In this paper, we study the self-healing problem of unmanned aerial vehicle (UAV) swarm network (USNET) that is required to quickly rebuild the communication connectivity under unpredictable external disruptions (UEDs). Firstly, to cope with the one-off UEDs, we propose a graph convolutional neural network (GCN) and find the recovery topology of the USNET in an on-line manner. Secondly, to cope with general UEDs, we develop a GCN based trajectory planning algorithm that can make UAVs rebuild the communication connectivity during the self-healing process. We also design a meta learning scheme to facilitate the on-line executions of the GCN. Numerical results show that the proposed algorithms can rebuild the communication connectivity of the USNET more quickly than the existing algorithms under both one-off UEDs and general UEDs. The simulation results also show that the meta learning scheme can not only enhance the performance of the GCN but also reduce the time complexity of the on-line executions.

翻译：在本文中,我们研究了无人驾驶飞行器(UAV)群温网络(USNET)的自我愈合问题,这是在无法预测的外部干扰下快速重建通信连接所必需的。 首先,为了应对一次性的UED,我们提议了一张图形革命神经网络(GCN),并以在线方式找到USNET的恢复表层。第二,为了应对通用UED,我们开发了基于GCN的轨迹规划算法,使无人驾驶飞行器能够在自愈过程期间重建通信连接。我们还设计了一个元学习计划,以便利GCN的在线处决。数字结果显示,拟议的算法可以比在一次性UED和通用UED下的现有算法更快地重建美国网络的通信连接。模拟结果还表明,元学习计划不仅可以提高GCN的性能,还可以降低在线处决的时间复杂性。