Sub-optimal Multiuser Detector Using a Time-varying Gain Chaotic Simulated Annealing Neural Network

This paper proposes a sub-optimal multiuser detector (MUD) algorithm for CDMA system based on the neural network with Time-varying Gain Chaotic Simulated Annealing Neural Network (TGCSANN), and gives a concrete model of the MUD after appropriate transformations and mappings. By refraining from the serious local optimal problem of Hopfield-type neural networks, the TGCSANN makes use of the time-varying and chaotic simulated annealing parameters of the recurrent neural network to control the evolving behavior of the network so that the network undergoes the transition from chaotic behavior to gradient convergence. It has richer and more flexible dynamics rather than conventional neural networks, so that it can be expected to have much ability to search for globally optimal or sub-optimal solutions. Simulation experiments have been performed to show the effectiveness and validation of the proposed neural network based method for MUD.