| With the development of wireless communication technology,5G and post 5G communication networks can support various applications and services.Due to the limited battery capacity and computing resources,mobile devices cannot afford intelligent services and computing intensive applications.In order to overcome these problems,mobile edge computing(MEC)concept is applied by providing users with storage and computing services similar to cloud server at the network edge.It can reduce task delay and alleviate network congestion effectively.However,due to the limited resources of MEC servers,it is difficult to meet the needs of all users.This article selects the technologies of device-to-device(D2D)and unmanned aerial vehicle(UAV)to assist MEC communication networks.Therefore,the focus of research is designing efficient computing offloading strategies in MEC communication networks with device collaboration.The main works are as follows:First,the theoretical knowledge related to mobile edge computing technology is introduced.Two cooperative device modes and the relevant optimization algorithms,e.g,particle swarm optimization and Lyapunov optimization are analyzed.Second,considering the overload problem caused by many offloading requests in the MEC scenario,D2D offloading is added for collaborative computing of system.An algorithm is proposed based on particle swarm optimization and matching to find optimal offloading strategy,MEC server resource allocation and channel allocation strategies to minimize system costs.Third,UAV assistance devices are introduced to solve the problem of minimizing the average system energy consumption during long-term dynamic task offloading.The Lyapunov optimization algorithm is used to establish buffer queues for terminal devices,and a method based on particle swarm optimization and simulated annealing algorithm is proposed to optimize the offloading strategy and transmission power.Simulation results show that the two joint optimization algorithms can utilize resources and reduce system costs compared to existing schemes effectively.This paper studies the joint optimization problems of task offloading and resource allocation in the MEC network with device collaboration,establishes the different offloading models,proposes two joint optimization algorithms,which meet the business needs in different scenarios,and provides a good reference value for the simulation and system design of the MEC network with device collaboration. |
