EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes
<|reference_start|>EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes: 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.<|reference_end|>
@article{wang2024em-gansim:,
    title={EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs
  for 3D Indoor Scenes},
    author={Ruichen Wang and Dinesh Manocha},
    journal={arXiv preprint arXiv:2405.17366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17366},
    primaryClass={cs.LG eess.SP}
}