Loading [MathJax]/extensions/TeX/enclose.js
An artificial neural network based nonlinear behavioral model for RF power transistors | IEEE Conference Publication | IEEE Xplore

An artificial neural network based nonlinear behavioral model for RF power transistors


Abstract:

In this paper, a frequency domain, nonlinear, behavioral model for RF power transistors, based on an artificial neural network (ANN), is proposed and validated. The model...Show More

Abstract:

In this paper, a frequency domain, nonlinear, behavioral model for RF power transistors, based on an artificial neural network (ANN), is proposed and validated. The model is identified using the back-propagation algorithm from the incident and scattered wave data of the RF transistor. The model has been extracted and validated on Cree GaN HEMT device. Both simulation and measurement examples are presented. Compared with existing nonlinear transistor behavioral models, which are all input-power dependent, the new model is able to effectively predict the behavior of a transistor over the entire Smith chart, at different levels of input power with only a single set of model coefficients.
Date of Conference: 13-16 November 2017
Date Added to IEEE Xplore: 11 January 2018
ISBN Information:
Conference Location: Kuala Lumpur, Malaysia
References is not available for this document.

I. Introduction

RF power amplifiers (PAs) play an important role in modern wireless communication systems, and the key part of RF PAs is the RF power transistor. Recently, transistors have been pushed to work under strong nonlinear conditions in newly developed PA architectures and operating modes, i.e., the Class PA, the Doherty PA [1] etc. The main motivation for these modern designs is to improve power added efficiency (PAE) when the PAs are excited with high peak-to-average power ratio (PAPR) digitally modulated signals.

Select All
1.
S. C. Cripps, RF Power Amplifiers for Wireless Communication, Norwood, MA, USA:Artech House, 1999.
2.
W. H. Leighton, Roger J. Chaffin and John G. Webb, "RF Amplifier Design with Large Signal S-parametrs", IEEE Transactions on Microwave Theory and Techniques, vol. 21, no. 12, pp. 809-814, Dec. 1973.
3.
Jan Verspecht and David E. Root, "Poly harmonic Distortion Modeling", IEEE Microwave Magazine, vol. 7, no. 3, pp. 44-57, Jun. 2006.
4.
Qi Hao, Benedikt Johannes, J. Paul and Tasker, "Nonlinear Data Utilization: From Direct Data Lookup to Behavioral Modeling", IEEE Transactions on Microwave Theory and Techniques, vol. 57, no. 6, pp. 1425-1432, Jun. 2009.
5.
Cai Jialin, J. King, B. Merrick and J. Thomas, "Pade-approximation-based Behavioral modeling", Brazil, vol. 61, no. 12, pp. 4418-4427, Jun. 2013.
6.
T. Liu, S. Boumaiza and F. M. Ghannouchi, "Dynamic Behavioral Modeling of 3G Power Amplifier using Real-Valued Time Delay Neural Networks", IEEE Transactions on Microwave Theory and Tech., vol. 52, no. 3, pp. 1025-1033, Mar. 2004.
7.
P. H. Aaen, J. A. Pla and J. Wood, Modeling and Characterization of RF and Microwave Power FETs, Cambridge, U.K.:Cambridge Univ. Press, 2007.

References

References is not available for this document.