IMAC: Impulsive-mitigation adaptive sparse channel estimation based on Gaussian-mixture model

TL;DR

This paper introduces IMAC, a robust adaptive sparse channel estimation method that effectively mitigates impulsive noise using a Gaussian mixture model, improving performance in non-Gaussian noise environments.

Contribution

It develops a novel impulsive-mitigation adaptive sparse channel estimation technique based on Gaussian mixture modeling and reweighted L1-norm penalized algorithms.

Findings

IMAC outperforms traditional methods in impulsive noise environments.

The Gaussian mixture model effectively characterizes non-Gaussian noise.

Simulation results validate the robustness and accuracy of IMAC.

Abstract

Broadband frequency-selective fading channels usually have the inherent sparse nature. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) methods, e.g., reweighted L1-norm least mean square (RL1-LMS), could bring a performance gain if additive noise satisfying Gaussian assumption. In real communication environments, however, channel estimation performance is often deteriorated by unexpected non-Gaussian noises which include conventional Gaussian noises and impulsive interferences. To design stable communication systems, hence, it is urgent to develop advanced channel estimation methods to remove the impulsive interference and to exploit channel sparsity simultaneously. In this paper, robust impulsive-mitigation adaptive sparse channel estimation (IMAC) method is proposed for solving aforementioned technical issues. Specifically, first of all, the non-Gaussian noise model is described by Gaussian mixture model (GMM). Secondly, cost function of reweighted L1-norm penalized least absolute error standard (RL1-LAE) algorithm is constructed. Then, RL1-LAE algorithm is derived for realizing IMAC method. Finally, representative simulation results are provided to corroborate the studies.