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 article, 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.

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

Uiara Celine de Moura, Ann Margareth Rosa Brusin, Andrea Carena, Darko Zibar, and Francesco Da Ros
Opt. Lett. 46(5) 1157-1160 (2021)

Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks

Mehran Soltani, Francesco Da Ros, Andrea Carena, and Darko Zibar
Opt. Lett. 46(11) 2650-2653 (2021)

Experimental validation of machine-learning based spectral-spatial power evolution shaping using Raman amplifiers

Mehran Soltani, Francesco Da Ros, Andrea Carena, and Darko Zibar
Opt. Express 30(25) 45958-45969 (2022)

Design, tuning, and blackbox optimization of laser systems

Jack Hirschman, Randy Lemons, Minyang Wang, Peter Kroetz, and Sergio Carbajo
Opt. Express 32(9) 15610-15622 (2024)

Intelligent gain flattening in wavelength and space domain for FMF Raman amplification by machine learning based inverse design

Yufeng Chen, Jiangbing Du, Yuting Huang, Ke Xu, and Zuyuan He
Opt. Express 28(8) 11911-11920 (2020)