EVT-enriched Radio Maps for URLLC
<|reference_start|>EVT-enriched Radio Maps for URLLC: 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.<|reference_end|>
@article{pérez2024evt-enriched,
    title={EVT-enriched Radio Maps for URLLC},
    author={Dian Echevarr'ia P'erez, Onel L. Alcaraz L'opez, Hirley Alves},
    journal={arXiv preprint arXiv:2404.04558},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04558},
    primaryClass={cs.NI}
}