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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU103323%21RIV15-MSM-26220___
rdf:type
skos:Concept n13:Vysledek
dcterms:description
Linearization techniques are required to reduce the undesirable effects of power amplifier nonlinearities in the wideband power-efficient radio communication systems. The polynomial model and its extension to the memory-polynomial are commonly used to model power amplifier nonlinearities. Determination of the appropriate order of the nonlinearity model is an actual issue to achieve effective methods for subsequent linearization techniques. Having in mind these facts, we compare two adaptive methods employing the determination of the appropriate order of the nonlinearity model. The first algorithm is the adaptive order recursive (ORLS) method, while the proposed solution is based on the recursive least squares method with the sequential updates. In the first stage the models were simulated in MATLAB. Depending on the mean square error estimate we decide on the appropriateness of the nonlinearity order increase. The parameters of the power amplifier models are based on the data experimentally obtained Linearization techniques are required to reduce the undesirable effects of power amplifier nonlinearities in the wideband power-efficient radio communication systems. The polynomial model and its extension to the memory-polynomial are commonly used to model power amplifier nonlinearities. Determination of the appropriate order of the nonlinearity model is an actual issue to achieve effective methods for subsequent linearization techniques. Having in mind these facts, we compare two adaptive methods employing the determination of the appropriate order of the nonlinearity model. The first algorithm is the adaptive order recursive (ORLS) method, while the proposed solution is based on the recursive least squares method with the sequential updates. In the first stage the models were simulated in MATLAB. Depending on the mean square error estimate we decide on the appropriateness of the nonlinearity order increase. The parameters of the power amplifier models are based on the data experimentally obtained
dcterms:title
Adaptive-order Polynomial Methods for Power Amplifier Model Estimation Adaptive-order Polynomial Methods for Power Amplifier Model Estimation
skos:prefLabel
Adaptive-order Polynomial Methods for Power Amplifier Model Estimation Adaptive-order Polynomial Methods for Power Amplifier Model Estimation
skos:notation
RIV/00216305:26220/13:PU103323!RIV15-MSM-26220___
n3:aktivita
n18:S n18:P
n3:aktivity
P(7H11097), P(ED2.1.00/03.0072), P(EE2.3.20.0007), S
n3:dodaniDat
n6:2015
n3:domaciTvurceVysledku
n10:6043372 n10:5119294 n10:3387429
n3:druhVysledku
n14:D
n3:duvernostUdaju
n21:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
59504
n3:idVysledku
RIV/00216305:26220/13:PU103323
n3:jazykVysledku
n12:eng
n3:klicovaSlova
power amplifier, adaptive polynomial model, order recursive least squares, sequential least squares
n3:klicoveSlovo
n9:order%20recursive%20least%20squares n9:sequential%20least%20squares n9:power%20amplifier n9:adaptive%20polynomial%20model
n3:kontrolniKodProRIV
[9EA1FF63623E]
n3:mistoKonaniAkce
Pardubice
n3:mistoVydani
Neuveden
n3:nazevZdroje
Proceedings of 23th Intenational Conference Radioelektronika 2013
n3:obor
n16:JA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n4:ED2.1.00%2F03.0072 n4:7H11097 n4:EE2.3.20.0007
n3:rokUplatneniVysledku
n6:2013
n3:tvurceVysledku
Maršálek, Roman Blumenstein, Jiří Dvořák, Jiří
n3:typAkce
n20:WRD
n3:wos
000326877900068
n3:zahajeniAkce
2013-04-16+02:00
s:numberOfPages
4
n17:hasPublisher
Neuveden
n8:isbn
978-1-4673-5517-9
n11:organizacniJednotka
26220