Improved Bayesian MIMO Channel Tracking for Wireless Communications: Incorporating Dynamical Channel Model, submitted for publication

Abstract — This paper investigates the improved decoder per-formance 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 will be 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. Index Terms — MIMO, wireless channel tracking, dynamic linear modelling, particle filtering