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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TL;DR: In this article , a tensor-based localization algorithm that fully exploits the multipath components (MPCs) within the channel state information is proposed to achieve high-accuracy localization via fusing time-difference of arrival and angle-of-arrival information from all MPCs.
Abstract: Wi-Fi indoor localization has witnessed an increasing interest, benefiting from its low cost, easy accessibility and wide deployment. However, its applications are often restricted due to harsh multipath propagation in indoor environments. In this letter, we propose a tensor-based localization algorithm that fully exploits the multipath components (MPCs) within the channel state information. We first formulate the location parameter estimation problem as a tensor decomposition problem and ensure the uniqueness by a preprocessing step. Then, a multipath-aided localization method is proposed to achieve high-accuracy localization via fusing time-difference-of-arrival and angle-of-arrival information from all MPCs. Experimental results show that the proposed method can provide a stable sub-meter-level localization performance in multipath environments.
6 citations
20 May 2019
TL;DR: The experiment results show that the accuracy of the deep learning algorithms on Wi-Fi datasets achieves beyond 95% which may generate notable market values which may stimulate further research on accelerating deep learning methods in a software/hardware co-design approach.
Abstract: It is common that the elderly may fall and injure severely. This problem has attracted worldwide attention and becomes a major challenge in the public health care. In the past decade, extensive studies have been conducted to detect fall using wearable sensors and cameras. Given the pervasive WiFi penetration in our daily life, behavior recognition based on the channel state information (CSI) of WiFi signals has shown its potentials in detecting falls for the elderly with less constraint compared with clumsy sensors. In this paper, we conducted a performance evaluation study of three deep learning methods on a public dataset to detect falls. The experiment results show that the accuracy of the deep learning algorithms on Wi-Fi datasets achieves beyond 95% which may generate notable market values. Nevertheless, the long training time of deep learning models is likely to be the hampering factor before commercialization. Our study may stimulate further research on accelerating deep learning methods in a software/hardware co-design approach.
6 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...The CSI collected with [4] contains information on 30 sub-carriers collected by three antennas....
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...This data set uses Intel 5300 NIC [4] with the sampling frequency 1kHz....
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TL;DR: The result shows that the localisation accuracy can be significantly enhanced using the proposed hybrid indoor localisation scheme, which can significantly reduce the computational overhead by the introduction of the distance between transceivers.
Abstract: In this study, a hybrid indoor localisation scheme based on ranging and fingerprinting using the calibrated channel state information is presented. In ranging, regarding the serious indoor multipath effect, the authors propose a ranging scheme based on channel impulse response to improve the ranging accuracy and stability of the existing ranging scheme using received signal strength. In fingerprint extraction, they utilise phase difference between antennae to generate fingerprint. The phase difference is more stable and better represents a specific location. Different from the traditional fingerprint-based scheme including establishing fingerprint database and matching all the fingerprints, they use multi-layer perceptron to learn a classification model by training on a fingerprint dataset and the new fingerprints are feed to the model to predict the corresponding locations. Their scheme can significantly reduce the computational overhead by the introduction of the distance between transceivers. Finally, they conduct and validate the proposed scheme in two typical indoor environments by extensive experiments. The result shows that the localisation accuracy can be significantly enhanced using their scheme.
6 citations
TL;DR: Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error reached 0.49m in the integrated room and 0.81 m in the complex garage, respectively.
Abstract: With the wide deployment of commercial WiFi devices, the fine-grained channel state information (CSI) has received widespread attention with broad application domain including indoor localization and intrusion detection. From the perspective of practicality, dynamic intrusion may be confused under non-line-of-sight (NLOS) conditions and the continuous operation of passive positioning system will bring much unnecessary computation. In this paper, we propose an enhanced CSI-based indoor positioning system with pre-intrusion detection suitable for NLOS scenarios (C-InP). It mainly consists of two modules: intrusion detection and positioning estimation. The introduction of detection module is a prerequisite for positioning module. In order to improve the discrimination of features under NLOS conditions, we propose a modified calibration method for phase transformation while the amplitude outliers are filtered by the variance distribution with the median sequence. In addition, binary and improved multiple support vector classification (SVC) models are established to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. Comprehensive experimental verification is carried out in typical indoor scenarios. Experimental results show that C-InP outperforms the existing system in NLOS environments, where the mean distance error (MDE) reached 0.49 m in the integrated room and 0.81 m in the complex garage, respectively.
6 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...11n CSI Tool [20] and operating in monitor mode at 2....
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...CSI depicts frequency diversity [20] at the OFDM subcarrier level and provides detailed CSI fine-grained PHY layer information....
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...In practical applications, CSI is collected at considerable speed [20], so we set a fixed time window and judge the dynamic intrusion based on the signal changes between adjacent time windows....
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...According to the IEEE 802.11n protocol, the acquisition of CSI features is achieved by Linux 802.11n CSI Tool [20] and operating in monitor mode at 2.4 GHz at the intervals of 5 ms....
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01 Dec 2018
TL;DR: This paper converts the acquired Channel State Information data to feature maps using complex wavelet transform and extends the fingerprint database by the proposed Wavelet Transform-Feature Deep Convolutional Generative Adversarial Network model to improve the accuracy of indoor localization system with reduced human effort.
Abstract: Wi-Fi positioning is currently the mainstream indoor localization method, and the construction of fingerprint database is crucial to the Wi-Fi based localization system. However, the accuracy requirement needs enough data sampled at many reference points, which consumes significant manpower and time. In this paper, we convert the acquired Channel State Information (CSI) data to feature maps using complex wavelet transform and then extend the fingerprint database by the proposed Wavelet Transform-Feature Deep Convolutional Generative Adversarial Network model. With this model, the convergence process in training phase can be accelerated and the diversity of generated feature maps can be increased significantly. Based on the extended fingerprint database, the accuracy of indoor localization system can be improved with reduced human effort.
6 citations
Additional excerpts
...11n CSI Tool [15], containing information of all subcarriers....
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...The firmware and driver of IWL5300 are modified to export CSI of each packet from wireless channel measurements with Linux 802.11n CSI Tool [15], containing information of all subcarriers....
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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