Swarm-Based Particle Filtering Detector for Wireless Communications over Flat Fading Channels

In this paper, an improved particle filtering technique is presented to estimate channel coefficients and detect transmitted signals over fading channels. A common fading phenomenon known as flat Rayleigh fading is typically modeled as an autoregressive (AR) process. For non-Gaussian systems, the channel coefficients and transmitted symbols can be estimated using the particle filter (PF) technique. The basic idea of PF is to estimate the posterior probabilities sequentially using importance sampling (IS) when the observations are available. The traditional PF method uses the prior density as the importance function. Although this choice reduces computational complexity, it is not efficient enough because the particle prediction only depends on the previous state and not the current observations. The proposed PF technique called swarm-based particle filter (SPF) tends to move particles to higher probability region of the likelihood function according to the current observation and the previous channel state using particle swarm optimization propagation. Simulation results are presented to show that SPF detector has better performance with lower computational complexity, especially in the case of high signal-to-noise ratio (SNR) at the receiver.