Density based user grouping for massive MIMO downlink in FDD system

Large-scale multiple-input multiple-output (MIMO) system with tens to hundreds of antennas is getting increasing attention for its very high spectral efficiency and energy efficiency. However, for frequency division duplexing (FDD) system, the cost of CSIT feedback becomes a bottleneck with the BS antennas increasing. To reduce the cost of the downlink training and the CSIT uplink feedback, joint spatial division and multiplexing (JSDM) scheme is proposed by Ansuman Adhikary, et al., which divides users into different groups according to their channel covariance eigenvectors and adopts the two-stage precoding. In this paper, a Density Based Spatial Clustering of Application with Noise (DBSCAN) user grouping algorithm is proposed for JSDM scheme, which divides all the users based on the density distribution of users. DBSCAN can find the groups with arbitrary shape and size, while DBSCAN has two parameters which is hard to determine. In this paper the parameters are determined by detecting the density distribution using k-dist graph. The simulation results show that the proposed DBSCAN grouping algorithm has better performance than K-Means and K-Medoids.