Juan A. Becerra, Daniel Herrera, M. J. Madero-Ayora, C. Crespo-Cadenas
{"title":"Sparse Model Selection of Digital Predistorters Using Subspace Pursuit","authors":"Juan A. Becerra, Daniel Herrera, M. J. Madero-Ayora, C. Crespo-Cadenas","doi":"10.23919/EUMIC.2018.8539910","DOIUrl":null,"url":null,"abstract":"This communication presents a new technique for the digital predistortion of power amplifiers (PAs) based on sparse behavioral models. The subspace pursuit algorithm formulation is adapted to work in the nonlinear series framework. Experiments driven on a test bench based on a GaN PA driven by a 15-MHz filter bank multicarrier (FBM C) signal were conducted in order to validate the algorithm. Experimental results in a digital predistortion scenario and the comparison with the orthogonal matching pursuit highlight the enhancement of this pruning method.","PeriodicalId":248339,"journal":{"name":"2018 13th European Microwave Integrated Circuits Conference (EuMIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 13th European Microwave Integrated Circuits Conference (EuMIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/EUMIC.2018.8539910","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
This communication presents a new technique for the digital predistortion of power amplifiers (PAs) based on sparse behavioral models. The subspace pursuit algorithm formulation is adapted to work in the nonlinear series framework. Experiments driven on a test bench based on a GaN PA driven by a 15-MHz filter bank multicarrier (FBM C) signal were conducted in order to validate the algorithm. Experimental results in a digital predistortion scenario and the comparison with the orthogonal matching pursuit highlight the enhancement of this pruning method.