Flexible Raman Amplifier Optimization Based on Machine Learning-Aided Physical Stimulated Raman Scattering Model

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.

Automated summary

The problem of Raman amplifier optimization is studied by using machine learning to obtain a differentiable interpolation function for the Raman gain coefficient. The frequency and power of forward-propagating Raman pumps are optimized for arbitrary data channel load and span length. By combining the forward propagation model and a backward-pumping Raman amplifier ML model, the joint optimization of forward and backward amplifiers is demonstrated for unrepeatered transmission. A gain flatness of < 1 dB over 4 THz is achieved, and the optimized amplifiers are validated using a numerical simulator.