arXiv subjects

Fangrui Wu

Publications and source records attributed to Fangrui Wu.

2 recordsLinked to original sources

arXiv2026

CLF-ULP: Cross-Layer Fusion-Based Link Prediction in Dynamic Multiplex UAV Networks

In complex Unmanned Aerial Vehicle (UAV) networks, UAVs can establish dynamic and heterogeneous links with one another for various purposes, such as communication coverage, collective sensing, and task collaboration. These interactions give rise to dynamic multiplex UAV networks, where each layer represents a distinct type of interaction among UAVs. Understanding how such links form and evolve is both of theoretical interest and of practical importance for the control and maintenance of networked UAV systems. In this paper, we first develop a dynamic multiplex network model for UAV networks to characterize their dynamic and heterogeneous link properties. We then propose a cross-layer fusion-based deep learning model, termed CLF-ULP, to predict future inter-UAV links based on historical topology data. CLF-ULP incorporates graph attention networks to extract topological features within each layer and perform a cross-layer attention fusion to capture inter-layer dependencies. Furthermore, a shared-parameter long short-term memory network is employed to model the temporal evolution of each layer. To improve embedding quality and link prediction performance, we develop a joint loss function that considers both intra-layer and inter-layer UAV adjacency. Extensive experiments on simulated UAV datasets under diverse mobility patterns demonstrate that CLF-ULP achieves state-of-the-art performance in predicting links within dynamic multiplex UAV networks.

cs.NI↗

arXiv2025

MUST: Multi-Scale Structural-Temporal Link Prediction Model for UAV Ad Hoc Networks

Link prediction in unmanned aerial vehicle (UAV) ad hoc networks (UANETs) aims to predict the potential formation of future links between UAVs. In adversarial environments where the route information of UAVs is unavailable, predicting future links must rely solely on the observed historical topological information of UANETs. However, the highly dynamic and sparse nature of UANET topologies presents substantial challenges in effectively capturing meaningful structural and temporal patterns for accurate link prediction. Most existing link prediction methods focus on temporal dynamics at a single structural scale while neglecting the effects of sparsity, resulting in insufficient information capture and limited applicability to UANETs. In this paper, we propose a multi-scale structural-temporal link prediction model (MUST) for UANETs. Specifically, we first employ graph attention networks (GATs) to capture structural features at multiple levels, including the individual UAV level, the UAV community level, and the overall network level. Then, we use long short-term memory (LSTM) networks to learn the temporal dynamics of these multi-scale structural features. Additionally, we address the impact of sparsity by introducing a sophisticated loss function during model optimization. We validate the performance of MUST using several UANET datasets generated through simulations. Extensive experimental results demonstrate that MUST achieves state-of-the-art link prediction performance in highly dynamic and sparse UANETs.

cs.LG↗