Load Aware Raman Gain Profile Prediction in Dynamic Multi-Band Optical Networks

A. Margareth Rosa Brusin, Uiara C. De Moura, Andrea D'Amico, Vittorio Curri, Darko Zibar, Andrea Carena

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

Abstract

We introduce a load aware machine learning method for prediction of Raman gain profiles. It enables future network controllers to manage seamless upgrades toward multi-band optical line systems with dynamic loads.

Original languageEnglish
Title of host publicationProceedings of 2020 Optical Fiber Communications Conference and Exhibition
Number of pages3
PublisherIEEE
Publication dateMar 2020
Article number9083232
ISBN (Electronic)9781943580712
Publication statusPublished - Mar 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

Cite this

Brusin, A. M. R., De Moura, U. C., D'Amico, A., Curri, V., Zibar, D., & Carena, A. (2020). Load Aware Raman Gain Profile Prediction in Dynamic Multi-Band Optical Networks. In Proceedings of 2020 Optical Fiber Communications Conference and Exhibition [9083232] IEEE.