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

doi:10.1109/ICTC.2017.8191021

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/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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 language	English
Title of host publication	International Conference on Information and Communication Technology Convergence
Subtitle of host publication	ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	466-470
Number of pages	5
ISBN (Electronic)	9781509040315
DOIs	https://doi.org/10.1109/ICTC.2017.8191021
State	Published - 12 Dec 2017
Externally published	Yes
Event	8th International Conference on Information and Communication Technology Convergence, ICTC 2017 - Jeju Island, Korea, Republic of Duration: 18 Oct 2017 → 20 Oct 2017

Publication series

Name	International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017
Volume	2017-December

Conference

Conference	8th International Conference on Information and Communication Technology Convergence, ICTC 2017
Country/Territory	Korea, Republic of
City	Jeju Island
Period	18/10/17 → 20/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Least mean fourth (LMF)
Least mean square (LMS)
channel estimation
fractional LMS (FLMS)
fractional calculus
fractional least mean fourth (FLMF)
non-Gaussian noise

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Information Systems
Information Systems and Management

Access to Document

10.1109/ICTC.2017.8191021

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 International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017 (pp. 466-470). (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTC.2017.8191021

Khan, Shujaat ; Ahmed, Naveed ; Malik, Muhammad Ammar et al. / FLMF : Fractional least mean fourth algorithm for channel estimation in non-Gaussian environment. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 466-470 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017).

@inproceedings{2948987babe348448f4e08ac7f4d37a7,

title = "FLMF: Fractional least mean fourth algorithm for channel estimation in non-Gaussian environment",

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 fourth (LMF), Least mean square (LMS), channel estimation, fractional LMS (FLMS), fractional calculus, fractional least mean fourth (FLMF), non-Gaussian noise",

author = "Shujaat Khan and Naveed Ahmed and Malik, \{Muhammad Ammar\} and Imran Naseem and Roberto Togneri and Mohammed Bennamoun",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th International Conference on Information and Communication Technology Convergence, ICTC 2017 ; Conference date: 18-10-2017 Through 20-10-2017",

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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 International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 466-470, 8th International Conference on Information and Communication Technology Convergence, ICTC 2017, Jeju Island, Korea, Republic of, 18/10/17. https://doi.org/10.1109/ICTC.2017.8191021

FLMF: Fractional least mean fourth algorithm for channel estimation in non-Gaussian environment. / Khan, Shujaat; Ahmed, Naveed; Malik, Muhammad Ammar et al.
International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 466-470 (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017; Vol. 2017-December).

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

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AU - Khan, Shujaat

AU - Ahmed, Naveed

AU - Malik, Muhammad Ammar

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AU - Togneri, Roberto

AU - Bennamoun, Mohammed

PY - 2017/12/12

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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.

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ER -

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 International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 466-470. (International Conference on Information and Communication Technology Convergence: ICT Convergence Technologies Leading the Fourth Industrial Revolution, ICTC 2017). doi: 10.1109/ICTC.2017.8191021

FLMF: Fractional least mean fourth algorithm for channel estimation in non-Gaussian environment

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this