DOI码：10.1109/ACCESS.2020.2986816

发表刊物：IEEE Access

关键字：Nonlinear RF PA, digital predistortion, recurrent neural network, instant gated, behavioral modeling

摘要：This article presents two novel neural network models based on recurrent neural network (RNN) for radio frequency power amplifiers (RF PAs): instant gated recurrent neural network (IGRNN) model and instant gated implict recurrent neural network (IGIRNN) model. In IGRNN model, two state control units are introduced to ensure the linear transmission of hidden state and solve the problem of vanishing gradients of RNN model. In contrast with conventional RNN model, IGRNN can better describe the long-term memory effect of power amplifier, more in line with the physical distortion characteristics of power amplifier. Furthermore the instantaneous gates are used to express the input information implicitly to reduce the redundancy of the input information, and a simpler IGIRNN model is proposed. The complexity analysis indicates that the proposed models have significantly lower complexity than other RNN-based variant structures. A wideband Doherty RF PA excited by 100MHz and 120MHz OFDM signals was employed to evaluate the performance. Extensive experimental results reveal that the proposed IGRNN and IGIRNN models can achieve better linearization performance compared with RNN model and traditional GMP model, and have comparable performance with lower computational complexity compared with the state-of-the-art RNN-based variant models, such as gated recurrent unit (GRU) model.

合写作者：Yikang Zhang,Hongmin Li,Wen Qiao

第一作者：Gang Li (李刚)

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：8

期号：online

页面范围：67474-67483

是否译文：否

发表时间：2020-04-09

收录刊物：SCI、EI