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.
This article is part of the series Multiuser Detection and Blind Estimation.
Adaptive Near-Optimal Multiuser Detection Using a Stochastic and Hysteretic Hopfield Net Receiver
1 Mobile Communications Laboratory, Department of Telecommunications, Budapest University of Technology and Economics, Budapest, Hungary
2 Signal Processing Laboratory, Department of Telecommunications, Budapest University of Technology and Economics, Budapest, Hungary
3 Department of Mathematics, Katholieke Universiteit Leuven, Leuven, Belgium
EURASIP Journal on Advances in Signal Processing 2002, 2002:681909 doi:10.1155/S1110865702209130
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2002/12/681909
|Received:||31 January 2002|
|Revisions received:||20 June 2002|
|Published:||2 January 2003|
© 2002 Jeney et al.