Radio propagation modeling is essential in telecommunication research, as radio channels result from complex interactions with environmental objects. Recently, Machine Learning has been attracting attention as a potential alternative to computationally demanding tools, like Ray Tracing, which can model these interactions in detail. However, existing Machine Learning approaches often attempt to learn directly specific channel characteristics, such as the coverage map, making them highly specific to the frequency and material properties and unable to fully capture the underlying propagation mechanisms. Hence, Ray Tracing, particularly the Point-to-Point variant, remains popular to accurately identify all possible paths between transmitter and receiver nodes. Still, path identification is computationally intensive because the number of paths to be tested grows exponentially while only a small fraction is valid. In this paper, we propose a Machine Learning-aided Ray Tracing approach to efficiently sample potential ray paths, significantly reducing the computational load while maintaining high accuracy. Our model dynamically learns to prioritize potentially valid paths among all possible paths and scales linearly with scene complexity. Unlike recent alternatives, our approach is invariant with translation, scaling, or rotation of the geometry, and avoids dependency on specific environment characteristics.

翻译：无线电传播建模在通信研究中至关重要，因为无线电信道源于与环境物体的复杂相互作用。近年来，机器学习作为一种潜在替代方案受到关注，可替代计算需求高的工具（如射线追踪），后者能够详细建模这些相互作用。然而，现有的机器学习方法通常试图直接学习特定的信道特性，如覆盖图，这使得它们高度依赖于频率和材料属性，无法完全捕捉潜在的传播机制。因此，射线追踪，特别是点对点变体，在精确识别发射节点与接收节点之间所有可能路径方面仍然广受欢迎。然而，路径识别计算密集，因为待测试的路径数量呈指数增长，而其中仅有很小一部分是有效的。在本文中，我们提出一种机器学习辅助的射线追踪方法，以高效采样潜在的射线路径，在保持高精度的同时显著降低计算负载。我们的模型动态学习在所有可能路径中优先考虑潜在有效路径，并且其计算复杂度随场景复杂度线性增长。与近期替代方案不同，我们的方法对几何结构的平移、缩放或旋转具有不变性，并避免依赖于特定的环境特性。