Unmanned aerial vehicle (UAV) swarm networks face severe challenges of communication network split (CNS) issues caused by massive damage in hostile environments. In this paper, we propose a new paradigm to restore network connectivity by repositioning remaining UAVs based on damage information within local topologies. Particularly, the locations of destroyed UAVs distributed in gaps between disconnected sub-nets are considered for recovery trajectory planning. Specifically, we construct the multi-hop differential sub-graph (MDSG) to represent local damage-varying topologies. Based on this, we develop two distinct algorithms to address CNS issues. The first approach leverages an artificial potential field algorithm to calculate the recovery velocities via MDSG, enabling simple deployment on low-intelligence UAVs. In the second approach, we design an MDSG-based graph convolution framework to find the recovery topology for high-intelligence swarms. As per the unique topology of MDSG, we propose a novel bipartite graph convolution operation, enhanced with a batch-processing mechanism to improve graph convolution efficiency. Simulation results show that the proposed algorithms expedite the recovery with significant margin while improving the spatial coverage and topology degree uniformity after recovery.

翻译：无人机集群网络在敌对环境中面临因大规模损毁导致的通信网络分裂问题的严峻挑战。本文提出一种新范式，通过基于局部拓扑内的损毁信息重新部署剩余无人机来恢复网络连接。特别地，恢复轨迹规划考虑了分布在断开子网间间隙中的被毁无人机位置。具体而言，我们构建了多跳差分子图以表征局部损毁变化的拓扑结构。基于此，我们开发了两种不同的算法来解决通信网络分裂问题。第一种方法利用人工势场算法，通过多跳差分子图计算恢复速度，使其能够简单部署于低智能无人机。在第二种方法中，我们设计了一个基于多跳差分子图的图卷积框架，为高智能集群寻找恢复拓扑。针对多跳差分子图的独特拓扑结构，我们提出了一种新颖的二部图卷积操作，并通过批处理机制增强以提高图卷积效率。仿真结果表明，所提算法显著加快了恢复速度，同时在恢复后改善了空间覆盖度和拓扑度均匀性。