Adaptive Inference Pathway-Gated Neural Network Model for Digital Predistortion With Varying Transmission Configurations

DOI number:10.1109/TMTT.2024.3418014

Affiliation of Author(s):中国科学技术大学

Teaching and Research Group:电子工程与信息科学系、中科院电磁空间信息重点实验室

Journal:IEEE Transactions on Microwave Theory and Techniques (Early Access)

Funded by:国家自然科学基金 NSFC 62371436

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

Abstract: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.

Co-author:Chengye Jiang,Renlong Han,Guichen Yang,Junsen Wang,Hao Chang

First Author:Qianqian Zhang (张牵牵)

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TMTT.2024.3418014

Discipline:Engineering

Document Type:J

Volume:online

Issue:online

Page Number:1-12

Translation or Not:no

Date of Publication:2024-07-03