  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.
