Combining nonlinear Fourier transform and neural network-based processing in optical communications

Oleksandr Kotlyar*, Maryna Pankratova, Morteza Kamalian-Kopae, Anastasiia Vasylchenkova, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We propose a method to improve the performance of the nonlinear Fourier transform (NFT)-based optical transmission system by applying the neural network post-processing of the nonlinear spectrum at the receiver. We demonstrate through numerical modeling about one order of magnitude bit error rate improvement and compare this method with machine learning processing based on the classification of the received symbols. The proposed approach also offers a way to improve numerical accuracy of the inverse NFT; therefore, it can find a range of applications beyond optical communications.

Original languageEnglish
Pages (from-to)3462-3465
Number of pages4
JournalOptics Letters
Issue number13
Early online date22 Jun 2020
Publication statusPublished - 1 Jul 2020

Bibliographical note

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Funding: H2020 Marie Skłodowska-Curie Actions (GA2015-713694, 751561); Engineering and Physical Sciences
Research Council (Project TRANSNET EP/R035342/1);
Leverhulme Trust (Grant RP-2018-063).


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