A power amplifier linearization method for TETRA based on ANN digital predistorter

Le Ying, Qin Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication
EditorsWenzheng Li, Guomin Zuo
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages395-399
Number of pages5
ISBN (Electronic)9781728111896
DOIs
Publication statusPublished - Jul 2019
Event9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019 - Beijing, China
Duration: 12 Jul 201914 Jul 2019

Publication series

NameICEIEC 2019 - Proceedings of 2019 IEEE 9th International Conference on Electronics Information and Emergency Communication

Conference

Conference9th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2019
Country/TerritoryChina
CityBeijing
Period12/07/1914/07/19

Keywords

  • ANN
  • TETRA
  • digital predistorter
  • power amplifier linearization

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