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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10 Apr 2016
TL;DR: This paper develops two complementary methods for computing frame delivery rate to capture the bursty errors under the WiFi interleaver, and designs a new interleavers to reduce the burstiness of errors, and improve the frames delivery rate.
Abstract: The signal-to-noise ratio (SNR) is the gold standard metric for capturing wireless link quality, but offers limited predictability. Recent work shows that frequency diversity causes limited predictability in SNR, and proposes effective SNR. Owing to its significant improvement over SNR, effective SNR has become a widely adopted metric for measuring wireless channel quality and served as the basis for many recent rate adaptation schemes. In this paper, we first conduct trace driven evaluation, and find that the accuracy of effective SNR is still inadequate due to frequency diversity and bursty errors. While common wisdom says that interleaving should remove the bursty errors, bursty errors still persist under the WiFi interleaver. Therefore, we develop two complementary methods for computing frame delivery rate to capture the bursty errors under the WiFi interleaver. We then design a new interleaver to reduce the burstiness of errors, and improve the frame delivery rate. We further design a rate adaptation scheme based on our delivery rate estimation. It can support both WiFi and our interleaver. Using extensive evaluation, we show our delivery rate estimation is accurate and significantly out-performs effective SNR; our interleaver improves the delivery rate over the WiFi interleaver; and our rate adaptation improves both throughput and energy.
16 citations
TL;DR: This article proposes a fall detection system, called FallViewer, based on analyzing the channel state information (CSI) of Wi-Fi signals, which can detect fall events with an average accuracy of 95.8%, and applies a double sliding window to get a flexible threshold, which improves the robustness of Fall viewer to various environments.
Abstract: The safety of the elderly has attracted much attention nowadays. Among various daily activities, fall is one of the most dangerous events for the elderly, especially those who live alone. Most existing works on fall detection are based on wearable devices, which are inconvenient in using. Several solutions only use coarse-grained Wi-Fi signal information that contains many biases, and lack considerations on environmental changes. These situations motivate us to design a fine-grained and robust fall detection approach. In this article, we propose a fall detection system, called FallViewer, based on analyzing the channel state information (CSI) of Wi-Fi signals. To get fine-grained information, we propose phase and amplitude calibration methods for deviation correction. Then, an adjustment approach for antenna power is designed to eliminate the multipath interference. Furthermore, we apply a double sliding window to get a flexible threshold, which improves the robustness of FallViewer to various environments. Finally, FallViewer extracts features of the processed Wi-Fi signal and sends the features to a LibSVM for classification. Through experiments in different environments, FallViewer can detect fall events with an average accuracy of 95.8%, which indicates that FallViewer can work reliably and effectively.
16 citations
TL;DR: This work proposes Wi-Run, a complete model based non-invasive step estimation system that leverages commercial Wi-Fi devices to intelligently estimate steps and demonstrates the superior performance of the step estimation.
16 citations
20 May 2019
TL;DR: This paper proposes a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method that is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.
Abstract: Device free WiFi Sensing using channel state information (CSI) has been shown great potentials for human activity recognition (HAR). However, extracting reliable and concise feature signals remains as a challenging problem, especially in a dynamic and complex environment. In this paper, we propose a novel scheme for CSI-based HAR using deep learning network (CH-DLN), with an innovative CSI correlation feature extraction (CCFE) method. The CCFE method pre-processes the signals input to the DLN in two steps. Firstly, it uses a recursive algorithm to reduce non-activity-related information from the signal and hence enhance the activity-dependent signals. Secondly, it computes the correlation over both the time and frequency domain to disclose better signal structure and compress the signal. From such enhanced and compressed signals, we utilize the recurrent neural networking (RNN) to automatically extract deeper features, and then apply the softmax regression algorithm for classifying activities. Through extensive experimental results, our proposed scheme is shown to outperform state-of-the-art methods in recognition accuracy, with much less training time.
16 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...The receiver continues collecting and storing CSI with 30 subcarriers (S = 30), by utilizing the CSI tools [12]....
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...pair of transmitter-receiver antenna can be extracted using the CSI tools as discussed in [12]....
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..., the Intel 5300 network interface card (NIC), is utilized for CSI acquisition [12]....
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TL;DR: An Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI), and shows that the ranging estimation accuracy can be significantly enhanced compared with the typical algorithms.
Abstract: With the increasing demand of location-based services, the indoor ranging method based on Wi-Fi has become an important technique due to its high accuracy and low hardware requirements. The complicated indoor environment makes it difficult for wireless indoor ranging systems to obtain accurate distance measurements. This paper presents an Extended Kalman filter-based approach for indoor ranging by utilizing transmission channel quality metrics, including Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). The proposed ranging algorithm scheme is implemented and validated with experiments in two typical indoor environments. A real indoor experiment demonstrates that the ranging estimation accuracy of our algorithms can be significantly enhanced compared with the typical algorithms. The ranging estimation accuracy is defined as the cumulative distribution function of the distance error.
16 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