RECURRENT NEURAL NETWORKS FOR SUB-OPTIMAL MULTIUSER DETECTION

This paper explores the use of recurrent neural networks for sub-optimal detection in code division multiple access systems. Research has shown that detectors based on the Hopfield recurrent neural network suffer from localized optimization. The basic Hopfield model is reviewed and we illustrate its use as a multiuser receiver. We investigate the use of stochastic methods to achieve a global minimum solution. A stochastic Hopfield network that employs a probabilistic firing mechanism is proposed for multiuser detection. The performance of the proposed model is investigated via simulation with respect to common linear detectors.