UAV Swarm Network Topology Self-Healing via Graph-Based Deep Reinforcement Learning

Unmanned aerial vehicles (UAV) swarm network (USNET) is a promising solution for diverse applications and usually works in harsh environments. However, it is challenging to rebuild the communication connectivity in USNETs with low time complexity under unpredictable UAV damages. In this paper, we present a graph attention network (GAT)-based deep reinforcement learning topology self-healing (GDR-TS) framework to minimize the topology self-healing (TSH) time of remaining UAVs. Specifically, we decompose the TSH problem into a neighbor selection problem and a trajectory planning problem. For the former, we present an edge update GAT-based actor-critic structure to find an optimal adjacency matrix; for the latter, we present a node update GAT-based position fine-tuning module to compute an optimal position matrix. Simulation results show that under various disruption setups, our GDR-TS framework outperforms the other four baselines in terms of the average TSH time and the response time.