# Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimization

arXiv: https://arxiv.org/abs/2504.03961 · HTML: https://mlsanity.com/paper/2504.03961/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis](https://mlsanity.com/paper/1509.01655/) (2015)
- [Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning](https://mlsanity.com/paper/1801.07472/) (2018)
- [UAV Base Station Location Optimization for Next Generation Wireless Networks: Overview and Future Research Directions](https://mlsanity.com/paper/1812.11826/) (2018)
- [Downlink Interference Management in Dense Interference-Aware Drone Small Cells Networks Using Mean-Field Game Theory](https://mlsanity.com/paper/1806.02727/) (2018)

## Built on by (most cited first, 3 of 3)

- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [PPO-Based Dynamic Positioning of HAPS-BS in Wind-Disturbed Stratospheric Maritime Networks](https://mlsanity.com/paper/2605.05240/) (2026)
- [Centralized PPO-Based DRL for Multi-UAV-BS Positioning and Trajectory Optimization in Disaster Response Networks](https://mlsanity.com/paper/2607.02533/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicle (UAV)-based base stations offer a promising solution in emergencies where the rapid deployment of cutting-edge networks is crucial for maximizing life-saving potential. Optimizing the strategic positioning of these UAVs is essential for enhancing communication efficiency. This paper introduces an automated reinforcement learning approach that enables UAVs to dynamically interact with their environment and determine optimal configurations. By leveraging the radio signal sensing capabilities of communication networks, our method provides a more realistic perspective, utilizing state-of-the-art algorithm -- proximal policy optimization -- to learn and generalize positioning strategies across diverse user equipment (UE) movement patterns. We evaluate our approach across various UE mobility scenarios, including static, random, linear, circular, and mixed hotspot movements. The numerical results demonstrate the algorithm's adaptability and effectiveness in maintaining comprehensive coverage across all movement patterns.