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 ∼ 0.3 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.
Bibliographical noteFunding Information:
Funding. Villum Fonden (VYI OPTIC-AI grant no. 29344); Horizon 2020 Framework Programme (Marie Skłodowska-Curie grant agreement No 754462); European Research Council (ERC CoG FRECOM grant 771878); Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 2017, project FIRST).
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