A robust LMS adaptive algorithm over distributed networks

Muhammad O. Bin Saeed; Azzedine Zerguine; Salam A. Zummo

doi:10.1109/ACSSC.2011.6190061

A robust LMS adaptive algorithm over distributed networks

Muhammad O. Bin Saeed^*, Azzedine Zerguine, Salam A. Zummo

^*Corresponding author for this work

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

3 Scopus citations

Abstract

power estimates on the behavior of a noise constrained diffusion-based adaptive algorithm for distributed adaptive networks. For good performance, the noise constrained diffusion least mean square (NCDLMS) algorithm assumes knowledge of the noise variance is available at each node. In this work, it is shown that the NCDLMS algorithm is robust to large variations in noise variance estimation. Moreover, the mean and steady-state analyses of the NCDLMS algorithm are carried out and simulation results are found to corroborate the theoretical findings. Great improvement in performance is obtained through the use of the proposed algorithm even when no information on the noise variance is available. The increased computational complexity of the NCDLMS algorithm is justified through the performance improvement it offers.

Original language	English
Title of host publication	Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages	547-550
Number of pages	4
DOIs	https://doi.org/10.1109/ACSSC.2011.6190061
State	Published - 2011

Publication series

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

Keywords

Adaptive filters
Variable step-size least mean square
diffusion algorithm
noise constrained least mean square

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2011.6190061

Cite this

@inproceedings{7f764fc2089347a392a3ab39a64d71fb,

title = "A robust LMS adaptive algorithm over distributed networks",

abstract = "power estimates on the behavior of a noise constrained diffusion-based adaptive algorithm for distributed adaptive networks. For good performance, the noise constrained diffusion least mean square (NCDLMS) algorithm assumes knowledge of the noise variance is available at each node. In this work, it is shown that the NCDLMS algorithm is robust to large variations in noise variance estimation. Moreover, the mean and steady-state analyses of the NCDLMS algorithm are carried out and simulation results are found to corroborate the theoretical findings. Great improvement in performance is obtained through the use of the proposed algorithm even when no information on the noise variance is available. The increased computational complexity of the NCDLMS algorithm is justified through the performance improvement it offers.",

keywords = "Adaptive filters, Variable step-size least mean square, diffusion algorithm, noise constrained least mean square",

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

year = "2011",

doi = "10.1109/ACSSC.2011.6190061",

language = "English",

isbn = "9781467303231",

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

pages = "547--550",

booktitle = "Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011",

}

A robust LMS adaptive algorithm over distributed networks. / Bin Saeed, Muhammad O.; Zerguine, Azzedine ; Zummo, Salam A.
Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011. 2011. p. 547-550 6190061 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

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

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T1 - A robust LMS adaptive algorithm over distributed networks

AU - Bin Saeed, Muhammad O.

AU - Zerguine, Azzedine

AU - Zummo, Salam A.

PY - 2011

Y1 - 2011

N2 - power estimates on the behavior of a noise constrained diffusion-based adaptive algorithm for distributed adaptive networks. For good performance, the noise constrained diffusion least mean square (NCDLMS) algorithm assumes knowledge of the noise variance is available at each node. In this work, it is shown that the NCDLMS algorithm is robust to large variations in noise variance estimation. Moreover, the mean and steady-state analyses of the NCDLMS algorithm are carried out and simulation results are found to corroborate the theoretical findings. Great improvement in performance is obtained through the use of the proposed algorithm even when no information on the noise variance is available. The increased computational complexity of the NCDLMS algorithm is justified through the performance improvement it offers.

AB - power estimates on the behavior of a noise constrained diffusion-based adaptive algorithm for distributed adaptive networks. For good performance, the noise constrained diffusion least mean square (NCDLMS) algorithm assumes knowledge of the noise variance is available at each node. In this work, it is shown that the NCDLMS algorithm is robust to large variations in noise variance estimation. Moreover, the mean and steady-state analyses of the NCDLMS algorithm are carried out and simulation results are found to corroborate the theoretical findings. Great improvement in performance is obtained through the use of the proposed algorithm even when no information on the noise variance is available. The increased computational complexity of the NCDLMS algorithm is justified through the performance improvement it offers.

KW - Adaptive filters

KW - Variable step-size least mean square

KW - diffusion algorithm

KW - noise constrained least mean square

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AN - SCOPUS:84861313235

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

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BT - Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011

ER -

A robust LMS adaptive algorithm over distributed networks

Abstract

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Keywords

ASJC Scopus subject areas

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