A Multimodal Dataset for Indoor Radio Mapping with 3D Point Clouds and RSSI

2025-11-01

AI Summary

Radio environment map (REM) modeling in complex indoor settings faces dual challenges: insufficient representation of spatial structure and lack of explicit modeling for dynamic human interference. To address these, we introduce the first multimodal indoor RF mapping dataset integrating high-resolution 3D LiDAR point clouds with multi-configuration Wi-Fi received signal strength indicator (RSSI) measurements—covering 20 distinct access point (AP) deployment configurations and both static (unoccupied) and dynamic (human-present) scenarios. Our approach jointly models precise 3D geometric structure, multi-angle RSSI observations, and human presence states, significantly enhancing the physical interpretability and generalization capability of REMs under dynamic conditions. Data collection adheres to an IEEE 802.11be–compliant framework, enabling support for Wi-Fi 7 and other high-frequency, high-capacity communication system development. The dataset is publicly released, establishing a benchmark resource for data-driven intelligent wireless environment modeling.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Generating accurate indoor Radio Environment Maps for network planning
• Overcoming indoor space complexity in wireless signal modeling
• Studying dynamic environmental effects on Wi-Fi signal propagation

Innovation

• Integrates 3D LiDAR scans with Wi-Fi RSSI measurements
• Captures data under 20 distinct AP configurations
• Includes scenarios with and without human presence