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, Ihtesham; Tunesi, Lorenzo; Masood, MUHAMMAD UMAR; Ghillino, Enrico; Bardella, Paolo; Carena, Andrea; Curri, Vittorio. - ELETTRONICO. - (2021). (Intervento presentato al convegno Photonics in Switching and Computing 2021 tenutosi a Washington, DC United States nel 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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PSC-2021-M2A.3.pdf
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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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https://hdl.handle.net/11583/2936652