Machine learning techniques applied to multiband spectrum sensing in cognitive radios

This research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).

Detalles Bibliográficos
Autores: Molina Tenorio, Yanqueleth, Prieto Guerrero, Alfonso, Aguilar Gomez, Rafael, Ruiz Boqué, Sílvia|||0000-0002-4672-2493
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/175675
Acceso en línea:https://hdl.handle.net/2117/175675
https://dx.doi.org/10.3390/s19214715
Access Level:acceso abierto
Palabra clave:Software radio
Signal processing
Cognitive radios
Multiband spectrum sensing
Machine learning
Neural networks
Ràdio definida per programari
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
id ES_c5ba2fbda796091d58cf2b8be4c00355
oai_identifier_str oai:upcommons.upc.edu:2117/175675
network_acronym_str ES
network_name_str España
repository_id_str
spelling Machine learning techniques applied to multiband spectrum sensing in cognitive radiosMolina Tenorio, YanquelethPrieto Guerrero, AlfonsoAguilar Gomez, RafaelRuiz Boqué, Sílvia|||0000-0002-4672-2493Software radioSignal processingCognitive radiosMultiband spectrum sensingMachine learningNeural networksRàdio definida per programariTractament del senyalÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacionsThis research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signalsPeer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20192019-10-3020202020-01-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/175675https://dx.doi.org/10.3390/s19214715reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 3.0 Spainhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/1756752026-05-27T15:37:01Z
dc.title.none.fl_str_mv Machine learning techniques applied to multiband spectrum sensing in cognitive radios
title Machine learning techniques applied to multiband spectrum sensing in cognitive radios
spellingShingle Machine learning techniques applied to multiband spectrum sensing in cognitive radios
Molina Tenorio, Yanqueleth
Software radio
Signal processing
Cognitive radios
Multiband spectrum sensing
Machine learning
Neural networks
Ràdio definida per programari
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
title_short Machine learning techniques applied to multiband spectrum sensing in cognitive radios
title_full Machine learning techniques applied to multiband spectrum sensing in cognitive radios
title_fullStr Machine learning techniques applied to multiband spectrum sensing in cognitive radios
title_full_unstemmed Machine learning techniques applied to multiband spectrum sensing in cognitive radios
title_sort Machine learning techniques applied to multiband spectrum sensing in cognitive radios
dc.creator.none.fl_str_mv Molina Tenorio, Yanqueleth
Prieto Guerrero, Alfonso
Aguilar Gomez, Rafael
Ruiz Boqué, Sílvia|||0000-0002-4672-2493
author Molina Tenorio, Yanqueleth
author_facet Molina Tenorio, Yanqueleth
Prieto Guerrero, Alfonso
Aguilar Gomez, Rafael
Ruiz Boqué, Sílvia|||0000-0002-4672-2493
author_role author
author2 Prieto Guerrero, Alfonso
Aguilar Gomez, Rafael
Ruiz Boqué, Sílvia|||0000-0002-4672-2493
author2_role author
author
author
dc.subject.none.fl_str_mv Software radio
Signal processing
Cognitive radios
Multiband spectrum sensing
Machine learning
Neural networks
Ràdio definida per programari
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
topic Software radio
Signal processing
Cognitive radios
Multiband spectrum sensing
Machine learning
Neural networks
Ràdio definida per programari
Tractament del senyal
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions
description This research received funding of the Mexican National Council of Science and Technology (CONACYT), Grant (no. 490180). Also, this work was supported by the Program for Professional Development Teacher (PRODEP).
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-10-30
2020
2020-01-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/175675
https://dx.doi.org/10.3390/s19214715
url https://hdl.handle.net/2117/175675
https://dx.doi.org/10.3390/s19214715
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 3.0 Spain
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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