ROSA: Robust sparse adaptive channel estimation in the presence of impulsive noises

TL;DR

This paper introduces robust sparse adaptive channel estimation algorithms designed to perform effectively in impulsive noise environments, outperforming standard methods by incorporating sparsity constraints and variable step-size techniques.

Contribution

The paper proposes three novel sparse VSS-APSA algorithms that enhance robustness against impulsive noises in channel estimation, addressing limitations of Gaussian-based models.

Findings

Proposed algorithms outperform standard VSS-APSA in impulsive noise environments.

Simulation results confirm improved accuracy and robustness.

Algorithms effectively leverage channel sparsity for better estimation.

Abstract

Based on the assumption of Gaussian noise model, conventional adaptive filtering algorithms for reconstruction sparse channels were proposed to take advantage of channel sparsity due to the fact that broadband wireless channels usually have the sparse nature. However, state-of-the-art algorithms are vulnerable to deteriorate under the assumption of non-Gaussian noise models (e.g., impulsive noise) which often exist in many advanced communications systems. In this paper, we study the problem of RObust Sparse Adaptive channel estimation (ROSA) in the environment of impulsive noises using variable step-size affine projection sign algorithm (VSS-APSA). Specifically, standard VSS-APSA algorithm is briefly reviewed and three sparse VSS-APSA algorithms are proposed to take advantage of channel sparsity with different sparse constraints. To fairly evaluate the performance of these proposed algorithms, alpha-stable noise is considered to approximately model the realistic impulsive noise environments. Simulation results show that the proposed algorithms can achieve better performance than standard VSS-APSA algorithm in different impulsive environments.