Leveraging 5G-NR for finding mobile devices with UAVs: latency vs accuracy trade-off

Other authors

Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions

Universitat Politècnica de Catalunya. GRCM - Grup de Recerca en Comunicacions Mòbils

Publication date

2025

Abstract

Rapid and accurate localization of individuals during search-and-rescue (SAR) missions is essential for reducing casualties in emergencies. Traditional methods often struggle in disaster scenarios, where obstacles like debris or dense foliage hinder performance, and reliance on user-side actions proves impractical for mission-critical operations. This paper presents a novel approach that leverages a 5G-new radio (NR)-based unmanned aerial vehicle (UAV) localization framework, integrating hybrid techniques with adaptive clustering strategies to localize user equipments (UEs). Unlike traditional methods, our system dynamically adjusts its trajectory to balance latency and accuracy, achieving UEs positioning accuracy within tens of centimeters during simulation tests. By integrating 5G-NR technology into UAV-based localization, our approach provides a robust and scalable solution for mission-critical SAR operations, significantly enhancing the latency and reliability of locating individuals in emergency situations.


This work was supported by both European Union’s Horizon Europe programme (Grant nº 101139161 — INSTINCT project and Grant nº101192521 - MultiX project). In addition, this work received support from the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union – NextGeneration EU, in the framework of the Recovery Plan, Transformation and Resilience (PRTR) (Call UNICO I+D 5G 2021, Grant nº TSI-063000-2021-6) and by the CERCA Programme from Generalitat de Catalunya.


Peer Reviewed


Postprint (published version)

Document Type

Conference report

Language

English

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Related items

https://ieeexplore.ieee.org/document/10978770

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Rights

Open Access

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E-prints [73012]