  An urban tactical wireless network is considered wherein the base stations
are situated on unmanned aerial vehicles (UAVs) that provide connectivity to
ground assets such as vehicles located on city streets. The UAVs are assumed to
be randomly deployed at a fixed height according to a two-dimensional point
process. Millimeter-wave (mmWave) frequencies are used to avail of large
available bandwidths and spatial isolation due to beamforming. In urban
environments, mmWave signals are prone to blocking of the line-of-sight (LoS)
by buildings. While reflections are possible, the desire for consistent
connectivity places a strong preference on the existence of an unblocked LoS
path. As such, the key performance metric considered in this paper is the
connectivity probability, which is the probability of an unblocked LoS path to
at least one UAV within some maximum transmission distance. By leveraging tools
from stochastic geometry, the connectivity probability is characterized as a
function of the city type (e.g., urban, dense urban, suburban), density of UAVs
(average number of UAVs per square km), and height of the UAVs. The city
streets are modeled as a Manhattan Poisson Line Process (MPLP) and the building
heights are randomly distributed. The analysis first finds the connectivity
probability conditioned on a particular network realization (location of the
UAVs) and then removes the conditioning to uncover the distribution of the
connectivity; i.e., the fraction of network realizations that will fail to meet
an outage threshold. While related work has applied an MPLP to networks with a
single UAV, the contributions of this paper are that it (1) considers networks
of multiple UAVs, (2) characterizes the performance by a connectivity
distribution, and (3) identifies the optimal altitude for the UAVs.
