Experimental demonstration of arbitrary Raman gain-profile designs using machine learning

Uiara C. de Moura*, Francesco Da Ros, A. Margareth Rosa Brusin, Andrea Carena, Darko Zibar

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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    Abstract

    A machine learning framework for Raman amplifier design is experimentally tested. Performance in terms of maximum error over the gain profile is investigated for various fiber types and lengths, demonstrating highly-accurate designs.
    Original languageEnglish
    Title of host publicationOptical Fiber Communication Conference 2020
    Number of pages3
    PublisherIEEE
    Publication date2020
    Article numberT4B.2
    ISBN (Print)978-1-943580-71-2
    DOIs
    Publication statusPublished - 2020
    EventOptical Fiber Communication Conference 2020 - San Diego Convention Center, San Diego, United States
    Duration: 8 Mar 202012 Mar 2020

    Conference

    ConferenceOptical Fiber Communication Conference 2020
    LocationSan Diego Convention Center
    Country/TerritoryUnited States
    CitySan Diego
    Period08/03/202012/03/2020
    SponsorAcacia Communications Inc., AC Photonics, Inc., Alibaba Group, Ciena Corporation, Cisco Systems

    Bibliographical note

    From the session: Machine Learning for Fiber Amplifier and Sensors (T4B)

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