DOI码：10.1109/TMTT.2024.3418014

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、中科院电磁空间信息重点实验室

发表刊物：IEEE Transactions on Microwave Theory and Techniques (Early Access)

项目来源：国家自然科学基金 NSFC 62371436

关键字：Digital predistortion (DPD), gated model, high-way network, power amplifiers (PAs), varying transmission configurations.

摘要：To meet the challenge of digital predistortion (DPD) under dynamic scenarios, a structural adaptation method of neural network (NN) based on the gate mechanism is proposed. This method integrates highway network with a noise gate, achieving discrete gating network gradient backpropagation and smooth variations in the backbone network structure. Applying this method to gated dynamic NN (GDNN), the adaptive inference pathway-gated NN (AIPGNN) model is proposed. The AIPGNN is capable of adaptively activating specific finite impulse response (FIR) filter branches based on the current configuration information. In a sense, the input signal is processed only through the activated FIR filter branches, while directly passing through the inactivated FIR filter branches. This adaptive activation method allows for the training of a specialized set of FIR branches customized to the nonlinear characteristics of a particular class of configurations, while FIR branches in GDNN are required to accommodate all configurations, which results in challenging trade-offs for the FIR layer during training. Furthermore, AIPGNN model also supports the activation of a varying number of FIR filter branches under different transmission configurations. The adaptively changed network structure enables the proposed model to adequately correct the nonlinear behavior of the PA in more complex transmission configurations, without resource wastage in simpler transmission configurations, which meets the needs of time-varying configuration scenarios. The experimental results indicate that AIPGNN exhibits superior dynamic linearization performance and good generalization capability under varying transmission configurations.

合写作者：Chengye Jiang,Renlong Han,Guichen Yang,Junsen Wang,Hao Chang

第一作者：Qianqian Zhang (张牵牵)

论文类型：期刊论文

通讯作者：Falin Liu

论文编号：10.1109/TMTT.2024.3418014

学科门类：工学

文献类型：J

卷号：online

期号：online

页面范围：1-12

是否译文：否

发表时间：2024-07-03