Radio Propagation Modeling

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Overview

Radio propagation modeling studies how signals reflect, diffract, scatter, and weaken as they travel through buildings, streets, and other environments. Methods range from empirical path-loss models and computationally intensive ray tracing to learned radio maps, neural channel models, and digital twins that combine geometry with measurements. Current work uses physics-informed networks, differentiable simulation, and generative models to improve prediction while reducing computation and measurement needs. A central challenge is generalizing across environments and hardware configurations while keeping simulated predictions faithful to real-world measurements and downstream network performance.

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May 27, 2024

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. 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.

Ruichen Wang, Dinesh Manocha

Department of Computer Science and the Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742 USA

Wireless CommunicationsRadio Propagation ModelingConditional Generative ModelingPhysics-Based Simulation
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