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Communication Dans Un Congrès Année : 2020

An overview on machine learning-based solutions to improve lightpath QoT estimation

Résumé

Estimating lightpath Quality of Transmission (QoT) is crucial in network design and service provisioning. Recent studies have turned to Machine Learning (ML) techniques to improve the accuracy of QoT estimation. We distinguish two categories of solutions: the first category aims to build ML-based QoT estimation models that outperform the analytical model while the second category uses ML algorithms to reduce uncertainties on parameters provided as input to analytical model. In this overview, we describe the solutions in each category and discuss their practical feasibility and added benefit for operational networks.
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Dates et versions

hal-03161225 , version 1 (05-03-2021)

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Citer

Reda Ayassi, Ahmed Triki, M Laye, Noel Crespi, Roberto Minerva, et al.. An overview on machine learning-based solutions to improve lightpath QoT estimation. ICTON 2020: 22nd International Conference on Transparent Optical Networks, Jul 2020, Bari (online), Italy. pp.1-4, ⟨10.1109/ICTON51198.2020.9203755⟩. ⟨hal-03161225⟩
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