  A proper channel modeling methodology that characterizes the statistics of
extreme events is key in the design of a system at an ultra-reliable regime of
operation. The strict constraint of ultra-reliability corresponds to the packet
error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable
latency on the order of milliseconds. Extreme value theory (EVT) is a robust
framework for modeling the statistical behavior of extreme events in the
channel data. In this paper, we propose a methodology based on EVT to model the
extreme events of a non-stationary wireless channel for the ultra-reliable
regime of operation. This methodology includes techniques for splitting the
channel data sequence into multiple groups concerning the environmental factors
causing non-stationarity, and fitting the lower tail distribution of the
received power in each group to the generalized Pareto distribution (GPD). The
proposed approach also consists of optimally determining the time-varying
threshold over which the tail statistics are derived as a function of time, and
assessing the validity of the derived Pareto model. Finally, the proposed
approach chooses the best model with minimum complexity that represents the
time variation behavior of the non-stationary channel data sequence. Based on
the data collected within the engine compartment of Fiat Linea under various
engine vibrations and driving scenarios, we demonstrate the capability of the
proposed methodology in providing the best fit to the extremes of the
non-stationary data, significantly outperforming the channel modeling approach
in characterizing the extreme events with the stationary channel assumption.
