A Novel Wiener-Type Dynamic Neural Network Method for Large Signal Modeling of Power Amplifiers

A novel Wiener-type dynamic neural network (Wiener-type DNN) method for large signal modeling of power amplifiers (PAs) is proposed in this paper. In this method, the proposed model structure for the PA contains two Wiener-type DNNs to describe the input impedance and the amplification efficiency, respectively. The Wiener-type DNN includes a simplified linear dynamic equation module and a nonlinear static equation module. For the simplification process of the linear dynamic equation, it is proposed to process fundamental frequency components of the large signal harmonic data of the PA by vector fitting. The neural network is used to implement the static equation in the Wiener-type DNN. The formulas for training the proposed Wiener-type DNN model of the PA using large signal data are derived. The training algorithm of the PA model is proposed to improve the training efficiency. A Motorola MOSFET PA example and a Freescale lateral double-diffused MOSFET PA example are present to validate the proposed Wiener-type DNN method. For the Motorola MOSFET PA, a dynamic neural network (DNN) model and a time-delay deep neural network (TDDNN) are established for comparison. For the Freescale PA, the DNN model is used for comparison experiment. The comparison figures show that the Wiener-type DNN model has better convergence properties than the DNN model in the nonlinear region of the PA. The established accurate PA model is conducive to the design of subsequent communication circuits.