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

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 transmis...

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Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques 2023-08, Vol.71 (8), p.1-12
Main Authors: Jiang, Chengye, Yang, Guichen, Han, Renlong, Tan, Jingchao, Liu, Falin
Format: Article
Language:English
Subjects:
Adaptation models
Artificial neural networks
Behavioral sciences
Complexity
Complexity theory
Computer networks
Configuration management
Digital predistortion (DPD)
gated dynamic model
Logic gates
neural network (NN)
Neural networks
power amplifier (PA)
Power amplifiers
Radio frequency
Transmitters
varying transmission configurations
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Summary: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.
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2023.3241612