Gated Dynamic Neural Network Model for Digital Predistortion of RF Power Amplifiers With Varying Transmission Configurations

DOI number:10.1109/TMTT.2023.3241612

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

Journal:IEEE Transactions on Microwave Theory and Techniques

Key Words:Digital predistortion (DPD), gated dynamic model, neural network (NN), power amplifier (PA), varying transmission configurations.

Abstract:The future intelligent transmitter will dynamically adjust the transmission configuration on demand, which will bring new challenges to digital predistortion (DPD). In this article, we present a gated dynamic neural network (GDNN) DPD model to linearize the power amplifier (PA) with varying transmission configurations. The proposed GDNN model is composed of a gating network and a backbone network that can be any NN-based DPD model designed for a fixed configuration. The core idea of the GDNN model is that the backbone model can be dynamically adjusted using the configuration-dependent weights generated by the gating network to achieve transmission configuration-adaptive DPD. To further reduce the running complexity of the GDNN DPD, a sparse GDNN (SGDNN) DPD model is also proposed, which selectively activates the neurons of the backbone network according to the transmission configuration. Experiments are performed with a Doherty PA to validate the proposed method, where the varying transmission configuration includes power level, signal bandwidth (BW), and peak-to-average power ratio. The test results demonstrate that the proposed method can effectively linearize the PA with dynamic transmission configuration and has excellent configuration generalization capability. Moreover, the sparse gating technique can reduce the running complexity of the GDNN DPD by more than 50% with only a slight performance loss.

First Author:Chengye Jiang (姜成业)

Co-author:Guichen Yang,Renlong Han,Jingchao Tan

Indexed by:Journal paper

Correspondence Author:Falin Liu

Document Code:10.1109/TMTT.2023.3241612

Discipline:Engineering

Document Type:J

Volume:71

Issue:8

Page Number:3605-3616

Translation or Not:no

Date of Publication:2023-08-01

Included Journals:SCI、EI