Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems

Evgeny Averyanov, Alexey A. Redyuk, Oleg Sidelnikov, Mariia Sorokina, Mikhail P. Fedoruk, Sergei K. Turitsyn

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

We propose a perturbation-based receiver-side machine-learning equalizer for inter- and intra-channel nonlinearity compensation in WDM systems. We show 1.6 dB and 0.6 dB Q2 -factor improvement compared with linear equalization and DBP respectively for 1000km transmission of 3×128Gbit/s DP-16QAM signal.
Original languageEnglish
Title of host publication2018 European Conference on Optical Communication (ECOC)
PublisherIEEE
ISBN (Electronic)978-1-5386-4862-9
ISBN (Print)978-1-5386-4863-6
DOIs
Publication statusPublished - 15 Nov 2018

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    Averyanov, E., Redyuk, A. A., Sidelnikov, O., Sorokina, M., Fedoruk, M. P., & Turitsyn, S. K. (2018). Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems. In 2018 European Conference on Optical Communication (ECOC) IEEE. https://doi.org/10.1109/ECOC.2018.8535338