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 language | English |
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Journal | Optics Letters |
Volume | 46 |
Issue number | 11 |
Pages (from-to) | 2650-2653 |
ISSN | 0146-9592 |
DOIs | |
Publication status | Published - 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