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

被引：0

作者：

Wang, Zhenyu ^{[1
]}

Wang, Yanyun ^{[1
]}

Song, Chunfeng ^{[1
]}

Chen, Tao ^{[2
]}

Cheng, Wei ^{[1
]}

机构：

[1] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China

[2] Cent South Univ, Sch Business, Changsha 410083, Hunan, Peoples R China

来源：

2ND ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2017) | 2017年 / 887卷

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1088/1742-6596/887/1/012049

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页数：6

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