FLMF: Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment

Shujaat Khan, Naveed Ahmed, Muhammad Ammar Malik, Imran Naseem, Roberto Togneri, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperConference paper

Abstract

As the communication systems have been increasingly complex, the problem of channel estimation for such complex communication systems has also emerged as an equally challenging task. To solve this problem, various schemes based on Least Mean Square (LMS) and its improved variants have been proposed. This paper presents an adaptive algorithm for channel estimation in non-Gaussian environment. The proposed algorithm named as fractional least mean fourth (FLMF) is derived using the concept of fractional order calculus (FOC) and least mean fourth (LMF). The proposed algorithm is a convex combination of conventional and fractional order gradient descent method and by removing the computationally expensive Gamma function from fractional gradient it become not only computationally inexpensive but also achieves the high convergence rate with low steady state error. The proposed algorithm is evaluated for the channel estimation problem in multiple configurations and it achieves better results compared to both the fractional least mean square (FLMS) and the least mean fourth (LMF) algorithms.

Original languageEnglish
Title of host publication2017 International Conference on Information and Communication Technology Convergence
Place of PublicationUSA
PublisherWiley-IEEE Press
Pages466-470
Number of pages5
ISBN (Print)9781509040322
Publication statusPublished - 14 Dec 2017
EventInternational Conference on Information and Communication Technology Convergence (ICTC) -
Duration: 18 Oct 201720 Oct 2017

Publication series

NameInternational Conference on Information and Communication Technology Convergence
PublisherIEEE
ISSN (Print)2162-1233

Conference

ConferenceInternational Conference on Information and Communication Technology Convergence (ICTC)
Period18/10/1720/10/17

Cite this

Khan, S., Ahmed, N., Malik, M. A., Naseem, I., Togneri, R., & Bennamoun, M. (2017). FLMF: Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment. In 2017 International Conference on Information and Communication Technology Convergence (pp. 466-470). (International Conference on Information and Communication Technology Convergence). USA: Wiley-IEEE Press.
Khan, Shujaat ; Ahmed, Naveed ; Malik, Muhammad Ammar ; Naseem, Imran ; Togneri, Roberto ; Bennamoun, Mohammed. / FLMF : Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment. 2017 International Conference on Information and Communication Technology Convergence. USA : Wiley-IEEE Press, 2017. pp. 466-470 (International Conference on Information and Communication Technology Convergence).
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abstract = "As the communication systems have been increasingly complex, the problem of channel estimation for such complex communication systems has also emerged as an equally challenging task. To solve this problem, various schemes based on Least Mean Square (LMS) and its improved variants have been proposed. This paper presents an adaptive algorithm for channel estimation in non-Gaussian environment. The proposed algorithm named as fractional least mean fourth (FLMF) is derived using the concept of fractional order calculus (FOC) and least mean fourth (LMF). The proposed algorithm is a convex combination of conventional and fractional order gradient descent method and by removing the computationally expensive Gamma function from fractional gradient it become not only computationally inexpensive but also achieves the high convergence rate with low steady state error. The proposed algorithm is evaluated for the channel estimation problem in multiple configurations and it achieves better results compared to both the fractional least mean square (FLMS) and the least mean fourth (LMF) algorithms.",
keywords = "Least mean square (LMS), Least mean fourth (LMF), fractional calculus, channel estimation, fractional LMS (FLMS), fractional least mean fourth (FLMF), non-Gaussian noise, NONDIFFERENTIABLE FUNCTIONS, IDENTIFICATION, CALCULUS, SERIES, ORDER",
author = "Shujaat Khan and Naveed Ahmed and Malik, {Muhammad Ammar} and Imran Naseem and Roberto Togneri and Mohammed Bennamoun",
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Khan, S, Ahmed, N, Malik, MA, Naseem, I, Togneri, R & Bennamoun, M 2017, FLMF: Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment. in 2017 International Conference on Information and Communication Technology Convergence. International Conference on Information and Communication Technology Convergence, Wiley-IEEE Press, USA, pp. 466-470, International Conference on Information and Communication Technology Convergence (ICTC), 18/10/17.

FLMF : Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment. / Khan, Shujaat; Ahmed, Naveed; Malik, Muhammad Ammar; Naseem, Imran; Togneri, Roberto; Bennamoun, Mohammed.

2017 International Conference on Information and Communication Technology Convergence. USA : Wiley-IEEE Press, 2017. p. 466-470 (International Conference on Information and Communication Technology Convergence).

Research output: Chapter in Book/Conference paperConference paper

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T1 - FLMF

T2 - Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment

AU - Khan, Shujaat

AU - Ahmed, Naveed

AU - Malik, Muhammad Ammar

AU - Naseem, Imran

AU - Togneri, Roberto

AU - Bennamoun, Mohammed

PY - 2017/12/14

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N2 - As the communication systems have been increasingly complex, the problem of channel estimation for such complex communication systems has also emerged as an equally challenging task. To solve this problem, various schemes based on Least Mean Square (LMS) and its improved variants have been proposed. This paper presents an adaptive algorithm for channel estimation in non-Gaussian environment. The proposed algorithm named as fractional least mean fourth (FLMF) is derived using the concept of fractional order calculus (FOC) and least mean fourth (LMF). The proposed algorithm is a convex combination of conventional and fractional order gradient descent method and by removing the computationally expensive Gamma function from fractional gradient it become not only computationally inexpensive but also achieves the high convergence rate with low steady state error. The proposed algorithm is evaluated for the channel estimation problem in multiple configurations and it achieves better results compared to both the fractional least mean square (FLMS) and the least mean fourth (LMF) algorithms.

AB - As the communication systems have been increasingly complex, the problem of channel estimation for such complex communication systems has also emerged as an equally challenging task. To solve this problem, various schemes based on Least Mean Square (LMS) and its improved variants have been proposed. This paper presents an adaptive algorithm for channel estimation in non-Gaussian environment. The proposed algorithm named as fractional least mean fourth (FLMF) is derived using the concept of fractional order calculus (FOC) and least mean fourth (LMF). The proposed algorithm is a convex combination of conventional and fractional order gradient descent method and by removing the computationally expensive Gamma function from fractional gradient it become not only computationally inexpensive but also achieves the high convergence rate with low steady state error. The proposed algorithm is evaluated for the channel estimation problem in multiple configurations and it achieves better results compared to both the fractional least mean square (FLMS) and the least mean fourth (LMF) algorithms.

KW - Least mean square (LMS)

KW - Least mean fourth (LMF)

KW - fractional calculus

KW - channel estimation

KW - fractional LMS (FLMS)

KW - fractional least mean fourth (FLMF)

KW - non-Gaussian noise

KW - NONDIFFERENTIABLE FUNCTIONS

KW - IDENTIFICATION

KW - CALCULUS

KW - SERIES

KW - ORDER

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M3 - Conference paper

SN - 9781509040322

T3 - International Conference on Information and Communication Technology Convergence

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BT - 2017 International Conference on Information and Communication Technology Convergence

PB - Wiley-IEEE Press

CY - USA

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Khan S, Ahmed N, Malik MA, Naseem I, Togneri R, Bennamoun M. FLMF: Fractional Least Mean Fourth Algorithm for Channel Estimation in non-Gaussian Environment. In 2017 International Conference on Information and Communication Technology Convergence. USA: Wiley-IEEE Press. 2017. p. 466-470. (International Conference on Information and Communication Technology Convergence).