A Multimodal Dataset for Indoor Radio Mapping with 3D Point Clouds and RSSI
This dataset facilitates research in data-driven wireless modeling for indoor environments, supporting emerging standards like Wi-Fi 7 to improve communication systems.
The paper tackles the challenge of generating realistic Radio Environment Maps (REMs) for indoor wireless connectivity by introducing a multimodal dataset that integrates 3D LiDAR scans with Wi-Fi RSSI measurements under 20 AP configurations, including scenarios with and without human presence to study dynamic effects.
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, and Extended Reality (XR), necessitates reliable wireless connectivity in indoor environments. Therein, accurate estimation of Radio Environment Maps (REMs) enables adaptive wireless network planning and optimization of Access Point (AP) placement. However, generating realistic REMs remains challenging due to the complexity of indoor spaces. To overcome this challenge, this paper introduces a multimodal dataset that integrates high-resolution 3D LiDAR scans with Wi-Fi Received Signal Strength Indicator (RSSI) measurements collected under 20 distinct AP configurations in a multi-room indoor environment. The dataset captures two measurement scenarios: the first without human presence in the environment, and the second with human presence. Thus, the presented dataset supports the study of dynamic environmental effects on wireless signal propagation. This resource is designed to facilitate research in data-driven wireless modeling, particularly in the context of emerging high-frequency standards such as IEEE 802.11be (Wi-Fi 7), and aims to advance the development of robust, high-capacity indoor communication systems.