On channel estimation and optimal training for MIMO systems

The performance of MIMO channel estimation methods using training sequences is studied. We consider the popular least squares (LS) and minimum mean-square-error (MMSE) approaches, and propose new scaled LS (SLS) and relaxed minimum mean-square-error (RMMSE) techniques which require less knowledge of the channel second-order statistics and/or have better performance than the LS and MMSE techniques. The optimal choice of training signals is investigated for the aforementioned techniques. In the case of multiple LS channel estimates, the best linear unbiased estimation (BLUE) scheme for their linear combining is proposed and studied.