  Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable
platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations
(BSs). Implementing aerial networks in disaster management scenarios and
crowded areas can effectively enhance Quality of Service (QoS). In such
environments, maintaining Line-of-Sight (LoS), especially at higher
frequencies, is crucial for ensuring reliable communication networks with high
capacity, particularly in environments with obstacles. The main contribution of
this paper is a traffic- and obstacle-aware UAV positioning algorithm named
Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm
(RLTOPA), for such environments. RLTOPA determines the optimal position of the
UAV by considering the positions of ground users, the coordinates of obstacles,
and the traffic demands of users. This positioning aims to maximize QoS in
terms of throughput by ensuring optimal LoS between ground users and the UAV.
The network performance of the proposed solution, characterized in terms of
mean delay and throughput, was evaluated using the ns- 3 simulator. The results
show up to 95% improvement in aggregate throughput and 71% in delay without
compromising fairness.
