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

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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 languageEnglish
Title of host publicationProgress in Electromagnetics Research Symposium
PublisherElectromagnetics Academy
Pages2473-2477
Number of pages5
Volume2015-January
ISBN (Print)9781934142301
Publication statusPublished - 2015

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