Elements of information theory
Elements of information theory
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Optimization of training sequences for spatially correlated MIMO-OFDM
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Superimposed training designs for spatially correlated MIMO-OFDM systems
IEEE Transactions on Wireless Communications
Practical algorithms for a family of waterfilling solutions
IEEE Transactions on Signal Processing
Training Signal Design for Estimation of Correlated MIMO Channels With Colored Interference
IEEE Transactions on Signal Processing
Quadratically Constrained Beamforming Robust Against Direction-of-Arrival Mismatch
IEEE Transactions on Signal Processing
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
Robust adaptive beamforming for general-rank signal models
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
Simplified channel estimation for OFDM systems with multiple transmit antennas
IEEE Transactions on Wireless Communications
Multiple-antenna capacity in correlated Rayleigh fading with channel covariance information
IEEE Transactions on Wireless Communications
Optimum training symbol design for MIMO OFDM in correlated fading channels
IEEE Transactions on Wireless Communications
Optimal Superimposed Training Design for Spatially Correlated Fading MIMO Channels
IEEE Transactions on Wireless Communications
Iterative water-filling for Gaussian vector multiple-access channels
IEEE Transactions on Information Theory
Space-time block coding for wireless communications: performance results
IEEE Journal on Selected Areas in Communications
An introduction to convex optimization for communications and signal processing
IEEE Journal on Selected Areas in Communications
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In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequencydivision multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter.