Joint Optimization of Flight Trajectory and Resource Allocation for Energy-Efficient UAV Assisted Vehicular Edge Computing

Unmanned aerial vehicles (UAVs) are recently envisioned as assist nodes to empower mobile edge computing with the virtue of highly flexible mobility, whereby improving energy efficiency becomes a crucial design issue due to the limited capacity of UAV's battery. This paper presents a fight trajectory and resource allocation optimization scheme for energy-efficient UAV-assisted vehicular edge computing. The joint UAV trajectory planning and resource allocation to minimize the energy consumption while satisfying service requirements is formulated as mixed-integer nonlinear programming. By using successive convex approximation to decompose the joint optimization problem into two convex optimization subproblems, an iteration algorithm that combines geometric-block coordinate descent and convex optimization is proposed to solve it. Simulation results show that the proposed algorithm performs better in terms of optimization efficiency and convergence speed comparing with existing works.