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Application of the NARX neural network as a digital predistortion technique for linearizing microwave power amplifiers
Author(s) -
AguilarLobo Lina M.,
LooYau Jose R.,
RayasSánchez Jose E.,
OrtegaCisneros Susana,
Moreno Pablo,
ReynosoHernández J. A.
Publication year - 2015
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.29281
Subject(s) - predistortion , nonlinear autoregressive exogenous model , amplifier , linearization , microwave , electronic engineering , adjacent channel , artificial neural network , nonlinear system , bandwidth (computing) , linearity , computer science , engineering , control theory (sociology) , cmos , telecommunications , artificial intelligence , physics , control (management) , quantum mechanics
This work presents a digital predistortion (DPD) scheme to linearize power amplifiers (PAs) using a recurrent neural network called Nonlinear AutoRegressive with eXogenous input model (NARX) neural network (NARXNN). The architecture of the NARXNN is based on a class of discrete‐time nonlinear system named NARX. Its topology has embedded memory at the input and output of the neural architecture, which allows an efficient linearization of PAs. To show the benefits of the DPD with NARXNN, a commercial PA is fed with a long term evolution signal at 2.0 GHz with 10 MHz of bandwidth. Our experimental results show an adjacent channel leakage ratio improvement of 24 dB. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:2137–2142, 2015