Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

In this paper we propose low-complexity algorithms for estimating flat, time-varying and spatially correlated MIMO channels. The proposed estimators employ Kalman filters to track the channel in orthogonal space-time block coded systems. After developing a state-space model for spatially correlated MIMO channels, we show that the proposed estimators can be simplified by using the orthogonality inherent to orthogonal space-time block codes. We also show that the channel estimates provided by the proposed algorithms correspond to weighted sums of instantaneous maximum likelihood channel estimates. For constant modulus signal constellations, we reduce the receiver complexity even more by proposing a steady-state Kalman filter. Simulation results indicate that there is no significant performance degradation of the steady-state filter compared to the first proposed algorithm and that both algorithms outperform conventional adaptive filters.