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

Machine learning-based Raman amplifier design

D. Zibar, A. Ferrari, V. Curri, and A. Carena
M1J.1 Optical Fiber Communication Conference (OFC) 2019


You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
Login to access OSA Member Subscription