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.

翻译：室内无线电传播的精确建模对定位、监测及设备协调至关重要，但由于室内环境中无线电波可能沿数百条路径传播的复杂特性，这仍然是一项艰巨挑战。这些路径的形成不仅受房间布局、家具、电器甚至玻璃杯等小物体的影响，还与物体材质及表面粗糙度密切相关。先进的机器学习技术有望考虑此类非线性且难以建模的因素，然而亟需大规模精细化数据集的支持。本文提出WiSegRT，一个面向室内无线电传播建模的开源数据集。该数据集通过三维分割室内环境中的可微射线追踪器生成，针对给定发射器位置，为每个网格点提供特定场景的信道冲激响应。我们期望WiSegRT能支持广泛的应用场景，包括基于机器学习的信道预测、高精度室内定位、无线电目标检测、无线数字孪生等。