Drift-Plus-Penalty-Based Joint Optimization of Computational Resource Scheduling, Power Control, and UAV Flight Decisions in UAV-Enabled Mobile Edge Computing
TL;DR
A Lyapunov-based joint optimization framework for UAV-enabled MEC systems achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.
Abstract
With the rapid growth of distributed Internet of Things (IoT) services and edge-intelligence applications, conventional cloud computing is increasingly limited in latency-sensitive scenarios. Mobile Edge Computing (MEC) reduces latency by moving computation closer to end devices, while Unmanned Aerial Vehicles (UAVs) further extend MEC services to remote, emergency, or congested areas through flexible aerial deployment. However, UAV-enabled MEC still faces coupled challenges caused by heterogeneous tasks, limited resources, device energy constraints, time-varying channels, and UAV mobility. To address these challenges, this paper develops a Lyapunov-based joint optimization framework for UAV-enabled MEC systems. A dual-queue model is established to characterize local task uploading and UAV-MEC task execution, and a long-term stochastic energy minimization problem is formulated under queue-stability, resource-capacity, and energy constraints. By applying the drift-plus-penalty principle, the problem is transformed into online per-slot control decisions that jointly coordinate MEC scheduling, uplink power control, and UAV flight decisions. Structure-matched solutions are then developed, including a Lyapunov-drift-based MEC scheduling scheme, a queue-weighted closed-form water-filling power-control policy, and a gradient-based UAV flight controller with exponential smoothing and a fly-or-hover gate. A power–position alternating optimization algorithm is further introduced to handle the coupling between transmit power and UAV position. The simulation results demonstrate that the proposed framework maintains queue stability while reducing system energy consumption under heterogeneous and bursty workloads. It also achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation.