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Báo cáo hóa học: " FPGA Prototyping of RNN Decoder for Convolutional Codes"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: FPGA Prototyping of RNN Decoder for Convolutional Codes | Hindawi Publishing Corporation EURASIP Journal on Applied Signal Processing Volume 2006 Article ID 15640 Pages 1-9 DOI 10.1155 ASP 2006 15640 FPGA Prototyping of RNN Decoder for Convolutional Codes Zoran Salcic Stevan Berber and Paul Secker Department of Electrical and Electronic Engineering the University of Auckland 38 Princess Street Auckland 1020 New Zealand Received 30 May 2005 Revised 29 November 2005 Accepted 21 January 2006 Recommended for Publication by Roger Woods This paper presents prototyping of a recurrent type neural network RNN convolutional decoder using system-level design specification and design flow that enables easy mapping to the target FPGA architecture. Implementation and the performance measurement results have shown that an RNN decoder for hard-decision decoding coupled with a simple hard-limiting neuron activation function results in a very low complexity which easily fits into standard Altera FPGA. Moreover the design methodology allowed modeling of complete testbed for prototyping RNN decoders in simulation and real-time environment same FPGA thus enabling evaluation of BER performance characteristics of the decoder for various conditions of communication channel in real time. Copyright 2006 Hindawi Publishing Corporation. All rights reserved. 1. INTRODUCTION Recurrent type neural networks RNN have been successfully used in various fields of digital communications primarily due to their nonlinear processing possible parallel processing that could accommodate recent requirements for high-speed signal transmission and also expected efficient hardware implementations 1 . In the past several years substantial efforts have been made to apply RNNs in error control coding theory. Initially these networks were applied for block codes decoding 2 3 and then for convolutional 4-7 and turbo codes decoding 8 .In 5-7 it was shown that the decoding problem could be formulated as a function minimization problem and the gradient descent algorithm was .