Adaptive Blind Sparse-Channel Equalization

TL;DR

This paper introduces a novel blind adaptive equalization algorithm that leverages sparsity-inducing penalties to improve convergence speed and exploit channel sparsity effectively.

Contribution

The paper proposes a fractional-norm constrained blind adaptive algorithm that enhances sparse channel equalization by integrating an -norm penalty into the CM criterion.

Findings

Faster convergence compared to existing methods

Effective exploitation of channel sparsity

Improved equalization performance

Abstract

In this article, a fractional-norm constrained blind adaptive algorithm is presented for sparse channel equalization. In essence, the algorithm improves on the minimization of the constant modulus (CM) criteria by adding a sparsity inducing \(\ell_p\)-norm penalty. Simulation results demonstrate that the proposed regularized equalizer exploits the inherent channel sparsity effectively and exhibits faster convergence compared to its counterparts.