Research On D2D Assisted MEC Computation Offloading And Resource Allocation Joint Optimization Algorithm Based On Game Theory

With the rapid development of mobile communication technology and the explosive growth of massive mobile data,various new network services and applications continue to emerge.Mobile users are increasingly demanding network service performance such as high quality,low latency,and low energy consumption.In order to meet the requirements of high bandwidth,low latency,and low energy consumption required for the rapid development of mobile networks,and reduce the network burden.The industry has proposed mobile edge computing(MEC)technology which combines IT service environment and cloud computing.MEC technology "sinks" cloud servers to the edge of mobile networks and can provide cloud computing services for close mobile users.The proximity of MEC reduces the link load of the core network,reduces the task transmission delay and energy consumption of mobile devices,and greatly improves the user’s service experience.However,only relying on MEC technology can not guarantee the low latency and low energy consumption service requests of mobile users in the dense terminal scenario.Therefore,it is necessary to jointly consider the short-range Device-to-Device(D2D)offloading technology with idle computing resources and the long-range mobile cloud computing(MCC)technology with rich computing resources.It is particularly important to design a reasonable computing offload and resource allocation scheme in the MCC-MEC-D2 D collaborative network to achieve low latency and low energy consumption service requirements in dense terminal scenarios.Therefore,this paper studies the computation offloading and resource allocation of MCC-MEC-D2 D cooperative network,and optimizes the delay and energy consumption of the communication system respectively.The main innovative research works are as follows:(1)For the problem that the computing resources of network edge are limited which cannot meet the demand of more terminal devices for low latency computing services,and the mobile cloud computing link is congested which has a long transmission latency.A MCC-MEC-D2 D collaborative offloading network model including cloud offloading mode,MEC offloading mode,and D2 D offloading mode is proposed,and a joint optimization algorithm for computing offload and resource allocation oriented to latency is studied.Based on the analysis of network model,communication model,and computing model,an optimization goal of minimizing total system latency is constructed.A joint optimization algorithm based on offloading mode selection,offloading proportion allocation,and computing resource allocation is proposed.The offloading mode selection subproblem is constructed as a precise potential game process,which solves the offloading mode selection subproblem by proving the existence of a Nash equilibrium point.Then,the problem of unloading proportion allocation and computing resource allocation is solved by using mathematical analysis,Lagrange multiplier method,and KKT condition.The simulation results show that compared to MEC networks and MEC-D2 D collaborative networks,the total latency of the proposed MCC-MEC-D2 D collaborative network is reduced by 31.64% and 18.92%,respectively.Moreover,the proposed joint optimization algorithm based on game theory can achieve efficient utilization of resources in collaborative networks and better leverage the low latency performance of collaborative networks.(2)The timeliness of large-scale data computing responses is limited caused by the limited battery capacity of terminal devices,as well as the high energy consumption problem caused by the improvement of software and hardware performance of 5G terminal devices.In MCC-MEC-D2 D collaborative networks,a joint optimization algorithm for computing offload and resource allocation with the goal of minimizing energy consumption under latency constraints is studied.Based on the analysis of network model,communication model,and computing model,the problem of minimizing total energy consumption of the system with latency constraints is constructed.A joint optimization algorithm based on offloading mode selection,offloading proportion allocation,transmit power allocation,and computing resource allocation is proposed.The offloading mode selection subproblem is constructed as a precise potential game process,and solved by proving the existence of a Nash equilibrium point.Then,the block coordinate descent method,Dinkelbach method,Lagrange multiplier method,and KKT condition are used to solve the resource allocation problem under the three unloading modes,respectively.The simulation results show that when the number of terminal devices is 20,the total energy consumption of the proposed algorithm is 40.86%,25.93%,and 13.46% lower than that of local computing,random algorithm,and PA algorithm,respectively.Moreover,the proposed joint optimization algorithm based on game theory and block coordinate descent method can achieve efficient scheduling and full utilization of network resources,and better play to the low energy performance of collaborative networks.