State-space approach to spatially correlated MIMO OFDM channel estimation

In this paper we address the problem of channel parameter estimation and tracking for multiple-input-multiple-output (MIMO) OFDM systems where the channels are spatially correlated. Starting from the OFDM transmission model with independent MIMO channels, we derive a state-space model that accounts for a spatial correlation structure. An advanced spatial MIMO correlation model validated by measurements is used. The parameters describing the dynamics of the state (i.e. the state transition matrix, the spatial MIMO correlation and the state noise statistics) are estimated from the received data. The Kalman filter is then applied to estimate and track the time-varying channels in time domain. Our examples, using measured MIMO channels, show that reliable parameter and channel estimation can be performed under realistic conditions.