DBNN, FDNN, discriminative learning, and back-propagation neural networks in DS/CDMA systems

The conventional single user detector in DS/CDMA (direct sequence code division multiple access) systems involves multiple access interference and near-far effect which cause the limitation in capacity. The complexity of optimum multiuser detectors also grows up exponentially with the number of users. There has been a lot of interest in suboptimal multiuser detectors with less complexity and reasonable performance. In this paper, we apply decision based neural network (DBNN), fuzzy decision neural network (FDNN), and discriminative learning and backpropagation neural networks employing multilayer perceptron for detection of signals of users in DS/CDMA systems in additive white Gaussian noise (AWGN) channel. We also show that FDNN and discriminative learning neural net have almost the same performance