SDMA Grouping Based on Unsupervised Learning for Multi-User MIMO Systems

Abstract

In this study, we investigate a spatial division multiple access (SDMA) grouping scheme to maximize the total data rate of a multi-user multiple input multiple output (MU-MIMO) system. Initially, we partition the set of mobile stations (MSs) into subsets according to their spatial compatibility. We explore different clustering algorithms, comparing them in terms of computational complexity and capability to partition MSs properly. Since we consider a scenario with a massive arrange of antenna elements and that operates on the mmWave scenario, we employ a hybrid beamforming scheme and analyze its behavior in terms of the total data rate. The analog and digital precoders exploit the channel information obtained from clustering and scheduling, respectively.  The simulation results indicate that a proper partition of MSs into clusters can take advantage of the spatial compatibility effectively and reduce the multi-user (MU) interference. The hierarchical clustering (HC) enhances the total data rate 25% compared with the baseline approach, while the density-based spatial clustering of applications with noise (DBSCAN) increases the total data rate 20%.