  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 ~0.3dB. This framework provides the comprehensive
characterization of the Raman amplifier and thus is a valuable tool for
predicting the performance of the next-generation optical communication
systems, expected to employ Raman amplification.
