Generalized Ridge Regression-Based Few-Sample Learning Digital Predistortion

DOI number:10.1109/LMWC.2022.3148407

Affiliation of Author(s):中国科学技术大学

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

Journal:IEEE Microwave and Wireless Components Letters

Funded by:国家自然科学基金 61471333

Key Words:Behavioral modeling, digital predistortion (DPD), generalized ridge regression (GRR), power amplifiers (PAs).

Abstract:Learning from fewer samples can effectively reduce the computational complexity of the parameter identification in digital predistortion (DPD). We refer to this kind of approach as few-sample learning (FSL). However, FSL is always challenging since the ill-conditioning of the matrix will lead to overfitting. In this letter, we explore a stable parameter identification method for FSL DPD based on generalized ridge regression (GRR) and give two closed-form expressions of GRR for fast implementation. Experiments confirm that the proposed method can achieve better performance than the previous methods without any prior knowledge.

First Author:Guichen Yang (杨贵晨)

Co-author:Wen Qiao,Chengye Jiang,Lei Su

Indexed by:Journal paper

Correspondence Author:Falin Liu

Discipline:Engineering

Document Type:J

Volume:32

Issue:6

Page Number:603-606

Translation or Not:no

Date of Publication:2022-06-01

Included Journals:SCI、EI