标题： vehicular任务卸载的动态改进框架 显示英文标题

标题： A Dynamic Improvement Framework for Vehicular Task Offloading

摘要： 本文提出了一种新颖的动态改进框架，用于优化具有随机速度的车辆的任务卸载问题。具体而言，在一个包含多辆车和基站（BS）的车载网络中，车辆的计算任务通过BS卸载到边缘服务器。由于车辆速度的随机性，无法提前预测它们的确切轨迹。因此，任务卸载期间多个车辆的小区关联、上行时间和吞吐量分配被建模为有限时间范围的马尔可夫决策过程，而不是确定性优化。在所提出的解决方案框架中，我们首先通过确定性优化获得小区关联、上行时间和吞吐量分配的参考调度方案。然后利用该参考调度方案来近似贝尔曼方程中的价值函数，并根据当前系统状态和近似价值函数在每个时隙内确定实际调度操作。这样就消除了传统解决方案中值迭代的大量计算。此外，还为所提出的解决方案框架提供了一个非平凡的平均成本上界。在仿真中，车辆的随机轨迹是从高保真交通模拟器生成的。结果显示，与基线方法相比，所提出的调度框架性能提升显著。 显示英文摘要

摘要： In this paper, the task offloading from vehicles with random velocities is optimized via a novel dynamic improvement framework. Particularly, in a vehicular network with multiple vehicles and base stations (BSs), computing tasks of vehicles are offloaded via BSs to an edge server. Due to the random velocities, the exact trajectories of vehicles cannot be predicted in advance. Hence, instead of deterministic optimization, the cell association, uplink time and throughput allocation of multiple vehicles in a period of task offloading are formulated as a finite-horizon Markov decision process. In the proposed solution framework, we first obtain a reference scheduling scheme of cell association, uplink time and throughput allocation via deterministic optimization at the very beginning. The reference scheduling scheme is then used to approximate the value functions of the Bellman's equations, and the actual scheduling action is determined in each time slot according to the current system state and approximate value functions. Thus, the intensive computation for value iteration in the conventional solution is eliminated. Moreover, a non-trivial average cost upper bound is provided for the proposed solution framework. In the simulation, the random trajectories of vehicles are generated from a high-fidelity traffic simulator. It is shown that the performance gain of the proposed scheduling framework over the baselines is significant.