Support Vector Regression Based Behavioral Model for Load Mismatched PAs

Jialin Cai,Lingling Sun,Jiangtao Su,Shichang Chen,Baicao Pan,Jun Liu,Chao Yu,Jing Xia

doi:10.1109/rfit.2019.8929153

Support Vector Regression Based Behavioral Model for Load Mismatched PAs

Jialin Cai, Lingling Sun + Show 6 more

https://doi.org/10.1109/rfit.2019.8929153

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Publication Date: Aug 1, 2019

Citations: 7

Affiliation: Hangzhou Dianzi University, Southeast University, Jiangsu University

#Double-input Double-output #Conventional Memory Polynomial + Show 8 more

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Abstract

A new double-input double-output (DIDO) power amplifier (PA) behavioral model, which takes into account output load termination mismatches, is presented in this paper. The support vector regression (SVR) methodology, from the field of machine learning, is utilized to build this new model. The model is intended for the system-level simulation of amplifier chains subjected to wideband modulated signals. It is validated by comparing the predicted and measured data of a 15 W Gallium Nitride (GaN) based PA under a wide range of voltage standing wave ratios (VSWR). Compared with the conventional memory polynomial (MP) based DIDO models, the obtained results from the new model demonstrate the improved prediction capabilities.

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