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dc.contributor.authorSolovyeva, E. B.
dc.date.accessioned2016-01-06T08:08:13Z
dc.date.available2016-01-06T08:08:13Z
dc.date.issued2015-12cs
dc.identifier.citationRadioengineering. 2015 vol. 24, č. 4, s. 1071-1076. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/51908
dc.description.abstractIn this paper, a cascade structure of nonlinear digital predistorter (DPD) synthesized by the direct learning adaptive algorithm is represented. DPD is used for linearization of power amplifier (PA) characteristic, namely for compensation of PA nonlinear distortion. Blocks of the cascade DPD are described by different models: the functional link artificial neural network (FLANN), the polynomial perceptron network (PPN) and the radially pruned Volterra model (RPVM). At synthesis of the cascade DPD there is possibility to overcome the ill conditionality problem due to reducing the dimension of DPD nonlinear operator approximation. Results of compensating nonlinear distortion in Wiener–Hammerstein model of PA at the GSM–signal with four carriers are shown. The highest accuracy of PA linearization is produced by the cascade DPD containing PPN and RPVM.en
dc.formattextcs
dc.format.extent1071-1076cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2015/15_04_1071_1076.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectDigital predistorteren
dc.subjectnonlinear compensationen
dc.subjectnonlinear distortionen
dc.subjectpower amplifieren
dc.subjectneural networksen
dc.titleCascade Structure of Digital Predistorter for Power Amplifier Linearizationen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue4cs
dc.coverage.volume24cs
dc.identifier.doi10.13164/re.2015.1071en
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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Except where otherwise noted, this item's license is described as Creative Commons Attribution 3.0 Unported License