标题： 基于图神经网络的数据驱动无线电传播建模 显示英文标题

标题： Data-Driven Radio Propagation Modeling using Graph Neural Networks

摘要： 建模无线电传播对于无线网络设计和性能优化至关重要。 传统方法依赖于无线电传播的物理模型，这些模型可能不准确或不够灵活。 在本工作中，我们提出使用图神经网络，直接从现实世界的网络数据中学习无线电传播行为。 我们的方法将无线电传播环境转换为图表示，其中节点对应于位置，边表示位置之间的空间和光线追踪关系。 该图是通过将环境的图像转换为图结构生成的，节点之间具有特定的关系。 该模型在此图表示上进行训练，使用传感器测量值作为目标数据。 我们证明，直接从数据中学习预测无线电传播的图神经网络，在性能上与传统启发式模型相当。 这种数据驱动的方法在速度和准确性方面都优于经典的数值求解器。 据我们所知，我们是第一个将图神经网络应用于现实世界无线电传播数据以生成覆盖图，仅使用点测量值即可实现信号传播的生成模型。 显示英文摘要

摘要： Modeling radio propagation is essential for wireless network design and performance optimization. Traditional methods rely on physics models of radio propagation, which can be inaccurate or inflexible. In this work, we propose using graph neural networks to learn radio propagation behaviors directly from real-world network data. Our approach converts the radio propagation environment into a graph representation, with nodes corresponding to locations and edges representing spatial and ray-tracing relationships between locations. The graph is generated by converting images of the environment into a graph structure, with specific relationships between nodes. The model is trained on this graph representation, using sensor measurements as target data. We demonstrate that the graph neural network, which learns to predict radio propagation directly from data, achieves competitive performance compared to traditional heuristic models. This data-driven approach outperforms classic numerical solvers in terms of both speed and accuracy. To the best of our knowledge, we are the first to apply graph neural networks to real-world radio propagation data to generate coverage maps, enabling generative models of signal propagation with point measurements only.