Open AccessNon‐linear dynamic behaviour modelling for broadband power amplifiers based on deep convolution generative adversarial networks
Author(s) -
Su Rina,
Liu Taijun,
Ye Yan,
Xu Gaoming
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12107
Subject(s) - predistortion , amplifier , convolution (computer science) , electronic engineering , computer science , bandwidth (computing) , deconvolution , broadband , power (physics) , algorithm , mathematics , artificial neural network , artificial intelligence , telecommunications , engineering , physics , quantum mechanics
This letter presents a non‐linear dynamic behaviour model for characterising the broadband power amplifiers (PAs) by using deep convolution generative adversarial networks (DCGAN). The DCGAN structure is based on the convolution neural network model and combines the generative adversarial networks to improve its linearisation ability of the digital predistortion. The DCGAN contains a generation model and a discriminant model. It imports convolution with steps and deconvolution into the structure, respectively, which makes the accuracy of the power amplifier non‐linear model to improve further. For verification, a 5G NR test signal with 100 MHz bandwidth is employed for testing a Doherty RF‐PA that operates at 1800 MHz. The experimental results illustrate that the normalised mean square error value is at least 12 dB higher than the traditional models, and the out‐of‐band suppression of the DCGAN predistorter can be up to 15 dB better than other models.