A novel shifted type of SLS estimator for estimation of Rician flat fading MIMO channels

This paper deals with the training-based channel estimation scheme in Rician distributed flat fading multiple-input multiple-output (MIMO) channels. We propose a new biased shifted scaled least squares (SSLS) technique that is generalized form of the scaled least squares (SLS) approach. This technique is suitable for estimation of both Rayleigh and Rician fading MIMO channels. The performance of the conventional least squares (LS) and SSLS channel estimators is investigated. Moreover, the optimal choice of training sequences is probed using mean square error (MSE) criteria. Analytical and numerical results show that the proposed SSLS estimator significantly outperforms the LS and SLS techniques. It is demonstrated that increasing the Rice factor leads to decreasing the MSE of offered technique, while the performance of LS and SLS estimators is independent of this factor.