Statistical Framework for Clustering MU-MIMO Wireless via Second Order Statistics
This work provides a theoretical foundation for performance prediction in wireless communication clustering, but it is incremental as it builds on existing statistical methods for covariance matrices.
The authors tackled the problem of clustering wireless users in MU-MIMO systems by analyzing distances between channel covariance matrices on a Riemannian manifold, developing a statistical framework with a central limit theorem for a consistent estimator of log-Euclidean distance.
This work explores the clustering of wireless users by examining the distances between their channel covariance matrices, which reside on the Riemannian manifold of positive definite matrices. Specifically, we consider an estimator of the Log-Euclidean distance between multiple sample covariance matrices (SCMs) consistent when the number of samples and the observation size grow unbounded at the same rate. Within the context of multi-user MIMO (MU-MIMO) wireless communication systems, we develop a statistical framework that allows to accurate predictions of the clustering algorithm's performance under realistic conditions. Specifically, we present a central limit theorem that establishes the asymptotic Gaussianity of the consistent estimator of the log-Euclidean distance computed over two sample covariance matrices.