Cognitive radio engine parametric optimization utilizing Taguchi analysis

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dc.contributor.authorAmanna, Ashwin E.en
dc.contributor.authorAli, Danielen
dc.contributor.authorGadhiok, Maniken
dc.contributor.authorPrice, Matthewen
dc.contributor.authorReed, Jeffrey H.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2012-08-24T10:49:01Zen
dc.date.available2012-08-24T10:49:01Zen
dc.date.issued2012-01-09en
dc.date.updated2012-08-24T10:49:02Zen
dc.description.abstractCognitive radio (CR) engines often contain multiple system parameters that require careful tuning to obtain favorable overall performance. This aspect is a crucial element in the design cycle yet is often addressed with ad hoc methods. Efficient methodologies are required in order to make the best use of limited manpower, resources, and time. Statistical methods for approaching parameter tuning exist that provide formalized processes to avoid inefficient ad hoc methods. These methods also apply toward overall system performance testing. This article explores the use of the Taguchi method and orthogonal testing arrays as a tool for identifying favorable genetic algorithm (GA) parameter settings utilized within a hybrid case base reasoning/genetic algorithm CR engine realized in simulation. This method utilizes a small number of test cases compared to traditional design of experiments that rely on full factorial combinations of system parameters. Background on the Taguchi method, its drawbacks and limitations, past efforts in GA parameter tuning, and the use of GA within CR are overviewed. Multiple CR metrics are aggregated into a single figure-of-merit for quantification of performance. Desirability functions are utilized as a tool for identifying ideal settings from multiple responses. Kiviat graphs visualize overall CR performance. The Taguchi method analysis yields a predicted best combination of GA parameters from nine test cases. A confirmation experiment utilizing the predicted best settings is compared against the predicted mean, and desirability. Results show that the predicted performance falls within 1.5% of the confirmation experiment based on 9 test cases as opposed to the 81 test cases required for a full factorial design of experiments analysis.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationEURASIP Journal on Wireless Communications and Networking. 2012 Jan 09;2012(1):5en
dc.identifier.doihttps://doi.org/10.1186/1687-1499-2012-5en
dc.identifier.urihttp://hdl.handle.net/10919/18775en
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderAshwin E Amanna et al.; licensee BioMed Central Ltd.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleCognitive radio engine parametric optimization utilizing Taguchi analysisen
dc.title.serialEURASIP Journal on Wireless Communications and Networkingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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