A Novel Training Design Algorithm for Kronecker-Inseparable MIMO Systems With General Power Constraints

In this paper, we study the training design for multiple-input multiple-output (MIMO) systems. Unlike the majority of existing works, we consider the most practical and general, yet challenging, scenario where the channel and noise covariance matrices are Kronecker-inseparable and general power constraints are imposed on the training signal. To effectively solve this challenging problem, we devise a novel and efficient iterative algorithm, in which the channel estimator and training signal are jointly optimized in an alternating manner by minimizing mean squared error (MSE) of the channel estimation. Simulation results demonstrate that the proposed scheme performs markedly better and more effective than the existing schemes.