Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > OFC > 2020 > T4B > T4B.2

Experimental demonstration of arbitrary Raman gain–profile designs using machine learning

Uiara C. de Moura, Francesco Da Ros, A. Margareth Rosa Brusin, Andrea Carena, and Darko Zibar

Not Accessible

Your library or personal account may give you access

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.

© 2020 The Author(s)

PDF Article
More Like This
Experimental Prediction and Design of Ultra-Wideband Raman Amplifiers Using Neural Networks

Xiaoyan Ye, Aymeric Arnould, Amirhossein Ghazisaeidi, Dylan Le Gac, and Jeremie Renaudier
W1K.3 Optical Fiber Communication Conference (OFC) 2020

Intelligent gain flattening of FMF Raman amplification by machine learning based inverse design

Yufeng Chen, Jiangbing Du, Yuting Huang, Ke Xu, and Zuyuan He
T4B.1 Optical Fiber Communication Conference (OFC) 2020

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, and Andrea Carena
T4B.3 Optical Fiber Communication Conference (OFC) 2020

View More...
Select as filters
Select Topics Cancel
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use