Robust Adaptive Sparse Channel Estimation in the Presence of Impulsive Noises

TL;DR

This paper introduces robust adaptive sparse channel estimation algorithms that effectively mitigate impulsive noise in broadband wireless systems by using sign least mean square methods with various sparsity penalties.

Contribution

It proposes novel SLMS-based algorithms with different sparsity penalties to improve channel estimation robustness against impulsive noise.

Findings

Algorithms outperform traditional methods in impulsive noise environments

SLMS-RZA and SLMS-LP show superior convergence properties

Proposed methods effectively mitigate impulsive noise effects

Abstract

Broadband wireless channels usually have the sparse nature. Based on the assumption of Gaussian noise model, adaptive filtering algorithms for reconstruction sparse channels were proposed to take advantage of channel sparsity. However, impulsive noises are often existed in many advance broadband communications systems. These conventional algorithms are vulnerable to deteriorate due to interference of impulsive noise. In this paper, sign least mean square algorithm (SLMS) based robust sparse adaptive filtering algorithms are proposed for estimating channels as well as for mitigating impulsive noise. By using different sparsity-inducing penalty functions, i.e., zero-attracting (ZA), reweighted ZA (RZA), reweighted L1-norm (RL1) and Lp-norm (LP), the proposed SLMS algorithms are termed as SLMS-ZA, SLMS-RZA, LSMS-RL1 and SLMS-LP. Simulation results are given to validate the proposed algorithms.