  Stochastic geometry models are used to study wireless networks, particularly
cellular phone networks, but most of the research focuses on the typical user,
often ignoring atypical events, which can be highly disruptive and of interest
to network operators. We examine atypical events when a unexpected large
proportion of users are disconnected or connected by proposing a hybrid
approach based on ray launching simulation and point process theory. This work
is motivated by recent results using large deviations theory applied to the
signal-to-interference ratio. This theory provides a tool for the stochastic
analysis of atypical but disruptive events, particularly when the density of
transmitters is high. For a section of a European city, we introduce a new
stochastic model of a single network cell that uses ray launching data
generated with the open source RaLaNS package, giving deterministic path loss
values. We collect statistics on the fraction of (dis)connected users in the
uplink, and observe that the probability of an unexpected large proportion of
disconnected users decreases exponentially when the transmitter density
increases. This observation implies that denser networks become more stable in
the sense that the probability of the fraction of (dis)connected users
deviating from its mean, is exponentially small. We also empirically obtain and
illustrate the density of users for network configurations in the disruptive
event, which highlights the fact that such bottleneck behaviour not only stems
from too many users at the cell boundary, but also from the near-far effect of
many users in the immediate vicinity of the base station. We discuss the
implications of these findings and outline possible future research directions.
