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
PublisherOptical Society of America (OSA)
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
CountryUnited 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)

Cite this

de Moura, U. C., Da Ros, F., Brusin, A. M. R., Carena, A., & Zibar, D. (2020). Experimental demonstration of arbitrary Raman gain-profile designs using machine learning. In Optical Fiber Communication Conference 2020 [T4B.2] Optical Society of America (OSA). https://doi.org/10.1364/OFC.2020.T4B.2