Standard

Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems. / Bogdanov, S. A.; Sidelnikov, O. S.; Fedoruk, M. P. et al.

Proceedings - International Conference Laser Optics 2020, ICLO 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9285392 (Proceedings - International Conference Laser Optics 2020, ICLO 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Bogdanov, SA, Sidelnikov, OS, Fedoruk, MP & Turitsyn, SK 2020, Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems. in Proceedings - International Conference Laser Optics 2020, ICLO 2020., 9285392, Proceedings - International Conference Laser Optics 2020, ICLO 2020, Institute of Electrical and Electronics Engineers Inc., 2020 International Conference Laser Optics, ICLO 2020, St. Petersburg, Russian Federation, 02.11.2020. https://doi.org/10.1109/ICLO48556.2020.9285392

APA

Bogdanov, S. A., Sidelnikov, O. S., Fedoruk, M. P., & Turitsyn, S. K. (2020). Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems. In Proceedings - International Conference Laser Optics 2020, ICLO 2020 [9285392] (Proceedings - International Conference Laser Optics 2020, ICLO 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICLO48556.2020.9285392

Vancouver

Bogdanov SA, Sidelnikov OS, Fedoruk MP, Turitsyn SK. Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems. In Proceedings - International Conference Laser Optics 2020, ICLO 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9285392. (Proceedings - International Conference Laser Optics 2020, ICLO 2020). doi: 10.1109/ICLO48556.2020.9285392

Author

Bogdanov, S. A. ; Sidelnikov, O. S. ; Fedoruk, M. P. et al. / Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems. Proceedings - International Conference Laser Optics 2020, ICLO 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings - International Conference Laser Optics 2020, ICLO 2020).

BibTeX

@inproceedings{02b66ba12e344e439d58d8465ce231a8,
title = "Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems",
abstract = "In this work we propose a receiver-side nonlinearity compensation method based on fully connected feed-forward neural networks applicable to polarization-division multiplexing transmission systems. We consider different neural network architectures and show that the use of information from both polarizations allows to effectively compensate the accumulated nonlinear distortion.",
keywords = "fully connected feed forward neural networks, machine learning, nonlinearity compensation, polarization-division multiplexing",
author = "Bogdanov, {S. A.} and Sidelnikov, {O. S.} and Fedoruk, {M. P.} and Turitsyn, {S. K.}",
note = "Funding Information: The work was supported by the Russian Science Foundation (Grant No. 17-72-30006). Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 International Conference Laser Optics, ICLO 2020 ; Conference date: 02-11-2020 Through 06-11-2020",
year = "2020",
month = nov,
day = "2",
doi = "10.1109/ICLO48556.2020.9285392",
language = "English",
series = "Proceedings - International Conference Laser Optics 2020, ICLO 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - International Conference Laser Optics 2020, ICLO 2020",
address = "United States",

}

RIS

TY - GEN

T1 - Fully connected feed-forward neural network based nonlinearity compensation method for polarization multiplexed transmission systems

AU - Bogdanov, S. A.

AU - Sidelnikov, O. S.

AU - Fedoruk, M. P.

AU - Turitsyn, S. K.

N1 - Funding Information: The work was supported by the Russian Science Foundation (Grant No. 17-72-30006). Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2020/11/2

Y1 - 2020/11/2

N2 - In this work we propose a receiver-side nonlinearity compensation method based on fully connected feed-forward neural networks applicable to polarization-division multiplexing transmission systems. We consider different neural network architectures and show that the use of information from both polarizations allows to effectively compensate the accumulated nonlinear distortion.

AB - In this work we propose a receiver-side nonlinearity compensation method based on fully connected feed-forward neural networks applicable to polarization-division multiplexing transmission systems. We consider different neural network architectures and show that the use of information from both polarizations allows to effectively compensate the accumulated nonlinear distortion.

KW - fully connected feed forward neural networks

KW - machine learning

KW - nonlinearity compensation

KW - polarization-division multiplexing

UR - http://www.scopus.com/inward/record.url?scp=85099392741&partnerID=8YFLogxK

U2 - 10.1109/ICLO48556.2020.9285392

DO - 10.1109/ICLO48556.2020.9285392

M3 - Conference contribution

AN - SCOPUS:85099392741

T3 - Proceedings - International Conference Laser Optics 2020, ICLO 2020

BT - Proceedings - International Conference Laser Optics 2020, ICLO 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 International Conference Laser Optics, ICLO 2020

Y2 - 2 November 2020 through 6 November 2020

ER -

ID: 27485954