Sequential Monte Carlo sampling detector for Rayleigh fast-fading channels

Detection of symbols transmitted over a frequency flat Rayleigh fast-fading channel is considered. This problem can be modeled as a dynamic state space model. A novel method for channel estimation and detection of transmitted data is presented based on the Monte Carlo sampling filter methodology. The channel fading coefficients and transmitted variables are treated as hidden variables. The channel coefficients are modeled as an autoregressive (AR) process. Particles (samples) of hidden variables are sequentially generated from the so called importance sampling density based on past observations. These are then propagated and weighted according to the required conditional posterior distribution. The particles along with their weights provide an estimate of the hidden variables. It can be seen through the simulations that the performance of this detector is comparable to the matched filter with known channel fading coefficients. Moreover, the Gaussian noise assumption in the noisy channel can be easily relaxed and a solution provided by the same methodology.