EURASIP Journal on Applied Signal Processing
Volume 2002 (2002), Issue 12, Pages 1401-1414
doi:10.1155/S1110865702209130

Adaptive Near-Optimal Multiuser Detection Using a Stochastic and Hysteretic Hopfield Net Receiver

Gábor Jeney,1 János Levendovszky,2 László Pap,1 and E. C. van der Meulen3

1Mobile Communications Laboratory, Department of Telecommunications, Budapest University of Technology and Economics, Budapest, Hungary
2Signal Processing Laboratory, Department of Telecommunications, Budapest University of Technology and Economics, Budapest, Hungary
3Department of Mathematics, Katholieke Universiteit Leuven, Leuven, Belgium

Received 31 January 2002; Revised 20 June 2002

Copyright © 2002 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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, the other identifies each channel of the corresponding communication links. The estimation of symbols is carried out either by a stochastic Hopfield net (SHN) or by a hysteretic neural network (HyNN) or 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.