Composite Adaptive Digital Predistortion with Improved Variable Step Size LMS Algorithm

被引：0
|
作者：
Gu, Linhai [1 ]
Gu, Lu [2 ]
Mao, Jian [2 ]
Ge, Lijia [2 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Key Lab Mobile Commun Technol, Chongqing, Peoples R China
[2] Chongqing Lynchpin Elect Technol Co Ltd, Chongqing, Peoples R China
来源：
PROCEEDINGS OF THE 2ND INFORMATION TECHNOLOGY AND MECHATRONICS ENGINEERING CONFERENCE (ITOEC 2016) | 2016年 / 24卷
关键词：
power amplifiers; linearization techniques; predistortion; adaptive algorithms; MEMORY; MODEL;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper presents a composite learning (CL) architecture. The CL can make use of the advantages of both direct and indirect learning. By an appropriate threshold, the indirect learning can be switched into direct learning. In order to further improve the performance of direct learning, an improved variable step size LMS algorithm is proposed. The proposed algorithm has the lower normalized mean-square error (NMSE) with low computation complexity. The outstanding performance is confirmed by simulation results.
引用
收藏
页码：157 / 162
页数：6
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