Experimental characterization of Raman amplifier optimization through inverse system design

TL;DR

This paper experimentally evaluates a machine learning-based inverse system design framework for Raman amplifiers, demonstrating high accuracy in gain-profile customization and robustness to input variations, supporting advanced optical communication needs.

Contribution

It provides the first thorough experimental validation of a machine learning approach for Raman amplifier design, including practical gain-profile control and input variation tolerance.

Findings

Errors below 0.5 dB in gain-profile accuracy

Effective flat and tilted gain-profile generation across fiber types

High robustness to input spectral profile variations

Abstract

Optical communication systems are always evolving to support the need for ever-increasing transmission rates. This demand is supported by the growth in complexity of communication systems which are moving towards ultra-wideband transmission and space-division multiplexing. Both directions will challenge the design, modeling, and optimization of devices, subsystems, and full systems. Amplification is a key functionality to support this growth and in this context, we recently demonstrated a versatile machine learning framework for designing and modeling Raman amplifiers with arbitrary gains. In this paper, we perform a thorough experimental characterization of such machine learning framework. The applicability of the proposed approach, as well as its ability to accurately provide flat and tilted gain-profiles, are tested on several practical fiber types, showing errors below 0.5~dB. Moreover, as channel power optimization is heavily employed to further enhance the transmission rate, the tolerance of the framework to variations in the input signal spectral profile is investigated. Results show that the inverse design can provide highly accurate gain-profile adjustments for different input signal power profiles even not considering this information during the training phase.