V2X异构车载网络下智能任务卸载策略研究

Intelligent task offloading strategy in V2X heterogeneous vehicular networks

随着自动驾驶技术的迅速发展,车辆日益增长的处理需求与资源受限的车载处理器之间的矛盾日渐突出.车载边缘计算的出现解决了车载资源的物理限制,增强了单个车辆的计算能力.然而,由于车载服务通常具有时延敏感性,如何选择合适的通信接入技术,更好地满足自动驾驶场景中时延要求便成为一个挑战性难题.鉴于此,综合考虑两种V2X通信接入技术,即短距通信(DSRC)和基于蜂窝网的车载通信(C-V2X),提出一种V2X异构车载网络任务卸载模型.首先分析车辆移动性特征,并对车载资源进行虚拟化处理;然后基于半马尔科夫决策过程原理对任务卸载问题进行建模,分别制定状态、动作、奖励和转移概率;最后基于强化学习智能算法获取最优任务卸载策略,并通过大量数值仿真实验验证其任务卸载性能优于贪婪算法.

With the rapid development of autonomous driving technology,the contradiction between the increasing processing requirements of vehicles and the resource-limited on-board processors is increasingly prominent.The emergence of vehicular edge computing solves the physical limitation of on-board resources and enhances the computing capacity of a single vehicle.However,due to the delay-sensitive of vehicular services in autonomous driving scenarios,how to choose the appropriate access technology to satisfy the delay constraint of vehicular services has become a challenge.In this paper,two kinds of V2X communication technologies,namely short range communication(DSRC)and cellular vehicular communication(C-V2X),are considered comprehensively,and a task offloading model of V2X heterogeneous vehiclular network is proposed.Firstly,the characteristics of vehicle mobility are analyzed,and the on-board resources are virtualized.Then,the task offloading problem is modeled based on the principle of semi-Markov decision processes(SMDP),and the state,action,reward and transition probability are defined respectively.Finally,the optimal task offloading strategy is obtained based on the reinforcement learning intelligent algorithm,and the performance of the algorithm is proved to be better than the greedy algorithm through a large number of numerical simulations.