Task Offloading And Resource Allocation Strategy In UAV-aided MEC Network

With the Fifth Generation Mobile Communication development,new applications with ultra-high computing power and ultra-low delay requirements,such as artificial intelligence,online games,and live broadcast,are constantly emerging.In order to satisfy the above emerging applications,Mobile Edge Computing technology has been proposed and widely used.However,computing servers are usually deployed in fixed base stations.The mobility of users and the damage of base stations in temporary hot spots and disaster areas make it difficult for fixed computing servers to meet temporary computing services’ needs.How to solve the dynamic communication demand and link congestion caused by multi-device access is particularly important.According to the existing research,the computing services can be deployed on the Unmanned Aerial Vehicle to meet the communication requirements.In order to give full play to the advantages of the UAV-assisted MEC network,there are still some key problems to be solved urgently,including ground user unloading strategy selection,UAV battery energy limitation,UAV trajectory planning,and location deployment,etc.By reasonably formulating the user unloading strategy and UAV flight trajectory,users can obtain a higher transmission rate and effectively reduce the calculation delay.Through the reasonable allocation of communication resources and optimal location deployment of UAVs,the service life of UAVs can be prolonged,and the energy consumption of the system can be reduced.To sum up,this paper studies two typical problems:offload strategy and resource allocation in a UAV-assisted MEC network.Firstly,aiming at the problem of low delay service guarantee for edge users,this paper designs a task offloading scheme in which UAV and ground base station cooperate.First of all,in the UAV-assisted MEC network,considering the limited flight cycle and computing power of UAV,aiming at the computing delay of users,the problem of low delay service guarantee for edge users is established.Then,considering the conflict between the mobile device delay and the UAV flight trajectory in the modeled problem,the original nonconvex problem is transformed into a convex problem by using the alternating optimization algorithm and a computational offload strategy based on the cooperation between the ground base station and the UAV is proposed.In this strategy,users get a good quality of service through three different offload choices.The alternating optimization algorithm is used to optimize user offload strategy,relay ratio,UAV trajectory,and bit allocation and minimize the computational delay.Finally,the simulation results show that the proposed scheme can effectively enhance the system’s computing power and reduce the user’s delay.Secondly,aiming at the task priority problem of ground users,this paper designs a new MEC architecture of double-layer UAV with multiple ground base stations.We first aiming at offloading tasks by ground users,the problem of maximizing user priority and minimizing system energy consumption is proposed.Besides,considering user task priority and UAV computing capacity limitation,a resource allocation algorithm based on double-layer UAV architecture is designed.In this algorithm,the upper UAV will be flexibly deployed to serve users with higher priority,while the lower UAV will fly periodically to relay other users’ tasks to the ground base station.Finally,simulation results show that the proposed algorithm can provide users with good service quality and reduce system energy consumption.