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 Communications
Doppler spectrum from moving scatterers in a random environment
IEEE Transactions on Wireless Communications
Large system spectral analysis of covariance matrix estimation
IEEE Transactions on Information Theory
Linear MMSE MIMO channel estimation with imperfect channel covariance information
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Shrinkage algorithms for MMSE covariance estimation
IEEE Transactions on Signal Processing
Covariance Matrix Estimation With Heterogeneous Samples
IEEE Transactions on Signal Processing
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing
CHANNEL ESTIMATION FOR WIRELESS OFDM SYSTEMS
IEEE Communications Surveys & Tutorials
IEEE Transactions on Wireless Communications
Performance Analysis of MIMO System with Linear MMSE Receiver
IEEE Transactions on Wireless Communications - Part 2
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
Hi-index | 0.01 |
The robustness of the linear minimum mean square error (LMMSE) channel estimator is studied with respect to the reliability of the estimated channel correlation matrix used for its implementation. The analysis is of interest in practical applications of multiple-input multiple-output (MIMO) systems, where a perfect estimate of the channel correlation matrix is not available. The channel estimation mean square error (MSE) is analytically analyzed assuming a general structure for the estimated channel correlation matrix used to implement the LMMSE channel estimator. The obtained results are successively detailed to the case of channel correlation matrices derived by sample correlation estimation methods. It is observed that the use of a coarse estimate of the channel correlation matrix can lead to a severe degradation on the LMMSE channel estimator performance, whereas the simpler least-square (LS) channel estimator may provide comparatively better results. Nevertheless, it is shown that a robust approach, although suboptimal, relies on implementing the LMMSE channel estimator by assuming transmissions over uncorrelated channels, since, with such an assumption, the resulting estimation MSE is certainly smaller than for the LS channel estimator.