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dc.contributor.authorRondeau, Thomas Warrenen_US
dc.date.accessioned2014-03-14T20:17:06Z
dc.date.available2014-03-14T20:17:06Z
dc.date.issued2007-09-20en_US
dc.identifier.otheretd-10052007-081332en_US
dc.identifier.urihttp://hdl.handle.net/10919/29199
dc.description.abstractThis dissertation provides the theory, design, and implementation of a cognitive engine, the enabling technology of cognitive radio. A cognitive radio is a wireless communications device capable of sensing the environment and making decisions on how to use the available radio resources to enable communications with a certain quality of service. The cognitive engine, the intelligent system behind the cognitive radio, combines sensing, learning, and optimization algorithms to control and adapt the radio system from the physical layer and up the communication stack. The cognitive engine presented here provides a general framework to build and test cognitive engine algorithms and components such as sensing technology, optimization routines, and learning algorithms. The cognitive engine platform allows easy development of new components and algorithms to enhance the cognitive radio capabilities. It is shown in this dissertation that the platform can easily be used on a simulation system and then moved to a real radio system. The dissertation includes discussions of both theory and implementation of the cognitive engine. The need for and implementation of all of the cognitive components is strongly featured as well as the specific issues related to the development of algorithms for cognitive radio behavior. The discussion of the theory focuses largely on developing the optimization space to intelligently and successfully design waveforms for particular quality of service needs under given environmental conditions. The analysis develops the problem into a multi-objective optimization process to optimize and trade-of of services between objectives that measure performance, such as bit error rate, data rate, and power consumption. The discussion of the multi-objective optimization provides the foundation for the analysis of radio systems in this respect, and through this, methods and considerations for future developments. The theoretical work also investigates the use of learning to enhance the cognitive engine's capabilities through feed-back, learning, and knowledge representation. The results of this work include the analysis of cognitive radio design and implementation and the functional cognitive engine that is shown to work in both simulation and on-line experiments. Throughout, examples and explanations of building and interfacing cognitive components to the cognitive engine enable the use and extension of the cognitive engine for future work.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartrondeau-dissertation.pdfen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectcase-based reasoningen_US
dc.subjectgenetic algorithmsen_US
dc.subjectartificial intelligenceen_US
dc.subjectwireless communicationsen_US
dc.subjectsoftware defined radioen_US
dc.subjectSDRen_US
dc.subjectmulti-objective optimizationen_US
dc.subjectcase-based decision theoryen_US
dc.subjectcognitive radioen_US
dc.titleApplication of Artificial Intelligence to Wireless Communicationsen_US
dc.typeDissertationen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairBostian, Charles W.en_US
dc.contributor.committeememberMacKenzie, Allen B.en_US
dc.contributor.committeememberMidkiff, Scott F.en_US
dc.contributor.committeememberBall, Sheryl B.en_US
dc.contributor.committeememberReed, Jeffrey Hughen_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-10052007-081332/en_US
dc.date.sdate2007-10-05en_US
dc.date.rdate2007-10-10
dc.date.adate2007-10-10en_US


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