EVT-Enriched Radio Maps for Ultrareliable Communication

This article introduces a sophisticated and adaptable framework combining extreme value theory with radio maps to spatially model extreme channel conditions accurately. Utilizing 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 generalized Pareto distribution, at unobserved locations. This innovative method offers a versatile solution adaptable to various resource allocation challenges in ultrareliable communications. We evaluate the performance of this method in a rate maximization 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. Finally, we analyze the impact of the localization error on the system performance, highlighting the need for accurate positioning algorithms to enable efficient resource allocation.