Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems

Averyanov, Evgeny, Redyuk, Alexey A., Sidelnikov, Oleg, Sorokina, Mariia, Fedoruk, Mikhail P. and Turitsyn, Sergei K. (2018). Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems. IN: 2018 European Conference on Optical Communication (ECOC). IEEE.

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.

Publication DOI: https://doi.org/10.1109/ECOC.2018.8535338
Divisions: Engineering & Applied Sciences > Institute of Photonics
Engineering & Applied Sciences
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Engineering & Applied Sciences > Electrical, electronic & power engineering
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Related URLs: https://ieeexpl ... ocument/8535338 (Publisher URL)
Published Date: 2018-11-15
Authors: Averyanov, Evgeny
Redyuk, Alexey A.
Sidelnikov, Oleg
Sorokina, Mariia
Fedoruk, Mikhail P.
Turitsyn, Sergei K. ( 0000-0003-0101-3834)

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