Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity

Alexey Redyuk, Evgeny Averyanov, Oleg Sidelnikov, Mikhail Fedoruk, Sergei K. Turitsyn

Research output: Contribution to journalArticle

Abstract

We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB Q2 -factor improvement for 2000 km transmission of 11x256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.
Original languageEnglish
JournalJournal of Lightwave Technology
Early online date5 Feb 2020
DOIs
Publication statusE-pub ahead of print - 5 Feb 2020

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machine learning
impairment
perturbation theory
perturbation
education
receivers
nonlinearity
requirements
fibers
propagation
coefficients

Bibliographical note

© 2020 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.

Cite this

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title = "Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity",
abstract = "We propose a modification of the conventional perturbation-based approach of fiber nonlinearity compensation that enables straight-forward implementation at the receiver and meets feasible complexity requirements. We have developed a model based on perturbation analysis of an inverse Manakov problem, where we use the received signal as the initial condition and solve Manakov equations in the reversed direction, effectively implementing a perturbative digital backward propagation enhanced by machine learning techniques. To determine model coefficients we employ machine learning methods using a training set of transmitted symbols. The proposed approach allowed us to achieve 0.5 dB and 0.2 dB Q2 -factor improvement for 2000 km transmission of 11x256 Gbit/s DP-16QAM signal compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We quantify the trade-off between performance and complexity.",
author = "Alexey Redyuk and Evgeny Averyanov and Oleg Sidelnikov and Mikhail Fedoruk and Turitsyn, {Sergei K.}",
note = "{\circledC} 2020 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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Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity. / Redyuk, Alexey; Averyanov, Evgeny; Sidelnikov, Oleg; Fedoruk, Mikhail; Turitsyn, Sergei K.

In: Journal of Lightwave Technology, 05.02.2020.

Research output: Contribution to journalArticle

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AU - Turitsyn, Sergei K.

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