  This paper introduces a sophisticated and adaptable framework combining
extreme value theory with radio maps to spatially model extreme channel
conditions accurately. Utilising existing signal-to-noise ratio (SNR)
measurements and leveraging Gaussian processes, our approach predicts the tail
of the SNR distribution, which entails estimating the parameters of a
generalised Pareto distribution, at unobserved locations. This innovative
method offers a versatile solution adaptable to various resource allocation
challenges in ultra-reliable low-latency communications. We evaluate the
performance of this method in a rate maximisation problem with defined outage
constraints and compare it with a benchmark in the literature. Notably, the
proposed approach meets the outage demands in a larger percentage of the
coverage area and reaches higher transmission rates.
