Reducing Parameter Estimation Error of Behavioral Modeling and Digital Predistortion via Transfer Learning for RF Power Amplifiers

DOI number:10.1109/TMTT.2023.3267117

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

Journal:IEEE Transactions on Microwave Theory and Techniques

Funded by:国际自然科学基金

Key Words:Behavioral modeling, digital predistortion (DPD), few-sample learning (FSL), power amplifiers (PA), transfer learning

Abstract:Digital predistortion (DPD) has been widely used in linearizing radio frequency (RF) power amplifiers (PAs). However, model coefficients could not always be estimated accurately for a variety of reasons. Several regularization methods have been developed for parameter identification. However, the performance improvement is limited due to the missing information. Fortunately, if parameters from earlier operating conditions are available, they can be employed to enhance the accuracy of DPD in the current state. Despite the fact that many adaptive DPD methods are based on related concepts, they merely use past parameters as initialization for the target task. In this article, we proposed some novel transfer learning-based parameter estimation techniques for PAs operating in time-varying operating configurations. By effectively utilizing the structure knowledge of noncurrent parameters as a priori rather than just initializing them, the estimation error can be significantly decreased. Applying few-sample learning (FSL), for instance, can help to simplify the computational process of parameter extraction, but its robustness is poor. And the experimental results prove that the proposed method is useful for reducing the parameter estimation bias in FSL with negligible extra computational complexity.

First Author:Guichen Yang (杨贵晨)

Co-author:Chengye Jiang,Renlong Han,Jingchao Tan

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TMTT.2023.3267117

Discipline:Engineering

Document Type:J

Volume:71

Issue:11

Page Number:4787-4799

Translation or Not:no

Date of Publication:2023-11-08

Included Journals:SCI