Title: Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks Show Chinese title

Title: 多级损伤感知图学习用于弹性无人机群网络

Abstract: Unmanned aerial vehicle (UAV) swarm networks leverage resilient algorithms to address communication network split issues and restore connectivity. However, existing graph learning-based resilient algorithms face over-aggregation and non-convergence problems caused by uneven and sparse topology under massive damage scenarios. To alleviate these problems, we propose a novel Multi-Level Damage-Aware Graph Learning (ML-DAGL) algorithm, which generates recovery trajectories by mining information from destroyed UAVs. We first introduce a Multi-Branch Damage Attention (MBDA) module, which forms a sequence of multi-hop Damage Attentive Graphs (mDAG) with different ranges of receptive fields. Each mDAG links only remaining and damaged nodes to ensure a more even degree distribution for mitigating over-aggregation, and utilizes multi-hop dilation to establish more links for sparse topology enhancement. To resort to the mDAG, we propose a Dilated Graph Convolution Network (DGCN), which generates the optimal recovery trajectories with theoretically proven convergence under massive damage cases. Simulation results show that the proposed algorithm can guarantee the connectivity restoration under large swarm and damage scales, while significantly expediting the recovery time by 75.94% and improving the topology uniformity after recovery. Show Chinese abstract

Abstract: 无人机（UAV）集群网络利用鲁棒性算法解决通信网络分裂问题并恢复连接。然而，现有的基于图学习的鲁棒性算法在面对大规模损伤情况下因拓扑分布不均和稀疏导致过聚合和非收敛问题。为缓解这些问题，我们提出了一种新颖的多级损伤感知图学习（ML-DAGL）算法，该算法通过挖掘受损无人机的信息生成恢复轨迹。我们首先引入一个多分支损伤注意力（MBDA）模块，形成一系列具有不同感受野范围的多跳损伤注意图（mDAG）。每个mDAG仅连接剩余节点和受损节点以确保更均匀的度分布来减轻过聚合，并利用多跳扩张建立更多链接以增强稀疏拓扑。为了使用mDAG，我们提出了一个扩张图卷积网络（DGCN），在大规模损伤情况下理论上证明了其收敛性并生成最优恢复轨迹。仿真结果显示，所提出的算法能够在大规模集群和损伤规模下保证连接恢复，同时显著加速恢复时间达75.94%，并在恢复后提高拓扑均匀性。