The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Precise Power Delay Profiling with Commodity WiFi
Abstract
Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance 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 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 $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.
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Cites
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ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization
Xiong, J., Sundaresan, K., & Jamieson, K. (2015) · Proceedings of the 21st Annual International Conference on Mobile Computing and Networking · doi:10.1145/2789168.2790125 · Cited by 331
Precise Power Delay Profiling with Commodity Wi-Fi
Xie, Y., Li, Z., & Li, M. (2019) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2860991 · Cited by 282
Self-citationLiFS: low human-effort, device-free localization with fine-grained subcarrier information
Wang, J., Jiang, H., Xiong, J., Jamieson, K., Chen, X., Fang, D., & Xie, B. (2016) · Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking · doi:10.1145/2973750.2973776 · Cited by 266
A Survey on Human Behavior Recognition Using Channel State Information
Wang, Z., Jiang, K., Hou, Y., Dou, W., Zhang, C., Huang, Z., & Guo, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2949123 · Cited by 149
Metadata
- Authors
- Xie, Y., Li, Z., & Li, M.
- Venue
- Proceedings of the 21st Annual International Conference on Mobile Computing and Networking
- Publisher
- ACM
- Published
- 2015-09-07
- Pages
- 53-64
- ISSN
- 1543-5679 (ISSN-L)
- OpenAlex
- W2064081294
- Cited by
- 600
- References
- 30
- FWCI
- 56.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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