End-to-End Learning in Optical Fiber Communications: Experimental Demonstration and Future Trends

Boris Karanov, Vinícius Oliari, Mathieu Chagnon, Gabriele Liga, Alex Alvarado, Vahid Aref, Domanic Lavery, Polina Bayvel, Laurent Schmalen

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
212 Downloads (Pure)

Abstract

Fiber-optic auto-encoders are demonstrated on an intensity modulation/direct detection testbed, outperforming state-of-the-art signal processing. Algorithms for end-to-end optimization using experimentally collected data are discussed. The end-to-end learning framework is extended for performing optimization of the symbol distribution in probabilistically-shaped coherent systems.

Original languageEnglish
Title of host publication2020 European Conference on Optical Communications, ECOC 2020
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781728173610
DOIs
Publication statusPublished - 4 Feb 2021
Event46th European Conference on Optical Communications (ECOC 2020) - Virtual, Brussels, Belgium
Duration: 6 Dec 202010 Dec 2020
Conference number: 46

Conference

Conference46th European Conference on Optical Communications (ECOC 2020)
Abbreviated titleECOC 2020
Country/TerritoryBelgium
CityVirtual, Brussels
Period6/12/2010/12/20

Bibliographical note

Funding Information:
The work received funding from the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project COIN (676448), UK EPSRC TRANSNET grant EP/R035342/1, and the Netherlands Organisation for Scientific Research (NWO) via the VIDI Grant ICONIC (15685). The work of G. Liga is funded by the EU-ROTECH postdoc programme under the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No 754462). The work of A. Alvarado received funding from the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme (757791).

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

The work received funding from the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie project COIN (676448), UK EPSRC TRANSNET grant EP/R035342/1, and the Netherlands Organisation for Scientific Research (NWO) via the VIDI Grant ICONIC (15685). The work of G. Liga is funded by the EU-ROTECH postdoc programme under the European Union’s Horizon 2020 research and innovation programme (Marie Skłodowska-Curie grant agreement No 754462). The work of A. Alvarado received funding from the European Research Council (ERC) under the EU’s Horizon 2020 research and innovation programme (757791).

FundersFunder number
EU-ROTECH
Marie Skłodowska‐Curie676448
European Union 's Horizon 2020 - Research and Innovation Framework Programme754462, 757791
Engineering and Physical Sciences Research CouncilEP/R035342/1
European Research Council
Nederlandse Organisatie voor Wetenschappelijk Onderzoek15685
Horizon 2020

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