SCOPE: Deterministic and Training-Free 3D UAV Deployment via Perimeter-based Heuristics
Chuan-Chi Lai

TL;DR
SCOPE is a deterministic, training-free 3D UAV deployment method that dynamically optimizes coverage using perimeter-based heuristics, ensuring high user satisfaction and real-time performance in unpredictable environments.
Contribution
This work introduces SCOPE, a novel perimeter-based heuristic framework with proven convergence and polynomial complexity, outperforming DRL methods in real-time UAV deployment scenarios.
Findings
Achieves 82-88% user satisfaction rate in simulations.
Maintains 40% coverage at 60m altitude, outperforming baselines.
Generates solutions within milliseconds on standard hardware.
Abstract
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…
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