The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the gradient descent optimization of forward-propagating Raman pumps. Both the frequency and power of an arbitrary number of pumps in a forward pumping configuration are then optimized for an arbitrary data channel load and span length. The forward propagation model is combined with an experimentally-trained ML model of a backward-pumping Raman amplifier to jointly optimize the frequency and power of the forward amplifier's pumps and the powers of the backward amplifier's pumps. The joint forward and backward amplifier optimization is demonstrated for an unrepeatered transmission of 250 km. A gain flatness of < 1~dB over 4 THz is achieved. The optimized amplifiers are validated using a numerical simulator.
@article{arxiv.2206.07650,
title = {Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model},
author = {Metodi Plamenov Yankov and Francesco Da Ros and Uiara Celine de Moura and Andrea Carena and Darko Zibar},
journal= {arXiv preprint arXiv:2206.07650},
year = {2022}
}
Comments
submitted to Journal of Lightwave Technology. Extended version of the previous conference paper M. Yankov, D. Zibar, A. Carena, and F. Da Ros, "Forward Raman amplifier optimization using machine learning-aided physical modeling," accepted, Optoelectronics and Communications Conference (OECC), 2022