Real-Time Large-Scale 6G Satellite-UAV Networks

Minh Le Nguyen, Tinh T. Bui, L. Nguyen, E. Garcia-Palacios, H. Zepernick, T. Duong
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Abstract

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). We propose an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. A non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. The total network latency is then minimised by using alternating optimisation technique. Numerical results prove the efficiency of our methods compared to other traditional ones.
实时大规模6G卫星-无人机网络
在本文中,我们考虑了一个由多颗卫星和多架缓存辅助无人机(uav)支持的物联网网络。我们提出了一个优化问题,目的是最小化总网络延迟。为了降低原问题的复杂性,将其划分为三个子问题,即与无人机相关的地面用户聚类问题、在无人机上放置缓存(以支持网络避免回程拥塞)以及卫星和无人机的功率分配问题。设计了一个非合作对策来求解聚类问题;采用遗传算法求解高复杂度缓存问题,该算法在多变量场景下具有强大的求解能力;并采用快速估计技术进行功率分配。然后使用交替优化技术将总网络延迟降至最低。数值结果证明了该方法与其他传统方法相比的有效性。
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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