Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
Integrating LEO Satellite and UAV Relaying via Reinforcement Learning for Non-Terrestrial Networks
复制标题
通过非地面网络的强化学习集成 LEO 卫星和无人机中继
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Young
中科院分区：
文献类型：
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作者：
Ju;Jihong Park;M. Bennis;Young
低地球轨道（LEO）卫星的巨型星座有可能实现低延迟的远程通信。将其与新兴的无人机（UAV）辅助非地面网络相结合，将成为提供大规模三维连接的5G系统的颠覆性解决方案。在这篇文章中，我们研究了两个遥远的地面终端之间的转发数据包的问题，通过一个低轨卫星从轨道星座和一个移动的高空平台（HAP），如固定翼无人机。为了最大化端到端数据速率，应该优化卫星关联和HAP位置，这是具有挑战性的，由于大量的轨道卫星和由此产生的时变网络拓扑。我们使用深度强化学习（DRL）和一种新的动作降维技术来解决这个问题。仿真结果证实，我们提出的方法实现了高达5.74倍的平均数据速率相比，没有SAT和HAP的直接通信基线。
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solution for beyond 5G systems provisioning large-scale three-dimensional connectivity. In this article, we study the problem of forwarding packets between two faraway ground terminals, through an LEO satellite selected from an orbiting constellation and a mobile high-altitude platform (HAP) such as a fixed-wing UAV. To maximize the end-to-end data rate, the satellite association and HAP location should be optimized, which is challenging due to a huge number of orbiting satellites and the resulting time-varying network topology. We tackle this problem using deep reinforcement learning (DRL) with a novel action dimension reduction technique. Simulation results corroborate that our proposed method achieves up to 5.74x higher average data rate compared to a direct communication baseline without SAT and HAP.