Advantages of MIMO channel estimation in Rician flat fading environments

Abstract

In this paper, the performance of Maximum Likelihood (ML) and Minimum Mean Square Error (MMSE) estimators in Rician flat fading Multiple-Input Multiple-Output (MIMO) systems is investigated. Optimum training sequences with Mean Square Error (MSE) criteria are achieved for these estimators. Clearly, the ML estimator cannot exploit the knowledge of the first and second-order statistics of the Rician channel. However, when the knowledge of the channel is known at the receiver, the MMSE estimator can be improved. Theoretical and numerical results show that increasing the channel Rice factor results in a better MSE performance of the MMSE estimator. Also, using these estimators, we probe the Bit Error Rate (BER) performance of the MIMO Rician fading channel. The ML detection and Binary Phase Shift Keying (BPSK) modulation are used. Simulation results show that the BER performance with MMSE estimation is significantly better than ML estimation especially at low Signal to Noise Ratios (SNRs).