  This paper considers a Massive multiple-input multiple-output (MIMO) network,
where the base station (BS) with a large number of antennas communicates with a
smaller number of users. The signals are transmitted using frequency division
duplex (FDD) mode. The problem of user scheduling with reduced overhead of
channel 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 new
user selection algorithm based on knowledge of the geometry of the service area
and of location of clusters, without having full channel state information
(CSI) at the BS. We 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 (CRLB)s, 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 although the BS receiver does not
require the channel information of all users, by the proposed geometry-based
user scheduling (GUS) algorithm the sum-rate of the system is only slightly
less than the well-known greedy weight clique (GWC) scheme
\cite{SUSGoldsmithGlobcom,ITC09_Userselection_GWC}. {Finally, the robustness of
the proposed algorithm to cluster localization is verified by the simulation
results.
