SparsePilot: Belief-Guided Network Planning under Sparse Wireless Measurements
2026-08-10 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the challenge of controlling drones to provide wireless coverage in cities when only limited signal data is available. They propose SparsePilot, a method that smartly selects a few important locations to measure signal strength and uses those spots to estimate overall coverage. Then, a smart controller moves the drone based on this limited info to improve coverage without needing full data. Their tests in simulated cities show their approach works well even with very little signal feedback and adapts to new environments.
unmanned aerial vehicleswireless coveragereceived signal strengthsparse feedbackmulti-armed banditupper confidence bounddeep reinforcement learningbelief stateurban wireless networks
Authors
Xuanhao Luo, Jiayuan Huang, Longyu Zhou, Mingzhe Chen, Yuchen Liu
Abstract
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.