Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf

TL;DR

This paper introduces a variable p-norm constrained LMS algorithm that adaptively adjusts p using gradient methods, leading to improved steady-state error and convergence in sparse system identification tasks with noise.

Contribution

The paper proposes a novel variable p-norm LMS algorithm that dynamically adjusts p for better performance in noisy sparse system identification.

Findings

Achieves lower steady-state error compared to traditional LMS algorithms.

Maintains fast convergence rates.

Effective in noisy sparse system identification environments.

Abstract

A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter. The parameter p is iteratively adjusted by the gradient method applied to the root relative deviation of the estimated weight vector. Numerical simulations show that this new algorithm achieves lower steady-state error as well as equally fast convergence compared with the traditional Lp-LMS and LMS algorithms in the application setting of sparse system identification in the presence of noise.