基于Q学习的蜂窝车联网边缘计算系统PC-5/Uu接口联合卸载策略OA北大核心CSTPCD

Intelligent transportation services,such as smart driving,put forward high requirements for latency.When the vehicle itself has insufficient computing power,the vehicle needs the surrounding vehicles and roadside edge computing units to help it complete the task computation.In this paper,based on the existing vehicular edge computing(VEC)offload-ing strategy,considering the differences between the 5G-NR interface and PC-5 interface link of cellular-V2X(C-V2X)sys-tem,we propose a Q-Learning based joint PC-5/Uu interface edge computing offloading strategy.The successful transmis-sion probability of PC-5 link in C-V2X system is modeled,and then the transmission rate characterization method of PC-5 link is deduced.We formulate a constrained Markov decision process(CMDP)to minimize the system latency,where the objective function is the task processing latency in C-V2X system,and constraints are transmission power at task vehicle and energy consumption of computation at vehicles with edge computing unit.By Lagrangian approach,the CMDP prob-lem is transformed into an equivalent min-max non-constrained MDP problem,and Q-Learning is introduced to design the offloading strategy,and then the offloading strategy of C-V2X based VEC system based on Q-Learning is proposed.Simu-lation results show that compared with other baseline schemes,the proposed algorithm can significantly improve the system latency performance by at least 27.3%.