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

Mehran Soltani; Francesco Da Ros; Andrea Carena; Darko Zibar

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arXiv · 2103.03837

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

Mehran Soltani·Francesco Da Ros·Andrea Carena·Darko Zibar

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Abstract

We present a Convolutional Neural Network (CNN) architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution, both in distance along the fiber and in frequency. Using the proposed framework, the prediction of the pump configuration required to achieve a target power profile is demonstrated numerically with high accuracy in C-band considering both counter-propagating and bidirectional pumping schemes. For a distributed Raman amplifier based on a 100 km single-mode fiber, a low mean set (0.51, 0.54 and 0.64 dB) and standard deviation set (0.62, 0.43 and 0.38 dB) of the maximum test error are obtained numerically employing 2 and 3 counter, and 4 bidirectional propagating pumps, respectively.

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Mehran Soltani, Francesco Da Ros, Andrea Carena, Darko Zibar. 2021-02-21. Inverse design of Raman amplifier in frequency and distance domain using Convolutional Neural Networks. https://arxiv.org/abs/2103.03837

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