用强化学习优化无人机基站飞行路径，提升应急通信覆盖效率。
Optimizing UAV Aerial Base Station Flights Using DRL-based Proximal Policy Optimization
- 基于近端策略优化算法，让无人机动态学习最优位置。
- 在静态、随机、线性等五种移动场景下均保持全覆盖。
- 适合应急通信、灾害救援等需要快速组网的场景。
基于无人机的基站为紧急情况下的快速网络部署提供了有前景的解决方案，对最大化救生潜力至关重要。优化无人机的战略定位对于提升通信效率至关重要。本文提出一种自动化强化学习方法，使无人机能够动态交互环境并确定最优配置。通过利用通信网络的无线信号感知能力，该方法提供了更真实的视角，采用前沿算法——近端策略优化（Proximal Policy Optimization），学习并泛化在多种用户设备（UE）移动模式下的定位策略。我们在包括静态、随机、线性、圆形及混合热点移动在内的多种用户设备移动场景中评估了该方法。数值结果表明，该算法在所有移动模式下均展现出良好的适应性和有效性，能持续维持全面覆盖。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。