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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01 Dec 2018
TL;DR: The prototype shows that the proposed WiFi-based activity detection system can work with WiFi signals with even higher accuracy and the powerful inference of deep neural networks simplify the feature extraction and improve the accuracy of the classification of indoor human states.
Abstract: This study proposes a WiFi-based activity detection system using deep neural networks to detect the indoor human states. This system captures useful amplitude information from the channel state information and converts the information to two-dimensional arrays. Next, the two-dimensional arrays are used as inputs to deep neural networks to distinguish the moving and stationary states of people. The powerful inference of deep neural networks simplify the feature extraction and also improve the accuracy of the classification of indoor human states. Our prototype shows that the proposed system can work with WiFi signals with even higher accuracy.
5 citations
Cites methods from "Tool release: gathering 802.11n tra..."
...In this study, we use an open CSI Tool built on Intel 5300 NIC to collect CSI signals [9]....
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16 May 2022
TL;DR: In this article , the authors identify ten major practical and theoretical problems that hinder real deployment of integrated sensing and communication (ISAC) applications, and provide possible solutions to those critical challenges, which will inspire further research to evolve existing WiFi/4G/5G networks into next-generation intelligent wireless network (i.e., 6G).
Abstract: Next-generation mobile communication network (i.e., 6G) has been envisioned to go beyond classical communication functionality and provide integrated sensing and communication (ISAC) capability to enable more emerging ap-plications, such as smart cities, connected vehicles, AIoT and health care/elder care. Among all the ISAC proposals, the most practical and promising approach is to empower existing wireless network (e.g., WiFi, 4G/5G) with the augmented ability to sense the surrounding human and environment, and evolve wireless communication networks into intelligent communication and sensing network (e.g., 6G). In this paper, based on our experience on CSI-based wireless sensing with WiFi/4G/5G signals, we intend to identify ten major practical and theoretical problems that hinder real deployment of ISAC applications, and provide possible solutions to those critical challenges. Hopefully, this work will inspire further research to evolve existing WiFi/4G/5G networks into next-generation intelligent wireless network (i.e., 6G).
5 citations
02 Oct 2020
TL;DR: This paper proposes CSI selective dictionary (CS-Dict), an accurate model-free indoor localization algorithm using only one access point simultaneously and mainly contains two parts: CSI feature enhancement and over-complete dictionary learning.
Abstract: With the increasingly growing demand for indoor location-based services(LBS) in the field of wireless sensing, Wi-Fi have been the mainstream method in indoor localization for the reasons of easy deployment and the popularity of signal. Channel State Information (CSI) is extracted from the physical layer of WiFi network interface cards and includes more fine-grained signal characteristics than received signal strength index (RSSI) which is commonly used in the literature. In this paper, we propose CSI selective dictionary (CS-Dict), an accurate model-free indoor localization algorithm using only one access point simultaneously. CS-Dict mainly contains two parts: CSI feature enhancement and over-complete dictionary learning. In the feature enhancement, CSI features with high reliability are selected as the input for dictionary learning. In the over-complete dictionary learning, we utilize the regularized K-SVD to perform a dictionary representation of selective CSI features in each reference point. Finally, a similarity measurement between the real-time measured CSI and the learned dictionary is performed to find the best match for position estimation. An extensive experiment is deployed in two typical indoor environments, the results show that the mean error are 0.12 m and 0.23 m respectively.
5 citations
10 Jun 2019
TL;DR: The key idea is to trace the probe packet flow to locate the positions of lost packets, derive the CSI Matrix from CSI measurements, and use improved compressive sensing technique to reconstruct the missing CSIs.
Abstract: Fine-grained and complete Channel State Information (CSI) is essential for emerging CSI-based activity recognition applications. However, many probe packets collected for CSI measurements may be lost due to co-channel interferences and other malfunctions in practice, such as link interruptions, and thus limit the further applications of these CSI-based activity recognitions. To overcome this limitation, we propose an IM proved cO mpressive Sensing bA sed mIssing packet reC overy method, named IMOSAIC, to locate the lost probe packets and to reconstruct the missing CSIs, and thus improve the accuracy and the robustness of CSI-based activity recognitions. The key idea is to trace the probe packet flow to locate the positions of lost packets, derive the CSI Matrix from CSI measurements, and use improved compressive sensing technique to reconstruct the missing CSIs. We mainly address challenges in locating the lost packets, transforming CSI measurements into CSI Matrix, and digging up CSI measurement correlations and inherent low-rank properties to reconstruct the lost packets. Furthermore, experiment results show that IMOSAIC outperforms existing interpolation methods on reconstructing the lost packets, and can achieve an average recovery accuracy of 80.21%, when 90% of packets are lost, and the reconstructed CSI datasets can improve the activity recognition accuracy obviously.
5 citations
Additional excerpts
...modified the wireless driver of a network interface card (NIC) and released a CSI tool, which can record detailed measurements of the wireless channel along with received probe packet traces [22]....
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01 Jan 2013
TL;DR: In this article, the authors consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers.
Abstract: We consider the problem of satisfying communication demands in a multi-agent system where several robots cooperate on a task and a fixed subset of the agents act as mobile routers. Our goal is to position the team of robotic routers to provide communication coverage to the remaining client robots. We allow for dynamic environments and variable client demands, thus necessitating an adaptive solution. We present an innovative method that calculates a mapping between a robot's current position and the signal strength that it receives along each spatial direction, for its wireless links to every other robot. We show that this information can be used to design a simple positional controller that retains a quadratic structure, while adapting to wireless signals in real-world environments. Notably, our approach does not necessitate stochastic sampling along directions that are counter-productive to the overall coordination goal, nor does it require exact client positions, or a known map of the environment.
5 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