基于图神经网络的数据驱动无线电传播建模
Data-Driven Radio Propagation Modeling using Graph Neural Networks
AI总结：
针对传统无线电传播物理模型准确性与灵活性不足的问题，提出将环境转为图表示、用图神经网络从真实数据学习传播特性的方法，在速度和精度上优于经典方法，首次实现仅靠点测量生成信号覆盖图。
摘要：
无线电传播建模对无线网络设计与性能优化至关重要。传统方法依赖无线电传播的物理模型，存在准确性不足或灵活性差的问题。本研究提出利用图神经网络，直接从真实网络数据中学习无线电传播特性。我们的方法将无线电传播环境转换为图表示，其中节点对应位置，边代表位置之间的空间关系与射线追踪关系。该图通过将环境图像转换为图结构生成，节点间具有特定关联关系。模型基于该图表示进行训练，以传感器测量值作为目标数据。\n 我们证明，这种可直接从数据中学习预测无线电传播的图神经网络，与传统启发式模型相比性能具有竞争力。该数据驱动方法在速度和准确性上均优于经典数值求解器。据我们所知，我们是首个将图神经网络应用于真实无线电传播数据以生成覆盖图的研究，实现了仅需点测量值的信号传播生成式模型。
英文摘要：
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.
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