Iterative-Promoting Variable Step-size Least Mean Square Algorithm For Adaptive Sparse Channel Estimation

TL;DR

This paper introduces three novel iterative-promoting variable step-size LMS algorithms for sparse channel estimation, improving the tradeoff between convergence speed and estimation accuracy over existing fixed step-size methods.

Contribution

The paper proposes three new sparse LMS algorithms with variable step-sizes that enhance convergence and estimation performance in sparse channel scenarios.

Findings

Effective in sparse channel estimation

Improved convergence speed

Enhanced estimation accuracy

Abstract

Least mean square (LMS) type adaptive algorithms have attracted much attention due to their low computational complexity. In the scenarios of sparse channel estimation, zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS) and reweighted -norm LMS (RL1-LMS) have been proposed to exploit channel sparsity. However, these proposed algorithms may hard to make tradeoff between convergence speed and estimation performance with only one step-size. To solve this problem, we propose three sparse iterative-promoting variable step-size LMS (IP-VSS-LMS) algorithms with sparse constraints, i.e. ZA, RZA and RL1. These proposed algorithms are termed as ZA-IPVSS-LMS, RZA-IPVSS-LMS and RL1-IPVSS-LMS respectively. Simulation results are provided to confirm effectiveness of the proposed sparse channel estimation algorithms.