Publication:
Online detection and SNR estimation in cooperative spectrum sensing

Simple item page
dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorPérez, Jesúses
dc.contributor.authorVía, Javieres
dc.contributor.authorVielva, Luises
dc.contributor.authorRamírez García, Davides
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2022-04-19T09:11:15Z
dc.date.available2022-04-19T09:11:15Z
dc.date.issued2022-04-01
dc.description.abstractCooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are time-varying. Simulation results show that its detection performance is close to the LRT in realistic conditions.en
dc.description.sponsorshipThis work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with European Commission [European Regional Development Fund (ERDF)], under Grant TEC2017-86921-C2-1-R and Grant TEC2017-86921-C2-2-R (CAIMAN) and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM).en
dc.description.statusPublicadoes
dc.format.extent12
dc.identifier.bibliographicCitationIEEE Transactions on Wireless Communications, (2022), 21(4), pp.: 2521-2533.en
dc.identifier.doihttps://doi.org/10.1109/TWC.2021.3113089
dc.identifier.issn1536-1276
dc.identifier.publicationfirstpage2521
dc.identifier.publicationissue4
dc.identifier.publicationlastpage2533
dc.identifier.publicationtitleIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONSen
dc.identifier.publicationvolume21
dc.identifier.urihttps://hdl.handle.net/10016/34567
dc.identifier.uxxiAR/0000028377
dc.language.isoengen
dc.publisherIEEEen
dc.relation.projectIDGobierno de España. TEC2017-86921-C2-2-Res
dc.relation.projectIDGobierno de España.TEC2017-86921-C2-1-R/CAIMANes
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705es
dc.rights© 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherCooperative spectrum sensingen
dc.subject.otherEnergy detectionen
dc.subject.otherExpectation-maximization (EM) algorithmen
dc.subject.otherMaximum likelihooden
dc.subject.otherProbabilistic mixture modelsen
dc.titleOnline detection and SNR estimation in cooperative spectrum sensingen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
online_IEEE-TWC_2022_ps.pdf
Size:
487.89 KB
Format:
Adobe Portable Document Format
Download
Collections
DTSC - GTSA - Artículos de Revistas