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PhotonIcs and Electromagnetics Research Symposium,
also known as Progress In Electromagnetics Research Symposium

PIERS Proceedings

Published by The Electromagnetics Academy

Published: 2015-07-09

Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM

By

Jinlong Wei,

Jinlong Wei

Peng Cheng Laboratory, China

Son T. Le,

Son T. Le

Aston University, UK

Elias Giacoumidis,

Elias Giacoumidis

Aston University, UK

Nick J. Doran,

Nick J. Doran

Aston University, UK

Ivan Aldaya,

Ivan Aldaya

Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico

Jan Bohata,

Jan Bohata

Czech Technical University in Prague, Czech Republic

Mohammad Ghanbarisabagh,

Mohammad Ghanbarisabagh

Multimedia University, Malaysia

Paul A. Haigh,

Paul A. Haigh

University of Bristol, UK

Mutsam A. Jarajreh,

Mutsam A. Jarajreh

Fahad Bin Sultan University, Kingdom of Saudi Arabia

Sofien Mhatli,

Sofien Mhatli

EPT Universite de Carthage, Tunisia

Andreas Perentos

Andreas Perentos

Aston University, UK

Proceedings of 2015 Photonics & Electromagnetics Research Symposium, Prague, July 6 - 9，Page(s)2473-2477

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 16 QAM 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 1000 km and compared to the benchmark IVSTF-NLE. Results show that in terms of Q-factor, for 100-Gb/s at 1000 km of transmission, ANN-NLE outperforms linear equalization and IVSTF-NLE by 3.2 dB and 1 dB, respectively.

Citation

Jinlong Wei, Son T. Le, Elias Giacoumidis, Nick J. Doran, Ivan Aldaya, Jan Bohata, Mohammad Ghanbarisabagh, Paul A. Haigh, Mutsam A. Jarajreh, Sofien Mhatli, and Andreas Perentos, "Numerical Analysis of Artificial Neural Network and Volterra-based Nonlinear Equalizers for Coherent Optical OFDM," Proceedings of 2015 Photonics & Electromagnetics Research Symposium, Prague, July 6 - 9，Page(s)2473-2477

References

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