  Base station densification is one of the key approaches for delivering high
capacity in radio access networks. However, current static deployments are
often impractical and financially unsustainable, as they increase both capital
and operational expenditures of the network. An alternative paradigm is the
moving base stations (MBSs) approach, by which part of base stations are
installed on vehicles. However, to the best of our knowledge, it is still
unclear if and up to which point MBSs allow decreasing the number of static
base stations (BSs) deployed in urban settings. In this work, we start tackling
this issue by proposing a modeling approach for a first-order evaluation of
potential infrastructure savings enabled by the MBSs paradigm. Starting from a
set of stochastic geometry results, and a traffic demand profile over time, we
formulate an optimization problem for the derivation of the optimal combination
of moving and static BSs which minimizes the overall amount of BSs deployed,
while guaranteeing a target mean QoS for users. Initial results on a
two-district scenario with measurement-based network traffic profiles suggest
that substantial infrastructure savings are achievable. We show that these
results are robust against different values of user density.
