Unmanned Aerial Vehicle (UAV) mounted Base Stations (UAV-BSs) provide flexible coverage for temporary hotspot scenarios; however, efficiently optimizing 3D deployment to satisfy heterogeneous user distributions remains a significant challenge. While Deep Reinforcement Learning (DRL) approaches have shown promise, they often suffer from prohibitive training overhead and poor generalization in cold-start scenarios where the user topology is unknown a priori. To address these limitations, this paper proposes Satisfaction-driven Coverage Optimization via Perimeter Extraction (SCOPE), which is a deterministic and training-free 3D deployment framework. Unlike existing heuristics that rely on fixed-altitude assumptions, SCOPE integrates a perimeter-based peeling strategy with the Welzl Smallest Enclosing Circle (SEC) algorithm to dynamically optimize 3D positions. Theoretically, we provide a rigorous convergence proof and derive a polynomial time complexity of $O(N^2 \log N)$, ensuring predictable execution for real-time applications. Experimentally, we evaluate SCOPE in unpredictable hotspot environments against both traditional heuristics and state-of-the-art DRL baselines under a matched hardware budget. Simulation results demonstrate that SCOPE maintains a high user satisfaction rate between 82% and 88% while generating solutions within millisecond-level latency on commodity hardware. Furthermore, SCOPE demonstrates exceptional resilience by maintaining an approximate 40% functional coverage rate at a minimum altitude constraint of 60 m; in this challenging regime, baseline methods suffer a significant performance degradation, dropping to approximately 20% due to altitude-induced path loss. These findings validate SCOPE as a robust and agile solution for establishing instantaneous digital lifelines in zero-day disaster response missions.

翻译：摘要：搭载基站的无人机（UAV-BSs）能为临时热点场景提供灵活覆盖，但如何高效优化三维部署以满足异构用户分布仍是一项重大挑战。尽管深度强化学习方法已展现出潜力，但在用户拓扑未知的冷启动场景中，这类方法常因训练开销过高及泛化能力不足而受限。为克服这些局限，本文提出基于满意度驱动的边界提取覆盖优化（SCOPE）框架——一种确定性且免训练的三维部署方案。与依赖固定高度假设的现有启发式方法不同，SCOPE将基于边界的剥离策略与Welzl最小包围圆算法相结合，实现三维位置的动态优化。理论上，我们给出了严格的收敛性证明，并推导出多项式时间复杂度$O(N^2 \log N)$，确保实时应用中的可预测性能。实验环节中，我们在硬件预算匹配条件下，针对不可预测热点环境，将SCOPE与传统启发式方法及最新DRL基线进行了对比评估。仿真结果表明：SCOPE在商用硬件上能以毫秒级延迟生成解决方案，同时将用户满意度维持在82%-88%的高水平。此外，当最小高度约束为60米时，SCOPE展现出卓越鲁棒性，保持约40%的功能覆盖率；而在该具有挑战性的场景下，基线方法因高度所致的路径损耗出现显著性能下降，覆盖率骤降至约20%。这些发现验证了SCOPE作为在零日灾害响应任务中建立即时数字生命线的稳健敏捷解决方案。