The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters

Matteo Lonardi; Jelena Pesic; Thierry Zami; Nicola Rossi

doi:10.1364/NETWORKS.2020.NeM3B.2

OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF)
OSA Technical Digest (Optica Publishing Group, 2020),
paper NeM3B.2
•https://doi.org/10.1364/NETWORKS.2020.NeM3B.2

The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters

Matteo Lonardi, Jelena Pesic, Thierry Zami, and Nicola Rossi

Photonic Networks and Devices 2020

Washington, DC United States
13–16 July 2020
ISBN: 978-1-943580-79-8

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Machine Learning for Optical Networks I (NeM3B)

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M. Lonardi, J. Pesic, T. Zami, and N. Rossi, "The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters," in OSA Advanced Photonics Congress (AP) 2020 (IPR, NP, NOMA, Networks, PVLED, PSC, SPPCom, SOF), L. Caspani, A. Tauke-Pedretti, F. Leo, and B. Yang, eds., OSA Technical Digest (Optica Publishing Group, 2020), paper NeM3B.2.

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Abstract

We compare the resilience to network parameters uncertainty between a GN-model based QoT tool and ML-based ones. We study the mean square error of the estimated G-OSNR and a novel metric based on the overestimation probability.

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