Simultaneous gain profile design and noise figure prediction for Raman amplifiers using machine learning

TL;DR

This paper presents a machine learning framework that accurately predicts pump powers and noise figure profiles for Raman amplifiers, enabling efficient design and performance prediction for next-generation optical communication systems.

Contribution

It introduces a neural network-based method for simultaneous gain profile design and noise figure prediction in Raman amplifiers, demonstrating high accuracy and practical utility.

Findings

Maximum average error of ~0.3dB in predictions

Highly accurate gain profile designs

Effective characterization of Raman amplifier performance

Abstract

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.