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Delay Optimization in Multi-UAV Edge Caching Networks: A Robust Mean Field Game
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2020-12-17 , DOI: 10.1109/tvt.2020.3045509
Lixin Li , Meng Wang , Kaiyuan Xue , Qianqian Cheng , Dawei Wang , Wei Chen , Miao Pan , Zhu Han

The data requirements of a large number of users incur significant delay to base stations/small base stations (BSs/SBSs) in edge caching networks (ECNs). Specially, in certain special scenarios, such as sports events, parades and nature disasters, it is a great challenge to deploy fixed BSs/SBSs. Thus, a kind of flexible replacement of BSs/SBSs, unmanned aerial vehicles (UAVs), are considered as the caching platform to serve users. Hence, this paper proposes a distributed delay optimization algorithm by using the massive UAVs to cache the request contents of users. The purpose of each UAV is to facilitate BSs/SBSs to minimize user delay in downloading content based on the popularity of content and the flight strategy. In addition, we consider the main disturbance caused by the atmospheric turbulence. For the control of large-scale UAVs, we take the robust mean field game theory to model this kind of caching and dynamic flight strategy problem, in which the atmospheric turbulence is described by the disturbance term in the drift function. The simulation results show that the proposed algorithm can reduce the download delay under the limited energy consumption and finally achieve the equilibrium.

中文翻译:

多UAV边缘缓存网络中的延迟优化:鲁棒的均值博弈

大量用户的数据需求导致边缘缓存网络(ECN)中的基站/小型基站(BS / SBS)明显延迟。特别地,在某些特殊情况下,例如体育赛事,游行和自然灾害,部署固定的BS / SBS是一个巨大的挑战。因此,无人驾驶飞行器(UAV)被认为是一种灵活替代BS / SBS的缓存平台,可以为用户提供服务。因此,本文提出了一种利用大型无人机来缓存用户请求内容的分布式时延优化算法。每个无人飞行器的目的是促进BS / SBS根据内容的受欢迎程度和飞行策略将用户下载内容时的延迟降到最低。另外,我们考虑了由大气湍流引起的主要扰动。为了控制大型无人机,我们采用鲁棒的平均场博弈理论对这种缓存和动态飞行策略问题进行建模,其中大气湍流由漂移函数中的扰动项描述。仿真结果表明,该算法可以在有限的能量消耗下减少下载延迟,最终达到平衡。
更新日期:2021-02-16
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