{"title":"A high-order model looking beyond the first-order harmonic superposition assumption","authors":"D. Bespalko, A. Amini, S. Boumaiza","doi":"10.1109/PAWR.2016.7440159","DOIUrl":null,"url":null,"abstract":"In this paper, a hybrid high-order behavioural model is proposed to mimic the response of strongly nonlinear unmatched RF transistors. In this model, a high-order Multi-Harmonic Volterra (MHV) behavioural model is used to predict the DC and fundamental frequency components of the output signal, while higher harmonic components are predicted by the Poly-Harmonic Distortion (PHD) model. The added coefficients of the MHV model augment the first-order expansion (harmonic superposition) of the PHD model to improve the model accuracy where it is needed. The hybrid MHV-PHD model improves the DC drain current prediction by 5dB and fundamental frequency output-power by 2dB in terms of Normalized Mean Squared Error (NMSE), while improving overall time-domain prediction by 1dB.","PeriodicalId":103290,"journal":{"name":"2016 IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications (PAWR)","volume":"56 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE Topical Conference on Power Amplifiers for Wireless and Radio Applications (PAWR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PAWR.2016.7440159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
In this paper, a hybrid high-order behavioural model is proposed to mimic the response of strongly nonlinear unmatched RF transistors. In this model, a high-order Multi-Harmonic Volterra (MHV) behavioural model is used to predict the DC and fundamental frequency components of the output signal, while higher harmonic components are predicted by the Poly-Harmonic Distortion (PHD) model. The added coefficients of the MHV model augment the first-order expansion (harmonic superposition) of the PHD model to improve the model accuracy where it is needed. The hybrid MHV-PHD model improves the DC drain current prediction by 5dB and fundamental frequency output-power by 2dB in terms of Normalized Mean Squared Error (NMSE), while improving overall time-domain prediction by 1dB.