DOI码：10.1109/TMTT.2024.3381151

所属单位：中国科学技术大学电子工程与信息科学系

发表刊物：IEEE Transactions on Microwave Theory and Techniques (Early Access)

项目来源：国家自然科学基金

关键字：Behavior modeling, digital predistortion (DPD), few-sample learning (FSL), linearization of power amplifiers (PAs), low complexity, sample selection

摘要：Sample selection methods have recently achieved success in low-complexity digital predistortion (DPD) parameter identification. However, obtaining appropriate sample sets still requires either large computing costs or careful tuning of hyperparameters. In this research, we present some novel and simplified strategies for selecting suitable samples in few-sample learning (FSL) DPD. First, as current approaches mostly rely on the maximum distance in the feature space, we provide a new centroid-based initialization method. This method effectively decreases the computational complexity and storage resource usage during sample selection. Then, in order to enhance the effectiveness of distinguishing samples, we suggest substituting the conventional Euclidean distance with the Manhattan distance. This modification enhances the robustness of sample selection to a certain degree, while eliminating the need for any multiplication operations. Finally, a memory feature weighting procedure has been devised to more accurately characterize the gradual decline of memory effects in radio frequency (RF) power amplifiers (PAs). Experimental results confirm that the suggested methods can improve the linearity performance of FSL DPD and facilitate rapid adjustment of the digital predistorter.

合写作者：Chengye Jiang,Renlong Han,Qianqian Zhang,Junsen Wang,Hao Chang

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.2024.3381151

学科门类：工学

文献类型：J

卷号：online

期号：online

页面范围：1-10

是否译文：否

发表时间：2024-04-01