Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems


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.

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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)
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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)



Version: Accepted Version

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