Rigorous Indoor Wireless Communication System Simulations With Deep Learning-Based Radio Propagation Models

• Published in: IEEE journal on multiscale and multiphysics computational techniques Vol. 10; pp. 58 - 68
• Main Authors: Bakirtzis, Stefanos, Qiu, Kehai, Chen, Jiming, Song, Hui, Zhang, Jie, Wassell, Ian
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna radiation patterns; Antennas; Artificial intelligence; Biological system modeling; Computational modeling; Computing time; Data models; Deep learning; digital twin; Directivity; Frequencies; Generative adversarial networks; Geometry; Propagation; Radio propagation; Radio transmission; ray-tracing; Simulators; Solid modeling; Tensors; Three-dimensional displays; Wireless communication; Wireless communication systems; Wireless communications
• ISSN: 2379-8815; 2379-8815
• DOI: 10.1109/JMMCT.2024.3506693

Recently, there has been a surge in the development of data-driven propagation models. These models aspire to distill knowledge from propagation solvers or measured data and eventually become capable of predicting characteristics related to radiowave propagation. In this paper, we present the functionality of a generalizable and robust data-driven propagation model that enables efficient and reliable simulations of indoor wireless communication systems (IWCSs). In particular, we modify our previously introduced model, EM DeepRay, to consider the impact of antenna directivity, and we present a training and inference strategy that allows the simulation of large-scale and complicated IWCSs. Our data-driven model is trained over a rich data set comprising diverse building geometries, frequency bands, and antenna radiation patterns. Benchmarking its performance with that of a ray-tracer in complicated IWCSs with real-world measured data yields similar results that have a distinct advantage in terms of computational time. Ultimately, our work paves the way for replacing legacy IWCSs simulators, with high-fidelity artificial intelligence-based models.