DOI码：10.1109/TMTT.2022.3199482

所属单位：中国科学技术大学

教研室：电子工程与信息科学系

发表刊物：IEEE Transactions on Microwave Theory and Techniques

关键字：Behavioral modeling, digital predistortion (DPD), few-sample learning (FSL), power amplifiers (PAs), sample selection method (SSM).

摘要：This article proposed a novel sample selection strategy for reducing the computational complexity of digital predistortion (DPD). Due to the memory effect of the power amplifier (PA), the PA's output is affected by the memory term. Thus, unlike existing sample selection methods (SSMs) that consider signal amplitude as the only feature, the proposed method regards signal points and their lagged terms (memory terms) as features of each sample point. We also introduce representative subset selection methods to further increase the selected samples' diversity, and these methods are improved to reduce their storage and computational complexity. By expanding the diversity among the selected samples, even a few samples for training can obtain satisfactory performance. In addition, the complexity analysis shows that the proposed method is effective and competitive. Based on the experimental results, the proposed method outperforms the existing techniques in performance, complexity, and stability.

合写作者：Wen Qiao,Chengye Jiang,Lei Su,Renlong Han,Jingchao Tan

第一作者：Guichen Yang (杨贵晨)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2022.3199482

学科门类：工学

文献类型：J

卷号：71

期号：2

页面范围：602-612

是否译文：否

发表时间：2023-02-06

收录刊物：EI、SCI