Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning

被引:294
作者
Le Thanh Tan [1 ]
Hu, Rose Qingyang [1 ]
机构
[1] Utah State Univ, Dept Elect & Comp Engn, Logan, UT 84322 USA
基金
美国国家科学基金会;
关键词
Vehicular networks; mobility; edge caching; edge computing; deep reinforcement learning; RESOURCE-ALLOCATION; COMMUNICATION; CHALLENGES; CLOUD;
D O I
10.1109/TVT.2018.2867191
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicular networks. Moreover, the resource allocation policy is designed by considering the vehicle's mobility and the hard service deadline constraint. These critical challenges have often been either neglected or addressed inadequately in the existing work on the vehicular networks because of their high complexity. We develop a deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper. Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space. Numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained.
引用
收藏
页码:10190 / 10203
页数:14
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