论文标题
在车辆边缘计算和网络中无人机的部署和资源分配的联合优化
Joint Optimization of the Deployment and Resource Allocation of UAVs in Vehicular Edge Computing and Networks
论文作者
论文摘要
随着智能车辆的开发，计算密集型任务被广泛生成。为了减轻车载CPU的负担，互联的车辆可以通过新兴的移动边缘计算（MEC）来卸载任务或从附近的边缘服务器提出请求。但是，这种方法可能会大大增加边缘服务器的工作量，并引起网络拥堵，尤其是在边缘服务器很少的农村和山区。为此，本文提出了一个无人机辅助的MEC系统，并提出了UAVS（JOAODR）的部署和资源分配的联合优化算法来决定位置并平衡无人机的资源和奖励。我们根据操作员解决了长期利润最大化问题。数值结果表明，我们的算法的表现优于其他基准算法，并验证了我们的解决方案。
With the development of smart vehicles, computing-intensive tasks are widely and rapidly generated. To alleviate the burden of on-board CPU, connected vehicles can offload tasks to or make request from nearby edge server thanks to the emerging Mobile Edge Computing (MEC). However, such approach may sharply increase the workload of an edge server, and cause network congestion, especially in rural and mountain areas where there are few edge servers. To this end, a UAV-assisted MEC system is proposed in this paper, and joint optimization algorithm of the deployment and resource allocation of UAVs (JOAoDR) is proposed to decide the location and balance the resource and rewards of the UAVs. We solve a long-term profit maximization problem in terms of the operator. Numerical results demonstrated that our algorithm outperforms other benchmarks algorithm, and validated our solution.
