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Proceedings Paper

Fast-converging adaptive training of DS-CDMA neural network receivers
Author(s): John D. Matyjas; Ioannis N. Psaromiligkos; Stella N. Batalama; Michael J. Medley
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Paper Abstract

In this work we consider the problem of detecting the information bit of a direct-sequence code-division-multiple-access (DS-CDMA) user in the presence of spread spectrum interference and AWGN using a multi-layer perceptron neural network receiver. We develop a fast converging adaptive training algorithm that minimizes the mean square error (MSE) at the output of the receiver. The proposed adaptive algorithm has two key features: (i) it utilizes constraints that are derived from properties of the optimum single-user decision boundary for AWGN multiple-access channels, and (ii) it embeds importance sampling principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Paper Details

Date Published: 23 July 2003
PDF: 11 pages
Proc. SPIE 5100, Digital Wireless Communications V, (23 July 2003); doi: 10.1117/12.487977
Show Author Affiliations
John D. Matyjas, SUNY/Buffalo (United States)
Ioannis N. Psaromiligkos, McGill Univ. (Canada)
Stella N. Batalama, SUNY/Buffalo (United States)
Michael J. Medley, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 5100:
Digital Wireless Communications V
Raghuveer M. Rao; Soheil A. Dianat; Michael D. Zoltowski, Editor(s)

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