End-to-End Deep Learning of Long-Haul Coherent Optical Fiber Communications via Regular Perturbation Model

Vladislav Neskorniuk*, Andrea Carnio, Vinod Bajaj, Domenico Marsella, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication

Abstract

We present a novel end-to-end autoencoder-based learning for coherent optical communications using a "parallelizable" perturbative channel model. We jointly optimized constellation shaping and nonlinear pre-emphasis achieving mutual information gain of 0.18 bits/sym./pol. simulating 64 GBd dual-polarization single-channel transmission over 30x80 km G.652 SMF link with EDFAs.
Original languageEnglish
Title of host publicationECOC Exhibition 2021
Number of pages4
Publication statusPublished - 26 Jul 2021
EventECOC 2021 - Bordeaux, France
Duration: 13 Sep 202115 Sep 2021

Conference

ConferenceECOC 2021
CountryFrance
CityBordeaux
Period13/09/2115/09/21

Bibliographical note

© 2021 The Authors.

Funding: This project has received funding from
EU Horizon 2020 program under the Marie Skłodowska-Curie
grant agreement No. 766115 (FONTE). JEP is supported by
Leverhulme Project RPG-2018-063. SKT acknowledges the
support of EPSRC project TRANSNET.

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