DOI码：10.1109/TVT.2024.3444028

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 Vehicular Technology ( Early Access )

项目来源：国家自然科学基金 NSFC 62371436

关键字：Digital predistortion (DPD), dynamic model, fully-connected hybrid beamforming (FC-HBF) transmitters, power amplifier (PA)

摘要：In practical applications, the main beam direction of massive multi-input multi-output (mMIMO) transmitters varies with the user’s location. However, different directions will result in different distortions in mMIMO transmitters, and this poses a big challenge to digital pre-distortion (DPD). This paper first analyzes the relationship between direction configuration and main beam distortion in the fully-connected hybrid beamforming (FC-HBF) transmitters. In order to transmit the signal correctly under dynamic directions, two DPD models using heterogeneous neural networks (HNNs) based on vector decomposition (VD) and block-oriented (BO) structure, called HNN-VD and HNN-BO, respectively, have been proposed. The direction information is used directly as the input of HNNs. Then, a gating-based neural network model is proposed called direction adaptive neural network (DANN), which is composed of a gating network and a backbone network, and its core idea is to dynamically adjust the hidden layer features of the backbone network by utilizing direction information through the gating network. The simulation indicates that all of the proposed models exhibit good performance, but DANN has the best performance and low complexity among models, which provides a good digital pre-distortion scheme for FC-HBF transmitters under dynamic directions.

合写作者：Chengye Jiang,Renlong Han,Guichen Yang,Qianqian Zhang,Hao Chang

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/TVT.2024.3444028

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：2024-08-16

收录刊物：SCI、EI