We investigate a Machine Learning regression model for Optical Signal-toNoise Ratio (OSNR) distribution estimation of unestablished lightpaths. The regressor exposes the estimation uncertainty and how close to a threshold each lightpath resides.
Machine Learning Regression vs. Classification for QoT Estimation of Unestablished Lightpaths / Ibrahimi, Memedhe; Abdollahi, Hatef; Giusti, Alessandro; Rottondi, Cristina; Tornatore, Massimo. - ELETTRONICO. - (2020), p. NeM3B.1. ((Intervento presentato al convegno OSA Advanced Photonics Congress (AP) 2020 tenutosi a Washington DC, USA nel 13–16 July 2020 [10.1364/NETWORKS.2020.NeM3B.1].
Titolo: | Machine Learning Regression vs. Classification for QoT Estimation of Unestablished Lightpaths | |
Autori: | ||
Data di pubblicazione: | 2020 | |
Abstract: | We investigate a Machine Learning regression model for Optical Signal-toNoise Ratio (OSNR) distri...bution estimation of unestablished lightpaths. The regressor exposes the estimation uncertainty and how close to a threshold each lightpath resides. | |
ISBN: | 978-1-943580-79-8 | |
Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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APC_invited_ML_Regression__Submitted_.pdf | articolo principale | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
Rottondi-Machine.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia |
http://hdl.handle.net/11583/2855059