Research On Edge Computing Task Offloading And Resource Allocation Algorithms In Internet Of Vehicles

Internet of Vehicles(Io V)is one of the key technologies in the field of unmanned driving in the future,but the heterogeneous characteristic of the communication network and the insufficient computing ability of vehicles will seriously affect the performance of the Io V system.As a promising technology,Mobile Edge Computing(MEC)can provide a low-latency and high-reliability computing environment for the Io V,relieve the task computing pressure of a single intelligent driving vehicle,and improve the data transmission efficiency of the whole Io V system.In the Io V,reasonable MEC task offloading and resource allocation strategies will further improve the performance of the Io V.Therefore,this thesis mainly studies edge computing task offloading and resource allocation algorithms in Io V,which are the joint access strategy and task offloading algorithm based on the minimization of task computing delay and the joint short packet block length optimization and resource allocation algorithm based on the maximization of task offloading.Combined with the current situation of shortage of spectrum resources and insufficient computing ability of vehicles,this thesis studies the offloading strategy and spectrum access strategy of vehicles in the Io V environment,and proposes the spectrum access strategy and task offloading strategy aiming at minimizing the task computing delay.Then,the joint optimization problem is constructed as a mixed integer programming problem,and the solution of the optimization problem is obtained by using the joint task offloading and resource allocation algorithm based on Multi-Agent Reinforcement Learning(MARL).Numerical simulations show that the task offloading and resource allocation algorithm proposed in this thesis can effectively reduce the task computing delay of the system,realize spectrum sharing among different vehicles,and improve system resource utilization.For the Io V scenario with high vehicle density and low individual data demand,how to improve the data transmission capacity of the communication system to meet the needs of more users is the current research hotspot.This thesis constructs an information transmission mode of vehicles-relay-base station to enhance communication reliability,adopts the communication mode of short packet transmission to reduce transmission delay,and uses the way that vehicles transmit information in different time slots to suppress the inter-vehicle interference.Then,a task offloading maximization problem with short packet block length and transmission time slot selection as optimization variables is constructed.In this thesis,a joint short packet block length optimization and resource allocation algorithm is proposed.The Block Coordinate Descent(BCD)algorithm is used to decouple the problem into two non-convex subproblems.The vehicle transmission slot selection problem is solved by introducing auxiliary variables.The Successive Convex Approximation(SCA)algorithm is used to solve the short packet block length optimization problem.Numerical simulations show that the algorithm proposed in this thesis can effectively achieve the maximum task offloading and has good convergence performance.