Online UAV Trajectory Planning Under QoS Constraints to Mobile Users in Urban Environments

TL;DR

This paper proposes an online reinforcement learning method for real-time UAV trajectory planning and resource allocation in urban environments, ensuring QoS for mobile users while maximizing throughput.

Contribution

It introduces a novel RL-based approach for joint trajectory and resource optimization under complex urban QoS constraints and practical network limitations.

Findings

The method satisfies QoS, fronthaul, and resource constraints.

It balances throughput and user fairness effectively.

Simulation results demonstrate improved system performance.

Abstract

This paper studies real-time trajectory planning and radio resource allocation for a single uncrewed aerial vehicle (UAV) serving multiple mobile ground users in an urban environment. The downlink system considers heterogeneous user mobility, where independent users and group users coexist and interact. To ensure reliable communication, quality-of-service (QoS) constraints are imposed by requiring the instantaneous data rate of each user to satisfy a minimum threshold whenever feasible. A capacity limited high-altitude platform (HAP)-assisted wireless fronthaul is further considered to capture practical network-side transmission limitations. Under these constraints, the UAV updates its position at each time slot, while QoS-aware bandwidth and power are jointly allocated under total bandwidth and transmit power constraints to maximize system throughput. Due to user mobility and urban blockages, the resulting problem is highly nonconvex and time-varying. An online reinforcement learning (RL) based approach is adopted for real-time UAV trajectory optimization. Simulation results show that the proposed method satisfies the QoS, fronthaul, and radio resource constraints and achieves a balanced trade-off between throughput and user fairness.