EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes

TL;DR

EM-GANSim introduces a real-time, physically-inspired GAN-based method for accurate electromagnetic simulation in 3D indoor environments, significantly reducing computation time while maintaining high accuracy.

Contribution

This work presents the first real-time EM simulation method using a conditional GAN that incorporates geometry and transmitter data, enabling fast and accurate predictions.

Findings

Achieves comparable accuracy to ray tracing with lower mean squared error.

Provides a 5X speedup in EM simulation on complex indoor scenes.

Capable of computing signal strength in milliseconds across 3D environments.

Abstract

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. We evaluated our method on 15 complex 3D indoor environments, with 4 additional scenarios later included in the results, showcasing the generalizability of the model across diverse conditions. 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.