Mostra el registre d'ítem simple
Machine learning techniques applied to multiband spectrum sensing in cognitive radios
dc.contributor.author | Molina Tenorio, Yanqueleth |
dc.contributor.author | Prieto Guerrero, Alfonso |
dc.contributor.author | Aguilar Gomez, Rafael |
dc.contributor.author | Ruiz Boqué, Sílvia |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2020-01-24T13:56:58Z |
dc.date.available | 2020-01-24T13:56:58Z |
dc.date.issued | 2019-10-30 |
dc.identifier.citation | Molina, Y. [et al.]. Machine learning techniques applied to multiband spectrum sensing in cognitive radios. "Sensors", 30 Octubre 2019, vol. 19, núm. 21, p. 1-22. |
dc.identifier.issn | 1424-8220 |
dc.identifier.uri | http://hdl.handle.net/2117/175675 |
dc.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). |
dc.description.abstract | 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 signals |
dc.format.extent | 22 p. |
dc.language.iso | eng |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) |
dc.rights | Attribution 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament del senyal en les telecomunicacions |
dc.subject.lcsh | Software radio |
dc.subject.lcsh | Signal processing |
dc.subject.other | Cognitive radios |
dc.subject.other | Multiband spectrum sensing |
dc.subject.other | Machine learning |
dc.subject.other | Neural networks |
dc.title | Machine learning techniques applied to multiband spectrum sensing in cognitive radios |
dc.type | Article |
dc.subject.lemac | Ràdio definida per programari |
dc.subject.lemac | Tractament del senyal |
dc.contributor.group | Universitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils |
dc.identifier.doi | 10.3390/s19214715 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.mdpi.com/1424-8220/19/21/4715 |
dc.rights.access | Open Access |
local.identifier.drac | 25967290 |
dc.description.version | Postprint (published version) |
local.citation.author | Molina, Y.; Prieto, A.; Aguilar, R.; Ruiz, S. |
local.citation.publicationName | Sensors |
local.citation.volume | 19 |
local.citation.number | 21 |
local.citation.startingPage | 1 |
local.citation.endingPage | 22 |
Fitxers d'aquest items
Aquest ítem apareix a les col·leccions següents
-
Articles de revista [2.526]
-
Articles de revista [100]