# UAV Access Point Placement for Connectivity to a User with Unknown Location Using Deep RL

## TL;DR

This paper presents a deep reinforcement learning approach for UAV placement to connect with ground users in urban environments without prior knowledge of user location or channel conditions, using SINR measurements and 3D maps.

## Contribution

It introduces a novel deep RL algorithm that optimally positions UAVs in urban settings without relying on known user locations or simple channel models.

## Key findings

- 90% success rate in converging to target SINR within limited iterations
- Effective UAV placement in complex urban environments using only SINR and 3D topology
- Applicable to any urban environment without prior user location data

## Abstract

In recent years, unmanned aerial vehicles (UAVs) have been considered for telecommunications purposes as relays, caches, or IoT data collectors. In addition to being easy to deploy, their maneuverability allows them to adjust their location to optimize the capacity of the link to the user equipment on the ground or of the link to the basestation. The majority of the previous work that analyzes the optimal placement of such a UAV makes at least one of two assumptions: the channel can be predicted using a simple model or the locations of the users on the ground are known. In this paper, we use deep reinforcement learning (deep RL) to optimally place a UAV serving a ground user in an urban environment, without the previous knowledge of the channel or user location. Our algorithm relies on signal-to-interference-plus-noise ratio (SINR) measurements and a 3D map of the topology to account for blockage and scatterers. Furthermore, it is designed to operate in any urban environment. Results in conditions simulated by a ray tracing software show that with the constraint on the maximum number of iterations our algorithm has a 90% success rate in converging to a target SINR.