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Machine Learning Assisted Model of QoT Penalties for Photonics Switching Systems

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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.

© 2021 The Author(s)

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More Like This
Machine learning Assisted Accurate Estimation of QoT Impairments of Photonics Switching System on 400ZR

Ihtesham Khan, Lorenzo Tunesi, M Umar Masood, Enrico Ghillino, Paolo Bardella, Andrea Carena, and Vittorio Curri
T2B.2 Asia Communications and Photonics Conference (ACPC) 2021

Machine Learning Assisted Management of Photonic Switching Systems

Ihtesham Khan, M Umar Masood, Lorenzo Tunesi, Paolo Bardella, Enrico Ghillino, Andrea Carena, and Vittorio Curri
JTu3A.32 CLEO: Applications and Technology (CLEO_AT) 2021

Autonomous Control Model for C+L Multi-band Photonic Switch System using Machine Learning

Ihtesham Khan, Lorenzo Tunesi, M Umar Masood, Enrico Ghillino, Paolo Bardella, Andrea Carena, and Vittorio Curri
T4A.163 Asia Communications and Photonics Conference (ACPC) 2021

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