WiSegRT: Dataset for Site-Specific Indoor Radio Propagation Modeling with 3D Segmentation and Differentiable Ray-Tracing

TL;DR

WiSegRT is an open-source dataset created using differentiable ray tracing in segmented 3D indoor environments, providing detailed radio propagation data to enhance machine learning and localization applications.

Contribution

The paper introduces WiSegRT, a novel dataset generated with differentiable ray tracing in 3D environments, addressing the need for detailed data for indoor radio propagation modeling.

Findings

Supports ML-based channel prediction

Enables accurate indoor localization

Facilitates radio-based object detection

Abstract

The accurate modeling of indoor radio propagation is crucial for localization, monitoring, and device coordination, yet remains a formidable challenge, due to the complex nature of indoor environments where radio can propagate along hundreds of paths. These paths are resulted from the room layout, furniture, appliances and even small objects like a glass cup. They are also influenced by the object material and surface roughness. Advanced machine learning (ML) techniques have the potential to take such non-linear and hard-to-model factors into consideration. However, extensive and fine-grained datasets are urgently required. This paper presents WiSegRT, an open-source dataset for indoor radio propagation modeling. Generated by a differentiable ray tracer within the segmented 3-dimensional (3D) indoor environments, WiSegRT provides site-specific channel impulse responses for each grid point relative to the given transmitter location. We expect WiSegRT to support a wide-range of applications, such as ML-based channel prediction, accurate indoor localization, radio-based object detection, wireless digital twin, and more.