  We propose an algorithm for joint precoding and user selection in
multiple-input multiple-output systems with extremely-large aperture arrays,
assuming realistic channel conditions and imperfect channel estimates. The use
of long-term channel state information (CSI) for user scheduling, and a proper
selection of the set of users for which CSI is updated allow for obtaining an
improved achievable sum spectral efficiency. We also confirm that the effect of
imperfect CSI in the precoding vector design and the cost of training must be
taken into consideration for realistic performance prediction.
