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: Zhang et al. as mentioned in this paper proposed a dual-path prototypical network (Dual-Path PN) which consists of a deep feature extractor and a dualpath recognizer.
Abstract: Numerous deep learning studies have achieved remarkable advances in WiFi-based human gesture recognition (HGR) using channel state information (CSI). However, since the CSI patterns of the same gesture change across domains (i.e., users, environments, locations, and orientations), recognition accuracy might degrade significantly when applying the trained model to new domains. To overcome this problem, we propose a WiFi-based cross-domain gesture recognition system (WiGr) which has a domain-transferable mapping to construct an embedding space where the representations of samples from the same class are clustered, and those from different classes are separated. The key insight of WiGr is using the similarity between the query sample representation and the class prototypes in the embedding space to perform the gesture classification, which can avoid the influence of the cross-domain CSI patterns change. Meanwhile, we present a dual-path prototypical network (Dual-Path PN) which consists of a deep feature extractor and a dual-path (i.e., Path-A and Path-B substructures) recognizer. The trained feature extractor can extract the gesture-related domain-independent features from CSI, namely, the domain-transferable mapping. In addition, WiGr implements the cross-domain HGR based on only a pair of WiFi devices without retraining in the new domain. We conduct comprehensive experiments on three data sets, one is built by ourselves and the others are public data sets. The evaluation suggests that WiGr achieves 86.8%–92.7% in-domain recognition accuracy and 83.5%–93% cross-domain accuracy under the four-shot condition.
8 citations
TL;DR: An indoor localization method using multiple input, multiple output orthogonal frequency division multiplexing (MIMO-OFDM) channel state information (CSI) is proposed as a method that can be implemented on wireless local area networks of a current standard without affecting their protocol structures and that does not require a training process for adaptation to indoor environments.
Abstract: An indoor localization method using multiple input, multiple output orthogonal frequency division multiplexing (MIMO-OFDM) channel state information (CSI) is proposed as a method that can be implemented on wireless local area networks of a current standard without affecting their protocol structures and that does not require a training process for adaptation to indoor environments. In the proposed method, the CSI obtained by the MIMO-OFDM receivers of all access points upon successful reception of a data packet from a mobile terminal (MT) is processed in order to determine the location of the MT. The proposed method analyzes the multipath effect that appears in the CSI as multiple complex sinusoids by using the matrix pencil method in order to extract only terms that are contributed by direct paths from the MT to the access points. Localization is achieved using the direct-path terms on the basis of the maximum likelihood principle.
8 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...Therefore, a receiver can provide the CSI at no overhead [10]....
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...Recently, a method that uses channel state information (CSI) available through a network interface card [10] has been proposed [9]....
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TL;DR: A CSI-based orientation detection system based on existing WiFi infrastructure and its channel state information (CSI) is designed and the correlations between subcarriers using complex network and a VG-based feature extraction technique is proposed.
Abstract: Nonintrusive orientation detection is an important yet largely unaddressed area. It can be used in many important applications, e.g., interactive games, medical care, and various industrial scenarios. Moreover, our novel techniques can be readily implemented in other critical areas, such as indoor localization, objective tracking, movement detection, etc. Many factors can limit the performance of detection algorithms in real-word applications, an important one of which is the negligence of subcarrier correlations. Therefore, we propose to build our system based on existing WiFi infrastructure and its channel state information (CSI). To explore the correlations of adjacent subcarriers, we apply the visibility graph (VG)-based network analysis method to process the CSI data. Specifically, in this article we make the following contributions: 1) we design a CSI-based orientation detection system; 2) we model the correlations between subcarriers using complex network and propose a VG-based feature extraction technique; and 3) we demonstrate the performance and effectiveness of our system with commercial products in real-world deployments. The experimental results show that our technique can achieve more than 98% accuracy and at least 26% better than the baseline approaches.
8 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...The CSI data is collected by CSITOOL [10], which is installed on the receiving notebook....
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...channel can be evenly divided into multiple subcarriers [10]....
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...The RSSI is the aggregated signal strength of all the subcarriers, while CSI, which is derived directly from the physical layer of the communication protocol, has the amplitude and phase information of every single subcarrier [10]....
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TL;DR: A novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle by exploiting the mean-based nearest neighbor rule to further improve the efficiency of Sparse Representation based Classification (SRC).
Abstract: In the realm of intelligent vehicles, gestures can be characterized for promoting automotive interfaces to control in-vehicle functions without diverting the driver’s visual attention from the road. Driver gesture recognition has gained more attention in advanced vehicular technology because of its substantial safety benefits. This research work demonstrates a novel WiFi-based device-free approach for driver gestures recognition for automotive interface to control secondary systems in a vehicle. Our proposed wireless model can recognize human gestures very accurately for the application of in-vehicle infotainment systems, leveraging Channel State Information (CSI). This computationally efficient framework is based on the properties of K Nearest Neighbors (KNN), induced in sparse representation coefficients for significant improvement in gestures classification. In this typical approach, we explore the mean of nearest neighbors to address the problem of computational complexity of Sparse Representation based Classification (SRC). The presented scheme leads to designing an efficient integrated classification model with reduced execution time. Both KNN and SRC algorithms are complimentary candidates for integration in the sense that KNN is simple yet optimized, whereas SRC is computationally complex but efficient. More specifically, we are exploiting the mean-based nearest neighbor rule to further improve the efficiency of SRC. The ultimate goal of this framework is to propose a better feature extraction and classification model as compared to the traditional algorithms that have already been used for WiFi-based device-free gesture recognition. Our proposed method improves the gesture recognition significantly for diverse scale of applications with an average accuracy of 91.4%.
8 citations
03 Jan 2018
TL;DR: This study applied channel state information available in IEEE 802.11n networks to characterize the flow count and found that this deep neural network structure beats other popular classification algorithms including random forest, logistic regression, support vector machine and multilayer perceptron in predicting the flow counted with attractive speed performance.
Abstract: Human flow counting has many applications in space management. This study applied channel state information (CSI) available in IEEE 802.11n networks to characterize the flow count. Raw inputs including mean, standard deviation and five-number summary were extracted from windowed CSI data. Due to the large number of raw inputs, stacked denoising autoencoders were used to extract hierarchical features from raw inputs and a final layer of softmax regression was used to model the flow counting problem. It is found that this deep neural network structure beats other popular classification algorithms including random forest, logistic regression, support vector machine and multilayer perceptron in predicting the flow count with attractive speed performance.
8 citations
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30 Aug 2010
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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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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.
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