Tool release: gathering 802.11n traces with channel state information
22 Jan 2011-Vol. 41, Iss: 1, pp 53-53
TL;DR: The measurement setup comprises the customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab scripts for data analysis.
Abstract: We are pleased to announce the release of a tool that records detailed measurements of the wireless channel along with received 802.11 packet traces. It runs on a commodity 802.11n NIC, and records Channel State Information (CSI) based on the 802.11 standard. Unlike Receive Signal Strength Indicator (RSSI) values, which merely capture the total power received at the listener, the CSI contains information about the channel between sender and receiver at the level of individual data subcarriers, for each pair of transmit and receive antennas.Our toolkit uses the Intel WiFi Link 5300 wireless NIC with 3 antennas. It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel. The measurement setup comprises our customized versions of Intel's close-source firmware and open-source iwlwifi wireless driver, userspace tools to enable these measurements, access point functionality for controlling both ends of the link, and Matlab (or Octave) scripts for data analysis. We are releasing the binary of the modified firmware, and the source code to all the other components.
Citations
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03 Oct 2016
TL;DR: This paper verifies the effectiveness of OpenMili through benchmark communication/sensing experiments, and showcases its usage by prototyping a pairwise phased-array localization scheme, and a learning-assisted real-time beam adaptation protocol.
Abstract: The 60 GHz wireless technology holds great potential for multi-Gbps communications and high-precision radio sensing. But the lack of an accessible experimental platform has been impeding its progress. In this paper, we overcome the barrier with OpenMili, a reconfigurable 60 GHz radio architecture. OpenMili builds from off-the-shelf FPGA processor, data converters and 60 GHz RF front-end. It employs customized clocking, channelization and interfacing modules, to achieve Gsps sampling bandwidth, Gbps wireless bit-rate, and Gsps sample streaming from/to a PC host. It also incorporates the first programmable, electronically steerable 60 GHz phased-array antenna. OpenMili adopts programming models that ease development, through automatic parallelization inside signal processing blocks, and modular, rate-insensitive interfaces across blocks. It provides common reference designs to bootstrap the development of new network protocols and sensing applications. We verify the effectiveness of OpenMili through benchmark communication/sensing experiments, and showcase its usage by prototyping a pairwise phased-array localization scheme, and a learning-assisted real-time beam adaptation protocol.
54 citations
Additional excerpts
...11ax [12]) and sensing appliances [13]....
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University of Calgary1, Wuhan University2, Beijing University of Posts and Telecommunications3, China University of Geosciences (Beijing)4, University of Exeter5, Sun Yat-sen University6, Shanghai Jiao Tong University7, Southern University of Science and Technology8, National University of Defense Technology9, University of Edinburgh10
TL;DR: In this paper, the authors provide guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.
Abstract: Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.
54 citations
05 Jul 2018
TL;DR: This paper develops a smartphone-based indoor space mapping system that lets a regular user quickly map an indoor space by simply walking around while holding a phone in his/her hand, and shows that the constructed indoor contour can be used to predict wireless received signal strength (RSS).
Abstract: Constructing a map of indoor space has many important applications, such as indoor navigation, VR/AR, construction, safety, facility management, and network condition prediction. Existing indoor space mapping requires special hardware (e.g., indoor LiDAR equipment) and well-trained operators. In this paper, we develop a smartphone-based indoor space mapping system that lets a regular user quickly map an indoor space by simply walking around while holding a phone in his/her hand. Our system accurately measures the distance to nearby reflectors, estimates the user's trajectory, and pairs different reflectors the user encounters during the walk to automatically construct the contour. Using extensive evaluation, we show our contour construction is accurate: the median errors are 1.5 cm for a single wall and 6 cm for multiple walls (due to longer trajectory and the higher number of walls). We show that our system provides a median error of 30 cm and a 90-percentile error of 1 m, which is significantly better than the state-of-the-art smartphone acoustic mapping system BatMapper [64], whose corresponding errors are 60 cm and 2.5 m respectively, even after multiple walks. We further show that the constructed indoor contour can be used to predict wireless received signal strength (RSS).
54 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...In our experiments, we use a laptop with Intel 5300 chip acting as a transmitter and another laptop acting as a receiver logs the RSS for every packet using Intel CSI toolkit [37]....
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01 Mar 2016
TL;DR: This work designs an adaptive device-free human detection scheme that automatically predicts the detection threshold according to the richness of multipath propagation within monitored areas, and implements the scheme with commodity WiFi infrastructure and evaluates it in typical office environments.
Abstract: Wireless device-free passive human detection is a key enabler for a range of indoor location-based services such as asset security, emergency responses, privacy-preserving children and elderly monitoring, etc. Since the feature of received signal varies with different multipath propagation conditions, an labor-intensive on-site calibration procedure is almost indispensable to decide the optimal scenario-specific threshold for human detection. Such overhead, however, impedes readily and fast deployment of wireless device-free human detection systems in practical indoor environments. In this work, we explore PHY layer multipath profiling information to extract a novel quantitative metric Ks as an indicator for link sensitivity, and further exploit a linear detection threshold prediction model. We design an adaptive device-free human detection scheme that automatically predicts the detection threshold according to the richness of multipath propagation within monitored areas. We implement our scheme with commodity WiFi infrastructure and evaluate it in typical office environments. Extensive experimental results show that our scheme yields comparative performance with the state-of-the-art, yet requires no on-site threshold calibration.
53 citations
TL;DR: S-Phaser is an indoor localization system that uses a single Wi-Fi access point (AP) to locate terminals and uses a geometric positioning algorithm to determine the user's location, compared to traditional indoor localization systems based on the fingerprint positioning technology.
Abstract: In this paper, we propose S-Phaser, an indoor localization system that uses a single Wi-Fi access point (AP) to locate terminals. Compared to traditional indoor localization systems based on the fingerprint positioning technology, S-Phaser does not need to deploy a large number of APs and has better localization accuracy. S-Phaser utilizes channel state information (CSI) to compute the direct path length between a single AP and terminals, thereby efficiently improving the accuracy in the line-of-sight (LOS) scenario. S-Phaser is still able to get satisfied accuracy in the non-LOS scenario even not as good as in the LOS scenario. Using the feature of multiple channels in 802.11n, we design an interpolation-based algorithm, named as the interpolation elimination method to calibrate signal phases from CSI, and then we use two different algorithms, the Chinese residue theorem and broadband angle ranging, to compute the real distance of the direct path from the calibrated phases. Then, S-Phaser uses a geometric positioning algorithm to determine the user's location. To verify the practicability of the proposed S-Phaser, we set up a localization system in an actual environment. S-Phaser can improve the median localization error to 1.5 m with a single Wi-Fi AP.
53 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...CFR can be calculated by CSI, which can be obtained from commodity Wi-Fi network interface cards (NICs), such as Intel 5300 by CSI-Tools [13]....
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...The conversion from the CSI to the CFR is [14] CFR = CSI × √ SNR = CSI × √ PRSS · PC SI∑ n (2) where n is the power of noise, which can be obtained from network cards by CSI-Tools, SNR is the signal-to-noise ratio of the antennas on network cards, PRSS is the power to describe the received signal strength, and PCSI is the received power of CSI....
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References
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30 Aug 2010
TL;DR: It is shown that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide, and the rate prediction is as good as the best rate adaptation algorithms for 802.
Abstract: RSSI is known to be a fickle indicator of whether a wireless link will work, for many reasons. This greatly complicates operation because it requires testing and adaptation to find the best rate, transmit power or other parameter that is tuned to boost performance. We show that, for the first time, wireless packet delivery can be accurately predicted for commodity 802.11 NICs from only the channel measurements that they provide. Our model uses 802.11n Channel State Information measurements as input to an OFDM receiver model we develop by using the concept of effective SNR. It is simple, easy to deploy, broadly useful, and accurate. It makes packet delivery predictions for 802.11a/g SISO rates and 802.11n MIMO rates, plus choices of transmit power and antennas. We report testbed experiments that show narrow transition regions (
697 citations
"Tool release: gathering 802.11n tra..." refers methods in this paper
...It works on up-to-date Linux operating systems: in our testbed we use Ubuntu 10.04 LTS with the 2.6.36 kernel....
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01 Oct 2001
TL;DR: The Internet is going mobile and wireless, perhaps quite soon, with a number of diverse technologies leading the charge, including, 3G cellular networks based on CDMA technology, a wide variety of what is deemed 2.5G cellular technologies (e.g., EDGE, GPRS and HDR), and IEEE 802.11 wireless local area networks (WLANs).
Abstract: At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...
615 citations
07 Jan 2010
TL;DR: This tutorial provides a brief introduction to multiple antenna techniques, and describes the two main classes of those techniques, spatial diversity and spatial multiplexing.
Abstract: The use of multiple antennas and MIMO techniques based on them is the key feature of 802.11n equipment that sets it apart from earlier 802.11a/g equipment. It is responsible for superior performance, reliability and range. In this tutorial, we provide a brief introduction to multiple antenna techniques. We describe the two main classes of those techniques, spatial diversity and spatial multiplexing. To ground our discussion, we explain how they work in 802.11n NICs in practice.
89 citations