Archivio Istituzionale della RicercaTwo artificial neural network (ANN) models are presented to predict power profiles over C+L–band in presence of inter-channel stimulated Raman scattering (ISRS) and to support non-linear interference (NLI) modeling. High prediction accuracy is obtained with maximum errors ≤ 0.1 dB over thousands different partial loads.
Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering / Rosa Brusin, A. M.; Zefreh, M. Ranjbar; Poggiolini, P.; Piciaccia, S.; Forghieri, F.; Carena, A.. - ELETTRONICO. - (2021), pp. 1-4. (Intervento presentato al convegno 2021 European Conference on Optical Communication (ECOC) tenutosi a Bordeaux (France) nel 13-16 Sept. 2021) [10.1109/ECOC52684.2021.9605807].
Machine Learning for Power Profiles Prediction in Presence of Inter-channel Stimulated Raman Scattering
Rosa Brusin, A. M.;Poggiolini, P.;Carena, A.
2021
Abstract
Two artificial neural network (ANN) models are presented to predict power profiles over C+L–band in presence of inter-channel stimulated Raman scattering (ISRS) and to support non-linear interference (NLI) modeling. High prediction accuracy is obtained with maximum errors ≤ 0.1 dB over thousands different partial loads.
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Machine_Learning_for_Power_Profiles_Prediction_in_Presence_of_Inter-channel_Stimulated_Raman_Scattering.pdf
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https://hdl.handle.net/11583/2947716