Supervised Machine Learning Techniques for Quality of Transmission Assessment in Optical Networks

被引：0

作者：

Mata, Javier ^{[1
]}

de Miguel, Ignacio ^{[1
]}

Duran, Ramon J. ^{[1
]}

Carlos Aguado, Juan ^{[1
]}

Merayo, Noemi ^{[1
]}

Ruiz, Lidia ^{[1
]}

Fernandez, Patricia ^{[1
]}

Lorenzo, Ruben M. ^{[1
]}

Abril, Evaristo J. ^{[1
]}

Tomkos, Ioannis ^{[2
]}

机构：

[1] Univ Valladolid, ETSI Telecomunicac, Campus Miguel Delibes, E-47011 Valladolid, Spain

[2] Athens Informat Technol AIT Ctr, Maroussi, Greece

来源：

2018 20TH ANNIVERSARY INTERNATIONAL CONFERENCE ON TRANSPARENT OPTICAL NETWORKS (ICTON) | 2018年

关键词：

machine learning; quality of transmission; lightpath; impairment-aware optical networks;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We propose and compare a number of machine learning models to classify unestablished lightpaths into high or low quality of transmission (QoT) categories in impairment-aware wavelength-routed optical networks. The performance of these models is evaluated in long haul communication networks and compared to previous proposals. Results show that, especially random forests and bagging trees approaches, significantly reduce the required computing time to classify the QoT of a given lightpath, while accuracy remains around 99.9%.

引用

页数：4

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