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

Lookup NU author(s): Dr Paul Haigh

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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.


Publication metadata

Author(s): Giacoumidis E, Wei J, Jarajreh MA, Le ST, Haigh PA, Bohata J, Perentos A, Mhatli S, Ghanbarisabagh M, Aldaya I, Doran NJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Progress in Electromagnetics Research Symposium

Year of Conference: 2015

Pages: 2473-2477

Print publication date: 01/01/2015

Acceptance date: 02/04/2014

Publisher: Electromagnetics Academy

Library holdings: Search Newcastle University Library for this item

ISBN: 9781934142301


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