The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc., necessitates reliable wireless connectivity in indoor environments. In such environments, accurate design of Radio Environment Maps (REMs) enables adaptive wireless network planning and optimization of Access Point (AP) placement. However, generating realistic REMs remains difficult due to the variability of indoor environments and the limitations of existing modeling approaches, which often rely on simplified layouts or fully synthetic data. These challenges are further amplified by the adoption of next-generation Wi-Fi standards, which operate at higher frequencies and suffer from limited range and wall penetration. To support the efforts in addressing these challenges, we collected a dataset that combines high-resolution 3D LiDAR scans with Wi-Fi RSSI measurements collected across 20 setups in a multi-room indoor environment. The dataset includes two measurement scenarios, the first without human presence in the environment, and the second with human presence, enabling the development and validation of REM estimation models that incorporate physical geometry and environmental dynamics. The described dataset supports research in data-driven wireless modeling and the development of high-capacity indoor communication networks.

翻译：随着支持带宽密集型与延迟敏感型应用（如实时视频分析、智能感知、扩展现实等）的智能设备数量不断增长，室内环境对可靠无线连接的需求日益迫切。在此类环境中，精确设计无线电环境地图能够实现自适应无线网络规划与接入点布局优化。然而，由于室内环境的多样性及现有建模方法的局限性（通常依赖简化布局或完全合成数据），生成真实的无线电环境地图仍面临困难。下一代Wi-Fi标准采用更高频段，其传输距离受限且穿墙能力减弱，进一步加剧了这些挑战。为助力应对这些挑战，我们采集了一个结合高分辨率三维激光雷达扫描与Wi-Fi RSSI测量的数据集，该数据集涵盖多房间室内环境中20种布设场景的测量数据。数据集包含两种测量场景：第一种为无人环境下的测量，第二种为有人环境下的测量，从而支持融合物理几何与环境动态特性的无线电环境地图估计模型的开发与验证。本数据集可为数据驱动的无线建模研究及高容量室内通信网络的开发提供支持。