Deep Neural Network Behavioral Modeling Based on Transfer Learning for Broadband Wireless Power Amplifier

Abstract

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.