Related papers: Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks

Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks

URL: http://arxiv.org/abs/2410.16303v1
Date: Mon, 07 Oct 2024 08:59:04 GMT
Title: Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks
Authors: Tuomas Määttä, Sasan Sharifipour, Miguel Bordallo López, Constantino Álvarez Casado,
Abstract summary: Joint communication and sensing (JC&S) is emerging as a key component in 5G and 6G networks. We present a transformer-based architecture that processes temporal Channel State Information (CSI) data, specifically amplitude and phase, to generate 3D point clouds of indoor environments.
Score: 2.084922791522632
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Abstract: Joint communication and sensing (JC\&S) is emerging as a key component in 5G and 6G networks, enabling dynamic adaptation to environmental changes and enhancing contextual awareness for optimized communication. By leveraging real-time environmental data, JC\&S improves resource allocation, reduces latency, and enhances power efficiency, while also supporting simulations and predictive modeling. This makes it a key technology for reactive systems and digital twins. These systems can respond to environmental events in real-time, offering transformative potential in sectors like smart cities, healthcare, and Industry 5.0, where adaptive and multimodal interaction is critical to enhance real-time decision-making. In this work, we present a transformer-based architecture that processes temporal Channel State Information (CSI) data, specifically amplitude and phase, to generate 3D point clouds of indoor environments. The model utilizes a multi-head attention to capture complex spatio-temporal relationships in CSI data and is adaptable to different CSI configurations. We evaluate the architecture on the MM-Fi dataset, using two different protocols to capture human presence in indoor environments. The system demonstrates strong potential for accurate 3D reconstructions and effectively distinguishes between close and distant objects, advancing JC\&S applications for spatial sensing in future wireless networks.

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