Standard

Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity. / Redyuk, Alexey; Averyanov, Evgeny; Sidelnikov, Oleg и др.

в: Journal of Lightwave Technology, Том 38, № 6, 8984221, 15.03.2020, стр. 1250-1257.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Redyuk, A, Averyanov, E, Sidelnikov, O, Fedoruk, M & Turitsyn, S 2020, 'Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity', Journal of Lightwave Technology, Том. 38, № 6, 8984221, стр. 1250-1257. https://doi.org/10.1109/JLT.2020.2971768

APA

Redyuk, A., Averyanov, E., Sidelnikov, O., Fedoruk, M., & Turitsyn, S. (2020). Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity. Journal of Lightwave Technology, 38(6), 1250-1257. [8984221]. https://doi.org/10.1109/JLT.2020.2971768

Vancouver

Redyuk A, Averyanov E, Sidelnikov O, Fedoruk M, Turitsyn S. Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity. Journal of Lightwave Technology. 2020 март 15;38(6):1250-1257. 8984221. doi: 10.1109/JLT.2020.2971768

Author

Redyuk, Alexey ; Averyanov, Evgeny ; Sidelnikov, Oleg и др. / Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity. в: Journal of Lightwave Technology. 2020 ; Том 38, № 6. стр. 1250-1257.

BibTeX

@article{a87d2fbfeb5543df9430980fd9e0575f,
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 11 × 256 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.",
keywords = "Fiber nonlinearity compensation, machine learning, manakov equations, nonlinear signal distortions, optical communication system, perturbation-based detection technique, SIGNAL, EQUALIZER",
author = "Alexey Redyuk and Evgeny Averyanov and Oleg Sidelnikov and Mikhail Fedoruk and Sergei Turitsyn",
year = "2020",
month = mar,
day = "15",
doi = "10.1109/JLT.2020.2971768",
language = "English",
volume = "38",
pages = "1250--1257",
journal = "Journal of Lightwave Technology",
issn = "0733-8724",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "6",

}

RIS

TY - JOUR

T1 - Compensation of Nonlinear Impairments Using Inverse Perturbation Theory with Reduced Complexity

AU - Redyuk, Alexey

AU - Averyanov, Evgeny

AU - Sidelnikov, Oleg

AU - Fedoruk, Mikhail

AU - Turitsyn, Sergei

PY - 2020/3/15

Y1 - 2020/3/15

N2 - 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 11 × 256 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.

AB - 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 11 × 256 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.

KW - Fiber nonlinearity compensation

KW - machine learning

KW - manakov equations

KW - nonlinear signal distortions

KW - optical communication system

KW - perturbation-based detection technique

KW - SIGNAL

KW - EQUALIZER

UR - http://www.scopus.com/inward/record.url?scp=85082400294&partnerID=8YFLogxK

U2 - 10.1109/JLT.2020.2971768

DO - 10.1109/JLT.2020.2971768

M3 - Article

AN - SCOPUS:85082400294

VL - 38

SP - 1250

EP - 1257

JO - Journal of Lightwave Technology

JF - Journal of Lightwave Technology

SN - 0733-8724

IS - 6

M1 - 8984221

ER -

ID: 23891852