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The accurate modeling of indoor radio propagation is crucial forlocalization, monitoring, and device coordination, yet remains a formidablechallenge, due to the complex nature of indoor environments where radio canpropagate along hundreds of paths. These paths are resulted from the roomlayout, furniture, appliances and even small objects like a glass cup. They arealso influenced by the object material and surface roughness. Advanced machinelearning (ML) techniques have the potential to take such non-linear andhard-to-model factors into consideration. However, extensive and fine-graineddatasets are urgently required. This paper presents WiSegRT, an open-sourcedataset for indoor radio propagation modeling. Generated by a differentiableray tracer within the segmented 3-dimensional (3D) indoor environments, WiSegRTprovides site-specific channel impulse responses for each grid point relativeto the given transmitter location. We expect WiSegRT to support a wide-range ofapplications, such as ML-based channel prediction, accurate indoorlocalization, radio-based object detection, wireless digital twin, and more.

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