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.

本文研究了移动地面用户的空地超可靠低延迟通信问题，提出了一种名为 GAXNet 的多智能体深度强化学习框架，通过避免无人机之间的碰撞来控制多个无人机，从而实现低误差率和低延迟。