Resilient UAV Swarm Communications with Graph Convolutional Neural Network

TL;DR

This paper introduces a graph convolutional neural network-based approach for self-healing UAV swarm networks, enabling rapid recovery of communication connectivity after disruptions through online topology recovery and trajectory planning.

Contribution

It presents a novel GCN-based method with meta learning for real-time self-healing of UAV swarm communications under various external disruptions.

Findings

Faster connectivity recovery compared to existing algorithms.

Meta learning improves GCN performance and reduces execution time.

Effective in both one-off and general external disruptions.

Abstract

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.