Robust Training Sequence Design for Spatially Correlated MIMO Channel Estimation

Abstract

A robust superimposed training sequence design is proposed for spatially correlated multiple-input-multiple-output (MIMO) channel estimation. The proposed scheme does not require accurate knowledge of the spatial correlation matrix, and it is shown to outperform previously proposed robust correlated MIMO channel estimators, such as relaxed minimum mean square error (RMMSE) and least-square RMMSE. Since the training sequence is overlaid into the data stream, the spectral efficiency of the system is higher than those that use time-multiplexed pilots. A solution for the sequence can easily be obtained by using a projection on convex-set-based iterative algorithm that is guaranteed to converge as long as the training sequence matrix is initialized to have full rank. Furthermore, it is shown that the proposed scheme is identical to the RMMSE-based schemes when the MIMO channel is spatially uncorrelated. The computational complexity of the proposed algorithm is also illustrated.