用强化学习动态调整高空基站位置，抗风干扰保海上通信稳定
PPO-Based Dynamic Positioning of HAPS-BS in Wind-Disturbed Stratospheric Maritime Networks
- 用PPO算法让高空基站自主学习最佳位置，实时应对风扰
- 仿真显示能有效减少风引起的定位偏差，提升覆盖稳定性
- 适合研究空基通信、智能调度的科研人员参考
高空平台基站（HAPS）为缺乏地面设施的海区提供广域无线覆盖的潜在解决方案。然而，由于船舶移动性和大气扰动（特别是平流层风对HAPS定位的影响），维持可靠性能面临挑战。本文提出一种基于深度强化学习（DRL）的框架，用于在受风扰动的海事网络中动态调整部署于高空的基站位置。一个集中式DRL智能体部署在协调型HAPS上，通过无线测量和网络反馈控制多个服务型HAPS，以捕捉真实信道条件和用户移动性。采用近端策略优化（PPO）算法学习鲁棒的定位策略，在风扰条件下提升覆盖稳定性与系统吞吐量。仿真结果表明，该方法能有效缓解风致定位偏移，确保海事用户的可靠广域连接。
原文摘要 · Abstract (English)
High-Altitude Platform Stations (HAPS) offer a promising solution for wide-area wireless coverage in maritime regions lacking terrestrial infrastructure. However, maintaining reliable performance is challenging due to dynamic ship mobility and atmospheric disturbances, particularly stratospheric wind effects on HAPS positioning. This paper proposes a deep reinforcement learning (DRL)-based framework for dynamic positioning of wind-disturbed HAPS-mounted base stations in maritime networks. A centralized DRL agent deployed on a coordinator HAPS controls multiple serving HAPS using radio measurements and network feedback, capturing realistic channel conditions and user mobility. A Proximal Policy Optimization (PPO) algorithm is employed to learn robust positioning policies that enhance coverage stability and system throughput under wind disturbances. Simulation results show that the proposed approach effectively mitigates wind-induced positioning deviations while ensuring reliable wide-area connectivity for maritime users.
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。