Optimal Superimposed Training Design for Spatially Correlated Fading MIMO Channels

The problem of channel estimation for spatially correlated fading Multiple-Input Multiple-Output (MIMO) systems 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 first developed. The problem of designing the optimal superimposed signal is then addressed and solved with an iterative optimization algorithm. Results show that under the constraint of equal training power and bandwidth efficiency, the authors' 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, especially for slowly time-varying channels with a large coherence time.