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EnglishWe propose the use of machine-learning based regression model to predict the quality of transmission (QoT) of an un-established lightpath (LP) in an un-seen network prior to its actual deployment, based on telemetry data of already established LPs of different network. This advance prediction of the QoT of un-established LP in an un-seen network has a promising factor not only for the optimal designing of this network but also enables the possibility to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the Generalized Signal-to-Noise Ratio (GSNR) which includes the effect of both Amplified Spontaneous Emission (ASE) noise and Non-Linear Interference (NLI) accumulation. In the response of present simulation scenario, the real field telemetry data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated data set, a machine-learning technique is applied, demonstrating the GSNR prediction of an un-established LP in an unrevealed network with maximum error of 0.53 dB.
QoT Estimation for Light-path Provisioning in Un-Seen Optical Networks using Machine Learning / Khan, Ihtesham; Bilal, Muhammad; Siddiqui, Mehek; Khan, Mahnoor; Ahmad, Arsalan; Shahzad, Muhammad; Curri, Vittorio. - ELETTRONICO. - (2020), pp. 1-4. ((Intervento presentato al convegno International Conference on Transparent Optical Networks (ICTON) tenutosi a Bari, Italy [10.1109/ICTON51198.2020.9203364].
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| Titolo: | QoT Estimation for Light-path Provisioning in Un-Seen Optical Networks using Machine Learning | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | We propose the use of machine-learning based regression model to predict the quality of transmiss...ion (QoT) of an un-established lightpath (LP) in an un-seen network prior to its actual deployment, based on telemetry data of already established LPs of different network. This advance prediction of the QoT of un-established LP in an un-seen network has a promising factor not only for the optimal designing of this network but also enables the possibility to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the Generalized Signal-to-Noise Ratio (GSNR) which includes the effect of both Amplified Spontaneous Emission (ASE) noise and Non-Linear Interference (NLI) accumulation. In the response of present simulation scenario, the real field telemetry data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated data set, a machine-learning technique is applied, demonstrating the GSNR prediction of an un-established LP in an unrevealed network with maximum error of 0.53 dB. | |
| ISBN: | 978-1-7281-8423-4 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| khan2020.pdf | 2a Post-print versione editoriale / Version of Record | Non Pubblico - Accesso privato/ristretto | Administrator Richiedi una copia | |
| ICTON_2020_QoT_Estimation_for_Light_path_Provisioning_in_Un_Seen_OpticalNetworks_using_Machine_Learning.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2847113
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