Abstract
In most of the previous researches on the multiple-input multiple-output (MIMO) channel estimation, the fading model has been assumed to be Rayleigh distributed. However, the Rician fading model is suitable for microcellular mobile systems or line of sight mode of WiMAX. In this paper, the training based channel es-timation (TBCE) scheme in the spatially correlated Rician flat fading MIMO channels is investigated. First, the least squares (LS) channel estimator is probed. Simulation results show that the Rice factor has no effect on the performance of this estimator. Then, a new linear minimum mean square error (LMMSE) technique, appropriate for Rician fading channels, is proposed. The optimal choice of training sequences with mean square error (MSE) criteria is investigated for these estimators. Analytical and numerical results show that the performance of proposed estimator in the Rician channel model compared with Rayleigh one is much better. It is illustrated that when the channel Rice factor and/or the correlation coefficient increase, the per-formance of the proposed estimator significantly improves.
Highlights
Due to high capacity and diversity gain, multiple input multiple output (MIMO) systems have received considerable attention in wireless communications
We have proposed a new channel estimator (GLMMSE) that is suitable for spatially correlated Rician fading multiple-input multiple-output (MIMO) channel estimation
Analytical and numerical results confirm the superiority of the GLMMSE estimator in the mentioned channel model
Summary
Due to high capacity and diversity gain, multiple input multiple output (MIMO) systems have received considerable attention in wireless communications. In [3] it is indicated that Rician fading can improve the capacity of a multiple antenna system, especially if the transmitter knows the value of the Rice factor. In order to attain the advantages of MIMO systems, it is necessary that the receiver and/or transmitter have access channel state information (CSI). One of the most usual approaches to identify MIMO CSI is training based channel estimation (TBCE). This class of estimation is attractive especially when it decouples symbol detection from channel estimation and simplifies the receiver implementation and relaxes the required identification conditions
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