Improved bayesian MIMO channel tracking for wireless communications: incorporating a dynamical model

This paper investigates the improved decoder performance offered by incorporating dynamic linear modelling techniques when applied to particle filters for use in tracking the MIMO wireless channel. Conventional Bayesian-based receivers that perform channel tracking necessarily require a wireless channel model, typified by the use of a low order auto-regressive (AR) model. Normally, the model parameters are static in nature and are estimated a priori of any transmission; thus if the channel conditions change, a model mismatch occurs and system performance is degraded. Our method allows for time-varying channel statistics by modelling the channel fading rate as a Markov random walk. This new procedure allows the channel model to assume a time-varying behavior. As shown through simulations, the incorporation of dynamic modelling of time-dispersive channels not only offers superior performance, but at high SNR eliminates the error-rate floor commonly seen in systems using the static AR models