Harnessing machine learning for fiber-induced nonlinearity mitigation in long-haul coherent optical OFDM

Elias Giacoumidis, Yi Lin, Jinlong Wei, Ivan Aldaya, Athanasios Tsokanos and Liam P. Barry
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Elias Giacoumidis: Radio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, Ireland
Yi Lin: Radio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, Ireland
Jinlong Wei: Huawei Technologies Düsseldorf GmbH, European Research Center, Riesstrasse 25, 80992 München, Germany
Ivan Aldaya: Campus São Joao da Boa Vista, State University of São Paulo (UNESP), 13876-750 São Paulo, Brazil
Athanasios Tsokanos: Centre for Computer Science and Informatics Research, School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Liam P. Barry: Radio and Optical Laboratory, School of Electronic Engineering, Dublin City University, Glasnevin 9, Dublin D09 Y5N0, Ireland

Future Internet, 2018, vol. 11, issue 1, 1-20

Abstract: Coherent optical orthogonal frequency division multiplexing (CO-OFDM) has attracted a lot of interest in optical fiber communications due to its simplified digital signal processing (DSP) units, high spectral-efficiency, flexibility, and tolerance to linear impairments. However, CO-OFDM’s high peak-to-average power ratio imposes high vulnerability to fiber-induced non-linearities. DSP-based machine learning has been considered as a promising approach for fiber non-linearity compensation without sacrificing computational complexity. In this paper, we review the existing machine learning approaches for CO-OFDM in a common framework and review the progress in this area with a focus on practical aspects and comparison with benchmark DSP solutions.

Keywords: fiber optics communications; machine learning; artificial neural network; support vector machine; clustering; nonlinear equalization; coherent optical OFDM (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
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