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: This paper considers a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO, and proposes a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI.
Abstract: The various offsets existed in the commodity WiFi devices greatly limit the use of ubiquitous WiFi signals for indoor applications. In this paper, we focus on the estimation and compensation of the residual carrier frequency offset (CFO) for the commodity WiFi devices. Specifically, we consider a distorted channel state information (CSI) model by taking into consideration various CSI errors such as packet detection delay (PDD) and CFO. We propose a multiscale sparse recovery algorithm to get rid of the effect of PDD and extract the carrier frequency component out of CSI. Then, we formulate the residual CFO estimation as a spectrum estimation problem and utilize the MUSIC algorithm to estimate the residual CFO. Real experiments and numerical simulations are conducted to evaluate the performance of the proposed method. The experimental results and simulation results show that the residual CFO is time-varying, and compared with existing methods, the proposed method can better estimate and compensate the residual CFO, and thus achieve better results.
24 citations
Posted Content•
TL;DR: A fully convolutional network (FCN) is proposed, termed WiSPPN, to estimate single person pose from the collected data and annotations and replies to the natural question: can WiFi devices work like cameras for vision applications?
Abstract: WiFi human sensing has achieved great progress in indoor localization, activity classification, etc. Retracing the development of these work, we have a natural question: can WiFi devices work like cameras for vision applications? In this paper We try to answer this question by exploring the ability of WiFi on estimating single person pose. We use a 3-antenna WiFi sender and a 3-antenna receiver to generate WiFi data. Meanwhile, we use a synchronized camera to capture person videos for corresponding keypoint annotations. We further propose a fully convolutional network (FCN), termed WiSPPN, to estimate single person pose from the collected data and annotations. Evaluation on over 80k images (16 sites and 8 persons) replies aforesaid question with a positive answer. Codes have been made publicly available at this https URL.
24 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...Recall that our WiFi system is comprised of a sender and a receiver both with 3 antennas, which outputs CSI samples with size of 30× 3× 3 through a open-source tool [25], where the 30 is the number of OFDM carriers described in Section....
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...We set WiFi working within a 20MHz band, the CSI of 30 carriers can be obtained through a open-source tool [25]....
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...The sender broadcasts WiFi signals, meanwhile the receiver parses CSI through [25] when receiving the broadcasting WiFi....
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01 Dec 2020
TL;DR: Wang et al. as discussed by the authors used a CNN (Convolutional Neural Network) for binary classification of the spectrogram images of the fall and non-fall motions, which achieved 0.90 accuracy.
Abstract: Fall detection system has a great demand for elderly people living alone. Wi-Fi CSI (Channel State Information) based fall detection method can be used to build non-intrusive and nonspace- limited fall detection systems. In the conventional work on Wi-Fi CSI based fall detection, a classification performance degradation has been observed when data in different environments is used for learning and testing data. Also, that method can not capture accurate features of motion due to the signal distortion during the noise reduction, and it can not segment signals accurately when the SNR (Signal to Noise power Ratio) is small. In this paper, we propose a spectrogram image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding time window, and then the classifier detects fall by using the spectrogram image generated from segmented CSI. We use a CNN (Convolutional Neural Network) for binary classification of the spectrogram images of the fall and non-fall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing data. As a result, we confirmed that our proposed method outperformed conventional one and reached 0.90 accuracy.
24 citations
TL;DR: The results demonstrate that the WiGesID system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.
Abstract: Gesture recognition is the central enabler of human-computer interaction (HCI). In addition to the semantic information contained in gestures, gesture-based user identification can effortlessly enhance HCI system security. Recently, the Wi-Fi-integrated sensing and communication (ISAC) technology has shown great potential in a field hitherto occupied by computer vision and radar sensing. In this work, leveraging Wi-Fi sensing, we propose a system called WiGesID that achieves joint gesture recognition and human identification (JGRHI). The basic idea behind WiGesID is to identify personalized spatiotemporal dynamic patterns from the gestures of different users. Moreover, we develop an effective approach to recognize new categories of gestures and users by computing relation scores between the features of the new category samples and the support samples. To evaluate the performance, we implemented WiGesID and conducted extensive experiments. The results demonstrate that our system outperforms the state-of-the-art method for cross-domain sensing and accurately recognizes new categories, which promotes the use of this application of Wi-Fi sensing in HCI.
24 citations
20 May 2018
TL;DR: This is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions, and achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal strength based solution using an already deployed infrastructure.
Abstract: We present the first WiFi based driver state recognition system: SafeDrive-Fi. Our proposed framework extracts fine-grain Channel State Information (CSI) of WiFi signal to accurately predict driver states through gestures and body movements. Different from vision based techniques, SafeDrive-Fi provides a simple, cost-effective and ubiquitous solution to prevent accidents and loss of lives due to reckless driving. We incorporate a unique DETECT algorithm to differentiate between normal and dangerous driving in a challenging and noisy in-vehicle conditions. Using only commercially available products, SafeDrive-Fi is compatible with 802.11n/ac and can assist drivers and law enforcement in discovering dangerous driving states. To the best of our knowledge, this is the first system that aggregates information from all the channel subcarriers and use multidomain CSI features to classify dangerous driving conditions. SafeDrive-Fi achieves an overall 98.04% recognition accuracy and 19.8% improvement over similar Received Signal Strength (RSS) based solution using an already deployed infrastructure.
24 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...We extract CSI of received packets using a tool designed to be used with Intel 5300 WiFi NIC [18]....
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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