Trajectory And Resource Allocation Optimization For Energy-Efficient UAV In Edge Computing

With the rapid development of beyond-fifth-generation(B5G)and sixth-generation(6G)wireless networks,a growing variety of Internet of Things(Io T)devices and applications,including mobile phones,wearable devices,face recognition,online gaming,automatic driving,real-time online gaming,etc.,demand greater computation capacity and lower latency to provide users with a better experience.These emerging applications are generally latency-sensitive and computation intensive,where the sufficient quality of service(Qo S)may not be guaranteed due to the small size and limited computing resources of the Io T devices.To address these challenges,researchers have introduced Mobile Edge Computing(MEC),which migrates computing services to the network edge to significantly reduce system latency and improve service quality.Furthermore,with their flexible and controllable mobility,Unmanned Aerial Vehicles(UAVs)are becoming an essential component in MEC for extending the coverage of computing services.In this dissertation,reducing the energy consumption of UAVs and ground devices and increasing the system’s computational throughput are two critical indicators for improving system performance.Focusing on system resources and energy consumption in wireless communication,this dissertation models the channel between UAVs and ground devices,the energy consumption model of UAVs,and the computing model of edge servers and devices,and proposes a UAV-assisted MEC communication system framework to investigate the system’s computational throughput and energy consumption issues.An efficient algorithm is designed to jointly optimize ground device computation offloading modes,UAV trajectories,and air-ground communication resource allocation.Additionally,based on real-world scenarios and application requirements,simulations and experiments are used to validate the performance of the proposed algorithm.By comparing with existing benchmark schemes,the effectiveness of the proposed method is demonstrated.The main contributions can be summarized into two aspects:Firstly,this dissertation investigates the computation throughput maximization problem of system in UAV-enabled MEC networks.To address the issue of indivisible computing tasks in practical scenarios,this dissertation adopts a binary offloading strategy and studies the computation throughput maximization problem of system in MEC networks by jointly optimizing the computation offloading selection,resource allocation,and UAV trajectory.To this end,a penalty-based successive convex approximation(SCA)algorithm is proposed for effective to solve this problem.Simulation results validate that the proposed algorithm significantly improves the overall computing throughput of the system compared to other benchmark methods.Secondly,this dissertation studies the tradeoff between air-ground energy consumption in UAV-enabled MEC networks.Specially,considering the threedimensional flight freedom of UAVs,this dissertation studies the UAV energy minimization problem and devices’ energy minimization problem based on the fixedwing UAV energy model in three-dimensional(3D)space,while taking into account the impact of angle-distance tradeoff on transmission related to UAV altitude.On this basis,to balance the energy tradeoff between air and ground,this dissertation further investigates the Pareto optimal solution of the air-ground energy with information causality constraints and task completion time,and proposes a new iterative algorithm based on a division blocks strategy for effective to solve it.Simulation results validate the effectiveness of the proposed approach and reveal the energy tradeoff between air and ground in 3D UAV-assisted MEC networks.