Unmanned Aerial Vehicle (UAV)-mounted Base Stations (UAV-BSs) offer a flexible solution for serving ground users in temporary hotspot scenarios. However, efficiently deploying UAV-BSs to satisfy heterogeneous user distributions remains a challenging optimization problem. While recent data-driven approaches, particularly Deep Reinforcement Learning (DRL), have shown promise in dynamic environments, they often suffer from prohibitive training overhead, poor generalization to topology changes, and high computational complexity. To address these limitations, this paper proposes Satisfaction-driven Coverage Optimization via Perimeter Extraction (SCOPE), a training-free and online 3D deployment framework. Unlike heuristic baselines that rely on fixed-altitude assumptions, SCOPE integrates a perimeter extraction mechanism with the Smallest Enclosing Circle (SEC) algorithm to dynamically optimize 3D UAV positions. Theoretically, we provide a rigorous convergence proof of the proposed algorithm and derive its polynomial time complexity of $O(N^2 \log N)$. Experimentally, we conduct a comprehensive comparative study against state-of-the-art DRL baselines (e.g., PPO). Simulation results demonstrate that SCOPE achieves comparable user satisfaction to DRL methods but significantly lower computational latency (milliseconds vs. hours of training) and superior energy efficiency, making it an ideal solution for real-time, on-demand emergency deployment.

翻译：无人机基站为临时热点场景下的地面用户服务提供了一种灵活的解决方案。然而，如何高效部署无人机基站以满足异构用户分布，仍然是一个具有挑战性的优化问题。尽管近期数据驱动方法，特别是深度强化学习，在动态环境中展现出潜力，但它们通常存在训练开销巨大、对拓扑变化泛化能力差以及计算复杂度高等问题。为应对这些局限，本文提出了一种基于满意度驱动的边界提取覆盖优化方法，即SCOPE——一种免训练、在线的三维部署框架。与依赖固定高度假设的启发式基线方法不同，SCOPE将边界提取机制与最小包围圆算法相结合，以动态优化无人机的三维位置。在理论上，我们为所提算法提供了严格的收敛性证明，并推导出其多项式时间复杂度为$O(N^2 \log N)$。在实验方面，我们针对先进的深度强化学习基线方法（如PPO）进行了全面的比较研究。仿真结果表明，SCOPE在用户满意度方面与深度强化学习方法相当，但计算延迟显著更低（毫秒级对比数小时的训练时间），且具有更优的能效，这使其成为实时、按需应急部署的理想解决方案。