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

doi:10.1109/ECOC.2018.8535338

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

Novosibirsk State University
Aston University
Institute for Computational Technologies

Research output: Chapter in Book/Published conference output › Conference publication

10 Citations (SciVal)

106 Downloads (Pure)

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 language	English
Title of host publication	2018 European Conference on Optical Communication (ECOC)
Publisher	IEEE
ISBN (Electronic)	978-1-5386-4862-9
ISBN (Print)	978-1-5386-4863-6
DOIs	https://doi.org/10.1109/ECOC.2018.8535338
Publication status	Published - 15 Nov 2018

Bibliographical note

© 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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10.1109/ECOC.2018.8535338

Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems
© 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.
Accepted author manuscript, 341 KB

Cite this

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title = "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.",

author = "Evgeny Averyanov and Redyuk, \{Alexey A.\} and Oleg Sidelnikov and Mariia Sorokina and Fedoruk, \{Mikhail P.\} and Turitsyn, \{Sergei K.\}",

note = "{\textcopyright} 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.",

year = "2018",

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AU - Averyanov, Evgeny

AU - Redyuk, Alexey A.

AU - Sidelnikov, Oleg

AU - Sorokina, Mariia

AU - Fedoruk, Mikhail P.

AU - Turitsyn, Sergei K.

N1 - © 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.

PY - 2018/11/15

Y1 - 2018/11/15

N2 - 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.

AB - 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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U2 - 10.1109/ECOC.2018.8535338

DO - 10.1109/ECOC.2018.8535338

M3 - Conference publication

SN - 978-1-5386-4863-6

BT - 2018 European Conference on Optical Communication (ECOC)

PB - IEEE

ER -

Perturbative Machine Learning Technique for Nonlinear Impairments Compensation in WDM Systems

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