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

arXiv: https://arxiv.org/abs/2206.07650 · HTML: https://mlsanity.com/paper/2206.07650/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 13 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Spectral and spatial power evolution design with machine learning-enabled Raman amplification](https://mlsanity.com/paper/2112.12637/) (2021)
- [SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure](https://mlsanity.com/paper/2106.03639/) (2021)

## Built on by (most cited first, 1 of 1)

- [A comparison between black-, grey- and white-box modeling for the bidirectional Raman amplifier optimization](https://mlsanity.com/paper/2310.05954/) (2023)

## Abstract (arXiv)

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.