Deep neural nets based power amplifier non-linear pre-distortion

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Wang, Zhenyu^1 ; Wang, Yanyun^1 ; Song, Chunfeng^1 ; Chen, Tao^2 ; Cheng, Wei^1

School of Control and Computer Engineering, North China Electric Power University, Beijing

102206, China^1

School of Business, Central South University, Changsha

410083, China^2

关键词: Deep neural nets;    Effectiveness and efficiencies;    Network structures;    Non-linear model;    Nonlinear features;    Nonlinear functions;    Object functions;    Predistortion techniques;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012049/pdf
DOI  :  10.1088/1742-6596/887/1/012049

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper proposed a novel method based on deep neural networks (auto-encoder) model, to construct the pre-distortion model for non-linear feature of power amplifier. As auto-encoder nets are high non-linear function, with the optimization of object function to tune the weights, the nets can reach any non-linear model. For widely used power amplifier, this method can help setting the pre-distortion model. In this paper, deep (more layers) network structure have been adopted in the auto-encoder model. The experimental results show the effectiveness and efficiency of deep neural network based power amplifier non-linear pre-distortion technique.