Artificial intelligence-driven radio propagation models provide agile and robust solutions for mobile network operators in their effort to ensure the optimal performance of the wireless ecosystem and support its efficient expansion. In this paper, we introduce GRAPHWAVE, a neural graph-driven propagation solver hinging on the governing principles of ray tracing. The proposed model leverages a digitized version of the propagation environment to build a point cloud and extract an equivalent graph representation of the radio environment. By applying neural message passing over the equivalent graph, it allows the model to accurately infer radio-related quantities, e.g., received signal strength, in a three-dimensional environment. We showcase the use of GRAPHWAVE as a radio environment digital twin and we demonstrate that the model can learn from synthetic and real-world data while achieving low inference times.

翻译：人工智能驱动的无线电传播模型为移动网络运营商提供了灵活且稳健的解决方案，助力其确保无线生态系统的最优性能并支持其高效扩展。本文提出GRAPHWAVE——一种基于光线追踪核心原理的神经图驱动传播求解器。该模型利用传播环境的数字化版本构建点云，提取无线电环境的等效图表示。通过在等效图上应用神经消息传递机制，模型能够在三维环境中准确推断与无线电相关的物理量（例如接收信号强度）。我们展示了GRAPHWAVE作为无线电环境数字孪生的应用，并证明该模型能够从合成数据和真实数据中学习，同时实现较低的推理时间。