Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910477896908800 |
|---|---|
| author | Li, Xin Hu, Jingzhi Luo, Jun |
| author_facet | Li, Xin Hu, Jingzhi Luo, Jun |
| contents | Wi-Fi sensing leveraging plain-text beamforming feedback information (BFI) in multiple-input-multiple-output (MIMO) systems attracts increasing attention. However, due to the implicit relationship between BFI and the channel state information (CSI), quantifying the sensing capability of BFI poses a challenge in building efficient BFI-based sensing algorithms. In this letter, we first derive a mathematical model of BFI, characterizing its relationship with CSI explicitly, and then develop a closed-form expression of BFI for 2x2 MIMO systems. To enhance the efficiency of BFI-based sensing by selecting only the most informative features, we quantify the sensing capacity of BFI using the Cramer-Rao bound (CRB) and then propose an efficient CRB-based BFI feature selection algorithm. Simulation results verify that BFI and CSI exhibit comparable sensing capabilities and that the proposed algorithm halves the number of features, reducing 20% more parameters than baseline methods, at the cost of only slightly increasing positioning errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05671 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection Li, Xin Hu, Jingzhi Luo, Jun Signal Processing Wi-Fi sensing leveraging plain-text beamforming feedback information (BFI) in multiple-input-multiple-output (MIMO) systems attracts increasing attention. However, due to the implicit relationship between BFI and the channel state information (CSI), quantifying the sensing capability of BFI poses a challenge in building efficient BFI-based sensing algorithms. In this letter, we first derive a mathematical model of BFI, characterizing its relationship with CSI explicitly, and then develop a closed-form expression of BFI for 2x2 MIMO systems. To enhance the efficiency of BFI-based sensing by selecting only the most informative features, we quantify the sensing capacity of BFI using the Cramer-Rao bound (CRB) and then propose an efficient CRB-based BFI feature selection algorithm. Simulation results verify that BFI and CSI exhibit comparable sensing capabilities and that the proposed algorithm halves the number of features, reducing 20% more parameters than baseline methods, at the cost of only slightly increasing positioning errors. |
| title | Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2406.05671 |