Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Blind multiuser detector for chaos-based CDMA using support vector machine
IEEE Transactions on Neural Networks
Wavelet and subband transforms: fundamentals and communication applications
IEEE Communications Magazine
Hopfield neural network implementation of the optimal CDMA multiuser detector
IEEE Transactions on Neural Networks
Fast converging minimum probability of error neural network receivers for DS-CDMA communications
IEEE Transactions on Neural Networks
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Multicarrier Code Division Multiple Access (MCCDMA) is one of the most promising techniques for high bit rate and high user capacity transmission in future broadband mobile services. The use of carrier interferometry (CI) codes further improves this user capacity relative to the conventional spreading codes. Neural networks (NN) are trained to optimize the weight factor of different users in minimum mean square error combining receiver (MMSEC) via back propagation type algorithm. Optimum values of weight factors give stable decision variables that lead to improve receiver performance without having the knowledge of channel state information and transmit signal powers. Decision variables are then used for realization of efficient block parallel interference cancellation (BPIC) as multiuser detection (MUD). Simulation results show that BER performance using NN is better than conventional network.