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.

中文翻译：

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基于 GNN 的数字孪生增强多无人机雷达网络资源分配


相互干扰一直是多无人机 （multi-UAV） 网络中的关键问题。数字孪生 （DT） 作为一种先进技术，将物理实体映射到虚拟域中，实现实时监控和动态更新，从而增强多无人机网络的适应性和性能。在本文中，我们研究了多无人机雷达传感网络的联合频谱分配和功率控制，其中多个无人机 （UAV） 同时分别执行雷达传感以检测目标并避免碰撞。通过将多无人机网络建模为图形，我们采用图神经网络 （GNN） 来捕获环境特征、构建 DT 网络并解决资源分配问题。特别是，我们提出了一种基于消息传递神经网络的频谱分配方法和一种基于图注意力网络的功率控制方法，以最大限度地提高所有无人机中的最小雷达回波信干噪比 （SINR）。仿真结果表明，所提出的基于 DT 增强的 GNN 资源分配方法可以显著提高最小 SINR 并扩大感知覆盖范围。