It has been recently demonstrated that neural networks can learn the complex pump–signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly–accurate Raman amplifier designs and flexible configuration for ultra–wideband optical communication systems.
Optimization of Raman amplifiers using machine learning / De Moura, U. C.; Da Ros, F.; Zibar, D.; Rosa Brusin, A. M.; Carena, A.. - ELETTRONICO. - (2021), pp. 1-2. (Intervento presentato al convegno 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM) tenutosi a Cabo San Lucas (Mexico) nel 19-21 July 2021) [10.1109/SUM48717.2021.9505708].
Optimization of Raman amplifiers using machine learning
Rosa Brusin A. M.;Carena A.
2021
Abstract
It has been recently demonstrated that neural networks can learn the complex pump–signal relations in Raman amplifiers. Here we experimentally show how these neural network models are applied to provide highly–accurate Raman amplifier designs and flexible configuration for ultra–wideband optical communication systems.File | Dimensione | Formato | |
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https://hdl.handle.net/11583/2973378