Research On Optimization Of Mobile Edge Computing Based On Joint Offloading And Computing

With the development of information technology,the era of Internet of Everything is coming.At the same time,the number of network edge devices is increasing every day,which leads to an explosive growth of data and poses a major challenge to the design of real-time communication systems.Mobile edge computing(MEC)is considered as a potential solution to improve the computing capability of mobile users and realize low-latency communication.By using the nearby MEC,mobile users with limited resources can offload computing tasks to a more powerful MEC server for remote execution.At the same time,MEC applied to the Internet of Vehicles can effectively solve the current urban traffic congestion problem,and can also support a variety of vehicle applications,including autonomous driving,road monitoring,real-time navigation,etc.This paper takes the MEC system of joint offloading and computing as the research object,and studies the energy-efficient MEC systems based on cooperative multicarrier relay,as well as the vehicular edge computing network(VECN)based on time-varying fading channels.The MEC technology for joint offloading and computing in the 5G Internet of Things scenario is deeply studied from four aspects: protocol design,problem formulation,problem solving and system performance simulation.In the energy-efficient MEC transmission network based on cooperative multicarrier relay,an access point and a relay node serve a user terminal over multicarrier subchannels.The relay can assist not only task offloading but also task computation.First,a novel cooperative MEC protocol is designed,where multicarrier subchannels are utilized for parallel task offloading by integrating the rateless coding technique.Then,under the newly protocol,considering a given task computation delay constraint,by jointly optimizing subcarrier allocation,power allocation,task partition,and offloading time and computation time allocation,the total energy consumption at the user terminal and the relay are minimized.The resource allocation optimization problem is formulated as a mixed-integer programming problem.Using the continuous relaxation and the successive convex approximation techniques,the lower bound of energy consumption performance and an iterative optimization algorithm that can approach the lower bound performance are obtained in turn.Simulation results show that the proposed jointly cooperative task offloading and computation scheme can significantly reduce the energy consumption as compared to the baseline schemes where the relay only assists the task offloading or task computation.For the design of VECN based on time-varying fading channels,our work considers that the wireless channels in VECN are time-varying due to high mobility of vehicles.Compared with the VECN based on invariant channels,our work is more practical.First,the monetary cost of energy consumption at vehicles and rental cost at roadside units are minimized by jointly optimizing task offloading and computing in the VECN.Since the formulated optimization problem involves noncausal channel state information(CSI),this problem cannot be solved directly.Then,this paper uses a path prediction model to predict the vehicles' future positions,so that noncausal CSI can be estimated.However,solving the problem directly would result in high computational complexity.For this reason,an algorithm based on the consensus alternating directions method of multipliers is proposed to solve the problem in a distributed manner with lower complexity.The simulation results prove the superior performance of our proposed scheme.