Fast converging adaptive training of DS-CDMA neural network receivers

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 axe 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.