Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based method for inverse design of Raman amplifiers, enabling the determination of pump configurations for desired signal power profiles in both frequency and distance domains with high accuracy.

Contribution

The work presents a novel CNN architecture for inverse Raman amplifier design, capable of predicting pump parameters for target power evolutions in complex fiber configurations.

Findings

High prediction accuracy in C-band for various pumping schemes

Low mean and standard deviation of maximum test error

Effective in both counter-propagating and bidirectional setups

Abstract

We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution, both in distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and 0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum test error are obtained numerically employing 2 and 3 counter, and 4 bidirectional propagating pumps, respectively.