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
Citations are 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.

Cites in Papers - |

Cites in Papers - IEEE (9)

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1.
João Louro, Luís C. Nunes, Filipe M. Barradas, José C. Pedro, "Robust Small-to-Large Signal Transition Extrapolation for ANN-based Transistor Models", 2024 19th European Microwave Integrated Circuits Conference (EuMIC), pp.87-90, 2024.
2.
Jiahao Wang, Jiangtao Su, Ruijin Wang, Weiyu Xie, Jun Liu, "An Efficient Transistor Modelling Method using Convolutional Neural Network with Transfer Learning", 2024 IEEE MTT-S International Wireless Symposium (IWS), pp.1-3, 2024.
3.
João Louro, Luís C. Nunes, Filipe M. Barradas, Telmo R. Cunha, Pedro M. Cabral, José C. Pedro, "A Hammerstein-Like Broadband ANN-Based Transistor Behavioral Model", IEEE Transactions on Microwave Theory and Techniques, vol.72, no.6, pp.3288-3299, 2024.
4.
Ruijin Wang, Zhongjie Lin, Jiangtao Su, "A Gaussian Curvature based method for optimizing measurement speed in RF device ANN model", 2023 International Conference on Microwave and Millimeter Wave Technology (ICMMT), pp.1-3, 2023.
5.
Mengyue Tian, James Bell, Ehsan Azad, Roberto Quaglia, Paul Tasker, "A Novel Cardiff Model Coefficients Extraction Process Based on Artificial Neural Network", 2023 IEEE Topical Conference on RF/Microwave Power Amplifiers for Radio and Wireless Applications, pp.1-3, 2023.
6.
João Louro, Catarina Belchior, Diogo R. Barros, Filipe M. Barradas, Luís C. Nunes, Pedro M. Cabral, José C. Pedro, "New Transistor Behavioral Model Formulation Suitable for Doherty PA Design", IEEE Transactions on Microwave Theory and Techniques, vol.69, no.4, pp.2138-2147, 2021.
7.
Jialin Cai, Jiangtao Su, Jun Liu, "Large Signal Behavioral Modeling of Power Transistor from Active Load-pull Systems", 2019 IEEE International Symposium on Radio-Frequency Integration Technology (RFIT), pp.1-3, 2019.
8.
Ahmad Khusro, Mohammad S. Hashmi, Abdul Quaiyum Ansari, Medet Auyenur, "A new and Reliable Decision Tree Based Small-Signal Behavioral Modeling of GaN HEMT", 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS), pp.303-306, 2019.
9.
Aleš Chvála, Lukáš Nagy, Juraj Marek, Juraj Priesol, Daniel Donoval, Alexander Šatka, "Neural Network for Circuit Models of Monolithic InAlN/GaN NAND and NOR Logic Gates", 2019 14th International Conference on Design & Technology of Integrated Systems In Nanoscale Era (DTIS), pp.1-4, 2019.

Cites in Papers - Other Publishers (2)

1.
Ruijin Wang, Jiangtao Su, Weiyu Xie, Zhongjie Lin, "Knowledge?based neural network with Bayesian optimization for efficient nonlinear RF device modeling", International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, 2023.
2.
Haode Li, Jiangtao Su, Ruijin Wang, Zhenyu Liu, Mengmeng Xu, "Review of RF Device Behavior Model: Measurement Techniques, Applications, and Challenges", Micromachines, vol.15, no.1, pp.46, 2023.

References

References is not available for this document.