A Neural Network-Based Blind Multiuser Receiver for DS-CDMA Communication Systems

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 in order 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 under multipath-fading conditions.