SDR receiver using commodity wifi via physical-layer signal reconstruction

With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz - the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RF fingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.

[1]  Yantian Hou,et al.  Cooperative cross-technology interference mitigation for heterogeneous multi-hop networks , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[2]  Marwan Krunz,et al.  Cross-Technology Interference Mitigation in Body Area Networks: An Optimization Approach , 2015, IEEE Transactions on Vehicular Technology.

[3]  Onur Avci,et al.  1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.

[4]  David Wetherall,et al.  Tool release: gathering 802.11n traces with channel state information , 2011, CCRV.

[5]  Matthias Hollick,et al.  Shadow Wi-Fi: Teaching Smartphones to Transmit Raw Signals and to Extract Channel State Information to Implement Practical Covert Channels over Wi-Fi , 2018, MobiSys.

[6]  K. P. Soman,et al.  Low cost digital transceiver design for Software Defined Radio using RTL-SDR , 2013, 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s).

[7]  Richard Han,et al.  Investigating Cost-effective RF-based Detection of Drones , 2016, DroNet@MobiSys.

[8]  Pietro Siciliano,et al.  In-home hierarchical posture classification with a time-of-flight 3D sensor. , 2014, Gait & posture.

[9]  Onur Avci,et al.  1-D Convolutional Neural Networks for Signal Processing Applications , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Revathy Narayanan,et al.  Revisiting Software Defined Radios in the IoT Era , 2018, HotNets.

[11]  Yasamin Mostofi,et al.  Magnitude-Based Angle-of-Arrival Estimation, Localization, and Target Tracking , 2018, 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[12]  Tien Dang Vo-Huu,et al.  Fingerprinting Wi-Fi Devices Using Software Defined Radios , 2016, WISEC.

[13]  Kamin Whitehouse,et al.  Multipath Triangulation: Decimeter-level WiFi Localization and Orientation with a Single Unaided Receiver , 2018, MobiSys.

[14]  Matthew Peacock,et al.  Towards detection and control of civilian unmanned aerial vehicles , 2013 .

[15]  Jiangchuan Liu,et al.  RoArray: Towards More Robust Indoor Localization Using Sparse Recovery with Commodity WiFi , 2019, IEEE Transactions on Mobile Computing.

[16]  Wenchao Jiang,et al.  C-Morse: Cross-technology communication with transparent Morse coding , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[17]  Tian He,et al.  FreeBee: Cross-technology Communication via Free Side-channel , 2015, MobiCom.

[18]  Xiuzhen Guo,et al.  LEGO-Fi: Transmitter-Transparent CTC with Cross-Demapping , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.

[19]  Wenchao Jiang,et al.  Transparent cross-technology communication over data traffic , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[20]  Srdjan Capkun,et al.  Implications of radio fingerprinting on the security of sensor networks , 2007, 2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007.

[21]  Jie Xiong,et al.  mD-Track: Leveraging Multi-Dimensionality for Passive Indoor Wi-Fi Tracking , 2018, MobiCom.

[22]  Yuan He,et al.  WiZig: Cross-technology energy communication over a noisy channel , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[23]  Joshua R. Smith,et al.  Inter-Technology Backscatter: Towards Internet Connectivity for Implanted Devices , 2016, SIGCOMM.

[24]  Kameswari Chebrolu,et al.  Esense: communication through energy sensing , 2009, MobiCom '09.

[25]  Zhijun Li,et al.  WEBee: Physical-Layer Cross-Technology Communication via Emulation , 2017, MobiCom.

[26]  Zhijun Li,et al.  LongBee: Enabling Long-Range Cross-Technology Communication , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[27]  Xiang-Yang Li,et al.  ZIMO: building cross-technology MIMO to harmonize zigbee smog with WiFi flash without intervention , 2013, MobiCom.

[28]  Jae-Hyun Lee,et al.  Deep Learning Based NLOS Identification With Commodity WLAN Devices , 2017, IEEE Transactions on Vehicular Technology.

[29]  Zhijun Li,et al.  Explicit Channel Coordination via Cross-technology Communication , 2018, MobiSys.

[30]  Richard Han,et al.  Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication , 2017, MobiSys.

[31]  Marco Gruteser,et al.  Wireless device identification with radiometric signatures , 2008, MobiCom '08.

[32]  Jeffrey H. Reed,et al.  Specific Emitter Identification for Cognitive Radio with Application to IEEE 802.11 , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[33]  Zhijun Li,et al.  BlueBee: a 10,000x Faster Cross-Technology Communication via PHY Emulation , 2017, SenSys.

[34]  Yongrui Chen,et al.  TwinBee: Reliable Physical-Layer Cross-Technology Communication with Symbol-Level Coding , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[35]  Han Zou,et al.  WiFi-Based Human Identification via Convex Tensor Shapelet Learning , 2018, AAAI.

[36]  Vincent Lenders,et al.  A software-defined sensor architecture for large-scale wideband spectrum monitoring , 2015, IPSN.

[37]  Mirza Mansoor Baig,et al.  Mobile healthcare applications: system design review, critical issues and challenges , 2014, Australasian Physical & Engineering Sciences in Medicine.

[38]  Ali Najafi,et al.  TinySDR: Low-Power SDR Platform for Over-the-Air Programmable IoT Testbeds , 2020, NSDI.

[39]  David Blaauw,et al.  A low power software-defined-radio baseband processor for the Internet of Things , 2016, 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[40]  Parth H. Pathak,et al.  WiWho: WiFi-Based Person Identification in Smart Spaces , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[41]  Srinivasan Seshan,et al.  Clearing the RF smog: making 802.11n robust to cross-technology interference , 2011, SIGCOMM.

[42]  Xiang-Yang Li,et al.  Taming Cross-Technology Interference for Wi-Fi and ZigBee Coexistence Networks , 2016, IEEE Transactions on Mobile Computing.

[43]  Yuan He,et al.  StripComm: Interference-Resilient Cross-Technology Communication in Coexisting Environments , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[44]  Ramachandran Ramjee,et al.  WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots , 2011, MobiCom.

[45]  Shuai Wang,et al.  Symbol-Level Cross-Technology Communication via Payload Encoding , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[46]  James Gross,et al.  CrossZig: Combating Cross-Technology Interference in Low-Power Wireless Networks , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[47]  Jeyanthi Hall,et al.  Detection of rogue devices in wireless networks , 2006 .

[48]  Adam Wolisz,et al.  Enabling Cross-technology Communication between LTE Unlicensed and WiFi , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.

[49]  Jie Yang,et al.  E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures , 2014, MobiCom.

[50]  Linsong Cheng,et al.  Walls Have No Ears: A Non-Intrusive WiFi-Based User Identification System for Mobile Devices , 2019, IEEE/ACM Transactions on Networking.

[51]  Tao Wang,et al.  The Tick Programmable Low-Latency SDR System , 2018, GETMBL.

[52]  Qun Li,et al.  HoWiES: A holistic approach to ZigBee assisted WiFi energy savings in mobile devices , 2013, 2013 Proceedings IEEE INFOCOM.

[53]  Mo Li,et al.  Precise Power Delay Profiling with Commodity Wi-Fi , 2015, IEEE Transactions on Mobile Computing.

[54]  Yuan He,et al.  ZIGFI: Harnessing Channel State Information for Cross-Technology Communication , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.