A Unified Analysis Approach for LMS-based Variable Step-Size Algorithms

TL;DR

This paper introduces a unified analytical framework for variable step-size LMS algorithms, simplifying their theoretical analysis and enabling comparison of different strategies through theoretical and simulation results.

Contribution

It provides a novel unified approach to analyze various variable step-size LMS algorithms, addressing the complexity in their theoretical understanding.

Findings

Unified analysis simplifies understanding of variable step-size LMS algorithms.

Theoretical results align well with simulation outcomes.

Framework applicable to multiple variable step-size strategies.

Abstract

The least-mean-squares (LMS) algorithm is the most popular algorithm in adaptive filtering. Several variable step-size strategies have been suggested to improve the performance of the LMS algorithm. These strategies enhance the performance of the algorithm but a major drawback is the complexity in the theoretical analysis of the resultant algorithms. Researchers use several assumptions to find closed-form analytical solutions. This work presents a unified approach for the analysis of variable step-size LMS algorithms. The approach is then applied to several variable step-size strategies and theoretical and simulation results are compared.