Ar9380 Atheros

AR9380 is the single-chip, dual-band (2.4/5 GHz), 3-stream 11n solution with PCIe interface. It packs the breakthrough Signal Sustain Technology 3 (SST3) technology that enhances the rate-over-range (RoR) performance. SST3 is a set of advanced technologies and features enabled by 802.11n including LDPC, TxBF and MLD. Atheros driver for AR9380 Windows 7 (64bit). Atheros 802.11 a/b/g/n Dualband Wireless Network Module, Atheros AR1111 WB-EG, Atheros AR1111, AR2427, AR5002G, AR5002X.

AR9380 is the single-chip, dual-band (2.4/5 GHz), 3-stream 11n solution with PCIe interface.

It packs the breakthrough Signal Sustain Technology 3 (SST3) technology that enhances the rate-over-range (RoR) performance.
SST3 is a set of advanced technologies and features enabled by 802.11n including LDPC, TxBF and MLD.

The end result is an impressive increase in rate-over-range of ~+100% at short range, ~+50% at mid range and ~+25% at long range.

Despite its mighty performance and high degree of integration, AR9380 consumes even less power in every operation mode active TX, active RX, idle associated and sleep. It keeps the notebook and other computing platforms running much longer on a single battery-charge, while providing TCP throughput of more than 300 Mbps when used in 3×3 mode.

AR9380 is also optimized for P2P applications compliant with Wi-Fi Direct.

TM With its Fast Channel Switch (FCS) feature, the channel switching time between 2.4 GHz and 5 GHz is reduced from 10 ms to as little as 1 ms.

Conserves power with 1×1 downshift using Dynamic MIMO Power Save Enables PHY rates of up to 450 Mbps

Specifications

Brand: Atheros
Model: AR9380 AR5BXB112
Interface: PCi-E / PCi-1X
Chipset: Atheros AR9380
Standards: IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n
Frequency range: 2.4GHz/5.1~5.8GHz
Antenna: 6dBi antenna x 3
Transmission rate: 450Mbps+450Mbps
Antenna Interface: SMA
Security: 16/128-bit WEP Encryption,WPA, WPA-PSK, WPA2, WPA2-PSK, TKIP/AES
Support Operating System: Windows XP, 7,8,8.1,10 / Linux / Mac OS (V10.8 NO need to install drivers)

Atheros AR9380 Driver Download for ALL OS

AR9380 WHQL Driver Download

Mirror Download (zippyshare.com)

Atheros CSI Tool

Overview (Cite this tool)

Channel State Information (CSI)

CSI in thoery. Channel state information (CSI) represents the coefficent of a wireless channel. In Wi-Fi systems using OFDM modulation, the CSI of every sub-carrier is a complex number, i.e., csi = a+bj, as shown in following figure. The CSI of a packet transmitted with M transmitting antennas, N receiving antennas, 20MHz channel bandwitdh, is a complex matrix of size M×N×56. If the bandwith is 40MHz, then the size of CSI matrix becomes M×N×114.

CSI from Wi-Fi NIC. Wi-Fi NIC use several bits to reprensent the value of a and b. For example, Atheros Wi-Fi NIC uses 10 bits to give the value of a and another 10 bits to describe b. (We note that other Wi-Fi NIC may give the CSI with different resolution, e.g., Intel 5300 Wi-Fi NIC uses 8 bits to describe the value of a and b.) The CSI obtained from Atheros Wi-Fi NIC are thus complex numbers whose imag and real part fall into the range of [-512, 512]. The right figure plots the distribution of four CSI matrix (1×56) we measured using Atheros QCA9558. During the measurement, the channel is stable. Theoretically, the CSI should be the same in the complex plane. The measurements data, however, exhibit the opposite patterns. Let's then take a closer look at the amplitude and phase of the measured CSI.

The following figure (a) and (b) depicts the amplitude and amplitude in dB level of four CSI. We see that the amplitudes are similar in shape with each other but with offsets with each other. Figure c plots the phase of four CSI. Similarly, the phase has obvious offset across CSIs. The phase offsets results in the rotation of CSI in the complex plane, as shown in above figure.

Atheros CSI tool

Atheros-CSI-Tool is an open source 802.11n measurement and experimentation tool. It enables extraction of detailed PHY wireless communication information from the Atheros WiFi NICs, including the Channel State Information (CSI), the received packet payload, and other additional information (the time stamp, the RSSI of each antenna, the data rate, etc.). Atheros-CSI-Tool is built on top of ath9k, which is an open source Linux kernel driver supporting Atheros 802.11n PCI/PCI-E chips, so theoretically this tool is supposed to be able to support all types of Atheros 802.11n WiFi chipsets. We have tested it on Atheros AR9580, AR9590, AR9344 and QCA9558. Atheros-CSI-Tool is open source and all functionalities are implemented in software without any modification to the firmware. Therefore, you are able to extend the functionalities of Atheros-CSI-Tool with your own codes under the GPL license. You are welcome to get back to us with new functionalities contributed to the tool.

Atheros-CSI-Tool works on various Linux distribution, e.g., Ubuntu, OpenWRT, Linino, and so on. Different Linux distribution works on different hardware. Ubuntu works for personal computers like laptops or desktops. OpenWRT works for embeded devices such as Wi-Fi routers. Linino works for IoT devices, such as Arduino YUN. We provides the source code for Ubuntu version and OpenWRT version of Atheros CSI tool. The installation instructions can be found from our WiKi. We also provide the PDF version of the installation guide for Ubuntu version and OpenWRT version .

SWAN: Stitched Wi-Fi ANtennas

Ar9380 Atheros Model

We build a general-purpose antenna extension solution with commodity Wi-Fi using our Atheros CSI tool. The proposed solution, SWAN, builds an array of stitched antennas extended from the radio chains of commodity Wi-Fi. SWAN has low hardware cost and provides easy-to-use interfaces embedded in the Linux kernel. Integrated with Atheros CSI tool, SWAN collects CSI from all the antennas in the array and group the CSI into a large matrix, which is further used for Wi-Fi sensing purposes such as estimating the AoA. The architecture of SWAN is shown in the following figure. We also build a prototype of SWAN using commodity devices, including Wi-Fi AP, Arduino and RF switches. More information about SWAN could be found from this page.

Users

The tool has been used by over 1100 registered users from top research institutes, universities, technology companies including MIT, Stanford, Princeton, Cambridge, TUBerlin, Tsinghua, Huawei, Samsung, etc. Below are the list of published papers from our users based on this tool.

OpenWRT version

[BuildSys]Soltanaghaei, Elahe and Sharma, Rahul Anand and Wang, Zehao and Chittilappilly, Adarsh and Luong, Anh and Giler, Eric and Hall, Katie and Elias, Steve and Rowe, Anthony
Robust and Practical WiFi Human Sensing Using On-device Learning with a Domain Adaptive Model, BuildSys 2020
[Sensors]Wenxu Wang, Damián Marelli, Minyue Fu
Fingerprinting-Based Indoor Localization Using Interpolated Preprocessed CSI Phases and Bayesian Tracking, Sensors 2020
[IJSEN]Mohammad Hadi Kefayati , Vahid Pourahmadi , and Hassan Aghaeinia
Wi2Vi: Generating Video Frames From WiFi CSI Samples, IEEE Sensors Journal 2020
[MSN] Si Huang, Dong Wang, Run Zhao and Qian Zhang
Wiga: A WiFi-Based Contactless Activity Sequence Recognition System Based on Deep Learning, MSN 2020
[JIoT] L. Wang, H. An, H. Zhu and W. Liu
MobiKey: Mobility-Based Secret Key Generation in Smart Home, IEEE Internet of Things Journal 2020
[Arxiv]Zhiping Jiang, Tom H. Luan, Han Hao, Jing Wang, Xincheng Ren, Kun Zhao, Wei Xi, Yueshen Xu, Rui Li
Eliminating the Barriers: Demystify Wi-Fi Baseband Design And Introduce PicoScenes Wi-Fi Sensing Platform,Arxiv 2020
[Arxiv]Paul Staat, Harald Elders-Boll, Markus Heinrichs, Rainer Kronberger, Christian Zenger, Christof Paar
Intelligent Reflecting Surface-Assisted Wireless Key Generation for Low-Entropy Environments, Arxiv 2020
[WiMob] Israel Elujide, Yonghe Liu
An Entropy-Based WLAN Channel Allocation using Channel State Information, WiMob 2020
[Arxiv]Yang Liu, Tiexing Wang, Yuexin Jiang, Biao Chen
Harvesting Ambient RF for Presence Detection Through Deep Learning , Arxiv 2020
[Arxiv] Jianfei Yang, Han Zou, Yuxun Zhou, Lihua Xie
Towards Stable and Comprehensive Domain Alignment: Max-Margin Domain-Adversarial Training, Arxiv 2020
[GLOBECOM] Pengming Hu, Weidong Yang, Xuyu Wang, and Shiwen Mao
MiFi: Device-free Wheat Mildew Detection Using Off-the-shelf WiFi Devices, GLOBECOM 2019
[MobiCom] Yaxiong Xie, Jie Xiong, Mo Li, and Kyle Jamieson
mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking, MobiCom 2019
[MobiCom]Jian Ding and Ranveer Chandra
Towards Low Cost Soil Sensing Using Wi-Fi, MobiCom 2019
[INFOCOM]Shuyu Shi, Yaxiong Xie, Mo Li, Alex X. Liu and Jun Zhao
Synthesizing Wider WiFi Bandwidth for Respiration Rate Monitoring in Dynamic Environments, INFOCOM 2019
[TMC] Yi-Jie Lin, Po-Hsuan Tseng, Yao-Chia Chan , Jie He, and Guan-Sian Wu
A Super-Resolution-Assisted Fingerprinting Method Based on Channel Impulse Response Measurement for Indoor Positioning,TMC 2019
[IEEE TWC]Navid Tadayon, Muhammed Tahsin Rahman, Shuo Han, Shahrokh Valaee, Wei Yu
Decimeter Ranging With Channel State Information, IEEE TWC 2019
[VTC] Simon Tewes, Alaa Alameer Ahmad, Jaber Kakar, Udaya Miriya Thanthrige, Stefan Roth, Aydin Sezgin
Ensemble-based Learning in Indoor Localization: A Hybrid Approach, VTC, 2019
[WCNC]Lingchao Guo, Xiangming Wen, Zhaoming Lu, Xinbin Shen, Zijun Ha
WiRoI : Spatial Region of Interest Human Sensing with Commodity WiFi, WCNC , 2019
[Electronics]Yong Lu,Shaohe Lv and Xiaodong Wang
Towards Location Independent Gesture Recognition with Commodity WiFi Devices Electronics, 2019
[EWSN] Zhiping Jiang, Xu Wang, Chen He, Rui Li
Demo: Enabling UWB Sensing Array on COTS Wi-Fi Platform, EWSN, 2019
[CVPR workshops]Han Zou, Jianfei Yang, Hari Prasanna Das, Huihan Liu, Yuxun Zhou, Costas J. Spanos
WiFi and Vision Multimodal Learning for Accurate and Robust Device-Free Human Activity RecognitionCVPR workshops, 2019
[Master Thesis]Abhinav Kumar
Leveraging Channel State Information From Cots WiFi Router to Detect Water Flow Pattern, Master Thesis 2019
[Journal on Communications] Xiaochao DANG, Yaning HUANG, Zhanjun HAO,Xiong SI
Passive indoor human daily behavior detection method based on channel state information Journal on Communications, 2019
[SAGE journal]Xiaochao Dang, Jiaju Ren, Zhanjun Hao, Yili Hei, Xuhao Tang and Yan Yan
A novel indoor localization method using passive phase difference fingerprinting based on channel state information SAGE journal, 2019
[IEEE Systems Journal ] Dongheng Zhang, Yang Hu, Yan Chen, and Bing Zeng
Calibrating Phase Offsets for Commodity WiFi, IEEE Systems Journal 2019
[Sensors]Jijun Zhao, Lishuang Liu, Zhongcheng Wei, Chunhua Zhang, Wei Wang and Yongjian Fan
R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi, Sensors 2019
[Mobile Networks and Applications]Bingxian Lu, Lei Wang, Jialin Liu, Wei Zhou, Linlin Guo, Myeong-Hun Jeong, Shaowen Wang3, Guangjie Han
LaSa: Location Aware Wireless Security Access Control for IoT Systems, Mobile Networks and Applications 2019
[Sensors] Xiaochao Dang, Xiong Si, Zhanjun Hao, and Yaning Huang
A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information, Sensors 2019
[Chinese Journal on Internet of Things]Xiaochao DANG, Xiong SI,Zhanjun HAO,Yaning HUANG,Yili HEI
Indoor localization method based on CSI in complex environment, Chinese Journal on Internet of Things 2019
[CWSN] Xiaochao Dang, Jiaju Ren, Zhanjun Hao, Yan Yan, Yili Hei
The Improvement of Indoor Localization Precision Through PCA-Based Channel Combination Method, 2018
[MobiCom] Xie, Yaxiong and Zhang, Yanbo and Liando, Jansen Christian and Li, Mo,
SWAN: Stitched Wi-Fi ANtennas, MobiCom, 2018
[AAAI] Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, Costas Spanos,
WiFi-based Human Identification via Convex Tensor Shapelet Learning, AAAI, 2018
[ICMLA] Han Zou, Jianfei Yang, Yuxun Zhou, and Costas J. Spanos
Joint Adversarial Domain Adaptation for Resilient WiFi-enabled Device-free Gesture Recognition, ICMLA 2018
[arXiv] Peter Hillyard, Anh Luong, Alemayehu Abrar, Neal Patwari, Krishna Sundar, Robert Farney, Jason Burch, Christina Porucznik, Sarah Pollard
Comparing Respiratory Monitoring Performance of Commercial Wireless Devices , arXiv 2018
[CCECE ]Bruno Soares da Silva, Gustavo Teodoro Laureano, Abdallah S. Abdallah, Kleber Vieira Cardoso
WiDMove: Sensing Movement Direction using IEEE 802.11n Interfaces, CCECE 2018
[ICCA] H. Zou, Y. Zhou, J. Yang, H. Jiang, L. Xie and C. J. Spanos,
WiFi-enabled Device-free Gesture Recognition for Smart Home Automation, IEEE ICCA, 2018
[ICC] H. Zou, Y. Zhou, J. Yang, H. Jiang, L. Xie and C. J. Spanos,
DeepSense: Device-Free Human Activity Recognition via Autoencoder Long-Term Recurrent Convolutional Network, IEEE ICC, 2018
[ICCCN] H. Zou, J. Yang, Y. Zhou, L. Xie and C. J. Spanos,
Robust WiFi-Enabled Device-Free Gesture Recognition via Unsupervised Adversarial Domain Adaptation, IEEE ICCCN, 2018
[Energy and Buildings] Zou, Han, Yuxun Zhou, Jianfei Yang, and Costas J. Spanos,
Towards occupant activity driven smart buildings via WiFi-enabled IoT devices and deep learning, Energy and Buildings, 2018
[Energy and Buildings] Zou, Han, Yuxun Zhou, Jianfei Yang, and Costas J. Spanos,
Device-Free Occupancy Detection and Crowd Counting in Smart Buildings with WiFi-enabled IoT., Energy and Buildings, 2018
[ICNC] R. Cwalinski and H. Koenig,
Identifying Malicious Traffic in Software-Defined Wireless Local Area Networks., IEEE ICNC, 2018
[WCNC] Jianfei Yang, Han Zou, Hao Jiang, and Lihua Xie,
Fine-grained adaptive location-independent activity recognition using commodity WiFi, IEEE WCNC, 2018
[JIOT] Jianfei Yang, Han Zou, Hao Jiang, and Lihua Xie,
Device-free Occupant Activity Sensing using WiFi-enabled IoT Devices for Smart Homes, IEEE Internet of Things Journal, 2018
[SIGCOMM] Zhenyu Song, Longfei Shangguan, Kyle Jamieson,
Wi-Fi Goes to Town: Rapid Picocell Switching for Wireless Transit Networks, ACM SIGCOMM, 2017
[ICMLA] H. Zou, Y. Zhou, J. Yang, W. Gu, L. Xie and C. Spanos,
Multiple Kernel Representation Learning for WiFi-Based Human Activity Recognition, IEEE ICMLA, 2017
[GLOBECOM] Zou, Han, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, and Costas Spanos,
Freecount: Device-free crowd counting with commodity WiFi., IEEE GLOBECOM 2017
[MobiCom] Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie, and Costas Spanos,
Poster: WiFi-based Device-Free Human Activity Recognition via Automatic Representation Learning, ACM MobiCom, 2017
[SBRC] da Silva, B. S., Laureano, G. T., & Cardoso, K. V.,
WiDMove-um sensor de movimento direcional baseado em perturbações do sinal eletromagnético de interfaces 802.11. SBRC (Vol. 36).
[SECON] Han Zou, Yuxun Zhou, Jianfei Yang, Weixi Gu, Lihua Xie and Costas Spanos,
FreeDetector: Device-Free Occupancy Detection with Commodity WiFi, IEEE SECON Workshops, 2017
[INFOCOM] Yaxiong Xie, Zhenjiang Li, Mo Li, Kyle Jamieson,
Augmenting Wide-band 802.11 Transmissions via Unequal Packet Bit Protection, IEEE INFOCOM, 2016
[MobiCom] Yaxiong Xie, Zhenjiang Li, Mo Li,
Precise Power Delay Profiling with Commodity WiFi, ACM MobiCom, 2015
[MobiCom] Zhenjiang Li, Yaxiong Xie, Mo Li, Kyle Jamieson,
Recitation: Rehearsing Wireless Packet Reception in Software, ACM MobiCom, 2015

Ubuntu version

[ICC]Qi Shi, Yangyu Liu, Shunqing Zhang, Shugong Xu, Shan Cao, Vincent Lau
Channel Estimation for WiFi Prototype Systems with Super-Resolution Image Recovery, ICC 2019
[SECON] Hua Xue, Jiadi Yu, Yanmin Zhu, Li Lu, Shiyou Qian, Minglu Li
WiZoom: Accurate Multipath Profiling using Commodity WiFi Devices with Limited Bandwidth, SECON 2019
[MobiCom] Chan, Justin and Wang, Anran and Iyer, Vikram and Gollakota, Shyamnath,
Surface MIMO: Using Conductive Surfaces For MIMO Between Small Devices, MobiCom, 2018
[MobiCom] H. Peter, A. Luong, A. Abrar, N. Patwari, K. Sundar, R. Farney, J. Burch, C. Porucznik, and S. Pollard,
Experience: Cross-Technology Radio Respiratory Monitoring Performance Study, MobiCom, 2018
[ICCCN] S. Tan, L. Zhang and J. Yang,
Sensing Fruit Ripeness Using Wireless Signals, ICCCN, 2018
[ Mobile Networks and Applications] B. Lu, L. Wang, J. Liu, W. Zhou, L. Guo, M. Jeong, S. Wang, and G. Han,
LaSa: Location Aware Wireless Security Access Control for IoT Systems, Mobile Networks and Applications, 2018
[EURASIP] Dang, Xiaochao, Yaning Huang, Zhanjun Hao, and Xiong Si,
PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi, EURASIP Journal on Wireless Communications and Networking, 2018
[ICNC] G. Wu and P. Tseng,
A Deep Neural Network-Based Indoor Positioning Method using Channel State Information, IEEE ICNC, 2018
[CCECE] B. Soaresda Silva, G. TeodoroLaureano, A. S. Abdallah and K. VieiraCardoso,
WiDMove: Sensing Movement Direction Using IEEE 802.11n Interfaces, IEEE CCECE, 2018
[TMC] H. Zhu, Y. Zhuo, Q. Liu and S. Chang,
π-Splicer: Perceiving Accurate CSI Phases with Commodity WiFi Devices, IEEE Trans. on Mobile Computing, 2018
[IJWIN] Duan, Shihong, Tianqing Yu, and Jie He,
WiDriver: Driver Activity Recognition System Based on WiFi CSI, International Journal of Wireless Information Networks, 2018
[GLOBECOM] A. Mukherjee, A. W. Garvin, S. E. Sanchez and Z. Zhang,
Experimental Evaluation of Time Bounded Anti-Spoofing (TBAS) in MIMO Systems, IEEE GLOBECOM, 2017
[GLOBECOM] A. Mukherjee and Z. Zhang,
Fast Compression of OFDM Channel State Information with Constant Frequency Sinusoidal Approximation, IEEE GLOBECOM, 2017
[WCSP] Jinsong Li, Yunzhou Li, and Xinsheng Ji,
A novel method of Wi-Fi indoor localization based on channel state information, IEEE WCSP, 2017
[arxiv] Jeong-Sik Choi, Woong-Hee Lee, Jae-Hyun Lee, Jong-Ho Lee and Seong-Cheol Kim,
Deep Learning Based NLOS Identification with Commodity WLAN Devices, arxiv, 2017
[INFOCOM] Yiwei Zhuo, Hongzi Zhu, Hua Xue, Shan Chang,
Perceiving accurate CSI phases with commodity WiFi devices, IEEE INFOCOM, 2017
[ICPADS] Yiwei Zhuo, Hongzi Zhu, Hua Xue,
Identifying a New Non-Linear CSI Phase Measurement Error with Commodity WiFi Devices, IEEE ICPADS, 2016

Ar9380 Atheros Wifi

Credits

Ar9380 Atheros Smart

Maintainer: Yaxiong Xie Author: Mo Li, Yaxiong Xie Collaborators: Zhenjiang Li, Kyle Jamieson