2024-03-29T14:26:01Zhttp://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/335522021-06-23T13:30:36Zcom_10324_1191com_10324_931com_10324_894col_10324_1381
Mata, Javier
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500
Miguel Jiménez, Ignacio de
7113cd0f4e4a295a
500
0000-0002-1084-1159
Durán Barroso, Ramón José
72e6ffac37e460e9
500
0000-0003-1423-1646
Aguado Manzano, Juan Carlos
1d6043b2f88c458f
500
0000-0002-2495-0313
Merayo Álvarez, Noemí
fe9385c725979c89
500
0000-0002-6920-0778
Ruiz Pérez, Lidia
1dd41dc3e751c9e1
500
0000-0001-6241-5998
Fernández Reguero, Patricia
99d11cf6950c28bc
500
0000-0001-5520-0948
Lorenzo Toledo, Rubén Mateo
2d213aefd2a0fae2
500
0000-0001-8729-3085
Abril Domingo, Evaristo José
e4e2ea80f96dd812
500
0000-0003-4164-2467
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
ORIGINAL
SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf
SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf
application/pdf
625435
https://uvadoc.uva.es/bitstream/10324/33552/1/SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf
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LICENSE
license.txt
license.txt
text/plain
4250
https://uvadoc.uva.es/bitstream/10324/33552/2/license.txt
909e634ba52becf192e4e9b4bcde7863
MD5
2
THUMBNAIL
SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf.jpg
SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf.jpg
IM Thumbnail
image/jpeg
1005
https://uvadoc.uva.es/bitstream/10324/33552/3/SVM-Approach-Lightpath-2017_IEEEBigData_JMata_postprint.pdf.jpg
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10324/33552
oai:uvadoc.uva.es:10324/33552
2021-06-23 15:30:36.453
UVaDOC
repositorio@uva.es
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