DOI码：10.1109/TMTT.2021.3124211

所属单位：中国科学技术大学

教研室：电子工程与信息科学系

发表刊物：IEEE Transactions on Microwave Theory and Techniques

项目来源：国家自然科学基金 61471333

关键字：Block-oriented models, digital predistortion (DPD), low feedback sampling rate, neural network, power amplifiers (PAs).

摘要：A novel block-oriented time-delay neural network (BOTDNN) model for dynamic nonlinear modeling and digital predistortion (DPD) of RF power amplifiers (PAs) is proposed. The proposed model consists of a dynamic linear network and a static nonlinear network to characterize dynamic nonlinear systems. The dynamic linear network simulates multiple linear filters using a fully connected layer with the linear activation function. The static nonlinear network is constructed based on vector decomposition and phase recovery mechanism. To validate the proposed model, experiments have been carried out with two different PAs operating at 2.4 and 39 GHz, respectively. The test results demonstrate that the proposed model has better PA modeling and nonlinear compensation capabilities than state-of-the-art PA behavioral models, while with significantly lower model complexity. Furthermore, to reduce the system cost, we investigate the problems that arise when the neural network-based behavioral models are applied to low feedback sampling rate DPD and propose an improved method. The experiments confirm that the proposed low feedback sampling rate DPD method can effectively alleviate the deterioration of linearization performance caused by undersampling.

合写作者：Hongmin Li,Wen Qiao,Guichen Yang,Qiao Liu,Guangjian Wang

第一作者：Chengye Jiang (姜成业)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2021.3124211

学科门类：工学

文献类型：J

卷号：70

期号：3

页面范围：1461-1473

是否译文：否

发表时间：2022-03-01

收录刊物：SCI、EI