用图神经网络增强强化学习，动态太赫兹无人机网络资源分配更高效。
Graph Neural Network Aided Deep Reinforcement Learning for Resource Allocation in Dynamic Terahertz UAV Networks
- 通过图神经网络建模无人机间关系，结合自节点特征优化资源分配。
- 相比基准方法，资源效率提升显著，训练全程零丢包，延迟更低。
- 适合研究动态无线网络、智能资源调度的科研人员和工程师。
太赫兹（THz）无人机（UAV）网络具有灵活拓扑和超高速率，有望在安防监控、灾害响应和环境监测等领域广泛应用。然而，动态拓扑导致各无人机间太赫兹链路的长期联合功率与天线阵列资源分配难以高效实现。由于功率连续、天线离散，该问题属于非凸、NP难的混合整数非线性规划（MINLP）。受深度强化学习（DRL）进展启发，本文提出一种图神经网络（GNN）辅助的DRL算法（GLOVE），聚焦自节点特征，以最大化资源效率（RE）。GLOVE在训练每架UAV的分配策略时，利用GNN学习其与邻近UAV的关系，同时强调自身节点特征。此外，采用多任务结构协同训练所有UAV的功率与子阵列分配决策。实验表明，GLOVE在最高资源效率和最低延迟方面优于基准方案，且在整个训练过程中保持零丢包，展现出在高度动态的太赫兹无人机网络中的更强鲁棒性。
原文摘要 · Abstract (English)
Terahertz (THz) unmanned aerial vehicle (UAV) networks with flexible topologies and ultra-high data rates are expected to empower numerous applications in security surveillance, disaster response, and environmental monitoring, among others. However, the dynamic topologies hinder the efficient long-term joint power and antenna array resource allocation for THz links among UAVs. Furthermore, the continuous nature of power and the discrete nature of antennas cause this joint resource allocation problem to be a mixed-integer nonlinear programming (MINLP) problem with non-convexity and NP-hardness. Inspired by recent rapid advancements in deep reinforcement learning (DRL), a graph neural network (GNN) aided DRL algorithm for resource allocation in the dynamic THz UAV network with an emphasis on self-node features (GLOVE) is proposed in this paper, with the aim of resource efficiency (RE) maximization. When training the allocation policy for each UAV, GLOVE learns the relationship between this UAV and its neighboring UAVs via GNN, while also emphasizing the important self-node features of this UAV. In addition, a multi-task structure is leveraged by GLOVE to cooperatively train resource allocation decisions for the power and sub-arrays of all UAVs. Experimental results illustrate that GLOVE outperforms benchmark schemes in terms of the highest RE and the lowest latency. Moreover, unlike the benchmark methods with severe packet loss, GLOVE maintains zero packet loss during the entire training process, demonstrating its better robustness under the highly dynamic THz UAV network.
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