Regularization Parameter Selection Method for Sign LMS with Reweighted L1-Norm Constriant Algorithm

TL;DR

This paper introduces a Monte Carlo-based method for selecting the regularization parameter in the sign LMS-RL1 algorithm, enhancing stability and sparsity exploitation in adaptive channel estimation under mixed noise conditions.

Contribution

It proposes a novel Monte Carlo-based approach for optimal regularization parameter selection in sign LMS-RL1, improving stability and performance in non-Gaussian noise environments.

Findings

Enhanced stability of SLMS-RL1 with the proposed REPA selection.

Improved channel estimation accuracy in impulsive noise conditions.

Validation through simulation results.

Abstract

Broadband frequency-selective fading channels usually have the inherent sparse nature. By exploiting the sparsity, adaptive sparse channel estimation (ASCE) algorithms, e.g., least mean square with reweighted L1-norm constraint (LMS-RL1) algorithm, could bring a considerable performance gain under assumption of additive white Gaussian noise (AWGN). In practical scenario of wireless systems, however, channel estimation performance is often deteriorated by unexpected non-Gaussian mixture noises which include AWGN and impulsive noises. To design stable communication systems, sign LMS-RL1 (SLMS-RL1) algorithm is proposed to remove the impulsive noise and to exploit channel sparsity simultaneously. It is well known that regularization parameter (REPA) selection of SLMS-RL1 is a very challenging issue. In the worst case, inappropriate REPA may even result in unexpected instable convergence of SLMS-RL1 algorithm. In this paper, Monte Carlo based selection method is proposed to select suitable REPA so that SLMS-RL1 can achieve two goals: stable convergence as well as usage sparsity information. Simulation results are provided to corroborate our studies.