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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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ZIGFI: Harnessing Channel State Information for Cross-Technology Communication

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Adaptive communication in multi-robot systems using directionality of signal strength

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Deep Spatial–Temporal Model Based Cross-Scene Action Recognition Using Commodity WiFi

TL;DR: A deep learning framework that integrates spatial features learned from the convolutional neural network (CNN) into the temporal model multilayer bidirectional long short-term memory (Bi-LSTM) and is the first work to explore deep spatial–temporal features for CSI-based action recognition.
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DeFi: Robust Training-Free Device-Free Wireless Localization With WiFi

TL;DR: This work develops a novel robust training-free DFL system, named DeFi, which could directly estimate the target location by refining the angle-of-arrival (AOA) of the target reflection path based on WiFi channel state information.
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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