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Dynamic behavioral modeling of RF power amplifiers based on decomposed piecewise machine learning technique

Published online by Cambridge University Press:  28 August 2020

Jialin Cai Open the ORCID record for Jialin Cai [Opens in a new window] ,
Justin B. King ,
Chao Yu ,
Baicao Pan ,
Lingling Sun  and
Jun Liu
Jialin Cai*
Affiliation:
Key Laboratory of RF Circuit and System, Ministry of Education, Hangzhou Dianzi University, Hangzhou, China State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing, China
Justin B. King
Affiliation:
RF and Microwave Research Group at Trinity College Dublin, Dublin, Ireland
Chao Yu
Affiliation:
State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing, China
Baicao Pan
Affiliation:
Key Laboratory of RF Circuit and System, Ministry of Education, Hangzhou Dianzi University, Hangzhou, China
Lingling Sun
Affiliation:
Key Laboratory of RF Circuit and System, Ministry of Education, Hangzhou Dianzi University, Hangzhou, China
Jun Liu
Affiliation:
Key Laboratory of RF Circuit and System, Ministry of Education, Hangzhou Dianzi University, Hangzhou, China
*
Author for correspondence: Jialin Cai, E-mail: caijialin@hdu.edu.cn
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Abstract

Multi-device radio frequency power amplifiers (PAs) often exhibit strongly non-linear behavior in combination with long-term memory effects, leading to an extremely challenging model development cycle. This paper presents a new, dynamic, behavioral modeling technique, based on a combination of the real-valued decomposed piecewise method and concepts from the field of machine learning. The underlying theory of the proposed modeling technique is provided, along with a detailed modeling procedure. Experimental results show that the proposed decomposed piecewise support vector regression (SVR) model leads to significant performance improvements when compared with standard SVR models for both single transistor and multi-transistor PAs. Different model thresholds are used to test the proposed model performance for both PA types. For the single-transistor PA, modeled using only one partition, an approximately 10 dB normalized mean square error (NMSE) reduction is seen when compared with the standard SVR model. For the same PA, when utilizing two partitions, the reduction improves to 14 dB. When applied to a multi-device Doherty PA, the NMSE between model and measurement data is −50 dB, representing more than 10 dB improvement compared with the standard SVR model.

Keywords

Linear and non-linear CAD techniquesmodelingsimulation and characterizations of devices and circuits
Type
Power Amplifiers
Information
International Journal of Microwave and Wireless Technologies , Volume 13 , Issue 4 , May 2021 , pp. 315 - 321
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press in association with the European Microwave Association.

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References

Cripps, SC (2006) RF Power Amplifier for Wireless Communications, 2nd Edn. Boston, MA, USA: Artech House.Google Scholar
Schreurs, D, O'Droma, M, Goacher, AA and Gardringer, M (2009) RF Power Amplifier Behavioral Modeling. Cambridge, UK: Cambridge University Press.Google Scholar
Ghannouchi, FM, Hammi, O and Helaoui, M (2015) Behavioral Modeling and Predistortion of Wideband Wireless Transmitters. West Sussex, UK: John Wiley & Sons.Google Scholar
Kim, J and Konstantinou, K (2001) Digital predistortion of wideband signals based on power amplifier model with memory. Electronics Letters 37, 1417–1418.CrossRefGoogle Scholar
Morgan, DR, Ma, Z, Kim, J, Zierdt, MG and Pastalan, J (2006) A generalized memory polynomial model for digital predistortion of RF power amplifiers. IEEE Transactions on Signal Processing 54, 3852–3860.CrossRefGoogle Scholar
Houssam Eddine Hamoud and al. A comparative overview of digital predistortion behavioral modeling for multi-standards applications. IEEE International Workshop on Integrated Nonlinear Microwave and Millimetre-wave Circuits (INMMiC), Brive La Gaillarde, France, Aug. 2018.Google Scholar
Grebennikov, A and Bulja, S (2012) High-efficiency Doherty power amplifiers: historical aspect and modern trends. Proceedings of the IEEE 100, 3190–3219.CrossRefGoogle Scholar
Zhu, A (2015) Decomposed vector rotation-based behavioral modeling for digital predistortion of RF power amplifier. IEEE Transactions on Microwave Theory and Techniques 63, 737–744.Google Scholar
Cai, J, Yu, C, Sun, L, Chen, S and King, J (2019) Dynamic behavioral modeling of RF power amplifier based on time-delay support vector regression. IEEE Transactions on Microwave Theory and Techniques 67, 533–543.Google Scholar
Liu, T, Boumaiza, S and Ghannouchi, FM (2004) Dynamic behavioral modeling of 3G power amplifier using real-valued time delay neural networks. IEEE Transactions on Microwave Theory and Techniques 52, 1025–1033.CrossRefGoogle Scholar
Zhu, A, Draxler, PJ, Chin, H, Brazil, TJ, Kimball, DF and Asbeck, PM (Oct. 2008) Digital predistortion for envelope-tracking power amplifiers using decomposed piecewise Volterra series. IEEE Transactions on Microwave Theory and Techniques 56, 2237–2247.Google Scholar
Cortes, C and Vapnik, V (1997) Support vector network. Machine Learn 20, 73–297.Google Scholar
Gidoni, T, Socher, E and Cohen, E (2016) Digital predistortion using piecewise memory polynomial for 802.11 WiFi applications. Science of Electrical Engineering (ICSEE) IEEE International Conference on the, pp. 1–3.Google Scholar
Zhang, C, Wang, J and Wu, W (2015) A piecewise generalized memory polynomial model for envelope tracking power amplifiers. Microwave Conference (APMC) 2015 Asia-Pacific 3, 1–3.Google Scholar