Clustering Based UAV Base Station Positioning for Enhanced Network Capacity

Ozturk, M., Nadas, J. P.B., Klaine, P. H.V. , Hussain, S. and Imran, M. A. (2020) Clustering Based UAV Base Station Positioning for Enhanced Network Capacity. In: International Conference on Advances in the Emerging Computing Technologies (AECT 2019), Medina, Saudi Arabia, 08-10 Dec 2019, ISBN 9781728144528 (doi: 10.1109/AECT47998.2020.9194188)

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Abstract

Unmanned aerial vehicles (UAVs) are expected to be deployed in a variety of applications in future mobile networks due to several advantages they bring over the deployment of ground base stations. However, despite the recent interest in UAVs in mobile networks, some issues still remain, such as determining the placement of multiple UAVs in different scenarios. In this paper we propose a solution to determine the optimal 3D position of multiple UAVs in a capacity enhancement use-case, or in other words, when the ground network cannot cope with the user traffic demand. For this scenario, real data from the city of Milan, provided by Telecom Italia is utilized to simulate an event. Based on that, a solution based on k-means, a machine learning technique, to position multiple UAVs is proposed and it is compared with two other baseline methods. Results demonstrate that the proposed solution is able to significantly outperform other methods in terms of users covered and quality of service.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Nadas, Mr Joao Pedro and Imran, Professor Muhammad and Öztürk, Metin and Hussain, Dr Sajjad and Valente Klaine, Mr Paulo
Authors: Ozturk, M., Nadas, J. P.B., Klaine, P. H.V., Hussain, S., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781728144528
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in 2019 International Conference on Advances in the Emerging Computing Technologies (AECT)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300725Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy