Accurate MIMO Channel Modeling: Correlation Tensor vs. Directional Approaches

The ability of correlation tensor and directional modeling strategies to accurately capture the spatial behavior of multiple-input multiple-output (MIMO) channels is investigated. Correlation tensor methods are based on the reduced order approximations of the full channel covariance, and do not require any a priori knowledge about the physical scattering mechanism or antenna array. Directional methods require knowledge about the array configuration and usually assume a double-directional wave-propagation mechanism. Five different tensor methods (Kronecker, separable maximum entropy, Weichselberger, principal hyperplane, and sparse core tensor) and one directional method (unstructured diffuse spectrum estimation) are compared in terms of the number of parameters required and the match to the true full covariance matrix. Simulations with a realistic cluster channel model indicate that tensor methods suffer from poor accuracy when too few values in the core tensor are retained, especially when no joint transmit/receive information is available. The directional method, on the other hand, has good accuracy with the same number of parameters as the simplest tensor model. The results stress the importance of including joint transmit/receive information in MIMO models and suggest that directional modeling is a logical choice for high accuracy modeling with few parameters.