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
Introduction to Space-Time Wireless Communications
Introduction to Space-Time Wireless Communications
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals
IEEE Transactions on Signal Processing
Transmit signal design for optimal estimation of correlated MIMO channels
IEEE Transactions on Signal Processing
Interference cancellation for cellular systems: a contemporary overview
IEEE Wireless Communications
Training-based channel estimation for multiple-antenna broadband transmissions
IEEE Transactions on Wireless Communications
Optimal training for MIMO frequency-selective fading channels
IEEE Transactions on Wireless Communications
Capacity-approaching space-time codes for systems employing four transmitter antennas
IEEE Transactions on Information Theory
Optimal transmitter eigen-beamforming and space-time block coding based on channel correlations
IEEE Transactions on Information Theory
What is the value of limited feedback for MIMO channels?
IEEE Communications Magazine
An introduction to the multi-user MIMO downlink
IEEE Communications Magazine
Optimal training sequence for MIMO wireless systems in colored environments
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Hi-index | 0.09 |
In this paper, training-based estimation of correlated block fading channels in a multiple-antenna, multi-user environment is considered. The linear minimum mean squared error (LMMSE) estimator is presented first. The problem of optimally designing the training data set, so as to minimize the mean squared channel estimation error subject to a total transmit power constraint, is then addressed. The design is based on the assumption of the availability of the channel and interference second-order statistics at the transmitter. It is shown that the optimal transmission directions are dictated jointly by the eigen-decompositions of the channel and interference covariance matrices. Their roles, in the channel estimation and interference suppression tasks, respectively, are revealed in the optimal transmit beamformer structure. The simulation results demonstrate that the gain in the estimation performance from using the optimal training sequence increases considerably with increasing spatial fading correlation, especially in strong interference environments.