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.
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TL;DR: PicoScenes as discussed by the authors is a software implementation of the 802.11a/g/n/ac/ax baseband, which allows users to fully control the baseband and access the complete physical layer information.
Abstract: The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: 1) unknown baseband design and its influence; 2) inadequate hardware; and 3) the lack of versatile and flexible measurement software. This article tries to eliminate these barriers through the following work. First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing channel state information (CSI) model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high-performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third, we release the PicoScenes software, which supports concurrent CSI measurement from multiple QCA9300, Intel Wireless Link (IWL5300), and SDR hardware. PicoScenes features rich low-level controls, packet injection, and software baseband implementation. It also allows users to develop their own measurement plugins. Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2-GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj.io .
12 citations
01 Aug 2019
TL;DR: It is shown that it is possible to characterize activities and / or human body presence with high accuracy and two algorithms are proposed - one using a support vector machine (SVM) for classification and another using a long short-term memory (LSTM) recurrent neural network.
Abstract: Human Activity Recognition (HAR) is a rather broad research area. While there exist solutions based on sensors and vision-based technologies, these solutions suffer from considerable limitations. Thus in-order to mitigate or avoid these limitations, device free solutions based on radio signals like home WiFi are considered. Recently, channel state information (CSI), available in WiFi networks have been proposed for fine-grained analysis. We are able to detect the human activities like Walk, Stand, Sit, Run, etc. in a Line of Sight scenario (LOS) and a Non Line of Sight (N-LOS) scenario within an indoor environment. We propose two algorithms - one using a support vector machine (SVM) for classification and another one using a long short-term memory (LSTM) recurrent neural network. While the former uses sophisticated pre-processing and feature extraction techniques the latter processes the raw data directly (after denoising with wavelets). We show that it is possible to characterize activities and / or human body presence with high accuracy and we compare both approaches with regards to accuracy and performance.
12 citations
12 Sep 2016
TL;DR: This demo shows how a centimeter-scale position change affects the respiration detection performance when a subject is moving closer to the LoS.
Abstract: Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively using commodity WiFi devices. However, it is not always possible to sense human respiration when a subject is in different locations or faces different orientations. In this demo, we will show how a centimeter-scale position change affects the respiration detection performance. Counter-intuitively, when a subject is moving closer to the LoS, the performance of respiration sensing is not always getting better. In fact, the detectable and undetectable regions are determined by the Fresnel zone model based theory developed in [7].
12 citations
Cites background from "Tool release: gathering 802.11n tra..."
...The Fresnel Zone; WiFi; Channel State Information (CSI); Respiration Detection....
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...The receiver is a mini-PC equipped with an Intel WiFi link 5300 NIC which allows us to record fine-grained CSI information....
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...Our demo demonstrated that a centimeterscale position change can significantly influence the performance of WiFi CSI based respiration detection....
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...In order to come out with a cost-effective and non-intrusive home monitoring system, researchers turn their attention to the commodity Wi-Fi devices leveraging Channel State Information (CSI) [2] for contact-free respiration monitoring [5] [4] [9] [6]....
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...After the participant lies in the bed, the quality of the CSI signal can be good or bad....
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27 Jun 2022
TL;DR: A device-free respiration detection system, ResFi, utilizing the CSI data from COTS WiFi devices is proposed, which shows an accuracy up to 15% higher than that of the traditional machine-learning methods.
Abstract: Respiration, a vital basis for life, is a key indicator of health status for the human being. Recently, with contact-based devices, some breathing signal detection methods have been proposed, which can achieve high accuracy and signal-to-noise ratio performance. However, these methods require users to be contacted with the devices, leading to a series of problems, such as hindering the movement of users. Therefore, there is an urgent need to call for a contactless solution for respiration detection. With the popularity of indoor WiFi devices, respiration detection with WiFi sensors has drawn a lot of attention. Nevertheless, the multipath effects, which commonly exist in indoor environments, have serious impacts on the propagation of wireless signals, leading to signal attenuation and poor signal quality. Moreover, although the channel state information (CSI) can be readily collected from commercial off-the-shelf (COTS) WiFi devices, the received CSI is distorted due to various offsets introduced during the propagation of the wireless signals and hardware imperfections. In this paper, we try to resolve the challenges mentioned above and propose a device-free respiration detection system, ResFi, utilizing the CSI data from COTS WiFi devices. The final evaluation shows an accuracy of 96.05% for human respiration detection, which is up to 15% higher than that of the traditional machine-learning methods.
11 citations
TL;DR: In this article , the authors proposed HAR-SAnet, a novel RF-based human activity recognition (HAR) framework, which adopts an original signal adapted convolutional neural network architecture.
Abstract: Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. Therefore, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet outperforms state-of-the-art algorithms and systems.
11 citations
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