Data-driven optimization of drone-assisted cellular networks

Data-driven optimization of drone-assisted cellular networks

Pijnappel, T.R. (Eindhoven University of Technology)
van den Berg, J.L. (TNO; University of Twente; Centrum Wiskunde & Informatica (CWI))
Borst, S.C. (Eindhoven University of Technology)
Litjens, R. (TU Delft Network Architectures and Services)

2021

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.

Drone-assisted cellular networks
drone positioning
load management
performance assessment

http://resolver.tudelft.nl/uuid:ad10e1c6-dbdd-4ce4-b801-d726d3b841b8

17th International Conference on Wireless and Mobile Computing, Networking and Communications, 2021-10-11 → 2021-10-13, virtual event

Institutional Repository

conference paper

© 2021 T.R. Pijnappel, J.L. van den Berg, S.C. Borst, R. Litjens