AI 中文总结
针对无人机覆盖规划的稀疏无线测量难题，提出SparsePilot框架，结合多臂老虎机探测与深度强化学习，仅用3.1%测量预算即实现优异覆盖恢复，且跨场景泛化能力强。
AI 中文摘要
无人机（UAV）已成为城市环境中按需无线覆盖规划的有前景解决方案。然而，现有的基于学习的无人机控制方法通常依赖于对用户级接收信号强度（RSS）测量的连续密集访问。由于密集无线反馈的高成本和有限可用性，这种全观测假设在实际部署中难以满足。因此，在严格观测约束下的稀疏反馈决策是一个基本挑战。为填补这一空白，我们提出了SparsePilot，这是一种测量高效的感知-控制框架，将主动无线探测与基于置信度的网络控制相结合。SparsePilot将空间探测表述为网格单元上的多臂老虎机问题，使用上置信界探测选择信息区域，并将稀疏RSS测量聚合为覆盖置信度图。然后，深度强化学习控制器利用该置信度状态生成连续的无人机移动动作，而完整的无线状态对策略保持隐藏。我们进一步提供了连接稀疏探测、置信度估计误差和稀疏反馈性能差距的理论分析。在七个城市数字孪生上的实验表明，SparsePilot仅使用约3.1%的全观测测量预算，即可实现卓越的覆盖恢复性能，并展示了对未见过的城市规模无线环境的强大跨场景泛化能力。
英文摘要
Unmanned aerial vehicles (UAVs) have emerged as a promising solution for on-demand wireless coverage planning in urban environments. Existing learning-based UAV control methods, however, typically rely on continuous access to dense user-level received signal strength (RSS) measurements. Such full-observation assumptions are difficult to satisfy in real-world deployments due to the high cost and limited availability of dense wireless feedback. Sparse-feedback decision making under severe observation constraints therefore represents a fundamental challenge. To fill this gap, we propose SparsePilot, a measurement-efficient sensing-control framework that couples active wireless probing with belief-guided network control. SparsePilot formulates spatial probing as a multi-armed bandit problem over grid cells, uses upper confidence bound probing to select informative regions, and aggregates sparse RSS measurements into a coverage belief map. A deep reinforcement learning controller then uses this belief state to generate continuous UAV mobility actions, while the full wireless state remains hidden from the policy. We further provide a theoretical analysis connecting sparse probing, belief estimation error, and the sparse-feedback performance gap. Experiments across seven urban digital twins show that SparsePilot achieves superior coverage restoration performance while using only about 3.1% of the full-observation measurement budget and demonstrates strong cross-scene generalization to unseen urban-scale wireless environments.
