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
Convex Optimization
Majorization and matrix-monotone functions in wireless communications
Foundations and Trends in Communications and Information Theory
Training-based Bayesian MIMO channel and channel norm estimation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
IEEE Transactions on Signal Processing
Impact of spatial correlation and precoding design in OSTBC MIMO systems
IEEE Transactions on Wireless Communications
Training Signal Design for Estimation of Correlated MIMO Channels With Colored Interference
IEEE Transactions on Signal Processing
Pilot-assisted channel estimation based on second-order statistics
IEEE Transactions on Signal Processing
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
IEEE Transactions on Signal Processing - Part I
Optimal training for MIMO frequency-selective fading channels
IEEE Transactions on Wireless Communications
A stochastic MIMO channel model with joint correlation of both link ends
IEEE Transactions on Wireless Communications
How much training is needed in multiple-antenna wireless links?
IEEE Transactions on Information Theory
Correlated MIMO wireless channels: capacity, optimal signaling, and asymptotics
IEEE Transactions on Information Theory
Impact of antenna correlation on the capacity of multiantenna channels
IEEE Transactions on Information Theory
Multiple-input-multiple-output measurements and modeling in Manhattan
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Impact of spatial correlation and precoding design in OSTBC MIMO systems
IEEE Transactions on Wireless Communications
EURASIP Journal on Wireless Communications and Networking
Dynamic estimation of local mean power in GSM-R networks
Wireless Networks
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In this paper, we create a framework for trainingbased channel estimation under different channel and interference statistics. The minimum mean square error (MMSE) estimator for channel matrix estimation in Rician fading multi-antenna systems is analyzed, and especially the design of mean square error (MSE) minimizing training sequences. By considering Kronecker-structured systems with a combination of noise and interference and arbitrary training sequence length, we collect and generalize several previous results in the framework. We clarify the conditions for achieving the optimal training sequence structure and show when the spatial training power allocation can be solved explicitly. We also prove that spatial correlation improves the estimation performance and establish how it determines the optimal training sequence length. The analytic results for Kronecker-structured systems are used to derive a heuristic training sequence under general unstructured statistics. The MMSE estimator of the squared Frobenius norm of the channel matrix is also derived and shown to provide far better gain estimates than other approaches. It is shown under which conditions training sequences that minimize the non-convex MSE can be derived explicitly or with low complexity. Numerical examples are used to evaluate the performance of the two estimators for different training sequences and system statistics. We also illustrate how the optimal length of the training sequence often can be shorter than the number of transmit antennas.