An indoor radio mapping dataset combining 3D point clouds and RSSI

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 indoor wireless connectivity. In such environments, accurate Radio Environment Maps (REMs) enable 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 modelling approaches, which often rely on simplified layouts or synthetic data. These challenges are further amplified by the adoption of next-generation Wi-Fi standards, operating at higher frequencies with limited range and wall penetration. To support progress in this area, we collected a dataset that combines high-resolution 3D LiDAR scans with Wi-Fi RSSI measurements across 20 setups in a multi-room indoor environment. It includes two measurement scenarios, one with and one without human presence, enabling development and validation of REM estimation models that incorporate physical geometry and environmental dynamics. The described dataset supports research in data-driven wireless modelling and the development of high-capacity indoor communication networks.