Advanced Convolutional Neural Networks for Nonlinearity Mitigation in Long-Haul WDM Transmission Systems

Abstract

Practical implementation of digital signal processing for mitigation of transmission impairments in optical communication systems requires reduction of the complexity of the underlying algorithms. Here, we investigate the application of convolutional neural networks for compensating nonlinear signal distortions in a 3200~km fiber-optic 11x400-Gb/s WDM PDM-16QAM transmission link with a focus on the optimization of the corresponding algorithmic complexity. We propose a design that includes original initialisation of the weights of the layers by a filter predefined through the training a single-layer convolutional neural network. Furthermore, we use an enhanced activation function that takes into account nonlinear interactions between neighbouring symbols. To increase learning efficiency, we apply a layer-wise training scheme followed by joint optimization of all weights applying additional training to all of them together in the large multi-layer network. We examine application of the proposed convolutional neural network for the nonlinearity compensation using only one sample per symbol and evaluate complexity and performance of the proposed technique.

Publication DOI: https://doi.org/10.1109/JLT.2021.3051609
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ Funding: The work was supported by the Russian Science Foundation (Grant No. 17-72-30006). SS and SKT acknowledges support by the EPSRC Programme grant TRANSNET (EP/R035342/1).
Uncontrolled Keywords: Convolutional neural networks,nonlinearity mitigation in fiber-optic links,Atomic and Molecular Physics, and Optics
Full Text Link:
Related URLs: https://ieeexpl ... cument/9324921/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-04-15
Published Online Date: 2021-01-21
Accepted Date: 2021-01-01
Authors: Sidelnikov, Oleg
Redyuk, Alexey
Sygletos, Stylianos (ORCID Profile 0000-0003-2063-8733)
Fedoruk, Mikhail
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

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