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DeepRay: Deep Learning Meets Ray-Tracing

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Bakirtzis, S 
Qiu, K 
Zhang, J 

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.

Description

Keywords

Machine learning, deep learning, radio propagation modeling, ray-tracing, U-Net, LoRA

Journal Title

2022 16th European Conference on Antennas and Propagation, EuCAP 2022

Conference Name

2022 16th European Conference on Antennas and Propagation (EuCAP)

Journal ISSN

2164-3342

Volume Title

Publisher

IEEE
Sponsorship
This work was supported by European Commission through the Horizon 2020 Framework Programme, H2020-MSCA-ITN-2019, MSCA-ITN-EID, Grant No. 860239, BANYAN.