Precise Power Delay Profiling with Commodity Wi-Fi

By Yaxiong Xie, Zhenjiang Li, Mo Li. IEEE Transactions on Mobile Computing, 2015

863 citations recorded by the source corpus.

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\;\mathrm{m}$2m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95\;\mathrm{m}$0.95m.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b76ce675225748346e626b2ca9f6b7f7ee9e6310

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In Computer Networks and Communications

1. Alex X. Liu
2. Gang Zhou
3. Zheng Yang
4. Yunhao Liu
5. Kun Qian
6. Chenshu Wu
7. Guidong Zhang
8. Yi Zhang

In Electrical and Electronic Engineering

1. Zheng Yang
2. Yunhao Liu
3. Kun Qian
4. Chenshu Wu
5. Gang Zhou
6. Guidong Zhang
7. Yi Zhang
8. Kyle Jamieson

Citations in the corpus

• PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, background): “, Intel WiFi Link 5300 NIC [11] and the Atheros AR9580 chipset [12].”
• mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “The latest ToF based systems [38, 47, 52, 53] also provide high accuracies by combining channels to form a virtual wider bandwidth for finer resolution.”
• Wireless Sensing for Human Activity: A Survey (2020, methodology): “To quantify the changes of the received WiFi signal, researchers could measure the physical layer properties over wireless channel such as the received signal strength Indicator (RSSI) and channel state information (CSI), which are readily available on many commercial network interface cards (e.g., ”
• Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “variations in the phase of CSI [16], [32].”
• Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “Multi-channel JCAS systems which can offer an overall large signal bandwidth for sensing, while without increasing instantaneous communication bandwidth [79], [80].”
• WiFi Sensing with Channel State Information (2019, methodology): “Both 802.11n CSI Tool and Atheros CSI Tool can operate at 2.4GHz and 5GHz.”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, neutral): “In practice, the CSI measurements usually sufer from amplitude noise, and three types of phase ofset [105], including Carrier Frequency Ofset (CFO), Sampling Time Ofset (SFO), and Packet Detection Delay (PDD).”
• Widar2.0: Passive Human Tracking with a Single Wi-Fi Link (2018, methodology): “A pioneer work Splicer [37] splices the CSI measurements from multiple Wi-Fi channels, which greatly extends the bandwidth available and yields sub-nanosecond TDoA for accurate localization.”
• Free Your CSI: A Channel State Information Extraction Platform For Modern Wi-Fi Chipsets (2019, methodology): “Non-grouped CSI can be obtained using the Atheros CSI Tool maintained by the Wireless And Networked Distributed Sensing (WANDS) research group [11].”