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

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 (A...

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Bibliographic Details
Main Authors: Renteria-Cedano, J. A., Aguilar-Lobo, L. M., Loo-Yau, J. R., Ortega-Cisneros, S.
Format: Conference Proceeding
Language:English
Subjects:
ANN
Biological neural networks
Computer architecture
Field programmable gate arrays
FPGA
Hardware design languages
Mathematical model
NARX network
Neurons
Recurrent neural networks
Verilog
Xilinx
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Description
Summary: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.
ISSN:1548-3746
1558-3899
DOI:10.1109/MWSCAS.2014.6908389