Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification

TL;DR

This paper introduces a variable p-norm constraint LMS algorithm that adaptively adjusts the p parameter using gradient descent, improving sparse system identification performance over traditional methods.

Contribution

The paper proposes a novel variable p-norm LMS algorithm that dynamically adapts the p parameter for better sparse system identification.

Findings

Achieves lower steady-state error than traditional LMS algorithms.

Demonstrates faster convergence in numerical simulations.

Outperforms fixed p-norm LMS in various sparse scenarios.

Abstract

Sparse adaptive filtering has gained much attention due to its wide applicability in the field of signal processing. Among the main algorithm families, sparse norm constraint adaptive filters develop rapidly in recent years. However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified. To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification. The parameter p is iteratively adjusted by the gradient descent method applied to the instantaneous square error. Numerical simulations show that this new approach achieves better performance than the traditional Lp-LMS and LMS algorithms in terms of steady-state error and convergence rate.