面向信息年龄的车联网资源分配和计算卸载策略优化OA

As the scale of age of information(AoI)expands and communication resources become scarce,relying solely on roadside units(RSUs)for task processing may fail to achieve optimal AoI performance,therefore,a soft actor-critic(SAC)-based resource allocation and computation offloading strategy(SAC-RACO)optimization algorithm was proposed to solve this problem,with AoI as the performance metric.By jointly optimizing the resource allocation scheme of RSUs and vehicles as well as the computation offloading scheme for tasks,the algorithm aims to minimize AoI.The results show that,compared with proximal policy optimization(PPO)and twin delayed deep deterministic policy gradient(TD3),the proposed algorithm converges faster and achieves higher cumulative reward,effectively ensuring the freshness of computation-intensive tasks in IoV.