Spatial transmit prefiltering for frequency-flat MIMO transmission with mean and covariance information

In this paper techniques are proposed for combining information about the mean and the covariance of the channel for the purpose of MIMO transmission. (Partial) channel state information at the transmitter (CSIT) is typically used in MIMO systems for the design of spatial preltering and waterlling. For the purpose of generating CSIT, the cases of mean or covariance information have only been solved separately in the literature. A Bayesian approach is presented here incorporating both pieces of information, but in which correlations are limited to the transmitter side. The approach yields the existing cases of mean or (transmit) covariance information as special instances. Various cases of mean and covariance information are discussed, including prior mean and covariance (Ricean channel distribution) and posterior mean and covariance (based on a noisy channel estimate and prior covariance information). The case of a singular covariance matrix is treated in detail also, allowing to treat zero covariance (known channel) as a special case.