基于NOMA-MEC的车联网任务卸载、迁移与缓存策略

Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC

在移动边缘计算(MEC)与非正交多路接入(NOMA)技术相结合的车联网系统中,针对用户处理计算密集型和时延敏感型任务时面临的高时延问题,提出了一种基于博弈论和Q学习的任务卸载、迁移与缓存优化策略。首先,对基于NOMA-MEC的车联网任务卸载时延、迁移时延与缓存时延进行建模;其次,采用合作博弈算法获得最优用户分组,以实现卸载时延优化;最后,为避免出现局部最优,通过Q学习算法优化用户分组中的迁移缓存联合时延。仿真结果表明,所提方案相比对比方案,能有效提升卸载效率并降低约22%~43%的任务时延。

In the internet of vehicles systems that combining mobile edge computing(MEC) with non-orthogonal multiple access(NOMA) technology,to solve the high latency problem when user processes computationally intensive and latency-sensitive task,a strategy of task offloading,migration and cache optimization based on game theory and Q learning is proposed.Firstly,the mo-del of offloading delay,migration delay and cache delay of the internet of vehicles task based on NOMA-MEC is established.Se-condly,we use the cooperative game method to obtain the optimal user group to optimize the offloading delay.Finally,in order to avoid local optima,the Q learning algorithm is utilized to optimize the joint delay of the migration cache in the user group.The simulation results show that compared with other solutions,the proposed algorithm can effectively improve the offloading efficiency and reduce the task delay by about 22% to 43%.