Fast-converging adaptive training of DS-CDMA neural network receivers

John D. Matyjas; Ioannis N. Psaromiligkos; Stella N. Batalama; Michael J. Medley

doi:10.1117/12.487977

23 July 2003 Fast converging minimum probability of error neural network receivers for DS-CDMA communications

John D. Matyjas, Ioannis N. Psaromiligkos, Stella N. Batalama, Michael J. Medley

Proceedings Volume 5100, Digital Wireless Communications V; (2003) https://doi.org/10.1117/12.487977
Event: AeroSense 2003, 2003, Orlando, Florida, United States

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.

Citation Download Citation

John D. Matyjas, Ioannis N. Psaromiligkos, Stella N. Batalama, and Michael J. Medley "Fast converging minimum probability of error neural network receivers for DS-CDMA communications", Proc. SPIE 5100, Digital Wireless Communications V, (23 July 2003); https://doi.org/10.1117/12.487977

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