Interchannel nonlinearity compensation using a perturbative machine learning technique

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Interchannel nonlinearity compensation using a perturbative machine learning technique. / Kozulin, I. A.; Redyuk, A. A.

In: Optics Communications, Vol. 493, 127026, 15.08.2021.

Research output: Contribution to journal › Article › peer-review

BibTeX

@article{15963ff750ad4ca6a451529684afc2d6,

title = "Interchannel nonlinearity compensation using a perturbative machine learning technique",

abstract = "We propose an extension of the perturbation-based approach for fiber nonlinearity compensation that is capable of mitigating both intra- and interchannel nonlinearity with a moderate increase in implementation complexity. Being guided by inverse perturbation theory we develop a straight-forward modification of the conventional model that takes into account nonlinear interactions between symbols from neighboring spectral channels. We employ machine learning techniques such as the normal equation model with regularization for joint identification of perturbation coefficients that are responsible for intra- and interchannel interactions. We investigate the application of the proposed approach for compensating nonlinear signal distortions in a 1200 km fiber-optic 3 x 400 Gbit/s WDM DP-64QAM transmission link. It was shown up to 0.83 dB and 0.51 dB Q2-factor improvement compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We estimate the implementation complexity of the approach.",

keywords = "Fiber nonlinearity compensation, Inverse perturbation theory, Machine learning, Optical communication system",

author = "Kozulin, {I. A.} and Redyuk, {A. A.}",

note = "Funding Information: The work was supported by the Russian Science Foundation (Grant No. 17-72-30006 ). Publisher Copyright: {\textcopyright} 2021 Elsevier B.V.",

year = "2021",

month = aug,

day = "15",

doi = "10.1016/j.optcom.2021.127026",

language = "English",

volume = "493",

journal = "Optics Communications",

issn = "0030-4018",

publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Interchannel nonlinearity compensation using a perturbative machine learning technique

AU - Kozulin, I. A.

AU - Redyuk, A. A.

PY - 2021/8/15

Y1 - 2021/8/15

N2 - We propose an extension of the perturbation-based approach for fiber nonlinearity compensation that is capable of mitigating both intra- and interchannel nonlinearity with a moderate increase in implementation complexity. Being guided by inverse perturbation theory we develop a straight-forward modification of the conventional model that takes into account nonlinear interactions between symbols from neighboring spectral channels. We employ machine learning techniques such as the normal equation model with regularization for joint identification of perturbation coefficients that are responsible for intra- and interchannel interactions. We investigate the application of the proposed approach for compensating nonlinear signal distortions in a 1200 km fiber-optic 3 x 400 Gbit/s WDM DP-64QAM transmission link. It was shown up to 0.83 dB and 0.51 dB Q2-factor improvement compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We estimate the implementation complexity of the approach.

AB - We propose an extension of the perturbation-based approach for fiber nonlinearity compensation that is capable of mitigating both intra- and interchannel nonlinearity with a moderate increase in implementation complexity. Being guided by inverse perturbation theory we develop a straight-forward modification of the conventional model that takes into account nonlinear interactions between symbols from neighboring spectral channels. We employ machine learning techniques such as the normal equation model with regularization for joint identification of perturbation coefficients that are responsible for intra- and interchannel interactions. We investigate the application of the proposed approach for compensating nonlinear signal distortions in a 1200 km fiber-optic 3 x 400 Gbit/s WDM DP-64QAM transmission link. It was shown up to 0.83 dB and 0.51 dB Q2-factor improvement compared to chromatic dispersion equalization and one step per span two samples per symbol digital back-propagation technique, respectively. We estimate the implementation complexity of the approach.

KW - Fiber nonlinearity compensation

KW - Inverse perturbation theory

KW - Machine learning

KW - Optical communication system

UR - http://www.scopus.com/inward/record.url?scp=85104570939&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=46016032

U2 - 10.1016/j.optcom.2021.127026

DO - 10.1016/j.optcom.2021.127026

M3 - Article

AN - SCOPUS:85104570939

VL - 493

JO - Optics Communications

JF - Optics Communications

SN - 0030-4018

M1 - 127026

ER -

ID: 28454245