EM DeepRay: An Expedient, Generalizable, and Realistic Data-Driven Indoor Propagation Model

Abstract

Efficient and realistic indoor radio propagation modeling tools are inextricably intertwined with the design and operation of next-generation wireless networks. Machine-learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of wireless channel characteristics in a computationally efficient way. However, most of the existing research works on the ML-based propagation models focus on outdoor propagation modeling, while indoor data-driven propagation models remain site-specific with limited scalability. In this article, we present an efficient and credible ML-based radio propagation modeling framework for indoor environments. Specifically, we demonstrate how a convolutional encoderdecoder can be trained to replicate the results of a ray tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decoding it as the path loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. In addition, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its preestimate weights, allowing it to make predictions that are consistent with measurement data.