Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Optimal Training Sequences for Frequency-Selective MIMO Correlated Fading Channels
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
A Matrix Handbook for Statisticians
A Matrix Handbook for Statisticians
IEEE Transactions on Wireless Communications
IEEE Transactions on Information Theory
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
A stochastic MIMO radio channel model with experimental validation
IEEE Journal on Selected Areas in Communications
On the robustness of MIMO LMMSE channel estimation
IEEE Transactions on Wireless Communications
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In this paper, we investigate the effects of imperfect knowledge of the channel covariance matrix on the performance of a linear minimum mean-square-error (MMSE) estimator for multiple-input multiple-output (MIMO) channels. The estimation mean-square-error (MSE) is analytically analyzed by providing both a very tight lower bound and an upper bound. The proposed analysis is useful for the understanding of how estimation accuracy of the channel covariance matrix impacts on system performance, depending on the average signal-to-noise ratio (SNR) and specific propagation conditions. Conclusions are fully supported by numerical results.