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

  • Yufei Huang1Email author,

    Affiliated with

    • Jianqiu (Michelle) Zhang2,

      Affiliated with

      • Isabel Tienda Luna3,

        Affiliated with

        • Petar M Djurić4 and

          Affiliated with

          • Diego Pablo Ruiz Padillo3

            Affiliated with

            EURASIP Journal on Wireless Communications and Networking20052005:960165

            DOI: 10.1155/WCN.2005.130

            Received: 30 April 2004

            Published: 28 April 2005


            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

            Authors’ Affiliations

            Department of Electrical Engineering, The University of Texas at San Antonio
            Department of Electrical and Computer Engineering, University of New Hampshire
            Departamento de Física Aplicada, Universidad de Granada
            Department of Electrical and Computer Engineering, Stony Brook University


            © Huang et al. 2005

            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.