C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912255814139904 |
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| author | Jiang, Jun Xu, Shugong Yu, Wenjun Gao, Yuan |
| author_facet | Jiang, Jun Xu, Shugong Yu, Wenjun Gao, Yuan |
| contents | Next-generation mobile networks are set to utilize integrated sensing and communication (ISAC) as a critical technology, providing significant support for sectors like the industrial Internet of Things (IIoT), extended reality (XR), and smart home applications. A key challenge in ISAC implementation is the extraction of sensing parameters from radio signals, a task that conventional methods struggle to achieve due to the complexity of acquiring sensing channel data. In this paper, we introduce a novel auto-encoder (AE)-based framework to acquire sensing information using channel state information (CSI). Specifically, our framework, termed C2S (CSI to sensing)-AE, learns the relationship between CSI and the delay power spectrum (DPS), from which the range information can be readily accessed. To validate our framework's performance, we conducted measurements of DPS and CSI in real-world scenarios and introduced the dataset 'SHU7'. Our extensive experiments demonstrate that the framework excels in C2S extrapolation, surpassing existing methods in terms of accuracy for both delay and signal strength of individual paths. This innovative approach holds the potential to greatly enhance sensing capabilities in future mobile networks, paving the way for more robust and versatile ISAC applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework Jiang, Jun Xu, Shugong Yu, Wenjun Gao, Yuan Signal Processing Next-generation mobile networks are set to utilize integrated sensing and communication (ISAC) as a critical technology, providing significant support for sectors like the industrial Internet of Things (IIoT), extended reality (XR), and smart home applications. A key challenge in ISAC implementation is the extraction of sensing parameters from radio signals, a task that conventional methods struggle to achieve due to the complexity of acquiring sensing channel data. In this paper, we introduce a novel auto-encoder (AE)-based framework to acquire sensing information using channel state information (CSI). Specifically, our framework, termed C2S (CSI to sensing)-AE, learns the relationship between CSI and the delay power spectrum (DPS), from which the range information can be readily accessed. To validate our framework's performance, we conducted measurements of DPS and CSI in real-world scenarios and introduced the dataset 'SHU7'. Our extensive experiments demonstrate that the framework excels in C2S extrapolation, surpassing existing methods in terms of accuracy for both delay and signal strength of individual paths. This innovative approach holds the potential to greatly enhance sensing capabilities in future mobile networks, paving the way for more robust and versatile ISAC applications. |
| title | C2S-AE: CSI to Sensing enabled by an Auto-Encoder-based Framework |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2503.00941 |