TY - JOUR
T1 - GNN-Based Resource Allocation for Digital Twin-Enhanced Multi-UAV Radar Networks
AU - Luo, Jihao
AU - Fei, Zesong
AU - Wang, Xinyi
AU - Zhao, Le
AU - Li, Bin
AU - Zhou, Yiqing
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - digital twin
KW - graph neural network
KW - power control
KW - radar sensing
KW - spectrum allocation
KW - Unmanned aerial vehicles
UR - http://www.scopus.com/inward/record.url?scp=85204157737&partnerID=8YFLogxK
U2 - 10.1109/LWC.2024.3456247
DO - 10.1109/LWC.2024.3456247
M3 - Article
AN - SCOPUS:85204157737
SN - 2162-2337
VL - 13
SP - 3137
EP - 3141
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 11
ER -