Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Multiuser Detection
A Compact Neural Network Based CDMA Receiver for Multimedia Wireless Communication
ICCD '96 Proceedings of the 1996 International Conference on Computer Design, VLSI in Computers and Processors
A New Adaptive Neural Network Multiuser Detector in Synchronous CDMA Systems
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
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This paper proposes a novel adaptive MUD algorithm for a wide variety (practically any kind) of interference limited systems, for example, code division multiple access (CDMA). The algorithm is based on recently developed neural network techniques and can perform near optimal detection in the case of unknown channel characteristics. The proposed algorithm consists of two main blocks: one estimates the symbols sent by the transmitters and the other identifies each channel of the corresponding communication links. The estimation of symbols is carried out either by a stochastic Hopfield net (SHN), by a hysteretic neural network (HyNN) or by both. The channel identification is based on either the self-organizing feature map (SOM) or the learning vector quantization (LVQ). The combination of these two blocks yields a powerful real-time detector with near optimal performance. The performance is analyzed by extensive simulations.