Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks
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arXiv
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| Format: | Preprint |
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2024
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| author | Määttä, Tuomas Sharifipour, Sasan López, Miguel Bordallo Casado, Constantino Álvarez |
| author_facet | Määttä, Tuomas Sharifipour, Sasan López, Miguel Bordallo Casado, Constantino Álvarez |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks Määttä, Tuomas Sharifipour, Sasan López, Miguel Bordallo Casado, Constantino Álvarez Signal Processing Machine Learning 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. |
| title | Spatio-Temporal 3D Point Clouds from WiFi-CSI Data via Transformer Networks |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2410.16303 |