Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems

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: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
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.
ISBN: 978-1-5386-4863-6, 978-1-5386-4862-9
Last Modified: 20 Feb 2024 08:24
Date Deposited: 22 Nov 2018 11:19
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Related URLs: https://ieeexpl ... ocument/8535338 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2018-11-15
Accepted Date: 2018-06-22
Authors: Averyanov, Evgeny
Redyuk, Alexey A.
Sidelnikov, Oleg
Sorokina, Mariia (ORCID Profile 0000-0001-6082-0316)
Fedoruk, Mikhail P.
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

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