This paper describes deep learning models based on convolutional neural networks applied to the problem of predicting EM wave propagation over rural terrain. A surface integral equation formulation, solved with the method of moments and accelerated using the Fast Far Field approximation, is used to generate synthetic training data which comprises path loss computed over randomly generated 1D terrain profiles. These are used to train two networks, one based on fractal profiles and one based on profiles generated using a Gaussian process. The models show excellent agreement when applied to test profiles generated using the same statistical process used to create the training data and very good accuracy when applied to real life problems.

翻译：本文描述了基于卷积神经网络的深度学习模型，应用于预测农村地形中电磁波传播的问题。采用表面积分方程公式，通过矩量法求解并利用快速远场近似加速，生成合成训练数据，该数据包含在随机生成的一维地形剖面上计算的路径损耗。这些数据用于训练两个网络：一个基于分形剖面，另一个基于高斯过程生成的剖面。当应用于与训练数据生成过程相同的统计过程生成的测试剖面时，模型表现出极佳的一致性；而在应用于实际问题时，则显示出非常高的精度。