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Energy consumption fairness for multiple flying base stations

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

Original languageEnglish
Title of host publication2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728144900
DOIs
PublishedAug 2020
Event31st IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020 - Virtual, London, United Kingdom
Duration: 31 Aug 20203 Sep 2020

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Volume2020-August

Conference

Conference31st IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2020
CountryUnited Kingdom
CityVirtual, London
Period31/08/20203/09/2020

King's Authors

Abstract

Unmanned Aerial Vehicles (UAVs) operating as Flying Base Stations (FBSs) are emerging as a mean of furthering network capabilities in beyond 5G networks especially in terms of capacity and coverage. In such UAV assisted 5G networks the bottleneck becomes the inherent limited energy availability of the FBSs. In this paper the focus is on the provision of trajectories with fair energy consumption (EC) allocation between different FBSs that utilize a single macro-base station (BS) as their depot to serve end users. Despite the recent significant research efforts in FBS trajectory optimization schemes little attention has been placed in studying fair trajectories in terms of, inter alia, energy consumption. To this end, a number of optimal (using linear and non-linear mathematical programming formulations) and heuristic algorithms are presented to create fair EC allocation trajectories for FBSs. Via a wide set of numerical investigations a systematic comparison is presented of the different fair EC trajectory optimization algorithms as with respect to Jain's fairness index, computational times and optimality gap.

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