Support vector regression-based dynamic behavioral modeling for RF power amplifiers

Jialin Cai,Xiwei Huang,Jie Liu,Xiang Wang,Jun Liu,Chao Yu,Lingling Sun,Jiangtao Su

doi:10.1109/ieee-iws.2018.8400885

Support vector regression-based dynamic behavioral modeling for RF power amplifiers

Jialin Cai, Xiwei Huang + Show 6 more

https://doi.org/10.1109/ieee-iws.2018.8400885

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Publication Date: May 1, 2018

Citations: 7

Affiliation: Hangzhou Dianzi University, Southeast University

#Support Vector Regression #Advanced Machine Learning Technique + Show 8 more

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Abstract

A new dynamic behavioral modelling technique, based on support vector regression (SVR), is presented in this paper. As an advanced machine learning technique, the SVR method provides an effective option for behavioral modeling of radio frequency (RF) power amplifiers (PAs) including the nonlinear and memory effects. In this paper, the basic theory of the proposed SVR modelling method is provided, along with details on model implementation of RF PAs. Experimental test example for a LDMOS PA is presented, revealing that the new modelling methodology provides a very efficient and extremely accurate prediction.

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