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  • Research Article
  • Open Access

Blind Multiuser Detection by Kurtosis Maximization for Asynchronous Multirate DS/CDMA Systems

EURASIP Journal on Advances in Signal Processing20062006:084930

https://doi.org/10.1155/ASP/2006/84930

  • Received: 9 January 2006
  • Accepted: 16 July 2006
  • Published:

Abstract

Chi et al. proposed a fast kurtosis maximization algorithm (FKMA) for blind equalization/deconvolution of multiple-input multiple-output (MIMO) linear time-invariant systems. This algorithm has been applied to blind multiuser detection of single-rate direct-sequence/code-division multiple-access (DS/CDMA) systems and blind source separation (or independent component analysis). In this paper, the FKMA is further applied to blind multiuser detection for multirate DS/CDMA systems. The ideas are to properly formulate discrete-time MIMO signal models by converting real multirate users into single-rate virtual users, followed by the use of FKMA for extraction of virtual users' data sequences associated with the desired user, and recovery of the data sequence of the desired user from estimated virtual users' data sequences. Assuming that all the users' spreading sequences are given a priori, two multirate blind multiuser detection algorithms (with either a single receive antenna or multiple antennas), which also enjoy the merits of superexponential convergence rate and guaranteed convergence of the FKMA, are proposed in the paper, one based on a convolutional MIMO signal model and the other based on an instantaneous MIMO signal model. Some simulation results are then presented to demonstrate their effectiveness and to provide a performance comparison with some existing algorithms.

Keywords

  • Information Technology
  • Data Sequence
  • Convergence Rate
  • Performance Comparison
  • Quantum Information

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Authors’ Affiliations

(1)
Department of Electrical Engineering and Institute of Communications Engineering, National Tsing Hua University, Hsinchu, 30043, Taiwan

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Copyright

© Peng et al. 2006

This article is published under license to BioMed Central Ltd. 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.

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