The behavior model based on the artificial neural network has been widely used in the broadband power amplifier (PA). Although the deep neural network (DNN) performs well in the PA modeling with high-dimensional inputs, the training time of the DNN model is still long. This letter proposes a PA modeling method based on transfer learning to reduce training time without sacrificing modeling performance. In the proposed method, the model can be divided into two parts. The first part is defined as a predesigned filter that can extract the features of PA, and the second part is defined as adaptation layers that can be used to fit the real PA output. Experimental results show that the proposed method can effectively reduce the training time and ensure good modeling performance compared with the traditional DNN model.

中文翻译：

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基于迁移学习的宽带无线功率放大器深度神经网络行为建模


基于人工神经网络的行为模型已广泛应用于宽带功率放大器（PA）中。尽管深度神经网络（DNN）在高维输入的 PA 建模中表现良好，但 DNN 模型的训练时间仍然较长。这封信提出了一种基于迁移学习的 PA 建模方法，以在不牺牲建模性能的情况下减少训练时间。在所提出的方法中，模型可以分为两部分。第一部分定义为预先设计的滤波器，可以提取 PA 的特征，第二部分定义为适应层，可用于拟合真实的 PA 输出。实验结果表明，与传统的DNN模型相比，该方法能够有效减少训练时间并保证良好的建模性能。