Iterative-Promoting Variable Step Size Least Mean Square Algorithm for Accelerating Adaptive Channel Estimation

TL;DR

This paper introduces an iterative-promoting variable step size LMS algorithm that enhances adaptive channel estimation by balancing convergence speed and estimation accuracy, outperforming traditional fixed step size methods.

Contribution

The paper proposes a novel VSS-LMS algorithm that adaptively adjusts the step size to improve convergence and estimation performance in channel estimation tasks.

Findings

VSS-LMS achieves better estimation accuracy than ISS-LMS.

The proposed algorithm maintains convergence speed while improving performance.

Simulation results validate the effectiveness of the VSS-LMS method.

Abstract

Invariable step size based least-mean-square error (ISS-LMS) was considered as a very simple adaptive filtering algorithm and hence it has been widely utilized in many applications, such as adaptive channel estimation. It is well known that the convergence speed of ISS-LMS is fixed by the initial step-size. In the channel estimation scenarios, it is very hard to make tradeoff between convergence speed and estimation performance. In this paper, we propose an iterative-promoting variable step size based least-mean-square error (VSS-LMS) algorithm to control the convergence speed as well as to improve the estimation performance. Simulation results show that the proposed algorithm can achieve better estimation performance than previous ISS-LMS while without sacrificing convergence speed.