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has been cited by the following article:

• TITLE: Performance Improvement in Estimation of Spatially Correlated Rician Fading MIMO Channels Using a New LMMSE Estimator

  AUTHORS: Hamid Nooralizadeh, Shahriar Shirvani Moghaddam

  KEYWORDS: Channel Estimation, MIMO, Spatially Correlated Rician Fading, Optimal Training Sequences, LS, Generalized LMMSE

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.3 No.12, December 27, 2010

  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.