Kalman Filter-Based Channel Tracking in MIMO-OSTBC Systems

Abstract

Abstract-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.

Adaptive Channel Estimation in Space-Time Coded MIMO Systems

“…In spite of the significant complexity reduction, it is shown in (Loiola et al, 2009) that the SS-KCE suffers negligible performance degradation compared to the regular KCE, especially when channel variations are fast. However, the SS-KCE of (Loiola et al, 2009) depends on the solution of a Riccati equation. This chapter extends (Loiola et al, 2009) by providing an explicit expression for the SS-KCE and by proving that, under mild conditions, the SS-KCE is stable.…”

“…Furthermore, the complexity reduction in (Balakumar et al, 2007) cannot be achieved if the channels were correlated. A generalization of (Balakumar et al, 2007) for correlated channels is presented in (Loiola et al, 2009) with a complexity similar to that in (Balakumar et al, 2007). As with most Kalman channel estimators (KCEs), the KCE in (Loiola et al, 2009), is a time-varying filter whose coefficients need to be computed anew for each time instant.…”

“…A generalization of (Balakumar et al, 2007) for correlated channels is presented in (Loiola et al, 2009) with a complexity similar to that in (Balakumar et al, 2007). As with most Kalman channel estimators (KCEs), the KCE in (Loiola et al, 2009), is a time-varying filter whose coefficients need to be computed anew for each time instant. This computation increases the complexity of the filter, especially because it involves a matrix inversion.…”

“…However, the SS-KCE of (Loiola et al, 2009) depends on the solution of a Riccati equation. This chapter extends (Loiola et al, 2009) by providing an explicit expression for the SS-KCE and by proving that, under mild conditions, the SS-KCE is stable. It also proves that, at worst, the SS-KCE is marginally stable, but it is never unstable.…”

“…Taking advantage of this fact, the authors in (Loiola et al, 2009) derive a steady-state Kalman channel estimator (SS-KCE) (Simon, 2006), where the asymptotic value of the filter coefficients are computed at the initialization and used in a time-invariant filter. In spite of the significant complexity reduction, it is shown in (Loiola et al, 2009) that the SS-KCE suffers negligible performance degradation compared to the regular KCE, especially when channel variations are fast. However, the SS-KCE of (Loiola et al, 2009) depends on the solution of a Riccati equation.…”