The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Precise Power Delay Profiling with Commodity Wi-Fi
Abstract
Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. The performance of power delay profile obtained using commodity Wi-Fi devices is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in the presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than 2 m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of 0.95 m.
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SpotFi: Decimeter Level Localization Using WiFi
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Self-citationDecimeter-level localization with a single WiFi access point
Vasisht, D., Kumar, S., & Katabi, D. (2016) · Networked Systems Design and Implementation · Cited by 719
Precise Power Delay Profiling with Commodity WiFi
Xie, Y., Li, Z., & Li, M. (2015) · Proceedings of the 21st Annual International Conference on Mobile Computing and Networking · doi:10.1145/2789168.2790124 · Cited by 600
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Cited by
Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization
Choi, H., Fujimoto, M., Matsui, T., Misaki, S., & Yasumoto, K. (2022) · IEEE Access · doi:10.1109/access.2022.3155812 · Cited by 100
MobileDA: Toward Edge-Domain Adaptation
Yang, J., Zou, H., Cao, S., Chen, Z., & Xie, L. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2976762 · Cited by 67
Metadata
- Authors
- Xie, Y., Li, Z., & Li, M.
- Venue
- IEEE Transactions on Mobile Computing
- Published
- 2019-06-01
- Volume
- 18
- Issue
- 6
- Pages
- 1342-1355
- ISSN
- 1536-1233 (ISSN-L)
- 1558-0660
- 2161-9875
- OpenAlex
- W2944172587
- Cited by
- 282
- References
- 31
- FWCI
- 14.98
- Type
- journal article
- Language
- en
- Open access
- closed
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