The quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. TThe quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. Typically, the network administrator configures the network element (NE) working point according to the specified nominal values given by vendors. These operational NEs experienced some variation from the given nominal working point and thus put up uncertainty during their operation, resulting in the introduction of uncertainty in estimating LP QoT. Consequently, a substantial margin is required to avoid any network outage. In this context, to reduce the required margin provisioning, a machine learning (ML) based framework is proposed which is cross-trained using the information retrieved from the fully operational network and utilized to support the QoT estimation unit of an un-used sister network.

Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks / Khan, Ihtesham; Bilal, Muhammad; Curri, Vittorio. - ELETTRONICO. - 797:(2021), pp. 78-87. ((Intervento presentato al convegno 4th International Conference on Telecommunications and Communication Engineering [10.1007/978-981-16-5692-7_9].

Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks

Khan, Ihtesham;Curri, Vittorio
2021

Abstract

The quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. TThe quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. Typically, the network administrator configures the network element (NE) working point according to the specified nominal values given by vendors. These operational NEs experienced some variation from the given nominal working point and thus put up uncertainty during their operation, resulting in the introduction of uncertainty in estimating LP QoT. Consequently, a substantial margin is required to avoid any network outage. In this context, to reduce the required margin provisioning, a machine learning (ML) based framework is proposed which is cross-trained using the information retrieved from the fully operational network and utilized to support the QoT estimation unit of an un-used sister network.
978-981-16-5691-0
978-981-16-5692-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2921479