DOI码：10.1109/TMTT.2026.3683474

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 Micriwave Theory and Techniques

项目来源：NFSC 62371436

关键字：Behavioral modeling, digital predistortion(DPD), genetic algorithm (GA), joint optimization, power amplifier (PA).

摘要：In digital predistortion (DPD) of power amplifiers(PAs), the intricate nonlinear characteristics of PAs make it a critical challenge to construct a high-performance basis function space and to design an efficient basis function search algorithm. To address this challenge, this article first introduces the threshold amplitude polynomial (TAP) model and formulates it within a novel genetic algorithm (GA)-based model encoding framework, in which the basis function structures of the TAP model are sequentially encoded through binary bits. Building upon this framework, a piecewise polynomial modeling-based cascaded encoding strategy is further introduced to enhance the flexibility of the DPD scheme, where all encoded basis functions from the multiple submodels are concatenated to form a unified cascade chromosome. Subsequently, a joint-encoding-based multimodel optimization (JEMO) method is proposed, which leverages the encoded basis function structures within submodels through iterative GA operations to provide feedback for adjusting segmentation boundaries, thereby tightly integrating adaptive segmentation rules with the structural optimization of multiple submodels. To validate the effectiveness of the proposed model, experiments are conducted on two different Doherty PAs. The results demonstrate that the proposed JEMO model exhibits a significant advantage over other models in achieving the tradeoff between performance and complexity.

第一作者：Hao Chang (昌昊)

合写作者：Chengye Jiang,Junsen Wang,Qianqian Zhang,Renlong Hang

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2026.3683474

学科门类：工学

文献类型：J

卷号：74

期号：7

页面范围：5811-5825

是否译文：否

发表时间：2026-04-10