We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation that is used for wireless communication simulation in 3D indoor environments. Our approach uses a modified conditional Generative Adversarial Network (GAN) that incorporates encoded geometry and transmitter location while adhering to the electromagnetic propagation theory. The overall physically-inspired learning is able to predict the power distribution in 3D scenes, which is represented using heatmaps. Our overall accuracy is comparable to ray tracing-based EM simulation, as evidenced by lower mean squared error values. Furthermore, our GAN-based method drastically reduces the computation time, achieving a 5X speedup on complex benchmarks. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments. We also present a large dataset of 3D models and EM ray tracing-simulated heatmaps. To the best of our knowledge, EM-GANSim is the first real-time algorithm for EM simulation in complex 3D indoor environments. We plan to release the code and the dataset.

翻译：我们提出了一种新颖的机器学习方法（EM-GANSim），用于三维室内环境中无线通信仿真所需的实时电磁传播模拟。该方法采用改进的条件生成对抗网络，在编码几何结构与发射器位置的同时严格遵循电磁传播理论。这种整体受物理启发的学习框架能够预测三维场景中的功率分布，并以热力图形式呈现。实验表明，我们的方法在均方误差指标上优于传统射线追踪电磁仿真，整体精度相当。此外，基于GAN的方法大幅降低了计算时间，在复杂基准测试中实现了5倍加速。在实际应用中，该方法能在数毫秒内计算出三维室内环境任意位置的信号强度。我们还发布了包含三维模型与电磁射线追踪仿真热力图的大规模数据集。据我们所知，EM-GANSim是首个适用于复杂三维室内环境的实时电磁仿真算法。我们计划公开代码与数据集。