Phaser: enabling phased array signal processing on commodity WiFi access points

By Jon Gjengset, Jie Xiong, Graeme McPhillips, Kyle Jamieson. ACM/IEEE International Conference on Mobile Computing and Networking, 2014

296 citations recorded by the source corpus.

Signal processing on antenna arrays has received much recent attention in the mobile and wireless networking research communities, with array signal processing approaches addressing the problems of human movement detection, indoor mobile device localization, and wireless network security. However, there are two important challenges inherent in the design of these systems that must be overcome if they are to be of practical use on commodity hardware. First, phase differences between the radio oscillators behind each antenna can make readings unusable, and so must be corrected in order for most techniques to yield high-fidelity results. Second, while the number of antennas on commodity access points is usually limited, most array processing increases in fidelity with more antennas. These issues work in synergistic opposition to array processing: without phase offset correction, no phase-difference array processing is possible, and with fewer antennas, automatic correction of these phase offsets becomes even more challenging. We present Phaser, a system that solves these intertwined problems to make phased array signal processing truly practical on the many WiFi access points deployed in the real world. Our experimental results on three- and five-antenna 802.11-based hardware show that 802.11 NICs can be calibrated and synchronized to a 20° median phase error, enabling inexpensive deployment of numerous phase-difference based spectral analysis techniques previously only available on costly, special-purpose hardware.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/15b146471af8ebeaf470458c72ff2b7b85445b52

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In Electrical and Electronic Engineering

Citations in the corpus

Understanding and Modeling of WiFi Signal Based Human Activity Recognition (2015, background): “But, unfortunately, commercial WiFi devices have non-negligible carrier frequency offsets (CFO) due to hardware imperfections and environmental variations [8].”
ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization (2015, methodology): “ArrayTrack [59] employs a large antenna array at the AP coupled with AoA signal processing techniques to provide fine-grained indoor localization, and the most promising approaches to indoor localization involve analyzing the signals clients send jointly: both in the spatial domain, analyzing the Ao”
Device-Free Human Activity Recognition Using Commercial WiFi Devices (2017, background): “However, commercial WiFi devices have non-negligible CFO, represented as the f in Equation (1), due to hardware imperfections and environmental variations [9].”
A Survey of Indoor Localization Systems and Technologies (2017, background): “Phaser [97] is an extension of the ArrayTrack that works on commodity WiFi and uses AoA for indoor localization.”
Decimeter-Level Localization with a Single WiFi Access Point (2016, methodology): “Chronos builds on vast literature on indoor WiFi-based localization [40, 13, 9, 51, 18, 48, 32, 30, 50, 4].”
SpotFi: Decimeter Level Localization Using WiFi (2015, methodology): “However these systems are relatively difﬁcult to deploy, since they require hardware changes by introducing as high as 8 antennas [1, 8], or new boxes itself with rotating antennas [6], or require special APs to access IQ samples [1, 24].”
Gait recognition using wifi signals (2016, background): “However, the CSI measurements obtained from commercial WiFi cards contain noises from various sources such as interference coming from nearby devices, transmission power adaptation at the sender, and imperfect clock synchronization [10].”
Non-Invasive Detection of Moving and Stationary Human With WiFi (2015, unknown): “radios [38] and standard WiFi infrastructure [39].”
PhaseBeat: Exploiting CSI Phase Data for Vital Sign Monitoring with Commodity WiFi Devices (2017, background): “where Δ∠CSIi is the true phase difference of subcarrier i, Δβ is the unknown difference in phase offsets, which is in fact a constant [17], and ΔZ is the noise difference with variance 2σ(2).”
Caraoke: An E-Toll Transponder Network for Smart Cities (2015, result): “Our work is also related to past work on RFID localization and RF-based positioning [60, 29, 59, 63, 26].”
mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking (2018, background): “ajor challenge for these systems. Recent progress in this area has explored many ways of extracting differentparameters of the wireless signal, such as angle-of-arrival (AoA) and time-of-flight (ToF) [12, 30, 51] for localization and tracking. These approaches rely on accurately estimating the AoA o”