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 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.