Data-Driven Optimization of Drone-Assisted Cellular Networks

T. R. Pijnappel, J. L. Van Den Berg, S. C. Borst, R. Litjens

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2021 17th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2021
PublisherIEEE Computer Society
Pages233-240
Number of pages8
ISBN (Electronic)9781665428545
DOIs
Publication statusPublished - 2021
Event17th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2021 - Virtual, Online, Italy
Duration: 11 Oct 202113 Oct 2021

Conference

Conference17th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2021
Country/TerritoryItaly
CityVirtual, Online
Period11/10/2113/10/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • drone positioning
  • Drone-assisted cellular networks
  • load management
  • performance assessment

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