MIMO Channel Estimation in Correlated Fading Environments

This paper presents two analytic correlated multiple-input multiple-output (MIMO) block fading channel models and their time-variant extensions that encompass the popular Kronecker model and the more general Weichselberger model as special cases. Both static and time-variant models offer compact representations of spatial- and/or time-correlated channels. When the transmit antenna array is such that the associated MIMO channel has a small angle spread (AS), which occurs quite often in a cellular downlink, our models admit reduced-rank channel representations. They also provide compact channel state information (CSI) descriptions which are needed in feedback systems and in many post channel estimation applications. The latter has the important implication of reduced feedback channel bandwidth requirement and lower post-processing complexity. Based on one of the proposed channel models we present novel iterative algorithms for estimating static and time-variant MIMO channels. The proposed models make it natural to decompose each iteration of our algorithms into two successive stages that are responsible for estimating the correlation coefficients and the signal direction, respectively. Using popular industry-approved standard channel models, we verify through simulations that our algorithms yield good MSE performance which, in many practical cases, is better than that achievable by a conventional least-square estimator. The mean-squared error (MSE) performance of our estimators are analyzed and the resulting predictions are consistent with those estimated by simulations.