Title
Data-Driven Optimization of Drone-Assisted Cellular Networks
Author
Pijnappel, T.R.
van den Berg, J.L.
Borst, S.C.
Litjens, R.
Publication year
2021
Abstract
Drone base stations can help safeguard coverage and provide capacity relief when cellular networks are under stress. Examples of such stress scenarios are events with massive crowds or network outages. In this paper we focus on a disaster scenario with emergence of a traffic hotspot, where agile drone positioning and load management is a critical issue. In order to address this challenge, we propose and assess a data-driven algorithm which leverages real-time measurements to dynamically optimize the 3D position of the drone as well as a cell selection bias tuned for optimized load management. We compare the performance with three benchmark scenarios: i) no drone; ii) a drone positioned above the failing site; and iii) a drone with a statically optimized position and cell selection bias. The results demonstrate that the proposed algorithm significantly improves the call success rate and achieves close to optimal performance.
Subject
drone positioning
Drone-assisted cellular networks
load management
performance assessment
Benchmarking
Drones
Electric load management
Information management
Mobile telecommunication systems
Stress relief
Wireless networks
Cell selection
Cellular network
Data-driven optimization
Disaster scenario
Drone positioning
Drone-assisted cellular network
Network outages
OR-networks
Performance assessment
Selection bias
Electric power plant loads
To reference this document use:
http://resolver.tudelft.nl/uuid:b30f2e66-00da-44d4-9e93-ef9eee049f0d
TNO identifier
962962
Publisher
IEEE Computer Society
ISBN
9781665428545
ISSN
2161-9646
Source
International Conference on Wireless and Mobile Computing, Networking and Communications, 17th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2021, 11 October 2021 through 13 October 2021, 233-240
Document type
conference paper