Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Conferences > OFC > 2020 > W1K > W1K.3

Experimental Prediction and Design of Ultra-Wideband Raman Amplifiers Using Neural Networks

Xiaoyan Ye, Aymeric Arnould, Amirhossein Ghazisaeidi, Dylan Le Gac, and Jeremie Renaudier

Not Accessible

Your library or personal account may give you access

Abstract

A machine learning method for Raman gain prediction and multi-pump broadband amplifier design is experimentally demonstrated over a 100 nm-wide optical bandwidth. We show high accuracy and ultra-fast prediction of arbitrary gain profile over a 100 km-long SSMF span.

© 2020 The Author(s)

PDF Article
More Like This
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
T4B.2 Optical Fiber Communication Conference (OFC) 2020

150nm SCL-Band Transmission through 70km SMF using Ultra-wideband Dual-stage Discrete Raman Amplifier

Md Asif Iqbal, Lukasz Krzczanowicz, Ian Phillips, Paul Harper, and Wladek Forysiak
W3E.4 Optical Fiber Communication Conference (OFC) 2020

Neural Networks for Fiber Amplifier Design Optimization using Experimental Training Sets

Hamed Rabbani, Saber Jalilpiran, Sophie LaRochelle, and Leslie A. Rusch
SF3H.2 CLEO: Science and Innovations (CLEO:S&I) 2023

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