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arXiv · 2610.06356

Channel Knowledge Maps for FR3 UAV Base Stations: Dataset and ML-based Modeling

Guillem Moreno~Garcia·Sergi Abadal·Evgenii Vinogradov
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Abstract

This paper introduces a dense upper mid-band Frequency Range (FR3) Channel Knowledge Map (CKM) dataset for Uncrewed Aerial Vehicles (UAVs) with an on-board base station (UxNB). It contains 16,180 CKM samples from 3,236 urban locations in 66 cities at 7.125 GHz and UxNB heights of up to 500 meters. Every 513x513 sample stores the building geometry, the line-of-sight (LOS) state, attenuation, delay spread, angular spread, and variable transmitter height. As a focused first use-case, we propose an interpretable height-aware attenuation model. Its LOS branch combines free-space loss, two-ray term, and compact data-based height/range calibration. Its non-LOS (NLOS) branch combines a COST 231-Hata basis with local environment-specific linear calibration. The proposed model reaches 1.93 dB overall root mean square error, with 1.74 dB in LOS and 3.54 dB in NLOS on over 500 unique locations in 14 unseen cities (2,590 samples in total). The result provides attenuation data for FR3 UxNB deployments and a physical anchor for future FR3 modeling efforts.

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Guillem Moreno~Garcia, Sergi Abadal, Evgenii Vinogradov. 2026-10-05. Channel Knowledge Maps for FR3 UAV Base Stations: Dataset and ML-based Modeling. https://arxiv.org/abs/2610.06356

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