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The data rate of a multiple input, multiple output (MIMO) communication link can be improved if knowledge about the channel statistics is exploited at the transmitter. However, many realistic system scenarios require computationally prohibitive Monte Carlo methods in order to optimize the transmit covariance for maximization of the exact mutual information. This paper instead considers an approximative approach to maximize the performance of a communication link where covariance feedback is available at the transmitter. The algorithm presented is based on an asymptotic expression of the mutual information that allows for correlation at both the transmit and the receive side of the system. From simulations we demonstrate significant gain over beamforming and pure divversity schemes in many realistic system scenarios., NR 20140805

Second order blind deconvolution of single input multiple output (SIMO) FIR channels is considered herein. A major drawback of several blind diversity techniques using antenna arrays/temporal-oversampling is high sensitivity to the choice of model order. In this article, a robust method using only the second order statistics is described. It provides high estimation accuracy even for a limited sample size and an unknown model order. In contrast to other suggested approaches, that often exploit properties valid only in the large sample case, the proposed method is applicable both in the large sample and high signal-to-noise ratio (SNR) scenario; it also enjoys a simple implementation., NR 20140805

In this paper the performance of two second order based blind channel identification techniques is studied. The methods are compared theoretically and the formulas validated practically by simulations. The first method is a well known subspace approach and the second is a covariance matching estimator. This last estimator should attain the lower bound for the asymptotical estimation error covariance of any second order based blindidentification algorithm., NR 20140805