Autonomous unmanned aerial vehicles (UAVs) with on-board base station equipment can potentially provide connectivity in areas where the terrestrial infrastructure is overloaded, damaged, or absent. Use cases comprise emergency response, wildfire suppression, surveillance, and cellular communications in crowded events to name a few. A central problem to enable this technology is to place such aerial base stations (AirBSs) in locations that approximately optimize the relevant communication metrics. To alleviate the limitations of existing algorithms, which require intensive and reliable communications among AirBSs or between the AirBSs and a central controller, this paper leverages stochastic optimization and machine learning techniques to put forth an adaptive and decentralized algorithm for AirBS placement without inter-AirBS cooperation or communication. The approach relies on a smart design of the network utility function and on a stochastic gradient ascent iteration that can be evaluated with information available in practical scenarios. To complement the theoretical convergence properties, a simulation study corroborates the effectiveness of the proposed scheme.
|Original language||English (US)|
|Title of host publication||2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN)|
|Number of pages||6|
|State||Published - Apr 16 2020|
Bibliographical noteKAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: The work in this paper was supported in part by the Indo-Norwegian program of the Research Council of Norway under INDNOR project 280835 (LUCAT) and by the KAUST-MIT-TUD-Caltech consortium grant OSR-2015-Sensors-2700 Ext. 2018.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.