DOI码：10.1109/LMWT.2023.3263642

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 Microwave and Wireless Technology Letters

关键字：Behavioral modeling, digital predistortion (DPD), long short-term memory (LSTM), multi-output neural network (NN), power amplifiers (PAs)

摘要：In this letter, we propose a method for behavioral modeling and digital predistortion (DPD) of RF power amplifiers (PAs) based on multi-output recurrent neural networks (RNNs). RNN has high modeling accuracy, but it also has high running complexity due to the recurrent mechanism. For this reason, we propose a multi-output model architecture, which means that the DPD model produces multiple adjacent outputs simultaneously for a single input sample group. This approach greatly reduces the running complexity of DPD based on RNNs with essentially no deterioration in performance. The proposed multi-output mechanism is applied to both long short-term memory (LSTM) and gate recurrent unit (GRU), and excellent linearization performances are maintained.

合写作者：Chengye Jiang,Guichen Yang,Renlong Han

第一作者：Qianqian Zhang (张牵牵)

论文类型：期刊论文

通讯作者：Falin Liu

学科门类：工学

文献类型：J

卷号：33

期号：7

页面范围：1067-1070

是否译文：否

发表时间：2023-07-07

收录刊物：SCI、EI