A machine learning framework predicting pump powers and noise figure profile for a target distributed Raman amplifier gain profile is experimentally demonstrated. We employ a single-layer neural network to learn the mapping from the gain profiles to the pump powers and noise figures. The obtained results show highly-accurate gain profile designs and noise figure predictions, with a maximum error on average of ~0.3dB. This framework provides the comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of the next-generation optical communication systems, expected to employ Raman amplification.
@article{arxiv.2012.06050,
title = {Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning},
author = {Uiara Celine de Moura and Ann Margareth Rosa Brusin and Andrea Carena and Darko Zibar and Francesco Da Ros},
journal= {arXiv preprint arXiv:2012.06050},
year = {2021}
}