Minimum probability of error for asynchronous Gaussian multiple-access channels
IEEE Transactions on Information Theory
Convergent activation dynamics in continuous time networks
Neural Networks
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Multiuser Detection
Blind adaptive multiuser detection
IEEE Transactions on Information Theory
UMTS/IMT-2000 based on wideband CDMA
IEEE Communications Magazine
An overview of air interface multiple access for IMT-2000/UMTS
IEEE Communications Magazine
Adaptive interference cancellation for DS-CDMA systems using neural network techniques
IEEE Journal on Selected Areas in Communications
Hopfield neural network implementation of the optimal CDMA multiuser detector
IEEE Transactions on Neural Networks
Space-Time Blind Multiuser Detection for Multiuser DS-CDMA and Oversampled Systems
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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The Universal Mobile Telecommunications System (UMTS) which is based on Wideband-Code Division Multiple Access (W-CDMA) techniques is one of the most important broadband wireless communication systems. Adaptive Blind Multiuser Detection was widely considered for mobile receivers. The main drawback of this approach is that it achieves the optimum solution after a certain number of bit times. This paper deals with a new neural network approach inorder to reduce the convergence time in different application environments.In particular, a modified Kennedy-Chua neural network, based on the Hopfield model is proposed. The neural network stability was investigated by means of a suitable analytical approach, while the performance of the proposed receiver scheme was derived by means of computer simulations. The numerical results shown in this paper highlight a fast convergence behavior of the proposed scheme, in particular undermultipath-fading conditions.