In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multi-agent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.
翻译:在本篇文章中,我们研究了移动地面用户的空对地超可靠和低时空通信问题。这是通过实时控制多无人驾驶飞行器(UAVs)来完成的,同时避免了UAV之间的碰撞。为此,我们提议建立一个新型的多剂深层强化学习(MADRL)框架,创建了一个图形关注交换网络(GAXNet)。在GAXNet中,每个UAV都绘制了一个关注点图,测量对邻近无人驾驶飞行器的注意程度,同时与其他无人驾驶航空器交换关注的权重,以减少它们之间的注意力不匹配。模拟结果证实GAXNet在培训期间获得最高4.5x的奖励。在执行过程中,GAXNet在不引起UAV碰撞的情况下,与最先进的基线框架相比,在目标0.0000001的误差率方面实现了6.5x较低的延度。