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

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4 Author(s)
J. D. Matyjas ; Dept. of Electr. Eng., State Univ. of New York, Buffalo, NY, USA ; I. N. Psaromiligkos ; S. N. Batalama ; M. J. Medley

We consider a multilayer perceptron neural network (NN) receiver architecture for the recovery of the information bits of a direct-sequence code-division-multiple-access (DS-CDMA) user. We develop a fast converging adaptive training algorithm that minimizes the bit-error rate (BER) at the output of the receiver. The adaptive algorithm has three key features: i) it incorporates the BER, i.e., the ultimate performance evaluation measure, directly into the learning process, ii) it utilizes constraints that are derived from the properties of the optimum single-user decision boundary for additive white Gaussian noise (AWGN) multiple-access channels, and iii) it embeds importance sampling (IS) principles directly into the receiver optimization process. Simulation studies illustrate the BER performance of the proposed scheme.

Published in:

IEEE Transactions on Neural Networks  (Volume:15 ,  Issue: 2 )