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dc.contributor.advisorWoerner, Brianen_US
dc.contributor.advisorSweeney, Dennis G.en_US
dc.contributor.advisorMartin, Thomas L.en_US
dc.contributor.advisorMorgan, Georgeen_US
dc.contributor.advisorMidkiff, Scott F.en_US
dc.contributor.advisorBostian, Charles W.en_US
dc.contributor.authorRieser, Christian Jamesen_US
dc.date.accessioned2011-08-22T19:09:13Z
dc.date.available2011-08-22T19:09:13Z
dc.date.issued2004-09-29en_US
dc.identifier.otheretd-10142004-023653en_US
dc.identifier.urihttp://hdl.handle.net/10919/11283
dc.description.abstractThis research focuses on developing a cognitive radio that could operate reliably in unforeseen communications environments like those faced by the disaster and emergency response communities. Cognitive radios may also offer the potential to open up secondary or complimentary spectrum markets, effectively easing the perceived spectrum crunch while providing new competitive wireless services to the consumer. A structure and process for embedding cognition in a radio is presented, including discussion of how the mechanism was derived from the human learning process and mapped to a mathematical formalism called the BioCR. Results from the implementation and testing of the model in a hardware test bed and simulation test bench are presented, with a focus on rapidly deployable disaster communications. Research contributions include developing a biologically inspired model of cognition in a radio architecture, proposing that genetic algorithm operations could be used to realize this model, developing an algorithmic framework to realize the cognition mechanism, developing a cognitive radio simulation toolset for evaluating the behavior the cognitive engine, and using this toolset to analyze the cognitive engineà ­s performance in different operational scenarios. Specifically, this research proposes and details how the chaotic meta-knowledge search, optimization, and machine learning properties of distributed genetic algorithm operations could be used to map this model to a computable mathematical framework in conjunction with dynamic multi-stage distributed memories. The system formalism is contrasted with existing cognitive radio approaches, including traditionally brittle artificial intelligence approaches. The cognitive engine architecture and algorithmic framework is developed and introduced, including the Wireless Channel Genetic Algorithm (WCGA), Wireless System Genetic Algorithm (WSGA), and Cognitive System Monitor (CSM). Experimental results show that the cognitive engine finds the best tradeoff between a host radio's operational parameters in changing wireless conditions, while the baseline adaptive controller only increases or decreases its data rate based on a threshold, often wasting usable bandwidth or excess power when it is not needed due its inability to learn. Limitations of this approach include some situations where the engine did not respond properly due to sensitivity in algorithm parameters, exhibiting ghosting of answers, bouncing back and forth between solutions. Future research could be pursued to probe the limits of the engineà ­s operation and investigate opportunities for improvement, including how best to configure the genetic algorithms and engine mathematics to avoid engine solution errors. Future research also could include extending the cognitive engine to a cognitive radio network and investigating implications for secure communications.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.relation.haspartCJRieserVTPhDEEDissertation101804.pdfen_US
dc.rightsThe authors of the theses and dissertations are the copyright owners. Virginia Tech's Digital Library and Archives has their permission to store and provide access to these works.en_US
dc.source.urihttp://scholar.lib.vt.edu/theses/available/etd-10142004-023653en_US
dc.subjectDistributeden_US
dc.subjectCognitive Radioen_US
dc.subjectBiologically Inspireden_US
dc.subjectRapidly Deployableen_US
dc.subjectCommunicationsen_US
dc.subjectNetworkingen_US
dc.subjectSecureen_US
dc.subjectRobusten_US
dc.subjectGenetic Algorithmsen_US
dc.subjectDisaster Responseen_US
dc.titleBiologically Inspired Cognitive Radio Engine Model Utilizing Distributed Genetic Algorithms for Secure and Robust Wireless Communications and Networkingen_US
dc.typeOther - Dissertationen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreePHDen_US


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