An Effective Global Optimization Algorithm for Wireless Mimo Channel Estimation

Abstract

The problem of channel estimation for spatially correlated fading multiple-input multiple-output (MIMO) channels is considered. Based on the channel's second order statistic, the minimum mean-square error (MMSE) channel estimator that works with the superimposed training signal is proposed. The problem of designing the optimal superimposed signal is then addressed and solved with an iterative global optimization algorithm. Simulation results show that our optimal design of the superimposed training signal leads to a significant reduction in channel estimation error when compared to the conventional design of time-multiplexing training.