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.

Machine Learning Assisted Model of QoT Penalties for Photonics Switching System / 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 System

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.
978-1-943580-99-6
File in questo prodotto:
File Dimensione Formato  
PSC-2021-M2A.3.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 352.73 kB
Formato Adobe PDF
352.73 kB Adobe PDF   Visualizza/Apri   Richiedi una copia
C_IPC_Machine_Learning_Aided_QoT_Assessment_of_Photonics_Switching_System_in_Software_defined_Optical_Network_29042021 (2).pdf

Open Access dal 30/09/2022

Tipologia: 2. Post-print / Author's Accepted Manuscript
Licenza: PUBBLICO - Tutti i diritti riservati
Dimensione 273.59 kB
Formato Adobe PDF
273.59 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2936652