Journal
OPTICS LETTERS
Volume 46, Issue 11, Pages 2650-2653Publisher
Optica Publishing Group
DOI: 10.1364/OL.422884
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Funding
- European Research Council (ERC-CoG FRECOM) [771878]
- Villum Fonden (OPTIC-AI) [29334]
- Ministero dell'Istruzione, dell'Universita e della Ricerca (PRIN 2017, Project FIRST)
- European Research Council (ERC) [771878] Funding Source: European Research Council (ERC)
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A convolutional neural network architecture for inverse Raman amplifier design is presented, demonstrating high accuracy predictions of pump configurations required for achieving target power profiles in the C-band. The study achieved low mean and standard deviation sets of maximum test errors numerically for distributed Raman amplifiers based on a 100 km single-mode fiber.
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. (C) 2021 Optical Society of America
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