Signal and Noise Modeling of Microwave Transistors Using Characteristic Support Vector-based Sparse Regression

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dc.contributor.authorGunes, Filiz
dc.contributor.authorBelen, Mehmet Ali
dc.contributor.authorMahouti, Peyman
dc.contributor.authorDemirel, Salih
dc.coverage.issue3cs
dc.coverage.volume25cs
dc.date.accessioned2016-09-19T08:40:27Z
dc.date.available2016-09-19T08:40:27Z
dc.date.issued2016-09cs
dc.description.abstractIn this work, an accurate and reliable S- and Noise (N) - parameter black-box models for a microwave transistor are constructed based on the sparse regression using the Support Vector Regression Machine (SVRM) as a nonlinear extrapolator trained by the data measured at the typical bias currents belonging to only a single bias voltage in the middle region of the device operation domain of (VDS/VCE, IDS/IC, f). SVRMs are novel learning machines combining the convex optimization theory with the generalization and therefore they guarantee the global minimum and the sparse solution which can be expressed as a continuous function of the input variables using a subset of the training data so called Support Vector (SV)s. Thus magnitude and phase of each S- or N- parameter are expressed analytically valid in the wide range of device operation domain in terms of the Characteristic SVs obtained from the substantially reduced measured data. The proposed method is implemented successfully to modelling of the two LNA transistors ATF-551M4 and VMMK 1225 with their large operation domains and the comparative error-metric analysis is given in details with the counterpart method Generalized Regression Neural Network GRNN. It can be concluded that the Characteristic Support Vector based-sparse regression is an accurate and reliable method for the black-box signal and noise modelling of microwave transistors that extrapolates a reduced amount of training data consisting of the S- and N- data measured at the typical bias currents belonging to only a middle bias voltage in the form of continuous functions into the wide operation range.en
dc.formattextcs
dc.format.extent490-499cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2016 vol. 25, č. 3, s. 490-499. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2016.0490en
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/63194
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2016/16_03_0490_0499.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectScattering S-parametersen
dc.subjectnoise N- parametersen
dc.subjectSupport Vector Regression Machineen
dc.subjectSVRMen
dc.subjectGeneralized Regression Neural Networken
dc.subjectGRNNen
dc.titleSignal and Noise Modeling of Microwave Transistors Using Characteristic Support Vector-based Sparse Regressionen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
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