Recent Progresses in Digital Predistortion Based on Machine Learning（in Chinese）

Affiliation of Author(s):中国科学技术大学信息学院

Teaching and Research Group:电子工程与信息科学系

Journal:Journal of Microwaves

Place of Publication:China

Funded by:国家自然科学基金

Key Words:digital predistortion (DPD), machine learning (ML), power amplifier (PA), parameter extraction, varying transmission configurations

Abstract:Digital predistortion (DPD) techniques are now widely used to correct the nonlinearity of power amplifiers (PAs) and reduce power dissipation in the transmitter front-ends. With the development of communication technology, High-performance and low-complexity DPD technology has become a hot spot of current research. The development of machine learning (ML) provides new ideas for research and plays an important role in the development process of DPD. Based on ML, this paper focuses on the three research directions of model construction, parameter extraction and varying transmission configurations DPD, summarises the relevant literature, and elaborates the existing methods in each direction.

First Author:LIU Falin

Co-author:张牵牵,王俊森,昌昊,姜成业,杨贵晨,韩仁龙

Indexed by:Journal paper

Document Code:10.14183/j.cnki.1005-6122.202305007

Discipline:Engineering

Document Type:J

Volume:39

Issue:5

Page Number:62-69

Translation or Not:no

Date of Publication:2023-10-20