2024-03-29T00:45:19Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/335522021-06-23T13:30:36Zcom_10324_1191com_10324_931com_10324_894col_10324_1381
Mata, Javier
Miguel Jiménez, Ignacio de
Durán Barroso, Ramón José
Aguado Manzano, Juan Carlos
Merayo Álvarez, Noemí
Ruiz Pérez, Lidia
Fernández Reguero, Patricia
Lorenzo Toledo, Rubén Mateo
Abril Domingo, Evaristo José
2018-12-19T10:01:20Z
2018-12-19T10:01:20Z
2017
2017 IEEE International Conference on Big Data (Big Data), 11-14 Dec. 2017, Boston, USA.
978-1-5386-2715-0
http://uvadoc.uva.es/handle/10324/33552
https://doi.org/10.1109/BigData.2017.8258545
Producción Científica
A novel quality of transmission (QoT) estimator based on support vector machines (SVM) is proposed for classifying optical connections (lightpaths) into high or low quality categories in impairment-aware wavelength-routed optical networks (WRONs). The performance of the SVM-based estimator is evaluated in a long haul communications network and compared to previous semi-analytical and cognitive proposals. Results show that the SVM approach significantly reduces the necessary computing time to estimate the QoT of a given lightpath, critical aspect of design in these networks, and even slightly improves accuracy.
Ministerio de Economía, Industria y Competitividad (Projects (TEC2014-53071-C3-2-P and TEC2015-71932-REDT)
Ministerio de Educación, Cultura y Deporte (Proyect BES-2015-074514)
application/pdf
eng
Institute of Electrical and Electronics Engineers (IEEE)
info:eu-repo/semantics/openAccess
© 2018 IEEE
Redes de transporte óptico
Optical transport networks
A SVM approach for lightpath QoT estimation in optical transport networks
International Conference on Big Data (Big Data) (5º. 2017. Boston)
info:eu-repo/semantics/conferenceObject
https://ieeexplore.ieee.org/document/8258545