Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments

TL;DR

This paper introduces a robust sparse adaptive filtering algorithm based on the maximum correntropy criterion and correntropy induced metric, designed to improve channel estimation in non-Gaussian environments with impulsive noise.

Contribution

It proposes a novel MCC-CIM based algorithm that enhances robustness and sparsity exploitation in channel estimation under non-Gaussian noise conditions.

Findings

The proposed algorithm outperforms traditional methods in impulsive noise environments.

Theoretical analysis confirms the robustness of the MCC-CIM approach.

Simulation results demonstrate improved estimation accuracy and noise resilience.

Abstract

Sparse adaptive channel estimation problem is one of the most important topics in broadband wireless communications systems due to its simplicity and robustness. So far many sparsity-aware channel estimation algorithms have been developed based on the well-known minimum mean square error (MMSE) criterion, such as the zero-attracting least mean square (ZALMS), which are robust under Gaussian assumption. In non-Gaussian environments, however, these methods are often no longer robust especially when systems are disturbed by random impulsive noises. To address this problem, we propose in this work a robust sparse adaptive filtering algorithm using correntropy induced metric (CIM) penalized maximum correntropy criterion (MCC) rather than conventional MMSE criterion for robust channel estimation. Specifically, MCC is utilized to mitigate the impulsive noise while CIM is adopted to exploit the channel sparsity efficiently. Both theoretical analysis and computer simulations are provided to corroborate the proposed methods.