# Non-cooperative Aerial Base Station Placement via Stochastic Optimization

## TL;DR

This paper introduces a decentralized stochastic optimization algorithm for placing aerial base stations using UAVs, enabling effective network coverage without inter-UAV communication, suitable for emergency and disaster scenarios.

## Contribution

It proposes a novel adaptive, decentralized placement algorithm for AirBSs leveraging stochastic gradient ascent, eliminating the need for inter-UAV communication or centralized control.

## Key findings

- Algorithm converges reliably in simulations
- Effective in optimizing network utility without inter-UAV communication
- Suitable for emergency and disaster scenarios

## Abstract

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.