UrMap: UAV-assisted Spatio-Temporal Radio Mapping using Sparse Cellular Samples

Li, Ho Ming, Mead, Fin ORCID: https://orcid.org/0009-0004-5758-9765, Huang, Yunfeng, Tang, Cheng-Wei, Wu, Fang-Jing, Ren, Yi ORCID: https://orcid.org/0000-0001-7423-6719 and Wu, Taoyang ORCID: https://orcid.org/0000-0002-2663-2001 (2026) UrMap: UAV-assisted Spatio-Temporal Radio Mapping using Sparse Cellular Samples. In: ICC 2026 - IEEE International Conference on Communications, Proceedings. IEEE International Conference on Communications . UNSPECIFIED. ISBN 9798319542090

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Abstract

The rapid development of Unmanned Aerial Vehicles (UAVs) and wireless communication technologies has fueled the rise of the low-altitude economy, supporting applications such as logistics delivery, precision agriculture, and disaster response. Reliable UAV communication and navigation rely on accurate radio maps that characterize the spatial distribution of signal strength and channel quality. However, constructing such radio maps is challenging, as extensive data collection in complex 3D environments is constrained by flight regulations and energy limits. In addition, pre-constructed radio maps may degrade over time due to environmental dynamics. To address these challenges, this work presents UrMap, a radio map estimation system based on multi-task spatio-temporal modeling. In UrMap, a Gaussian Process Regression (GPR) model is developed to jointly model spatial and temporal correlations, enabling the generation of complete radio maps that remain robust under time-varying environmental conditions. Furthermore, the proposed GPR model generates diverse connectivity maps by exploiting their cross-modality correlations, thereby providing a comprehensive understanding of the radio environment. Extensive experiments show that UrMap outperforms baseline methods, achieving a Mean Absolute Error (MAE) of 1.1 dB in the best case and maintaining 4.2 dB even in the worst case for radio map estimation.

Item Type:	Book Section
Uncontrolled Keywords:	gaussian process regression,uav communications,multi-task learning,radio map estimation,spatio-temporal modeling,computer networks and communications,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1705
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Cyber Intelligence and Networks Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation Faculty of Science > Research Groups > Computational Biology
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	28 Oct 2026 15:36
Last Modified:	28 Oct 2026 15:36
URI:	https://ueaeprints.uea.ac.uk/id/eprint/104903
DOI:	10.1109/icc59461.2026.11587937

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