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Journal ArticleDOI

Tool release: gathering 802.11n traces with channel state information

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TLDR
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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Citations
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POSTER: WLAN Device Fingerprinting using Channel State Information (CSI)

TL;DR: This work shows that radio-metrics such as amplitude and phase can be used to create a unique fingerprint of a wireless device, based on as little as 100 CSI packets per device collected with an off-the-shelf Wi-Fi card.
Journal ArticleDOI

Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial

TL;DR: A benchmark, SenseFi, is proposed to study the effectiveness of various deep learning models for WiFi sensing, and is believed to be the first benchmark with an open-source library for deep learning in WiFi sensing research.
Proceedings ArticleDOI

WiFi Motion Detection: A Study into Efficacy and Classification

TL;DR: A motion detection system that utilizes WiFi Channel State Information (CSI), which describes how a wireless signal propagates from the transmitter to the receiver, and supervised machine learning algorithms to classify a set of simple motions using a proposed feature extraction methods.
Proceedings ArticleDOI

LetFi: Letter Recognition in the Air Using CSI

TL;DR: LetFi is designed, a high accuracy letter recognition in the air system, which could detect and recognize the letter written by a user by analyzing its influence on surrounding WiFi signals and proposes a coherence histogram based multi-domain feature extraction strategy.
Proceedings ArticleDOI

Human Identification Using WIFI Signal

TL;DR: A system that uses Channel State Information (CSI) System to extract unique features of an individual’s unique walking pattern and can uniquely identify human with an average accuracy of 78% to 97.5% by using Random Forest (RF) and 84% to 95% average accuracy by using Boosted Decision Tree (BDT) algorithm.
References
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Proceedings ArticleDOI

Predictable 802.11 packet delivery from wireless channel measurements

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.
Journal ArticleDOI

ACM SIGCOMM computer communication review

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).
Journal ArticleDOI

802.11 with multiple antennas for dummies

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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