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Efficient DRL-Based 3D UAV Deployment for Optimizing Mobile User Monitoring Under Wireless Service-Reliability Constraints
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DOI：10.1109/ojvt.2026.3705949.png)
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Abstract
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Indoor flying networks (IFNs) can provide flexible monitoring support in crowded indoor environments, but practical deployment must jointly account for user mobility, limited UAV energy, motion boundaries, and reliable UAV-to-access-point (AP) communication. This paper studies energy- and service-reliability-constrained three-dimensional (3D) UAV deployment in a hybrid LiFi-WiFi IFN. The optimization objective is to maximize the number of mobile users monitored by each UAV, while energy, boundary, speed, and AP-connectivity requirements are imposed as operational constraints. To solve the resulting sequential decision problem, we use a coverage-driven deep reinforcement learning (DRL) framework whose main state keeps the UAV position, residual energy, and current coverage. The paper also implements an aggregate density-aware state variant based on coarse cell-level occupancy, density centroid, and density-centroid displacement to quantify the benefit of explicit mobility-context information without requiring exact user localization. The UAV operates in a continuous physical space, yet the DQL controller selects actions from a discrete set of local 3D motion primitives. This discretization is motivated by the limited onboard computational resources of the indoor UAV and the need to make rapid local positioning decisions. Collision avoidance is provided structurally by non-overlapping sub-region assignment, while boundary and wireless-service feasibility are enforced through action screening and reward penalties. The proposed policy is evaluated against several practical schemes (random selection, centroid-based selection, greedy heuristics, particle swarm optimization, and genetic algorithms), as well as against an upper-bound benchmark based on finite-grid integer linear programming (ILP). The results show that the proposed DRL method substantially outperforms practical baselines in terms of the average number of monitored users, while still achieving 93.7% of the performance of the finite-grid ILP benchmark. The evaluation also reports multi-seed robustness, QoS-violation rates, reward-threshold sensitivity, scalability, UAV-speed effects, and LiFi/WiFi service-reliability behavior.
Keywords:
Indoor flying networks (IFNs)
deep reinforcement learning (DRL)
3D deployment
monitoring coverage
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