GNN-Based Resource Allocation for Digital Twin-Enhanced Multi-UAV Radar Networks

Jihao Luo, Zesong Fei, Xinyi Wang*, Le Zhao, Bin Li, Yiqing Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Mutual interference has been a critical issue in multiple unmanned aerial vehicles (multi-UAV) networks. As an advanced technology, digital twin (DT) maps physical entities into virtual domain, enables real-time monitoring and dynamic updates, thereby enhancing the adaptability and performance of multi-UAV networks. In this letter, we investigate joint spectrum allocation and power control for a multi-UAV radar sensing network, where multiple unmanned aerial vehicles (UAVs) simultaneously perform radar sensing separately to detect targets and avoid collision. By modeling the multi-UAV network as a graph, we employ graph neural network (GNN) to capture environmental features, construct the DT network, and address resource allocation issues. In particular, we propose a message-passing neural network based spectrum allocation method and a graph attention network based power control method to maximizing the minimum radar echo signal-to-interference-plus-noise ratio (SINR) among all UAVs. Simulation results show that the proposed DT-enhanced GNN based resource allocation method can significantly improve the minimum SINR and extend the sensing coverage.

Original languageEnglish
Pages (from-to)3137-3141
Number of pages5
JournalIEEE Wireless Communications Letters
Volume13
Issue number11
DOIs
Publication statusPublished - 2024

Keywords

  • digital twin
  • graph neural network
  • power control
  • radar sensing
  • spectrum allocation
  • Unmanned aerial vehicles

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