Neural Networks for Multiuser Detection of Signals in DS/CDMA Systems

Abstract

Multiple access interference and near-far effect cause the performance of the conventional single user detector in DS/CDMA systems to degrade. Due to the high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. In this paper, we analyse the performance of the multilayer perceptron backpropagation neural network as a multiuser detector of CDMA signals in AWGN and multipath fading channels. Our results show significant improvement over previous research. We compare neural network performance with the other detectors, and apply different neural networks and criteria, such as decision-based, fuzzy decision, discriminative learning, minimum classification, and cross entropy neural networks, and compare their performance. We propose a modified decision-based network which significantly improves the performance.