A q-Noise Constrained Least Mean Square Algorithm

Muhammad O. Bin Saeed; Azzedine Zerguine

doi:10.1109/IEEECONF51394.2020.9443480

A q-Noise Constrained Least Mean Square Algorithm

Muhammad O. Bin Saeed, Azzedine Zerguine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.

Original language	English
Title of host publication	Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1420-1424
Number of pages	5
ISBN (Electronic)	9780738131269
DOIs	https://doi.org/10.1109/IEEECONF51394.2020.9443480
State	Published - 1 Nov 2020

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2020-November
ISSN (Print)	1058-6393

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Noise constrained algorithm
least mean square algorithm
qq-Derivative

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/IEEECONF51394.2020.9443480

Cite this

Bin Saeed, M. O., & Zerguine, A. (2020). A q-Noise Constrained Least Mean Square Algorithm. In M. B. Matthews (Ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020 (pp. 1420-1424). Article 9443480 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF51394.2020.9443480

@inproceedings{cfa65a9e67d443cebca3a96f2acf7a10,

title = "A q-Noise Constrained Least Mean Square Algorithm",

abstract = "The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.",

keywords = "Noise constrained algorithm, least mean square algorithm, qq-Derivative",

author = "{Bin Saeed}, {Muhammad O.} and Azzedine Zerguine",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.",

year = "2020",

month = nov,

day = "1",

doi = "10.1109/IEEECONF51394.2020.9443480",

language = "English",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "1420--1424",

editor = "Matthews, {Michael B.}",

booktitle = "Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020",

address = "United States",

}

Bin Saeed, MO & Zerguine, A 2020, A q-Noise Constrained Least Mean Square Algorithm. in MB Matthews (ed.), Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020., 9443480, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2020-November, IEEE Computer Society, pp. 1420-1424. https://doi.org/10.1109/IEEECONF51394.2020.9443480

A q-Noise Constrained Least Mean Square Algorithm. / Bin Saeed, Muhammad O.; Zerguine, Azzedine.
Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020. ed. / Michael B. Matthews. IEEE Computer Society, 2020. p. 1420-1424 9443480 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2020-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A q-Noise Constrained Least Mean Square Algorithm

AU - Bin Saeed, Muhammad O.

AU - Zerguine, Azzedine

PY - 2020/11/1

Y1 - 2020/11/1

N2 - The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.

AB - The Least Mean Square (LMS) algorithm has an inherent trade-off issue between convergence speed and steady-state error performance. One of the algorithms proposed to tackle this issue is called the Noise Constrained LMS algorithm, which uses the noise variance to iteratively vary the step-size. This work uses the q-derivative to propose an improved Noise Constrained LMS algorithm. Simulation results show that the proposed algorithm shows better performance than the conventional algorithm at the cost of only a minimal increase in complexity. Steady-state analysis for the proposed algorithm has also been carried out.

KW - Noise constrained algorithm

KW - least mean square algorithm

KW - qq-Derivative

UR - http://www.scopus.com/inward/record.url?scp=85107766131&partnerID=8YFLogxK

U2 - 10.1109/IEEECONF51394.2020.9443480

DO - 10.1109/IEEECONF51394.2020.9443480

M3 - Conference contribution

AN - SCOPUS:85107766131

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 1420

EP - 1424

BT - Conference Record of the 54th Asilomar Conference on Signals, Systems and Computers, ACSSC 2020

A2 - Matthews, Michael B.

PB - IEEE Computer Society

ER -

A q-Noise Constrained Least Mean Square Algorithm

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this