标题： 大规模无线通信网络中的图神经网络：在随机几何图上的可扩展性 显示英文标题

标题： Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs

摘要： 无线系统的复杂性不断增加，加速了从传统方法向基于学习的解决方案的转变。 图神经网络（GNNs）在这一领域特别适合，因为无线网络可以自然地表示为图。 GNNs 的一个关键特性是可迁移性：在一张图上训练的模型通常能在更大图上泛化，且性能损失很小。 尽管实证研究显示基于 GNN 的无线策略能够有效迁移，但现有的理论保证并未捕捉到这一现象。 大多数研究集中在节点度数随网络规模增长的密集图上，这一假设在无线系统中并不成立。 在本工作中，我们为随机几何图（RGGs）上的可迁移性提供了正式的理论基础，RGGs 是一种稀疏且广泛用于无线网络的模型。 我们进一步通过功率分配的数值实验验证了我们的结果，功率分配是一项基本的资源管理任务。 显示英文摘要

摘要： The growing complexity of wireless systems has accelerated the move from traditional methods to learning-based solutions. Graph Neural Networks (GNNs) are especially well-suited here, since wireless networks can be naturally represented as graphs. A key property of GNNs is transferability: models trained on one graph often generalize to much larger graphs with little performance loss. While empirical studies have shown that GNN-based wireless policies transfer effectively, existing theoretical guarantees do not capture this phenomenon. Most works focus on dense graphs where node degrees scale with network size, an assumption that fails in wireless systems. In this work, we provide a formal theoretical foundation for transferability on Random Geometric Graphs (RGGs), a sparse and widely used model of wireless networks. We further validate our results through numerical experiments on power allocation, a fundamental resource management task.