Neural network-based decision feedback equalizer using a recursive least squares algorithm

Kashif Mahmood; Azzedine Zerguine

doi:10.1109/ISSPA.2005.1580201

Neural network-based decision feedback equalizer using a recursive least squares algorithm

Kashif Mahmood^*
, Azzedine Zerguine

^*Corresponding author for this work

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

1 Scopus citations

Abstract

In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used for a decision feedback equalization (DFE) scenario. Its performance is investigated and compared to those of MLP-DFE based on the back propagation (BP) algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improved performance obtained by the new structure in both time-invariant and time-varying fading channels.

Original language	English
Title of host publication	Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005
Publisher	IEEE Computer Society
Pages	82-85
Number of pages	4
ISBN (Print)	0780392434, 9780780392434
DOIs	https://doi.org/10.1109/ISSPA.2005.1580201
State	Published - 2005

Publication series

Name	Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005
Volume	1

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/ISSPA.2005.1580201

Cite this

Mahmood, K., & Zerguine, A. (2005). Neural network-based decision feedback equalizer using a recursive least squares algorithm. In Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005 (pp. 82-85). Article 1580201 (Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ISSPA.2005.1580201

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title = "Neural network-based decision feedback equalizer using a recursive least squares algorithm",

abstract = "In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used for a decision feedback equalization (DFE) scenario. Its performance is investigated and compared to those of MLP-DFE based on the back propagation (BP) algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improved performance obtained by the new structure in both time-invariant and time-varying fading channels.",

author = "Kashif Mahmood and Azzedine Zerguine",

year = "2005",

doi = "10.1109/ISSPA.2005.1580201",

language = "English",

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series = "Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005",

publisher = "IEEE Computer Society",

pages = "82--85",

booktitle = "Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005",

address = "United States",

}

Mahmood, K & Zerguine, A 2005, Neural network-based decision feedback equalizer using a recursive least squares algorithm. in Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005., 1580201, Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005, vol. 1, IEEE Computer Society, pp. 82-85. https://doi.org/10.1109/ISSPA.2005.1580201

Neural network-based decision feedback equalizer using a recursive least squares algorithm. / Mahmood, Kashif; Zerguine, Azzedine.
Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005. IEEE Computer Society, 2005. p. 82-85 1580201 (Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005; Vol. 1).

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

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AU - Mahmood, Kashif

AU - Zerguine, Azzedine

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N2 - In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used for a decision feedback equalization (DFE) scenario. Its performance is investigated and compared to those of MLP-DFE based on the back propagation (BP) algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improved performance obtained by the new structure in both time-invariant and time-varying fading channels.

AB - In this work, a recently derived recursive least-square (RLS) algorithm to train multi layer perceptron (MLP) is used for a decision feedback equalization (DFE) scenario. Its performance is investigated and compared to those of MLP-DFE based on the back propagation (BP) algorithm and the simple DFE based on the least-mean square (LMS) algorithm. The results show improved performance obtained by the new structure in both time-invariant and time-varying fading channels.

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SN - 0780392434

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T3 - Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005

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Mahmood K, Zerguine A. Neural network-based decision feedback equalizer using a recursive least squares algorithm. In Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005. IEEE Computer Society. 2005. p. 82-85. 1580201. (Proceedings - 8th International Symposium on Signal Processing and its Applications, ISSPA 2005). doi: 10.1109/ISSPA.2005.1580201

Neural network-based decision feedback equalizer using a recursive least squares algorithm

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