Support Vector Regression-Based Behavioral Modeling Technique for RF Power Transistors

A nonlinear behavioral modeling technique, based on support vector regression (SVR), is presented in this letter. As an advanced machine-learning technique, the SVR method provides a more effective way to determine the optimal model when compared with the more traditional modeling approaches based o...

Full description

Saved in:
Bibliographic Details
Published inIEEE microwave and wireless components letters Vol. 28; no. 5; pp. 428 - 430
Main Authors Cai, Jialin, King, Justin, Yu, Chao, Liu, Jun, Sun, Lingling
Format Journal Article
LanguageEnglish
Published IEEE 01.05.2018
Subjects
Computational modeling
Harmonic analysis
Integrated circuit modeling
Load modeling
Machine learning
nonlinear behavioral model
Predictive models
Radio frequency
support vector regression (SVR)
Transistors
Online AccessGet full text

Cover

More Information
Summary:A nonlinear behavioral modeling technique, based on support vector regression (SVR), is presented in this letter. As an advanced machine-learning technique, the SVR method provides a more effective way to determine the optimal model when compared with the more traditional modeling approaches based on artificial neural network (ANN) techniques. The proposed technique can overcome the well-known overfitting issue often associated with ANNs. In this letter, the basic theory of the proposed SVR modeling method is provided, along with details on model implementation in the context of RF transistor devices. Both simulation and experimental test examples for a 10-W gallium nitride (GaN) transistor are provided, revealing that the new modeling methodology provides a more efficient and robust prediction throughout the Smith chart when compared with ANNs, with the latest results showing excellent model fidelity at both the fundamental and at the second harmonic.
ISSN:1531-1309
1558-1764
DOI:10.1109/LMWC.2018.2819427