Transform domain LMF algorithm for sparse system identification under low SNR

Murwan Bashir; Azzedine Zerguine

doi:10.1109/ACSSC.2015.7421118

Transform domain LMF algorithm for sparse system identification under low SNR

Murwan Bashir, Azzedine Zerguine

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

4 Scopus citations

Abstract

In this work, a transform domain Least Mean Fourth (LMF) adaptive filter for a sparse system identification, in the case of low Signal-to-Noise Ratio (SNR), is proposed. Unlike the Least Mean Square (LMS) algorithm, the LMF algorithm, because of its error nonlinearity, performs very well in these environments. Moreover, its transform domain version has an outstanding performance when the input signal is correlated. However, it lacks sparse information capability. To overcome this limitation, a zero attractor mechanism, based on the l\ norm is implemented to yield the Zero-Attractor Transform-Domain LMF (ZA-TD-LMF) algorithm. The ZA-TD-LMF algorithm ensures fast convergence and attracts all the filter coefficients to zero. Simulation results conducted to substantiate our claim are found to be very effective.

Original language	English
Title of host publication	Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	221-224
Number of pages	4
ISBN (Electronic)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421118
State	Published - 26 Feb 2016

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2016-February
ISSN (Print)	1058-6393

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Least Mean Fourth (LMF)
Sparse solution
Transform Domain (TD)
Zero-Attractor ZA

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2015.7421118

Cite this

Bashir, M., & Zerguine, A. (2016). Transform domain LMF algorithm for sparse system identification under low SNR. In M. B. Matthews (Ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 (pp. 221-224). Article 7421118 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421118

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title = "Transform domain LMF algorithm for sparse system identification under low SNR",

abstract = "In this work, a transform domain Least Mean Fourth (LMF) adaptive filter for a sparse system identification, in the case of low Signal-to-Noise Ratio (SNR), is proposed. Unlike the Least Mean Square (LMS) algorithm, the LMF algorithm, because of its error nonlinearity, performs very well in these environments. Moreover, its transform domain version has an outstanding performance when the input signal is correlated. However, it lacks sparse information capability. To overcome this limitation, a zero attractor mechanism, based on the l\ norm is implemented to yield the Zero-Attractor Transform-Domain LMF (ZA-TD-LMF) algorithm. The ZA-TD-LMF algorithm ensures fast convergence and attracts all the filter coefficients to zero. Simulation results conducted to substantiate our claim are found to be very effective.",

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author = "Murwan Bashir and Azzedine Zerguine",

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Bashir, M & Zerguine, A 2016, Transform domain LMF algorithm for sparse system identification under low SNR. in MB Matthews (ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015., 7421118, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2016-February, IEEE Computer Society, pp. 221-224. https://doi.org/10.1109/ACSSC.2015.7421118

Transform domain LMF algorithm for sparse system identification under low SNR. / Bashir, Murwan; Zerguine, Azzedine.
Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. ed. / Michael B. Matthews. IEEE Computer Society, 2016. p. 221-224 7421118 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February).

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

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N2 - In this work, a transform domain Least Mean Fourth (LMF) adaptive filter for a sparse system identification, in the case of low Signal-to-Noise Ratio (SNR), is proposed. Unlike the Least Mean Square (LMS) algorithm, the LMF algorithm, because of its error nonlinearity, performs very well in these environments. Moreover, its transform domain version has an outstanding performance when the input signal is correlated. However, it lacks sparse information capability. To overcome this limitation, a zero attractor mechanism, based on the l\ norm is implemented to yield the Zero-Attractor Transform-Domain LMF (ZA-TD-LMF) algorithm. The ZA-TD-LMF algorithm ensures fast convergence and attracts all the filter coefficients to zero. Simulation results conducted to substantiate our claim are found to be very effective.

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Bashir M, Zerguine A. Transform domain LMF algorithm for sparse system identification under low SNR. In Matthews MB, editor, Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. IEEE Computer Society. 2016. p. 221-224. 7421118. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2015.7421118

Transform domain LMF algorithm for sparse system identification under low SNR

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this