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Online versus Offline Optimization Methods for Raman Amplifier Optimization

  • U. C. de Moura
  • , T. Pinto
  • , A. M. Rosa Brusin
  • , A. Carena
  • , A. Napoli
  • , D. Zibar
  • , F. Da Ros
  • Polytechnic University of Turin
  • Infinera

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

In this work, we evaluate machine learning (offline) and evolutionary strategy (online) techniques for the Raman pump power optimization. Experimental results show that, although reusable and accurate, online tools may be time-consuming for reconfigurable amplifiers.
Original languageEnglish
Title of host publicationProceedings of 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing 
Number of pages4
PublisherIEEE
Publication date6 Jul 2022
Pages1-4
Article number9850067
ISBN (Print)978-1-6654-8606-4
DOIs
Publication statusPublished - 6 Jul 2022
Event27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC) - Toyama International Conference Center, Toyama, Japan
Duration: 3 Jul 2022 → 6 Jul 2022
https://www.oeccpsc2022.org/about_oecc.html

Conference

Conference27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC)
LocationToyama International Conference Center
Country/TerritoryJapan
CityToyama
Period03/07/2022 → 06/07/2022
Internet address

Keywords

  • Stimulated emission
  • Shape
  • Optical switches
  • Pumps
  • Optimization methods
  • Machine learning
  • Artificial neural networks

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