Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks

TL;DR

This paper introduces a novel multi-level damage-aware graph learning algorithm for UAV swarm networks that improves resilience and recovery efficiency after damage by focusing on destroyed nodes and expanding neighbor receptive fields.

Contribution

It proposes a multi-level damage-aware graph learning approach with a damage attention module and dilated graph convolution, addressing over-aggregation and convergence issues in damaged topologies.

Findings

Guarantees connectivity restoration in UAV networks

Speeds up recovery time significantly

Improves topology uniformity after recovery

Abstract

Unmanned aerial vehicle (UAV) swarm networks leverage resilient algorithms to restore connectivity from communication network split issues. However, existing graph learning-based approaches face over-aggregation and non-convergence problems caused by uneven and sparse topology under massive damage. In this paper, we propose a novel Multi-Level Damage-Aware (MLDA) Graph Learning algorithm to generate recovery solutions, explicitly utilizing information about destroyed nodes to guide the recovery process. The algorithm first employs a Multi-Branch Damage Attention (MBDA) module as a pre-processing step, focusing attention on the critical relationships between remaining nodes and destroyed nodes in the global topology. By expanding multi-hop neighbor receptive fields of nodes to those damaged areas, it effectively mitigating the initial sparsity and unevenness before graph learning commences. Second, a Dilated Graph Convolution Network (DGCN) is designed to perform convolution on the MBDA-processed bipartite graphs between remaining and destroyed nodes. The DGCN utilizes a specialized bipartite graph convolution operation to aggregate features and incorporates a residual-connected architecture to extend depth, directly generating the target locations for recovery. We theoretically proved the convergence of the proposed algorithm and the computational complexity is acceptable. Simulation results show that the proposed algorithm can guarantee the connectivity restoration with excellent scalability, while significantly expediting the recovery time and improving the topology uniformity after recovery.