Estimation and equalization of fading channels with random coefficients
Signal Processing - Special issue on higher order statistics
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
A stochastic MIMO radio channel model with experimental validation
IEEE Journal on Selected Areas in Communications
Capacity limits of MIMO channels
IEEE Journal on Selected Areas in Communications
Kalman filter-based channel tracking in MIMO-OSTBC systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A new approach for joint channel estimation and data detection in MIMO wireless systems
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Blind Sequential Monte Carlo Joint Tracking of Channel State and Frequency Offset in OFDM Systems
Wireless Personal Communications: An International Journal
Superimposed training designs for spatially correlated MIMO-OFDM systems
IEEE Transactions on Wireless Communications
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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.