Open Access

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

  • Gábor Jeney1Email author,
  • János Levendovszky2,
  • László Pap1 and
  • EC van der Meulen3
EURASIP Journal on Advances in Signal Processing20032002:681909

DOI: 10.1155/S1110865702209130

Received: 31 January 2002

Published: 2 January 2003

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.

Keywords

CDMA system adaptive detection recurrent neural network self-organizing maps learning vector quantization

Authors’ Affiliations

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

Copyright

© Jeney et al. 2002