Inverse design of a Raman amplifier in frequency and distance domains using convolutional neural networks

Mehran Soltani*, Francesco da Ros, Andrea Carena, Darko Zibar

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

We present a convolutional neural network architecture for inverse Raman amplifier design. This model aims at finding the pump powers and wavelengths required for a target signal power evolution in both 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 two and three counter-, and four bidirectional propagating pumps, respectively.

Original languageEnglish
JournalOptics Letters
Volume46
Issue number11
Pages (from-to)2650-2653
ISSN0146-9592
DOIs
Publication statusPublished - 1 Jun 2021

Bibliographical note

Funding Information:
Funding. European Research Council (ERC-CoG FRECOM Grant NFo. 771878); Villum Fonden (OPTIC-AI Grant No. 29334); Ministero dell?Istruzione, dell?Universit? e della Ricerca (PRIN 2017, Project FIRST).

Funding Information:
Funding. European Research Council (ERC-CoG FRECOM Grant NFo. 771878); Villum Fonden (OPTIC-AI Grant No. 29334); Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 2017, Project FIRST).

Publisher Copyright:
© 2021 Optical Society of America

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