An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI

TL;DR

This paper presents a new dataset combining 3D point clouds and Wi-Fi RSSI measurements in indoor environments to improve the modeling of Radio Environment Maps for better wireless network planning.

Contribution

It introduces a comprehensive indoor radio mapping dataset with scenarios including human presence, supporting data-driven modeling and validation of REM estimation methods.

Findings

Dataset includes 20 setups in multi-room environments.

Two measurement scenarios: with and without human presence.

Supports research in indoor wireless modeling and network optimization.

Abstract

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.