Ahead of Print article · Preprint article · Journal article
Experimental characterization of Raman amplifier optimization through inverse system design
Department of Photonics Engineering, Technical University of Denmark1
Machine Learning in Photonic Systems, Department of Photonics Engineering, Technical University of Denmark2
Centre of Excellence for Silicon Photonics for Optical Communications, Centers, Technical University of Denmark3
Ultra-fast Optical Communication, Department of Photonics Engineering, Technical University of Denmark4
Polytechnic University of Turin5
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.
Language: | English |
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Publisher: | IEEE |
Year: | 2021 |
Pages: | 1162-1170 |
ISSN: | 15582213 and 07338724 |
Types: | Ahead of Print article , Preprint article and Journal article |
DOI: | 10.1109/JLT.2020.3036603 |
ORCIDs: | Moura, Uiara Celine de , Da Ros, Francesco and Zibar, Darko |