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Autonomous unmanned aerial vehicles (UAVs) with on-board base stationequipment can potentially provide connectivity in areas where the terrestrialinfrastructure is overloaded, damaged, or absent. Use cases comprise emergencyresponse, wildfire suppression, surveillance, and cellular communications incrowded events to name a few. A central problem to enable this technology is toplace such aerial base stations (AirBSs) in locations that approximatelyoptimize the relevant communication metrics. To alleviate the limitations ofexisting algorithms, which require intensive and reliable communications amongAirBSs or between the AirBSs and a central controller, this paper leveragesstochastic optimization and machine learning techniques to put forth anadaptive and decentralized algorithm for AirBS placement without inter-AirBScooperation or communication. The approach relies on a smart design of thenetwork utility function and on a stochastic gradient ascent iteration that canbe evaluated with information available in practical scenarios. To complementthe theoretical convergence properties, a simulation study corroborates theeffectiveness of the proposed scheme.

Non-cooperative Aerial Base Station Placement via Stochastic Optimization