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.

中文翻译：

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通过人工神经网络行为模型预测功率放大器的谐波干扰


射频功率放大器（PA）是发射机系统的重要组成部分，可以驱动众多的输出设备。然而PA的非线性特性会造成严重的谐波干扰，从而导致电磁干扰（EMI）问题。本文对PA的非线性特性和记忆效应进行了分析。根据非线性的强弱划分强非线性区域和弱非线性区域。针对强非线性，提出了一种基于编码器-解码器（ED-based）的人工神经网络模型来预测PA的谐波干扰。为了促进输入信号较小时对高次谐波的预测，对弱非线性区域使用了多层感知器模型。该模型可以有效预测PA的前5次谐波，其中基波的平均绝对误差约为0.1 dB，二、三阶谐波的平均绝对误差约为0.5 dB。由于迁移学习（TL）可以根据不同任务的相似性简化模型的训练，因此基于模型迁移的TL用于根据现有模型来预测其他PA的谐波干扰。应用TL可以大大减少PA建模所需的数据量并保证预测的准确性。最终，该方法可以根据已知的激励信号快速准确地预测谐波干扰，从而采取相应措施避免辐射杂散发射对敏感接收设备在同一电磁环境中使用的影响。