Hard Threshold Least Mean Squares Algorithm

TL;DR

This paper introduces a novel LMS variation incorporating a hard threshold operator for sparse signal identification, demonstrating improved performance in spectrum estimation for cognitive radios.

Contribution

It proposes a new LMS-based algorithm with a hard threshold operator for sparse signals, analyzing its properties and applications in spectrum estimation.

Findings

The new algorithm outperforms traditional LMS in sparse signal scenarios.

It effectively estimates spectrum in cognitive radio applications.

The method shows improved convergence and accuracy.

Abstract

This work presents a new variation of the commonly used Least Mean Squares Algorithm (LMS) for the identification of sparse signals with an a-priori known sparsity using a hard threshold operator in every iteration. It examines some useful properties of the algorithm and compares it with the traditional LMS and other sparsity aware variations of the same algorithm. It goes on to examine the application of the algorithm in the problem of spectrum estimation for cognitive radio devices.