Topics in matrix analysis
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas
Wireless Personal Communications: An International Journal
Parameter estimation problems with singular information matrices
IEEE Transactions on Signal Processing
On Estimation of Covariance Matrices With Kronecker Product Structure
IEEE Transactions on Signal Processing
Maximum likelihood parameter and rank estimation in reduced-rankmultivariate linear regressions
IEEE Transactions on Signal Processing
A generic model for MIMO wireless propagation channels in macro- and microcells
IEEE Transactions on Signal Processing
Reduced rank linear regression and weighted low rank approximations
IEEE Transactions on Signal Processing - Part I
Asymptotic eigenvalue distributions and capacity for MIMO channels under correlated fading
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels
IEEE Transactions on Information Theory
A stochastic MIMO radio channel model with experimental validation
IEEE Journal on Selected Areas in Communications
From theory to practice: an overview of MIMO space-time coded wireless systems
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
Linear MMSE MIMO channel estimation with imperfect channel covariance information
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
On the robustness of MIMO LMMSE channel estimation
IEEE Transactions on Wireless Communications
EURASIP Journal on Wireless Communications and Networking
Separable linear discriminant analysis
Computational Statistics & Data Analysis
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Many algorithms for transmission in multiple input multiple output (MIMO) communication systems rely on second order statistics of the channel realizations. The problem of estimating such second order statistics of MIMO channels, based on limited amounts of training data, is treated in this article. It is assumed that the Kronecker model holds. This implies that the channel covariance is the Kronecker product of one covariance matrix that is associated with the array and the scattering at the transmitter and one that is associated with the receive array and the scattering at the receiver. The proposed estimator uses training data from a number of signal blocks (received during independent fades of the MIMO channel) to compute the estimate. This is in contrast to methods that assume that the channel realizations are directly available, or possible to estimate almost without error. It is also demonstrated how methods that make use of the training data indirectly via channel estimates can be biased. An estimator is derived that can, in an asymptotically optimal way, use, not only the structure implied by the Kronecker assumption, but also linear structure on the transmit- and receive covariance matrices. The performance of the proposed estimator is analyzed and numerical simulations illustrate the results and also provide insight into the small sample behaviour of the proposed method.