Impulse-Noise-Tolerant Data-Selective LMS Algorithm
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E105-A No.2 pp.114-117
- Publication Date
- 2022/02/01
- Publicized
- 2021/08/02
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2021EAL2046
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Ying-Ren CHIEN
National Ilan University
Chih-Hsiang YU
National Taiwan University
Keyword
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Ying-Ren CHIEN, Chih-Hsiang YU, "Impulse-Noise-Tolerant Data-Selective LMS Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 2, pp. 114-117, February 2022, doi: 10.1587/transfun.2021EAL2046.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAL2046/_p
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@ARTICLE{e105-a_2_114,
author={Ying-Ren CHIEN, Chih-Hsiang YU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Impulse-Noise-Tolerant Data-Selective LMS Algorithm},
year={2022},
volume={E105-A},
number={2},
pages={114-117},
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.},
keywords={},
doi={10.1587/transfun.2021EAL2046},
ISSN={1745-1337},
month={February},}
Copy
TY - JOUR
TI - Impulse-Noise-Tolerant Data-Selective LMS Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 114
EP - 117
AU - Ying-Ren CHIEN
AU - Chih-Hsiang YU
PY - 2022
DO - 10.1587/transfun.2021EAL2046
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2022
AB - 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.
ER -
English
