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This paper considers a Massive multiple-input multiple-output (MIMO) network,where the base station (BS) with a large number of antennas communicates with asmaller number of users. The signals are transmitted using frequency divisionduplex (FDD) mode. The problem of user scheduling with reduced overhead ofchannel estimation in the uplink of Massive MIMO systems has been investigated.We consider the COST 2100 channel model. In this paper, we first propose a newuser selection algorithm based on knowledge of the geometry of the service areaand of location of clusters, without having full channel state information(CSI) at the BS. We then show that the correlation in geometry-based stochasticchannel models (GSCMs) arises from the common clusters in the area. Inaddition, exploiting the closed-form Cramer-Rao lower bounds (CRLB)s, theanalysis for the robustness of the proposed scheme to cluster position errorsis presented. It is shown by analysing the capacity upper-bound that thecapacity strongly depends on the position of clusters in the GSCMs and users inthe system. Simulation results show that although the BS receiver does notrequire the channel information of all users, by the proposed geometry-baseduser scheduling (GUS) algorithm the sum-rate of the system is only slightlyless than the well-known greedy weight clique (GWC) scheme\cite{SUSGoldsmithGlobcom,ITC09_Userselection_GWC}. {Finally, the robustness ofthe proposed algorithm to cluster localization is verified by the simulationresults.

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