DeepRay: Deep Learning Meets Ray-Tracing
Stefanos Bakirtzis, Kehai Qiu, Jie Zhang, Ian Wassell
Abstract
Efficient and accurate indoor radio propagation modeling tools are essential for the design and operation of wireless communication systems. Lately, several attempts to combine radio propagation solvers with machine learning (ML) have been made. In this paper, motivated by the recent advances in the area of computer vision, we present a new ML propagation model using convolutional encoder-decoders. Specifically, we couple a ray-tracing simulator with either a U-Net or an SDU-Net, showing that the use of atrous convolutions utilized in SDU-Net can enhance significantly the performance of an ML propagation model. The proposed data-driven framework, called DeepRay, can be trained to predict the received signal strength in a given indoor environment. More importantly, once trained over multiple input geometries, DeepRay can be employed to directly predict the signal level for unknown indoor environments. We demonstrate this approach in various indoor environments using long range (LoRa) devices operating at 868 MHz.