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

Abstract

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 M -algorithm (SMA), which, although having lower complexity than the generic PF implementation, maintains comparable performance.

Keywords

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