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

Abstract

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.