Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimization
This work addresses communication efficiency in emergency scenarios, but it is incremental as it applies an existing algorithm to a specific domain.
The paper tackled optimizing UAV base station positioning for emergency communication networks using a reinforcement learning approach, achieving comprehensive coverage across diverse user movement patterns.
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.