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This article is part of the series Advanced Signal Processing Algorithms for Wireless Communications.

Open Access Open Badges Research Article

Adaptive Blind Multiuser Detection over Flat Fast Fading Channels Using Particle Filtering

Yufei Huang1*, Jianqiu (Michelle) Zhang2, Isabel Tienda Luna3, Petar M Djurić4 and Diego Pablo Ruiz Padillo3

Author Affiliations

1 Department of Electrical Engineering, The University of Texas at San Antonio, San Antonio, TX 78249-06615, USA

2 Department of Electrical and Computer Engineering, University of New Hampshire, Durham, NH 03824, USA

3 Departamento de Física Aplicada, Universidad de Granada, Granada 18071, Spain

4 Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794-2350, USA

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EURASIP Journal on Wireless Communications and Networking 2005, 2005:960165  doi:10.1155/WCN.2005.130

The electronic version of this article is the complete one and can be found online at: http://jwcn.eurasipjournals.com/content/2005/2/960165

Received:30 April 2004
Revisions received:16 September 2004
Published:28 April 2005

© 2005 Huang et al.

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.

We propose a method for blind multiuser detection (MUD) in synchronous systems over flat and fast Rayleigh fading channels. We adopt an autoregressive-moving-average (ARMA) process to model the temporal correlation of the channels. Based on the ARMA process, we propose a novel time-observation state-space model (TOSSM) that describes the dynamics of the addressed multiuser system. The TOSSM allows an MUD with natural blending of low-complexity particle filtering (PF) and mixture Kalman filtering (for channel estimation). We further propose to use a more efficient PF algorithm known as the stochastic -algorithm (SMA), which, although having lower complexity than the generic PF implementation, maintains comparable performance.

multiuser detection; time-observation state-space model; fading channel estimation; particle filtering; mixture Kalman filter

Research Article