基于编码—解码模型的D类功率放大器行为建模

doi:10.16180/j.cnki.issn1007-7820.2020.02.004

Abstract

Abstract:

Class D power amplifiers have excellent transmission efficiency and are classified as power amplifiers. Their output signals have large nonlinear distortion. The behavior modeling of calss-D power amplifier should take into account both nonlinearity and memory characteristics. This study introduced wavelet transform into the encoder-decoder neural network model, and proposed sequence to sequence wavelet neural network model. In this paper, the encoder-decoder model and sequence to sequence wavelet model based on gated recurrent unit were used in the behavior modeling of class-D power amplifier. Experiments results demonstrated that the proposed behavior model of class-D power amplifier had higher precision in time and frequency domain than the traditional Voterra-Laguerre model.

Key words: class-D power amplifier, nonlinearsystem, behaviormodeling, gated recurrent unit, encoder-decoder neural network, wavelet transform

CLC Number:

TP274

ZHAO Yihe,SHAO Jie,CHENG Yongliang. Behavior Modeling of Class-D Power Amplifier Based on Encoder-Decoder Model[J].Electronic Science and Technology, 2020, 33(2): 20-24.

Figures/Tables 5

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Table 1

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Table 2

References 16

[1]	Roblin P, Root D E, Verspecht J , et al. Newtrends for the nonlinear measurement and modeling of high-power RF transistors and amplifiers with memory effects[J]. IEEE Transactions on Microwave Theory & Techniques, 2012,60(6):1964-1978.
[2]	Galaviz J A, Roblin P, Cárdenas J R , et al. Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation[J]. Soft Computing, 2017,32(10):1-19.
[3]	Amhaimar L, Ahyoud S, Asselman A , et al. Peak-to-Average power ratio reduction based varied phase for MIMO-OFDM systems[J]. International Journal of Advanced Computer Science and Applications, 2016,7(1):432-437.
[4]	Simbélie F, Laurent S, Medrel P , et al. Non-linear electrical modeling of MASMOS structure to design power amplifiers for 4G applications[J]. International Journal of Microwave and Wireless Technologies, 2018,10(1):9-17.
[5]	Hao R, Zhang X, Gao H , et al. Avolterra-series-based design technique for the bias circuit of class-AB RF power amplifier[C]. Xi’an:2 ^nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference , 2018.
[6]	Jing X, Lang Z. Frequency domain analysis and design of nonlinear systems based onvolterra series expansion[M]. Berlin: Springer International Publishing, 2015.
[7]	Jie Shao, Zhao Weisong, Jin Xiangjun , et al. Underwater broadband acoustic scattering modelling based on FDTD[J]. ElektronikaIr Elektrotechnika, 2015,21(2):1-7.
[8]	Jin X, Shao J, Zhang X , et al. Modeling of nonlinear system based on deep learning framework[J]. Nonlinear Dynamics, 2016,84(3):1327-1340.
[9]	Che Z, Purushotham S, Cho K , et al. Recurrent neural networks for multivariate time series with missing values[J]. Scientific Reports, 2018,8(1):6085-6090.
[10]	Zhu Y, Imamura M, Nikovski D , et al. Introducing time series chains: a new primitive for time series data mining[J]. Knowledge and Information Systems, 2018,6(3):1-27.
[11]	Luongvinh D, Kwon Y . Behavioral modeling of power amplifiers using fully recurrent neural networks[C]. Long Beach:IEEE MTT-S International Microwave Symposium Digest, 2005.
[12]	Yan S, Zhang C, Zhang Q . Recurrent neural network technique for behavioral modeling of power amplifier with memory effects[J]. International Journal of RF and Microwave Computer-Aided Engineering, 2015,25(4):289-298.
[13]	周祥全, 张津 . 深层网络中的梯度消失现象[J]. 科技展望, 2017,6(27):284-285.
	Zhou Xiangquan, Zhang Jin . Gradient disappearance in deep neural networks[J]. Technology Outlook, 2017,6(27):284-285.
[14]	Greff K, Srivastava R K, Koutnik J , et al. LSTM:a search space odyssey[J]. IEEE Transactions on Neural Networks & Learning Systems, 2016,28(10):2222-2232.
[15]	尹光花, 刘小明, 张露 , 等. 基于LSTM特征模板的短文本情感要素分析与研究[J]. 电子科技, 2018,11(10):38-41.
	Yin Guanghua, Liu Xiaoming, Zhang Lu , et al. Sentiment elements of internet short texts for analysis and research based on LSTM network mode[J]. Electronic Science and Technology, 2018,11(10):38-41.
[16]	Zhou G B, Wu J, Zhang C L , et al. Minimal gated unit for recurrent neural networks[J]. International Journal of Automation & Computing, 2016,13(3):226-234.

模型	平均时域误差/V
VL	0.093 5
S2S	9.45×10^-4
S2S-WL	5.28×10^-4

模型	VL	Seq2Seq	Seq2Seq-WL
平均频域误差	6.7114	0.5904	0.3572
f₁误差/dB	-0.0653	0.0013	0.0005
f₂误差/dB	0.0038	0.0003	0.0002
f₃误差/dB	5.5144	-0.0046	-0.0087
f₄误差/dB	9.0907	-0.0004	-0.0010
f₅误差/dB	4.4487	-0.0156	0.0257
f₆误差/dB	8.0684	0.0278	-0.0065

Behavior Modeling of Class-D Power Amplifier Based on Encoder-Decoder Model

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