By Topic

FPGA-implementation of an adaptive neural network for RF power amplifier modeling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Bahoura, M. ; Dept. of Eng., Univ. du Quebec a Rimouski, Rimouski, QC, Canada ; Chan-Wang Park

In this paper, we propose an architecture for FPGA-implementation of neural adaptive neural network RF power behavioral modeling. The real-valued time-delay neural network (RVTDNN) and the backpropagation (BP) learning algorithm were implemented on FPGA using Xilinx System Generator for DSP and the Virtex-6 FPGA ML605 Evaluation Kit. Performances obtained with 16-QAM modulated test signal and material resource requirement are presented for a network of six hidden layer neurons.

Published in:

New Circuits and Systems Conference (NEWCAS), 2011 IEEE 9th International

Date of Conference:

26-29 June 2011