期刊
OPTICS LETTERS
卷 46, 期 5, 页码 1157-1160出版社
OPTICAL SOC AMER
DOI: 10.1364/OL.417243
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资金
- Villum Fonden (VYI OPTIC-AI) [29344]
- Horizon 2020 Framework Programme (Marie Sklodowska-Curie grant) [754462]
- European Research Council (ERC CoG FRECOM) [771878]
- Ministero dell'Istruzione, dell'Universita e della Ricerca (PRIN 2017, project FIRST)
The machine learning framework successfully predicts pump powers and noise figure profile for a target distributed Raman amplifier gain profile, with highly accurate results. It provides a valuable tool for predicting the performance of next-generation optical communication systems.
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 similar to 0.3 dB. This framework provides a comprehensive characterization of the Raman amplifier and thus is a valuable tool for predicting the performance of next-generation optical communication systems, expected to employ Raman amplification. (C) 2021 Optical Society of America
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