DOI码：10.1109/TMTT.2024.3461952

所属单位：中国科学技术大学信息学院

教研室：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：NNSF 62371436

关键字：Adaptive model, behavioral modeling, decision tree, digital predistortion (DPD), Doherty power amplifier (DPA), neural networks

摘要：In this article, a novel input amplitude-based adaptive tuning (AT) technique is proposed to improve the performance of the existing neural network-based digital predistortion (NN-DPD) models in Doherty power amplifier (DPA) systems. The changes in the behavioral characteristics of DPA are first discussed to explain why NN-DPD models designed for single power amplifier (PA) degrade in performance in DPA systems. Then, the AT technique leverages the input selection module (ISM) to help NN-DPD models adapt to changes in the operating states of DPA, while the parameter tuning module (PTM) aids NN-DPD models in accommodating the dynamic behavioral characteristics arising from active load modulation in DPA. Furthermore, the AT technique is applied to both the block-oriented time-delay neural network (BOTDNN) and the augmented real-valued time-delay neural network (ARVTDNN) for developing two adaptive tuning NN (ATNN) models, named AT-BOTDNN and AT-ARVTDNN, respectively. The experimental results demonstrate that both ATNN models achieve superior linearization performance with reduced computational complexity, and that the performance improvement mainly comes from the ISM while the remaining performance gains come from the PTM and other designs.

合写作者：Chengye Jiang,Renlong Han,Qianqian Zhang,Hao Chang,Kang Zhou

第一作者：Junsen Wang (王俊森)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2024.3461952

学科门类：工学

文献类型：J

卷号：Online

期号：Online

页面范围：1-13

是否译文：否

发表时间：2024-09-26

收录刊物：SCI、EI