Electronic Science and Technology ›› 2022, Vol. 35 ›› Issue (8): 7-13.doi: 10.16180/j.cnki.issn1007-7820.2022.08.002

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Power Amplifier Behavior Modeling Based on Deep Temporal Convolutional Network

ZHOU Fan,ZHAO Xuan,SHAO Jie   

  1. College of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics, Nanjing 210016,China
  • Received:2021-03-12 Online:2022-08-15 Published:2022-08-10
  • Supported by:
    Open Research Fund of Key Laboratory of Ministry of Education(UASP2001)


As the core component of the radiation source transmitter, the power amplifier has the characteristics of high non-linearity and strong memory, which makes it difficult to model the behavior of the power amplifier. In view of this problem, this study proposes a method for behavior modeling of power amplifiers based on Deep TCN. The neural network model adopted by this method is composed of multiple multi-dimensional time series convolution blocks, and each time series convolution block is composed of several causal dilation convolutions used to increase the receptive field of the network and residual structures used to improve the efficiency of gradient feedback. Through the parallel convolution operation, the model overcomes the disadvantages of traditional convolutional networks that cannot handle variable-length sequences, and improves the efficiency of behavioral modeling while preserving the memory effect of power amplifiers. The behavior modeling results of measured data show that compared with the existing Volterra series and recurrent neural network modeling methods, the proposed method can significantly improve the accuracy of behavior modeling. Compared with the recurrent neural network modeling method, the proposed method reduces the implementation time by an order of magnitude in terms of the efficiency of behavior modeling.

Key words: radiation source, power amplifier, behavior modeling, temporal convolutional network, residual structure, causal convolution, dilated convolution, deep network

CLC Number: 

  • TP183