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

2024-05-27

arXiv.org

AI Summary

To address the challenge of real-time, high-fidelity electromagnetic (EM) propagation simulation in complex 3D indoor environments, this paper proposes a theory-driven conditional generative adversarial network (cGAN). For the first time, physics-based EM priors—including path loss models and specular reflection constraints—are explicitly embedded into the cGAN architecture; geometric scene structure and transmitter location are jointly encoded, and the model is supervised using ray-tracing-generated ground-truth data. This work presents the first end-to-end framework capable of generating real-time EM field-strength heatmaps for intricate 3D indoor scenes. Evaluated across 19 real-world environments, it achieves accuracy comparable to ray tracing—demonstrating lower mean squared error (MSE)—while accelerating inference by 5×, with single-prediction latency in the millisecond range. The core contribution lies in unifying physical interpretability with deep learning efficiency, thereby significantly expediting the deployment and optimization of wireless communication systems.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

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.

Problem

• Real-time EM simulation for 3D indoor wireless communication
• Accurate power distribution prediction using conditional GANs
• Reducing computation time while maintaining simulation accuracy

Innovation

• Uses conditional GANs for EM simulation
• Incorporates geometry and transmitter location
• Achieves real-time speed with 5X speedup