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Machine Learning Assisted Model of QoT Penalties for Photonics Switching Systems

Ihtesham Khan, Lorenzo Tunesi, Muhammad Umar Masood, Enrico Ghillino, Paolo Bardella, Andrea Carena, and Vittorio Curri

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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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