Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

Elias Giacoumidis; Jinlong Wei; Mutsam A. Jarajreh; Son T. Le; Paul A. Haigh; Jan Bohata; Andreas Perentos; Sofien Mhatli; Mohammad Ghanbarisabagh; Ivan Aldaya; Nick J. Doran

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

Elias Giacoumidis
, Jinlong Wei
, Mutsam A. Jarajreh
, Son T. Le
, Paul A. Haigh
, Jan Bohata
, Andreas Perentos
, Sofien Mhatli
, Mohammad Ghanbarisabagh
, Ivan Aldaya
, Nick J. Doran

Aston Institute of Photonic Technologies (AiPT)

Instituto Tecnologico y de Estudios Superiores de Monterrey
Czech Technical University
Fahad Bin Sultan University
University of Bristol
EPT Université de Carthage
Islamic Azad University
ADVA Optical Networking SE
University of Sydney

Research output: Chapter in Book/Published conference output › Conference publication

Abstract

One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

Original language	English
Title of host publication	PIERS 2015 Prague
Subtitle of host publication	Progress in Electromagnetics Research Symposium, proceedings
Place of Publication	Cambridge, MA (US)
Publisher	Electromagnetics Academy
Pages	2473-2477
Number of pages	5
ISBN (Print)	978-1-934142-30-1
Publication status	Published - 2015
Event	Progress in Electromagnetics Research Symposium - Prague, Czech Republic Duration: 6 Jul 2015 → 6 Jul 2015

Publication series

Name	PIERS proceedings
Publisher	Electromagnetics Academy
ISSN (Print)	1559-9450
ISSN (Electronic)	1931-7360

Symposium

Symposium	Progress in Electromagnetics Research Symposium
Abbreviated title	PIERS 2015 Prague
Country/Territory	Czech Republic
City	Prague
Period	6/07/15 → 6/07/15

Bibliographical note

Funding: EU 7FP (FP/2007-2013) under grant 318137 (DISCUS).

Cite this

Giacoumidis, E., Wei, J., Jarajreh, M. A., Le, S. T., Haigh, P. A., Bohata, J., Perentos, A., Mhatli, S., Ghanbarisabagh, M., Aldaya, I., & Doran, N. J. (2015). Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM. In PIERS 2015 Prague: Progress in Electromagnetics Research Symposium, proceedings (pp. 2473-2477). (PIERS proceedings). Electromagnetics Academy.

@inproceedings{31b26257858e483194e0dad4fc93b2fd,

title = "Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM",

abstract = "One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.",

author = "Elias Giacoumidis and Jinlong Wei and Jarajreh, \{Mutsam A.\} and Le, \{Son T.\} and Haigh, \{Paul A.\} and Jan Bohata and Andreas Perentos and Sofien Mhatli and Mohammad Ghanbarisabagh and Ivan Aldaya and Doran, \{Nick J.\}",

note = "Funding: EU 7FP (FP/2007-2013) under grant 318137 (DISCUS).; Progress in Electromagnetics Research Symposium, PIERS 2015 Prague ; Conference date: 06-07-2015 Through 06-07-2015",

year = "2015",

language = "English",

isbn = "978-1-934142-30-1",

series = "PIERS proceedings",

publisher = "Electromagnetics Academy",

pages = "2473--2477",

booktitle = "PIERS 2015 Prague",

}

Giacoumidis, E, Wei, J, Jarajreh, MA, Le, ST, Haigh, PA, Bohata, J, Perentos, A, Mhatli, S, Ghanbarisabagh, M, Aldaya, I & Doran, NJ 2015, Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM. in PIERS 2015 Prague: Progress in Electromagnetics Research Symposium, proceedings. PIERS proceedings, Electromagnetics Academy, Cambridge, MA (US), pp. 2473-2477, Progress in Electromagnetics Research Symposium, Prague, Czech Republic, 6/07/15.

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM. / Giacoumidis, Elias; Wei, Jinlong; Jarajreh, Mutsam A. et al.
PIERS 2015 Prague: Progress in Electromagnetics Research Symposium, proceedings. Cambridge, MA (US): Electromagnetics Academy, 2015. p. 2473-2477 (PIERS proceedings).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

AU - Giacoumidis, Elias

AU - Wei, Jinlong

AU - Jarajreh, Mutsam A.

AU - Le, Son T.

AU - Haigh, Paul A.

AU - Bohata, Jan

AU - Perentos, Andreas

AU - Mhatli, Sofien

AU - Ghanbarisabagh, Mohammad

AU - Aldaya, Ivan

AU - Doran, Nick J.

N1 - Funding: EU 7FP (FP/2007-2013) under grant 318137 (DISCUS).

PY - 2015

Y1 - 2015

N2 - One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

AB - One major drawback of coherent optical orthogonal frequency-division multiplexing (CO-OFDM) that hitherto remains unsolved is its vulnerability to nonlinear fiber effects due to its high peak-to-average power ratio. Several digital signal processing techniques have been investigated for the compensation of fiber nonlinearities, e.g., digital back-propagation, nonlinear pre- and post-compensation and nonlinear equalizers (NLEs) based on the inverse Volterra-series transfer function (IVSTF). Alternatively, nonlinearities can be mitigated using nonlinear decision classifiers such as artificial neural networks (ANNs) based on a multilayer perceptron. In this paper, ANN-NLE is presented for a 16QAM CO-OFDM system. The capability of the proposed approach to compensate the fiber nonlinearities is numerically demonstrated for up to 100-Gb/s and over 1000km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2dB and 1dB, respectively.

UR - https://www.scopus.com/pages/publications/84947232228

M3 - Conference publication

AN - SCOPUS:84947232228

SN - 978-1-934142-30-1

T3 - PIERS proceedings

SP - 2473

EP - 2477

BT - PIERS 2015 Prague

PB - Electromagnetics Academy

CY - Cambridge, MA (US)

T2 - Progress in Electromagnetics Research Symposium

Y2 - 6 July 2015 through 6 July 2015

ER -

Numerical analysis of artificial neural network and volterra-based nonlinear equalizers for coherent optical OFDM

Abstract

Publication series

Symposium

Bibliographical note

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