Hidden Markov model based spectrum sensing for cognitive radio

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dc.contributor.advisorMark, Brian L.
dc.contributor.advisorEphraim, Yariv
dc.contributor.authorNguyen, Thao Tran Nhu
dc.creatorNguyen, Thao Tran Nhu
dc.date.accessioned2013-08-09T15:40:06Z
dc.date.available2013-08-09T15:40:06Z
dc.date.issued2013
dc.description.abstractCognitive radio is an emerging technology for sensing and opportunistic spectrum access in wireless communication networks. It allows a secondary user to detect under-utilized spectrum of a primary user and to dynamically access the spectrum without causing harmful interference to the primary user. A number of spectrum sensing techniques has been proposed in the literature to identify the state of the primary user in the temporal domain. However, most of these techniques make instantaneous decisions based on current measurement received at the cognitive radio, and they do not consider the transmission pattern of the primary user which can be acquired from past measurements. Thus, sensing performance can be improved by incorporating measurement history into the sensing decision. Moreover, using all available data may enable prediction of the primary user activity, which will allow a cognitive radio to better plan for its spectrum usage.
dc.format.extent92 pages
dc.identifier.urihttps://hdl.handle.net/1920/8288
dc.language.isoen
dc.rightsCopyright 2013 Thao Tran Nhu Nguyen
dc.subjectEngineering
dc.subjectBaum algorithm
dc.subjectBivariate Markov chain
dc.subjectCognitive radio
dc.subjectHidden Markov model
dc.subjectSpectrum Sensing
dc.titleHidden Markov model based spectrum sensing for cognitive radio
dc.typeDissertation
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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