Improved Adaptive Sparse Channel Estimation Using Re-Weighted L1-norm Normalized Least Mean Fourth Algorithm

TL;DR

This paper introduces an improved adaptive sparse channel estimation method using a reweighted L1-norm normalized least mean fourth algorithm, enhancing accuracy by better exploiting sparsity in wireless channels.

Contribution

The paper proposes a novel RL1-NLMF algorithm that outperforms previous methods by more effectively leveraging sparsity for adaptive channel estimation.

Findings

RL1-NLMF outperforms ZA-NLMF and RZA-NLMF in simulations.

The proposed method achieves higher estimation accuracy.

Reweighted L1-norm enhances sparsity exploitation.

Abstract

In next-generation wireless communications systems, accurate sparse channel estimation (SCE) is required for coherent detection. This paper studies SCE in terms of adaptive filtering theory, which is often termed as adaptive channel estimation (ACE). Theoretically, estimation accuracy could be improved by either exploiting sparsity or adopting suitable error criterion. It motivates us to develop effective adaptive sparse channel estimation (ASCE) methods to improve estimation performance. In our previous research, two ASCE methods have been proposed by combining forth-order error criterion based normalized least mean fourth (NLMF) and L1-norm penalized functions, i.e., zero-attracting NLMF (ZA-NLMF) algorithm and reweighted ZA-NLMF (RZA-NLMF) algorithm. Motivated by compressive sensing theory, an improved ASCE method is proposed by using reweighted L1-norm NLMF (RL1-NLMF) algorithm where RL1 can exploit more sparsity information than ZA and RZA. Specifically, we construct the cost function of RL1-NLMF and hereafter derive its update equation. In addition, intuitive figure is also given to verify that RL1 is more efficient than conventional two sparsity constraints. Finally, simulation results are provided to confirm this study.