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Fast Converging Minimum Probability of Error Neural Network Receivers for DS-CDMA Communications
2004
IEEE Transactions on Neural Networks
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
doi:10.1109/tnn.2004.824409
pmid:15384536
fatcat:7rsybyrkl5fmvl3ufexu7yglee