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.

我们提出了一种用于在 3D 室内环境中进行无线通信模拟的实时电磁 (EM) 传播的新型机器学习 (ML) 方法 (EM-GANSim)。我们的方法使用了一种修改后的条件生成对抗网络 (GAN)，该网络融合了编码几何和发射机位置，并符合电磁传播理论。整体上，基于物理启发的学习能够预测 3D 场景中的功率分布，该分布用热度图表示。我们的整体准确度与基于射线追踪的 EM 模拟相当，这表现在较低的均方误差值上。此外，我们基于 GAN 的方法大幅减少了计算时间，在复杂基准测试上实现了 5 倍加速。实际上，它可以在几毫秒内计算 3D 室内环境中任何位置的信号强度。我们还提供了一个包含 3D 模型和 EM 射线追踪模拟热度图的大型数据集。据我们所知，EM-GANSim 是在复杂 3D 室内环境中进行 EM 模拟的第一个实时算法。我们计划发布代码和数据集。