Robust User Scheduling with COST 2100 Channel Model for Massive MIMO Networks

The problem of user scheduling with reduced overhead of channel estimation in the uplink of massive multiple-input multiple-output (MIMO) systems has been investigated. The authors consider the COST 2100 channel model. In this paper, they first propose a new user selection algorithm based on knowledge of the geometry of the service area and location of clusters, without having full channel state information at the BS. They then show that the correlation in geometry-based stochastic channel models (GSCMs) arises from the common clusters in the area. In addition, exploiting the closed-form Cramer–Rao lower bounds, the analysis for the robustness of the proposed scheme to cluster position errors is presented. It is shown by analysing the capacity upper bound that the capacity strongly depends on the position of clusters in the GSCMs and users in the system. Simulation results show that though the BS receiver does not require the channel information of all users, by the proposed geometry-based user scheduling algorithm the sum rate of the system is only slightly less than the well known greedy weight clique scheme.