  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.
