Speeding up VSLMS adaptation algorithms using dynamic adaptation gain: Analysis and Applications

TL;DR

This paper introduces a dynamic adaptation gain for variable step-size LMS algorithms, enhancing their transient performance through stability analysis, convergence properties, and practical applications in noise attenuation.

Contribution

It presents a generic implementation of dynamic adaptation gain in VS-LMS algorithms, along with stability, convergence analysis, and application demonstrations.

Findings

Improved transient performance with DAG in VS-LMS algorithms

Stability and convergence criteria for DAG-based VS-LMS

Successful application in noise attenuation systems

Abstract

The paper explores the use of dynamic adaptation gain/step size (DAG) for improving the adaptation transient performance of variable step-size LMS (VS-LMS) adaptation algorithms. A generic form for the implementation of the DAG within the VS-LMS algorithms is provided. The properties of the VS-LMS algorithms using dynamic adaptation gain are discussed in detail. Stability issues in deterministic environment and convergence properties in stochastic environment are examined. A transient performance analysis is proposed. Criteria for the selection of the coefficients of the DAG filter are provided.The potential of the VS-LMS adaptation algorithms using a DAG is then illustrated by simulation results (adaptive line enhancer, filter identification) and experimental results obtained on a relevant adaptive active noise attenuation system.