A Power Amplifier Linearization Method for TETRA Based on ANN Digital Predistorter

被引：0

作者：

Ying, Le ^{[1
]}

Zhang, Qin ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Informat & Elect, Beijing, Peoples R China

来源：

PROCEEDINGS OF 2019 IEEE 9TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2019) | 2019年

关键词：

TETRA; ANN; power amplifier linearization; digital predistorter;

D O I：

10.1109/iceiec.2019.8784459

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Baseband digital predistorters (DPDs) are used in wireless communication systems to linearize RF high power amplifiers (HPAs). Traditional digital predistorters based on look-up table (LUT) and polynomial (POLY) either bring in quantization noise or cost large amount of computational resources. In this paper, a baseband digital predistorter based on artificial neural network (ANN) is proposed for terrestrial trunked radio (TETRA). Simulations show that the linearization performance of the proposed DPD is better than the LUT based DPD and the POLY based DPD, and it costs less resources, which means it can be easily implemented.

引用

页码：395 / 399

页数：5

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