Archivio Istituzionale della RicercaWe propose a data-driven approach to provide augmented knowledge of the QoT impairments of photonic switches in a software-defined networking context. The pro- posed framework is topological and technological agnostic and can be operated in real- time.
Machine Learning Assisted Model of QoT Penalties for Photonics Switching Systems / Khan, I., Tunesi, L., Masood, M.U., Ghillino, E., Bardella, P., Carena, A., Curri, V.. - ELETTRONICO. - (2021). (Photonics in Switching and Computing 2021 Washington, DC United States 27–29 September 2021).
Machine Learning Assisted Model of QoT Penalties for Photonics Switching Systems
Ihtesham Khan;Lorenzo Tunesi;Muhammad Umar Masood;Paolo Bardella;Andrea Carena;Vittorio Curri
2021
Abstract
We propose a data-driven approach to provide augmented knowledge of the QoT impairments of photonic switches in a software-defined networking context. The pro- posed framework is topological and technological agnostic and can be operated in real- time.
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C_IPC_Machine_Learning_Aided_QoT_Assessment_of_Photonics_Switching_System_in_Software_defined_Optical_Network_29042021 (2).pdf
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/11583/2936652