An enhanced peak limited digital predistortion based on indirect learning architecture

被引：0

作者：

Huang, Guojing ^{[1
]}

Wang, Zhiyu ^{[1
]}

Liu, Jiarui ^{[1
]}

Chen, Hua ^{[1
]}

Yu, Faxin ^{[1
]}

机构：

[1] Zhejiang Univ, Sch Aeronaut & Astronaut, Hangzhou 310000, Peoples R China

来源：

IEICE ELECTRONICS EXPRESS | 2023年 / 20卷 / 19期

关键词：

digital predistortion; indirect learning architecture; power am-plifier; linearization; MEMORY POLYNOMIAL MODEL;

D O I：

10.1587/elex.20.20230358

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The peak power of the digital predistortion (DPD) will be un-controllably expand when the power amplifier (PA) is driven in the over-saturated state. To solve this problem, this letter proposes a peak limited digital predistortion method. By filtering the data used for predistortion coefficient extraction, the peak power of the predistorted signal can be ef-fectively controlled. The memory effect of PA is considered during the data filtering. Less data samples are needed for coefficient extraction, which re-duces the computational cost. Simulation verifies the effectiveness of the proposed method, and experimental results show that the adjacent channel leakage ratio (ACLR) can have 12.4 dB improvement when the tested PA is operated in an oversaturated state.

引用

页数：4