论文标题
车辆速度意识到计算任务卸载和资源分配，基于多代理增强学习在车辆边缘计算网络中
Vehicle Speed Aware Computing Task Offloading and Resource Allocation Based on Multi-Agent Reinforcement Learning in a Vehicular Edge Computing Network
论文作者
论文摘要
对于车载应用，具有不同速度的车辆具有不同的延迟要求。但是，尚未广泛探索车速，这可能会导致车速与其分配的计算和无线资源之间的不匹配。在本文中，我们提出了一个车辆速度意识到的任务卸载和资源分配策略，以减少执行任务的能源成本而不超过延迟约束。首先，我们根据不同的速度和任务类型建立车辆速度意识延迟约束模型。然后，计算VEC服务器和本地终端中任务执行的延迟和能源成本。接下来，我们制定了任务卸载和资源分配的联合优化，以最大程度地减少车辆的能源成本，但要延迟约束。 MADDPG方法用于获得卸载和资源分配策略。仿真结果表明，我们的算法可以在能源成本和任务完成延迟方面实现卓越的性能。
For in-vehicle application, the vehicles with different speeds have different delay requirements. However, vehicle speeds have not been extensively explored, which may cause mismatching between vehicle speed and its allocated computation and wireless resource. In this paper, we propose a vehicle speed aware task offloading and resource allocation strategy, to decrease the energy cost of executing tasks without exceeding the delay constraint. First, we establish the vehicle speed aware delay constraint model based on different speeds and task types. Then, the delay and energy cost of task execution in VEC server and local terminal are calculated. Next, we formulate a joint optimization of task offloading and resource allocation to minimize vehicles' energy cost subject to delay constraints. MADDPG method is employed to obtain offloading and resource allocation strategy. Simulation results show that our algorithm can achieve superior performance on energy cost and task completion delay.
