Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

Rafhael Medeiros de Amorim, Jeroen Wigard, Huan Cong Nguyen, Istvan Kovacs, Preben Elgaard Mogensen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experience
different radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.
OriginalsprogEngelsk
TitelGlobecom Workshops (GC Wkshps), 2017 IEEE
Antal sider6
ForlagIEEE
Publikationsdatodec. 2017
ISBN (Elektronisk)978-1-5386-3920-7
DOI
StatusUdgivet - dec. 2017
BegivenhedIEEE GLOBECOM 2017: Global Hub: Connecting East and West - , Singapore
Varighed: 4 dec. 20178 dec. 2017
http://globecom2017.ieee-globecom.org/

Konference

KonferenceIEEE GLOBECOM 2017
LandSingapore
Periode04/12/201708/12/2017
Internetadresse

Fingerprint

Unmanned aerial vehicles (UAV)
Learning systems
Identification (control systems)
Antennas
Antenna grounds
Network performance
Mobile phones
Railroad cars
Drones

Citer dette

Amorim, Rafhael Medeiros de ; Wigard, Jeroen ; Nguyen, Huan Cong ; Kovacs, Istvan ; Mogensen, Preben Elgaard. / Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements. Globecom Workshops (GC Wkshps), 2017 IEEE. IEEE, 2017.
@inproceedings{c13a2269b6744bfdaa975254c42ebfb3,
title = "Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements",
abstract = "The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experiencedifferent radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99{\%} in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100{\%}.",
author = "Amorim, {Rafhael Medeiros de} and Jeroen Wigard and Nguyen, {Huan Cong} and Istvan Kovacs and Mogensen, {Preben Elgaard}",
year = "2017",
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Amorim, RMD, Wigard, J, Nguyen, HC, Kovacs, I & Mogensen, PE 2017, Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements. i Globecom Workshops (GC Wkshps), 2017 IEEE. IEEE, Singapore, 04/12/2017. https://doi.org/10.1109/GLOCOMW.2017.8269067

Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements. / Amorim, Rafhael Medeiros de; Wigard, Jeroen; Nguyen, Huan Cong; Kovacs, Istvan; Mogensen, Preben Elgaard.

Globecom Workshops (GC Wkshps), 2017 IEEE. IEEE, 2017.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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