  In this paper, we investigate how metasurfaces can be deployed to deliver
high data rates in a millimeter-wave (mmWave) indoor dense space with many
blocking objects. These surfaces can either be static metasurfaces (SMSs) that
reflect with fixed phase-shifts or reconfigurable intelligent surfaces (RISs)
that can reconfigure their phase-shifts to the currently served user. The
latter comes with an increased power, cabling, and signaling cost. To see how
reconfigurability affects the network performance, we propose an iterative
algorithm based on the feasible point pursuit successive convex approximation
method. We jointly optimize the types and phase-shifts of the surfaces and the
time portion allocated to each user equipment to maximize the minimum data rate
achieved by the network. Our numerical results demonstrate that the minimum
data rate improves as more RISs are introduced but the gain diminishes after
some point. Therefore, introducing more reconfigurability is not always
necessary. Another result shows that to reach the same data rate achieved by
using 22 SMSs, at least 18 RISs are needed. This suggests that when it is
costly to deploy many RISs, as an inexpensive alternative solution, one can
reach the same data rate just by densely deploying more SMSs.
