Machine Learning-Based Raman Amplifier Design

Darko Zibar*, A. Ferrari, V. Curri, A. Carena

*Corresponding author for this work

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

Abstract

A multi-layer neural network is employed to learn the mapping between Raman gain profile and pump powers and wavelengths. The learned model predicts with high-accuracy, low-latency and low-complexity the pumping setup for any gain profile.
Original languageEnglish
Title of host publicationProceedings of 2019 Optical Fiber Communications Conference and Exhibition
Number of pages3
PublisherOptical Society of America
Publication date2019
Pages1-3
ISBN (Print)9781943580538
Publication statusPublished - 2019
Event2019 Optical Fiber Communications Conference and Exhibition - San Diego Convention Center, San Diego, United States
Duration: 3 Mar 20197 Mar 2019

Conference

Conference2019 Optical Fiber Communications Conference and Exhibition
LocationSan Diego Convention Center
CountryUnited States
CitySan Diego
Period03/03/201907/03/2019

Cite this

Zibar, D., Ferrari, A., Curri, V., & Carena, A. (2019). Machine Learning-Based Raman Amplifier Design. In Proceedings of 2019 Optical Fiber Communications Conference and Exhibition (pp. 1-3). Optical Society of America.