EURASIP Journal on Applied Signal Processing
Challenge of channel estimations and its way out in MIMO OFDM systems for mobile wireless channels
WSEAS TRANSACTIONS on COMMUNICATIONS
A signal perturbation free whitening-rotation-based semiblind approach for MIMO channel estimation
IEEE Transactions on Signal Processing
Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems
IEEE Transactions on Wireless Communications
Semi-blind gradient-Newton CMA and SDD algorithm for MIMO space-time equalisation
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
A signal-perturbation-free transmit scheme for MIMO-OFDM channel estimation
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems
International Journal of Automation and Computing
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This paper proposes a whitening-rotation (WR)-based algorithm for semi-blind estimation of a complex flat-fading multi-input multi-output (MIMO) channel matrix H. The proposed algorithm is based on decomposition of H as the matrix product H=WQH, where W is a whitening matrix and Q is unitary rotation matrix. The whitening matrix W can be estimated blind using only received data while Q is estimated exclusively from pilot symbols. Employing the results for the complex-constrained Cramer-Rao Bound (CC-CRB), it is shown that the lower bound on the mean-square error (MSE) in the estimate of H is directly proportional to its number of unconstrained parameters. Utilizing the bounds, the semi-blind scheme is shown to be very efficient when the number of receive antennas is greater than or equal to the number of transmit antennas. Closed-form expressions for the CRB of the semi-blind technique are presented. Algorithms for channel estimation based on the decomposition are also developed and analyzed. In particular, the properties of the constrained maximum-likelihood (ML) estimator of Q for an orthogonal pilot sequence is examined, and the constrained estimator for a general pilot sequence is derived. In addition, a Gaussian likelihood function is considered for the joint optimization of W and Q, and its performance is studied. Simulation results are presented to support the algorithms and analysis, and they demonstrate improved performance compared to exclusively training-based estimation.