A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

Shujaat Khan; Muhammad Usman; Imran Naseem; Roberto Togneri; Mohammed Bennamoun

doi:10.1109/CSPA.2017.8064914

A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

Shujaat Khan^*, Muhammad Usman, Imran Naseem, Roberto Togneri, Mohammed Bennamoun

^*Corresponding author for this work

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

11 Scopus citations

Abstract

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

Original language	English
Title of host publication	Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781509011841
DOIs	https://doi.org/10.1109/CSPA.2017.8064914
State	Published - 10 Oct 2017
Externally published	Yes

Publication series

Name	Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Least mean square (LMS)
adaptive filter
adaptive step-size modified fractional LMS (AMFLMS)
channel equalization
fractional LMS (FLMS)
fractional calculus
high convergence
low steady state error
modified fractional LMS (MFLMS)
plant identification
robust variable step size (RVSS)
robust variable step size FLMS (RVSS-FLMS)

ASJC Scopus subject areas

Signal Processing
Instrumentation

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10.1109/CSPA.2017.8064914

Cite this

Khan, S., Usman, M., Naseem, I., Togneri, R., & Bennamoun, M. (2017). A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017 (pp. 1-6). Article 8064914 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSPA.2017.8064914

Khan, Shujaat ; Usman, Muhammad ; Naseem, Imran et al. / A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-6 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017).

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title = "A robust variable step size fractional least mean square (RVSS-FLMS) algorithm",

abstract = "In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).",

keywords = "Least mean square (LMS), adaptive filter, adaptive step-size modified fractional LMS (AMFLMS), channel equalization, fractional LMS (FLMS), fractional calculus, high convergence, low steady state error, modified fractional LMS (MFLMS), plant identification, robust variable step size (RVSS), robust variable step size FLMS (RVSS-FLMS)",

author = "Shujaat Khan and Muhammad Usman and Imran Naseem and Roberto Togneri and Mohammed Bennamoun",

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Khan, S, Usman, M, Naseem, I, Togneri, R & Bennamoun, M 2017, A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. in Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017., 8064914, Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017, Institute of Electrical and Electronics Engineers Inc., pp. 1-6. https://doi.org/10.1109/CSPA.2017.8064914

A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. / Khan, Shujaat; Usman, Muhammad; Naseem, Imran et al.
Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-6 8064914 (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017).

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

TY - GEN

T1 - A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

AU - Khan, Shujaat

AU - Usman, Muhammad

AU - Naseem, Imran

AU - Togneri, Roberto

AU - Bennamoun, Mohammed

PY - 2017/10/10

Y1 - 2017/10/10

N2 - In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

AB - In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).

KW - Least mean square (LMS)

KW - adaptive filter

KW - adaptive step-size modified fractional LMS (AMFLMS)

KW - channel equalization

KW - fractional LMS (FLMS)

KW - fractional calculus

KW - high convergence

KW - low steady state error

KW - modified fractional LMS (MFLMS)

KW - plant identification

KW - robust variable step size (RVSS)

KW - robust variable step size FLMS (RVSS-FLMS)

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DO - 10.1109/CSPA.2017.8064914

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BT - Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017

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ER -

Khan S, Usman M, Naseem I, Togneri R, Bennamoun M. A robust variable step size fractional least mean square (RVSS-FLMS) algorithm. In Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-6. 8064914. (Proceedings - 2017 IEEE 13th International Colloquium on Signal Processing and its Applications, CSPA 2017). doi: 10.1109/CSPA.2017.8064914

A robust variable step size fractional least mean square (RVSS-FLMS) algorithm

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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