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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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
Publication date2020
Article numberT4B.2
ISBN (Print)978-1-943580-71-2
Publication statusPublished - 2020
EventOptical Fiber Communication Conference 2020 - San Diego Convention Center, San Diego, United States
Duration: 8 Mar 202012 Mar 2020


ConferenceOptical Fiber Communication Conference 2020
LocationSan Diego Convention Center
CountryUnited States
CitySan Diego
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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