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We present a Convolutional Neural Network (CNN) architecture for inverseRaman amplifier design. This model aims at finding the pump powers andwavelengths required for a target signal power evolution, both in distancealong the fiber and in frequency. Using the proposed framework, the predictionof the pump configuration required to achieve a target power profile isdemonstrated numerically with high accuracy in C-band considering bothcounter-propagating and bidirectional pumping schemes. For a distributed Ramanamplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximumtest error are obtained numerically employing 2 and 3 counter, and 4bidirectional propagating pumps, respectively.

Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks