Application of particle filters to MIMO wireless communications

The implementation of current space-time codes is often performed under the assumption that the additive channel noise is white and Gaussian, and that the receiver has precise knowledge of the realization of the fading process. Here we study the application of particle filters to MIMO systems in order to reduce the uncertainty in the estimation of the channel fading coefficients. Using known orthogonal training sequences for channel estimation, an analysis on the estimated fading gains reveals that they are stochastic in nature with mean equal to the true channel gain, and variance proportional to the inverse of the transmit power. Furthermore, this estimation uncertainty is shown to incur a penalty in the signal-to-noise ratio, thus reducing overall system efficiency. In order to mitigate the effects of estimation error suffered by current MIMO systems, we use particle filters for channel tracking. Modeling the wireless fading channel as an AR process, the particle filter is shown to be superior to conventional estimation techniques by providing a significant decrease in the mean-squared error (MSE) of the channel estimate. Simulations illustrate the robust nature of this new scheme.