Multisample receivers for time-varying channels using particle filtering

It has been reported that multisampling provides an implicit time diversity and that processing more than one sample per symbol results in achieving improved performance over processing a single sample. We explore this time diversity and develop a novel method for symbol detection from several samples per symbol. The method is based on a Bayesian formulation in which we use sequential Monte Carlo filtering, also known as particle filtering. Particle filtering has been applied to data detection problems with a single sample per symbol, and it has shown promising results. The proposed method is developed for communication systems characterized by time-varying channels. We demonstrate, via computer simulations, that significant performance improvement can be achieved by processing multisamples.