| 基于机器学习的数字预失真进展 |
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| 引用本文： | 刘发林,张牵牵,王俊森,昌 昊,姜成业,杨贵晨,韩仁龙.基于机器学习的数字预失真进展[J].微波学报,2023,39(5):62-69. |
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| 作者姓名： | 刘发林 张牵牵 王俊森 昌 昊 姜成业 杨贵晨 韩仁龙 |
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| 作者单位： | 1. 中国科学技术大学 电子工程与信息科学系,合肥 230027; 2. 中国科学院 电磁空间信息重点实验室,合肥 230027 |
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| 基金项目： | 国家自然科学基金(62371436) |
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| 摘 要： | 数字预失真技术目前被广泛用于矫正功率放大器的非线性，降低发射机前端的功率耗费。随着通信技术的发展，高性能、低复杂度的数字预失真技术已成为当前研究的热点。机器学习的发展也为研究提供了新的思路，在数字预失真技术发展历程中发挥了重要作用。文章以机器学习为基础，围绕数字预失真中的模型构建、参数求解和动态数字预失真这三个研究方向展开，总结了相关文献，对各方向的现有方法进行了阐述和总结。
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| 关 键 词： | 数字预失真 机器学习 功率放大器 参数提取 时变传输配置 |
Recent Progresses in Digital Predistortion Based on Machine Learning |
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| LIU Fa-lin,ZHANG Qian-qian,WANG Jun-sen,CHANG Hao,JIANG Cheng-ye,YANG Gui-chen,HAN Ren-long.Recent Progresses in Digital Predistortion Based on Machine Learning[J].Journal of Microwaves,2023,39(5):62-69. |
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| Authors: | LIU Fa-lin ZHANG Qian-qian WANG Jun-sen CHANG Hao JIANG Cheng-ye YANG Gui-chen HAN Ren-long |
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| Affiliation: | 1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China; 2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China |
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| 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. |
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