Adaptive Transfer Strategy for Digital Predistortion With Varying Transmission Configurations

DOI number:10.1109/TVT.2025.3624547

Affiliation of Author(s):中国科学技术大学信息学院

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

Journal:IEEE Transactions on Vehicular Technology ( Early Access )

Funded by:NSFC 62371436

Key Words:Digital predistortion (DPD), power amplifiers (PAs), Kolmogorov–Arnold network (KAN), transfer learning, time-varying transmission configurations.

Abstract:Power amplifier (PA) behaviors are highly correlated under different transmission configurations. Utilizing transfer learning to apply effective information from an already trained digital predistortion (DPD) model under a certain configuration to the current configuration can greatly reduce update resources and shorten the transition period. Fine-tuning is an effective method to implement transfer learning in neural networks (NNs). For the trade-off between performance and update resources, this paper proposes an adaptive fine-tuning strategy (ATS) for DPD networks in time-varying transmission configuration scenarios. ATS can determine the location of the fine-tuning operation according to the transmission configuration to obtain better gains with limited resources. Owing to the high fitting efficiency and good interpretability of Kolmogorov-Arnold network (KAN), the block-oriented KAN (BO-KAN) model is also proposed in this paper. Unlike NNs based on multilayer perceptron (MLP), BO-KAN has the ability to transfer information between networks of different capacities. Therefore, the BO-KAN model with a larger capacity can be used for fast updating in complex configurations without training from scratch. Experimental results demonstrate that the BO-KAN with ATS achieves excellent dynamic linearization performance, with low running complexity and acceptable update complexity.

First Author:Qianqian Zhang(张牵牵)

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

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TVT.2025.3624547

Discipline:Engineering

Document Type:J

Volume:Online

Issue:Online

Page Number:1-13

Translation or Not:no

Date of Publication:2025-10-23