Archivio Istituzionale della RicercaA differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.
Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling / Yankov, Metodi P.; Zibar, Darko; Carena, Andrea; Da Ros, Francesco. - ELETTRONICO. - (2022), pp. -3. (Intervento presentato al convegno 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC) tenutosi a Toyama, Japan nel 3-6 July 2022) [10.23919/OECC/PSC53152.2022.9849857].
Forward Raman Amplifier Optimization Using Machine Learning-aided Physical Modeling
Carena, Andrea;
2022
Abstract
A differentiable nonlinear interpolation function learns the Raman gain efficiency and enables gradient-descent-based optimization of a Raman amplifier with arbitrary number of pumps. Example is given for unrepeatered links with a remote pumping stage.
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Forward_Raman_Amplifier_Optimization_Using_Machine_Learning-aided_Physical_Modeling.pdf
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OECCPSC2022_HybridAmp_v2-finaldraft.pdf
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https://hdl.handle.net/11583/2984824