Neural Networks, IEEE - INNS - ENNS International Joint Conference on
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Abstract

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 detector also grows up exponentially with the number of users. There has been a lot of interest in sub-optimal 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 back-propagation 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.
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