@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",
}