Scalable Parameter Management using Casebased Reasoning for Cognitive Radio Applications

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dc.contributor.authorAli, Daniel Rayen
dc.contributor.committeechairPark, Jung-Min Jerryen
dc.contributor.committeememberAmanna, Ashwin E.en
dc.contributor.committeememberClancy, Thomas Charles IIIen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:34:57Zen
dc.date.adate2012-05-30en
dc.date.available2014-03-14T20:34:57Zen
dc.date.issued2012-05-01en
dc.date.rdate2012-05-30en
dc.date.sdate2012-05-03en
dc.description.abstractCognitive radios have applied various forms of artificial intelligence (AI) to wireless systems in order to solve the complex problems presented by proper link management, network traffic balance, and system efficiency. Casebased reasoning (CBR) has seen attention as a prospective avenue for storing and organizing past information in order to allow the cognitive engine to learn from previous experience. CBR uses past information and observed outcomes to form empirical relationships that may be difficult to model apriori. As wireless systems become more complex and more tightly time constrained, scalability becomes an apparent concern to store large amounts of information over multiple dimensions. This thesis presents a renewed look at an abstract application of CBR to CR. By appropriately designing a case structure with useful information both to the cognitive entity as well as the underlying similarity relationships between cases, an accurate problem description can be developed and indexed. By separating the components of a case from the parameters that are meaningful to similarity, the situation can be quickly identified and queried given proper design. A data structure with this in mind is presented that orders cases in terms of general placement in Euclidean space, but does not require the discrete calculation of distance between the query case and all cases stored. By grouping possible similarity dimension values into distinct partitions called "similarity buckets", a data structure is developed with constant (O(1)) access time, which is an improvement of several orders of magnitude over traditional linear approaches (O(n)).en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05032012-140854en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05032012-140854/en
dc.identifier.urihttp://hdl.handle.net/10919/32167en
dc.publisherVirginia Techen
dc.relation.haspartAli_DR_T.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCognitive radio networksen
dc.subjectCasebased Reasoningen
dc.subjectData Structureen
dc.subjectAccess Timeen
dc.titleScalable Parameter Management using Casebased Reasoning for Cognitive Radio Applicationsen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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