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dc.contributor.authorHuang, Ching-Chunen_US
dc.contributor.authorWang, Li-Chunen_US
dc.date.accessioned2017-04-21T06:55:45Z-
dc.date.available2017-04-21T06:55:45Z-
dc.date.issued2012-04en_US
dc.identifier.issn2162-2337en_US
dc.identifier.urihttp://dx.doi.org/10.1109/WCL.2012.010912.110136en_US
dc.identifier.urihttp://hdl.handle.net/11536/133700-
dc.description.abstractIn this paper, a dynamic sampling rate adjustment scheme is proposed for compressive spectrum sensing in cognitive radio network. Nowadays, compressive sensing (CS) has been proposed with a revolutionary idea to sense the sparse spectrum by using a lower sampling rate. However, many methods for compressive spectrum sensing assume that the sparse level is static and a fixed compressive sampling rate is applied over time. To adapt to time-varying sparse levels and adjust the sampling rate, we proposed to model sparse levels as a dynamic system and treat the dynamic rate selection as a tracking problem. By introducing the Sequential Monte Carlo (SMC) algorithm into a distributed compressive spectrum sensing framework, we could not only track the optimal sampling rate but determine the unoccupied channels accurately in a unified method.en_US
dc.language.isoen_USen_US
dc.subjectCognitive radioen_US
dc.subjectdynamic systemen_US
dc.subjectsequential Monte Carloen_US
dc.subjectcompressive spectrum sensingen_US
dc.titleDynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Networken_US
dc.identifier.doi10.1109/WCL.2012.010912.110136en_US
dc.identifier.journalIEEE WIRELESS COMMUNICATIONS LETTERSen_US
dc.citation.volume1en_US
dc.citation.issue2en_US
dc.citation.spage57en_US
dc.citation.epage60en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000209696000003en_US
Appears in Collections:Articles