Recently, traditional transportation systems have been gradually evolving to ITS, inspired by both artificial intelligence and wireless communications technologies. The vehicles get smarter and connected, and a variety of intelligent applications have emerged. Meanwhile, the shortage of vehicles' computing capacity makes it insufficient to support a growing number of applications due to their compute- intensive nature. This contradiction restricts the development of ICVs and ITS. Under this background, vehicular edge computing networks (VECNs), which integrate MEC and vehicular networks, have been proposed as a promising network paradigm. By deploying MEC servers at the edge of the network, ICVs' computational burden can be greatly eased via MEC offloading. However, existing task offloading schemes had insufficient consideration of fast-moving ICVs and frequent handover with the rapid changes in communications, computing resources, and so on. Toward this end, we design an intelligent task offloading scheme based on deep Q learning, to cope with such a rapidly changing scene, where software-defined network is introduced to achieve information collection and centralized management of the ICVs and the network. Extensive numerical results and analysis demonstrate that our scheme not only has good adaptability, but also can achieve high performance compared to traditional offloading schemes.

中文翻译：

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车辆边缘计算网络中的智能任务卸载


近年来，受人工智能和无线通信技术的启发，传统交通系统逐渐向智能交通系统演进。车辆变得更加智能、互联，各种智能应用不断涌现。同时，车辆计算能力的短缺使其不足以支持越来越多的计算密集型应用。这一矛盾制约了智能网联汽车和智能交通系统的发展。在此背景下，集成MEC和车辆网络的车辆边缘计算网络（VECN）被提出作为一种有前途的网络范式。通过在网络边缘部署 MEC 服务器，可以通过 MEC 卸载大大减轻智能网联汽车的计算负担。然而，现有的任务卸载方案对于智能网联汽车的快速移动和通信、计算资源等快速变化的频繁切换考虑不足。为此，我们设计了一种基于深度Q学习的智能任务卸载方案，以应对这种快速变化的场景，其中引入软件定义网络来实现智能网联汽车和网络的信息收集和集中管理。大量的数值结果和分析表明，与传统卸载方案相比，我们的方案不仅具有良好的适应性，而且能够实现高性能。