Impulse-Noise-Tolerant Data-Selective LMS Algorithm

Abstract

Exponential growth in data volumes has promoted widespread interest in data-selective adaptive algorithms. In a pioneering work, Diniz developed the data-selective least mean square (DS-LMS) algorithm, which is able to reduce specific quantities of computation data without compromising performance. Note however that the existing framework fails to consider the issue of impulse noise (IN), which can greatly undermine the benefits of reduced computation. In this letter, we present an error-based IN detection algorithm for implementation in conjunction with the DS-LMS algorithm. Numerical evaluations confirm the effectiveness of our proposed IN-tolerant DS-LMS algorithm.