NDLI: Implementation of a NARX neural network in a FPGA for modeling the inverse characteristics of power amplifiers

Content Provider	IEEE Xplore Digital Library
Author	Renteria-Cedano, J.A. Aguilar-Lobo, L.M. Loo-Yau, J.R. Ortega-Cisneros, S.
Copyright Year	2014
Description	Author affiliation: Dept. de Ing. Electr. y Cienc. Computacionales, Centro de Investig. y de Estudios Av. del I.P.N., Zapopan, Mexico (Renteria-Cedano, J.A.; Aguilar-Lobo, L.M.; Loo-Yau, J.R.; Ortega-Cisneros, S.)
Abstract	In this paper the hardware implementation of a NARX neural network algorithm using a Field Programmable Gate Array (FPGA) is presented. A NARX network is a Recurrent Neural Network (RNN) suitable for modeling nonlinear systems with promising results for the modeling of the inverse characteristics (AM/AM and AM/PM) of Power Amplifiers (PAs). The implementation is realized in the Xilinx ISE tool with the Virtex-6 FPGA ML 605 Evaluation Kit using Verilog language. Experimental results have shown a high correlation with the inverse model computed with SystemVue in co-simulation with MATLAB for a GaN class F PA working with a LTE signal center at 2 GHz.
Starting Page	209
Ending Page	212
File Size	1003223
Page Count	4
File Format	PDF
ISBN	9781479941346
ISSN	15483746
e-ISBN	9781479941322
DOI	10.1109/MWSCAS.2014.6908389
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Field programmable gate arrays Neurons Mathematical model Recurrent neural networks Computer architecture Hardware design languages Biological neural networks NARX network ANN Verilog Xilinx FPGA
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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