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
More filters
19 May 2018
TL;DR: Two CSI-based indoor localization algorithms based on the weighted linear discriminant analysis and a localization algorithm based on two-dimensional principal component analysis are proposed and shown to outperform the basic Bayesian algorithm on improving the localization accuracy and reducing the computational complexity.
Abstract: Indoor location-based mobile applications have been gaining momentum in reshaping the daily activities of Internet users. A large number of indoor localization techniques achieve the localization goal by analyzing the received signal strength indication (RSSI) of pervasive WiFi signals. Compared with RSSI, the channel state information (CSI) provides more comprehensive time and space information with more complex hardware and software cost. In this paper, we proposed two CSI-based indoor localization algorithms: 1) a localization algorithm based on the weighted linear discriminant analysis; 2) a localization algorithm based on two-dimensional principal component analysis. The experimental results show that the proposed algorithms outperform the basic Bayesian algorithm based on the principal component analysis on improving the localization accuracy and reducing the computational complexity.
20 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...The open source CSI measurement tool running on off-the-shelve commercial WiFi devices [2] has stimulated increasing research efforts on many CSI-based localization algorithms....
[...]
TL;DR: This paper provides a passive tracking system for WiFi signals with an enhanced range-only particle filter using fine-grained power and is equipped with a single coordinated turn model to address the challenges in passive positioning.
Abstract: Passive positioning systems produce user location information for third-party providers of positioning services. In this paper, we provide a passive tracking system for WiFi signals with an enhanced range-only particle filter using fine-grained power. Our proposed particle filter, WVT-bootstrap particle filter, provides improved observation likelihood and is equipped with a single coordinated turn model to address the challenges in passive positioning. The anchor nodes for WiFi signal sniffing use software defined radio techniques to extract channel state information for multipath mitigation and a non-linear regression method is used for the path-loss model. Our tracking system produces measured positioning errors that, in the 80th percentile, are equal to or less than 2 m; this represents a 33% improvement over the traditional bootstrap particle filter. Additionally, it requires (0.12 s for 1000 particles) only half of the computation efforts as a multi-model particle filter.
20 citations
Cites background or methods from "Tool release: gathering 802.11n tra..."
...We extract the long preambles from the decoded WiFi packets and design a channel estimation block based on MATLAB to estimate CSI in frequency domain....
[...]
...As mentioned in Section II, off-the-shelf network cards with firmware [19] can not be adopted for a passive local-...
[...]
...In FILA, the target laptop is equipped with an off-the-shelf WiFi network card (IWL5300) to extract CSI [19]....
[...]
...CSI reveals a set of channel measurements depicting the amplitudes and phases of every subcarrier in the frequency domain....
[...]
...Thirdly, we adopt block-type pilot channel estimation based on long preambles to estimate CSI in 64 subcarriers in MATLAB....
[...]
TL;DR: In this paper, the authors proposed a hybrid fingerprint location technology based on RSS and CSI, and the weighted k-nearest neighbor (WKNN) algorithm was applied to reduce the complexity of the algorithm during the online positioning stage.
Abstract: Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.
20 citations
01 Oct 2016
TL;DR: Experimental results show that the method considering the correlation of CSI streams could achieve promising accuracy above 90% in identifying six actions even testing in five different rooms, and the method based on Singular Value Decomposition (SVD) could effectively extract the channel information of signals reflected by human bodies.
Abstract: Due to the characteristics of ubiquity, non-occlusion, privacy preservation of WiFi, many researchers have devoted to human action recognition using WiFi signals. As demonstrated in [1], Channel State Information (CSI), a fine-grained information capturing the properties of WiFi signal propagation, could be transformed into images for achieving a promising accuracy on action recognition via vision-based methods. However, from the experimental results shown in [1], the CSI is usually location dependent, which affects the recognition performance if signals are recorded in different places. In this paper, we propose a location-dependency removal method based on Singular Value Decomposition (SVD) to eliminate the background CSI and effectively extract the channel information of signals reflected by human bodies. Experimental results show that our method considering the correlation of CSI streams could achieve promising accuracy above 90% in identifying six actions even testing in five different rooms.
20 citations
Cites background from "Tool release: gathering 802.11n tra..."
...Such raw signals, even though contain even more detailed channel information, can only be obtained by special hardware, nudging researchers toward CSI [9, 10, 11], which can, in practice, be accessed from today’s commodity devices [12]....
[...]
TL;DR: Rover is presented, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors that addresses practical issues including interference among multiple tags, real-time processing, and the data marginalization problem in dealing with degenerated motions.
Abstract: Recent years have witnessed the rapid proliferation of backscatter technologies that realize the ubiquitous and long-term connectivity to empower smart cities and smart homes. Localizing such backscatter tags is crucial for IoT-based smart applications. However, current backscatter localization systems require prior knowledge of the site, either a map or landmarks with known positions, which is laborious for deployment. To empower universal localization service, this paper presents Rover, an indoor localization system that localizes multiple backscatter tags without any start-up cost using a robot equipped with inertial sensors. Rover runs in a joint optimization framework, fusing measurements from backscattered WiFi signals and inertial sensors to simultaneously estimate the locations of both the robot and the connected tags. Our design addresses practical issues including interference among multiple tags, real-time processing, as well as the data marginalization problem in dealing with degenerated motions. We prototype Rover using off-the-shelf WiFi chips and customized backscatter tags. Our experiments show that Rover achieves localization accuracies of 39.3 cm for the robot and 74.6 cm for the tags.
20 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...11n Channel State Information (CSI) tool [17] to obtain wireless channel information for AoA estimation....
[...]
...11 CSI tool [17] to obtain the wireless channel information for each packet....
[...]
References
More filters
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....
[...]
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