Training-based MIMO channel Rice factor estimation algorithms

Recently, to estimate the Rician flat fading Multiple-Input Multiple-Output (MIMO) channels, we have proposed the Shifted Scaled Least Squares (SSLS) channel estimator. In this paper, it is analytically shown that the performance of this estimator is less sensitive to the erroneous estimation of the Rice factor. Moreover, to estimate the channel Rice factor in the above mentioned channel model, two algorithms are proposed. These algorithms work based on the optimal training signal and Least Squares (LS) technique. The estimated Rice factor is used in the SSLS estimator. The performance of these algorithms is numerically compared in the Rician fading channel estimation. Simulation results confirm the efficiency of these algorithms.