Harmonic Interference Prediction of Power Amplifiers by Artificial Neural Network Behavioral Model

Radio frequency power amplifier (PA) is an important part of the transmitter system, which can drive numerous output devices. However, the nonlinear characteristics of PA will cause serious harmonic interference, which leads to electromagnetic interference (EMI) problems. In this article, the nonlinear characteristics and the memory effect of PA are analyzed. The strong nonlinearity region and the weak nonlinearity region are divided according to the strength of the nonlinearity. For the strong nonlinearity, an encoder–decoder-based (E-D-based) artificial neural network model is proposed to predict the harmonic interference of PA. To promote the prediction of high-order harmonics when the input signal is small, a multilayer perceptron model is used for the weak nonlinearity region. The models can effectively predict the first five harmonics of PA, in which the mean absolute error of the fundamental wave is about 0.1 dB, the one of the second-order and the third-order harmonics is about 0.5 dB. Since transfer learning (TL) can simplify the training of the model based on the similarity of different tasks, TL based on model transfer is used to predict the harmonic interference of other PAs according to the existing models. The amount of data required for the modeling of PA can be greatly reduced and the accuracy of prediction can be guaranteed by applying TL. Ultimately, the proposed method can predict the harmonic interference rapidly and accurately according to the known excitation signal so that corresponding measures can be taken to avoid the influence of radiated spurious emission on the use of the sensitive receiving devices in the same electromagnetic environment.