  Estimating the probability of rare channel conditions is a central challenge
in ultra-reliable wireless communication, where random events, such as deep
fades, can cause sudden variations in the channel quality. This paper proposes
a sample-efficient framework for predicting the statistics of such events by
utilizing spatial dependency between channel measurements acquired from various
locations. The proposed framework combines radio maps with non-parametric
models and extreme value theory (EVT) to estimate rare-event channel statistics
under a Bayesian formulation. The framework can be applied to a wide range of
problems in wireless communication and is exemplified by rate selection in
ultra-reliable communications. Notably, besides simulated data, the proposed
framework is also validated with experimental measurements. The results in both
cases show that the Bayesian formulation provides significantly better results
in terms of throughput compared to baselines that do not leverage measurements
from surrounding locations. It is also observed that the models based on EVT
are generally more accurate in predicting rare-event statistics than
non-parametric models, especially when only a limited number of channel samples
are available. Overall, the proposed methods can significantly reduce the
number of measurements required to predict rare channel conditions and
guarantee reliability.
