy.

Better robustness to heavy-tailed noise distributions.

Abstract

We propose a diffusion least mean p-power (LMP) algorithm for distributed estimation in alpha stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least mean squares (LMS) algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.