DOI码：10.1109/ACCESS.2019.2927875

发表刊物：IEEE Access

关键字：Nonlinear RF PA, digital predistortion, artificial neural network, vector decomposition, behavioral modeling.

摘要：This article presents two novel neural network models for radio frequency (RF) power amplifiers (PAs): vector decomposed time-delay neural network (VDTDNN) model and augmented vector decomposed time-delay neural network (AVDTDNN) model. In contrast to conventional neural network based models, VDTDNN and AVDTDNN comply with the physical characteristics of RF PAs by employing carefully designed network structures. In particular, the nonlinear operations are conducted only on the magnitude of the input signals while the phase information is recovered with the linear weighting. Linear terms with shortcut connection, as well as high order terms, can be used to further boost the modeling performance. The complexity analysis shows that the proposed models have significantly lower complexity than existing neural network models. A wideband GaN RF PA excited by 40MHz and 60MHz OFDM signals was employed to evaluate the performance. Extensive experimental results reveal that the proposed VDTDNN and AVDTDNN models can achieve better linearization performance with lower computational complexity, compared with the existing neural network based models.

合写作者：Yue Li,Anding Zhu

第一作者：Yikang Zhang (张益康)

论文类型：期刊论文

通讯作者：Falin Liu

卷号：7

页面范围：91559-91568

是否译文：否

发表时间：2019-07-12