Joint Client Association and UAV Scheduling in Cache-Enabled UAV-Assisted Vehicular Networks

Shichao Zhu, Lin Gui, Qi Zhang, Xiupu Lang
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引用次数: 2

Abstract

In the case of explosive content requests being generated by numerous vehicles in rush hour, the cellular downlink resources become insufficient. This paper considers two supplementary schemes for exploiting the uplink resources: One is letting vehicles cache the browsed contents so that the vehicles can share contents with one another; and the other is dispatching a cache-enabled UAV to transmit contents to the vehicles nearby. We formulate a joint optimization problem for maximizing the average data rate of vehicles, and then decompose it into the subproblems of client association and UAV scheduling. The client association aims at matching the vehicles to the resources, and the UAV scheduling is to update the UAV’s caching and trajectory to adapt to the environment changing. The two subproblems focus respectively on the current condition and the long term profit, and are solved respectively by a matching-based algorithm and a deep reinforcement learning-based algorithm. Our simulation results based on a real-world traffic data set demonstrate the advantages of the proposed approaches.
基于缓存的无人机辅助车辆网络中的联合客户关联和无人机调度
在交通高峰期大量车辆产生爆炸性内容请求的情况下,蜂窝下行资源变得不足。本文考虑了两种利用上行资源的补充方案:一是让车辆缓存浏览的内容,使车辆之间可以共享内容;另一种是派遣具有缓存功能的无人机将内容传输到附近的车辆。提出了以车辆平均数据速率最大化为目标的联合优化问题,并将其分解为客户关联和无人机调度子问题。客户端关联的目的是实现车辆与资源的匹配,无人机调度的目的是更新无人机的缓存和轨迹,以适应环境的变化。这两个子问题分别关注当前状况和长期收益,分别采用基于匹配的算法和基于深度强化学习的算法进行求解。基于真实交通数据集的仿真结果证明了所提出方法的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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