  This paper deals with the stochastic geometry-based characterization of the
time-varying performance of a drone cellular network in which the initial
locations of drone base stations (DBSs) are modeled as a Poisson point process
(PPP) and each DBS is assumed to move on a straight line in a random direction.
This drone placement and trajectory model closely emulates the one used by the
third generation partnership project (3GPP) for drone-related studies. Assuming
the nearest neighbor association policy for a typical user equipment (UE) on
the ground, we consider two models for the mobility of the serving DBS: (i) UE
independent model, and (ii) UE dependent model. Using displacement theorem from
stochastic geometry, we characterize the time-varying interference field as
seen by the typical UE, using which we derive the time-varying coverage
probability and data rate at the typical UE. We also compare our model with
more sophisticated mobility models where the DBSs may move in nonlinear
trajectories and demonstrate that the coverage probability and rate estimated
by our model act as lower bounds to these more general models. To the best of
our knowledge, this is the first work to perform a rigorous analysis of the
3GPP-inspired drone mobility model and establish connection between this model
and the more general non-linear mobility models.
