Complexity-Optimized Sample Selection for Fast Adaptation of Digital Predistorter

DOI number:10.1109/TMTT.2024.3381151

Affiliation of Author(s):中国科学技术大学电子工程与信息科学系

Journal:IEEE Transactions on Microwave Theory and Techniques

Funded by:国家自然科学基金

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

Abstract: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.

Co-author:Chengye Jiang,Renlong Han,Qianqian Zhang,Junsen Wang,Hao Chang

First Author:Guichen Yang(杨贵晨)

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TMTT.2024.3381151

Discipline:Engineering

Document Type:J

Volume:72

Issue:10

Page Number:5779-5788

Translation or Not:no

Date of Publication:2024-10-10

Included Journals:SCI、EI