skip to main content
10.1145/3384419.3430723acmconferencesArticle/Chapter ViewAbstractPublication PagessensysConference Proceedingsconference-collections
research-article

X-MIMO: cross-technology multi-user MIMO

Authors Info & Claims
Published:16 November 2020Publication History

ABSTRACT

Multi-user MIMO (MU-MIMO) is a widely-known, fundamental technique to significantly improve the spectrum efficiency. While there is a great demand for spectrum efficiency and massive scalability under explosively increasing IoT, hardware limitations make it particularly challenging for the mechanism to be transferred to the IoT (e.g., ZigBee) domain. This paper presents X-MIMO, a zero-cost, software-only cross-technology MU-MIMO for commodity ZigBee. As the first work to shed the light on the feasibility of MU-MIMO on commodity IoT, X-MIMO leverages on cross-technology communication (CTC) to turn the pervasively-deployed WiFi AP into MU-MIMO transmitter, delivering different packets to multiple ZigBees in parallel. X-MIMO uniquely exploits WiFi CSI to extract the accurate physical layer signal of the ZigBee packet and the WiFi-ZigBee channel coefficient. Rigorous derivation shows that X-MIMO's precoding is inherently immune to the uncertainties of the commodity devices, making X-MIMO highly reliable in practice. Lastly, spectrum-efficient emulation is proposed to maximize the spectrum reuse. We implement and comprehensively evaluate the performance of X-MIMO on commodity devices (Atheros AR9334 WiFi NIC and TelosB CC2420) as well as on USRP B210 for in-depth analysis. Results reveal that X-MIMO achieves 495 Kbps with <1% symbol error rate (SER) and 704.24 Kbps with 6.1% SER for two and three streams, respectively. Near-linear increase of the throughput effectively demonstrates the feasibility of X-MIMO.

References

  1. CC2530 Datasheet. https://www.ti.com/product/CC2530.Google ScholarGoogle Scholar
  2. IEEE 802.11 Protocol. http://standards.ieee.org/getieee802/download/802.11-2012.pdf.Google ScholarGoogle Scholar
  3. IEEE 802.15.4 Protocol. http://standards.ieee.org/getieee802/download/802.15.4-2015.pdf.Google ScholarGoogle Scholar
  4. Implementation of IEEE 802.15.4 Protocol on USRP. https://github.com/bastibl/gr-ieee802-15-4.Google ScholarGoogle Scholar
  5. WirelessHART, an Industrial Wireless Technology. https://www.emerson.com/en-us/expertise/automation/industrial-internet-things/pervasive-sensing-solutions/wireless-technology.Google ScholarGoogle Scholar
  6. CC2420 Data Sheet. http://www.ti.com/lit/ds/symlink/cc2420.pdf, 2003.Google ScholarGoogle Scholar
  7. Isa standard, wireless systems for industrial automation: Process control and related applications. ISA-100.11 a-2009, 2009.Google ScholarGoogle Scholar
  8. Ieee standard for local and metropolitan area networks - part 15.4: Low-rate wireless personal area networks (lr-wpans) amendment 4: Alternative physical layer extension to support medical body area network (mban) services operating in the 2360 mhz - 2400 mhz band. IEEE Std 802.15.4j-2013 (Amendment to IEEE Std 802.15.4-2011 as amended by IEEE Std 802.15.4e-2012, IEEE Std 802.15.4f-2012, and IEEE Std 802.15.4g-2012), pages 1--24, 2013.Google ScholarGoogle Scholar
  9. B. Al Nahas, S. Duquennoy, and O. Landsiedel. Concurrent transmissions for multi-hop bluetooth 5. In EWSN, pages 130--141, 2019.Google ScholarGoogle Scholar
  10. J. Beysens, A. Galisteo, Q. Wang, D. Juara, D. Giustiniano, and S. Pollin. Densevlc: A cell-free massive mimo system with distributed leds. In Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies, pages 320--332, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Bhartia, Y.-C. Chen, L. Qiu, and G. P. Nychis. Embracing distributed mimo in wireless meshnetworks. In 2015 IEEE 23rd International Conference on Network Protocols (ICNP), pages 66--77. IEEE, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  12. M. Brachmann, O. Landsiedel, and S. Santini. Concurrent transmissions for communication protocols in the internet of things. In 2016 IEEE 41st Conference on Local Computer Networks (LCN), pages 406--414. IEEE, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  13. J. Chan, A. Wang, V. Iyer, and S. Gollakota. Surface mimo: Using conductive surfaces for mimo between small devices. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pages 3--18, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. Chauhan, Y. Hu, S. Seneviratne, A. Misra, A. Seneviratne, and Y. Lee. Breathprint: Breathing acoustics-based user authentication. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pages 278--291, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. B. Chen, V. Yenamandra, and K. Srinivasan. Tracking keystrokes using wireless signals. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services, pages 31--44, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Z. Chen, G. Zhu, S. Wang, Y. Xu, J. Xiong, J. Zhao, J. Luo, and X. Wang. m3: Multipath assisted wi-fi localization with a single access point. IEEE Transactions on Mobile Computing, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Ding and R. Chandra. Towards low cost soil sensing using wi-fi. In The 25th Annual International Conference on Mobile Computing and Networking, MobiCom '19, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarGoogle Scholar
  18. Y. Du, E. Aryafar, P. Cui, J. Camp, and M. Chiang. Samu: Design and implementation of selectivity-aware mu-mimo for wideband wifi. In 2015 12th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pages 229--237. IEEE, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. C. Gao, M. Hessar, K. Chintalapudi, and B. Priyantha. Blind distributed mu-mimo for iot networking over vhf narrowband spectrum. In The 25th Annual International Conference on Mobile Computing and Networking, pages 1--17, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. K. Geissdoerfer, R. Jurdak, B. Kusy, and M. Zimmerling. Getting more out of energy-harvesting systems: Energy management under time-varying utility with preact. In Proceedings of the 18th International Conference on Information Processing in Sensor Networks, pages 109--120, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. X. Guo, Y. He, J. Zhang, and H. Jiang. Wide: Physical-level ctc via digital emulation. In 2019 18th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), pages 49--60. IEEE, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. X. Guo, Y. He, X. Zheng, L. Yu, and O. Gnawali. Zigfi: Harnessing channel state information for cross-technology communication. IEEE/ACM Transactions on Networking, 28(1):301--311, 2020.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. X. Guo, Y. He, X. Zheng, Z. Yu, and Y. Liu. Lego-fi: Transmitter-transparent ctc with cross-demapping. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pages 2125--2133. IEEE, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. E. Hamed, H. Rahul, and B. Partov. Chorus: truly distributed distributed-mimo. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, pages 461--475, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. M. Hammouda, R. Zheng, and T. N. Davidson. Full-duplex spectrum sensing and access in cognitive radio networks with unknown primary user activities. In 2016 IEEE International Conference on Communications (ICC), pages 1--6. IEEE, 2016.Google ScholarGoogle ScholarCross RefCross Ref
  26. P. Hillyard, A. Luong, A. S. Abrar, N. Patwari, K. Sundar, R. Farney, J. Burch, C. Porucznik, and S. H. Pollard. Experience: Cross-technology radio respiratory monitoring performance study. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pages 487--496, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. C. Hua, H. Yu, R. Zheng, J. Li, and R. Ni. Online packet dispatching for delay optimal concurrent transmissions in heterogeneous multi-rat networks. IEEE Transactions on Wireless Communications, 15(7):5076--5086, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. C. Husmann, G. Georgis, K. Nikitopoulos, and K. Jamieson. Flexcore: massively parallel and flexible processing for large mimo access points. In 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI), pages 197--211, 2017.Google ScholarGoogle Scholar
  29. V. Iyer, V. Talla, B. Kellogg, S. Gollakota, and J. Smith. Inter-technology backscatter: Towards internet connectivity for implanted devices. In Proceedings of the 2016 ACM SIGCOMM Conference, pages 356--369, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. W. Jiang, S. M. Kim, Z. Li, and T. He. Achieving receiver-side cross-technology communication with cross-decoding. In Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, pages 639--652, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. W. Jiang, Z. Yin, R. Liu, Z. Li, S. M. Kim, and T. He. Bluebee: a 10,000 x faster cross-technology communication via phy emulation. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, pages 1--13, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. K. Jin, S. Fang, C. Peng, Z. Teng, X. Mao, L. Zhang, and X. Li. Vivisnoop: Someone is snooping your typing without seeing it! In 2017 IEEE Conference on Communications and Network Security (CNS), pages 1--9. IEEE, 2017.Google ScholarGoogle Scholar
  33. V. Jungnickel, K. Manolakis, W. Zirwas, B. Panzner, V. Braun, M. Lossow, M. Sternad, R. Apelfröjd, and T. Svensson. The role of small cells, coordinated multipoint, and massive mimo in 5g. IEEE communications magazine, 52(5):44--51, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  34. S. M. Kim and T. He. Freebee: Cross-technology communication via free side-channel. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 317--330. ACM, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. H. Kong, L. Lu, J. Yu, Y. Chen, L. Kong, and M. Li. Fingerpass: Finger gesture-based continuous user authentication for smart homes using commodity wifi. In Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pages 201--210, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Z. Li and T. He. Webee: Physical-layer cross-technology communication via emulation. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pages 2--14, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. K. C.-J. Lin, S. Gollakota, and D. Katabi. Random access heterogeneous mimo networks. ACM SIGCOMM Computer Communication Review, 41(4):146--157, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. R. Liu, Z. Yin, W. Jiang, and T. He. Lte2b: time-domain cross-technology emulation under lte constraints. In Proceedings of the 17th Conference on Embedded Networked Sensor Systems, pages 179--191, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. H. Lou, M. Ghosh, P. Xia, and R. Olesen. A comparison of implicit and explicit channel feedback methods for mu-mimo wlan systems. In 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 419--424. IEEE, 2013.Google ScholarGoogle Scholar
  40. A. R. Moghimi, H.-M. Tsai, C. U. Saraydar, and O. K. Tonguz. Characterizing intra-car wireless channels. IEEE Transactions on Vehicular Technology, 58(9):5299--5305, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  41. H. S. Rahul, S. Kumar, and D. Katabi. Jmb: scaling wireless capacity with user demands. ACM SIGCOMM Computer Communication Review, 42(4):235--246, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. M. Sha, G. Xing, G. Zhou, S. Liu, and X. Wang. C-mac: Model-driven concurrent medium access control for wireless sensor networks. In IEEE INFOCOM 2009, pages 1845--1853. IEEE, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  43. L. Shangguan, Z. Zhou, and K. Jamieson. Enabling gesture-based interactions with objects. In Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pages 239--251, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. C. Shepard, H. Yu, N. Anand, E. Li, T. Marzetta, R. Yang, and L. Zhong. Argos: Practical many-antenna base stations. In Proceedings of the 18th annual international conference on Mobile computing and networking, pages 53--64, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. J. Song, S. Han, A. Mok, D. Chen, M. Lucas, M. Nixon, and W. Pratt. Wirelesshart: Applying wireless technology in real-time industrial process control. In 2008 IEEE Real-Time and Embedded Technology and Applications Symposium, pages 377--386. IEEE, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. P. Sparks. The route to a trillion devices. 2017.Google ScholarGoogle Scholar
  47. Q. H. Spencer, C. B. Peel, A. L. Swindlehurst, and M. Haardt. An introduction to the multi-user mimo downlink. IEEE communications Magazine, 42(10):60--67, 2004.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. S. Sur, I. Pefkianakis, X. Zhang, and K.-H. Kim. Practical mu-mimo user selection on 802.11 ac commodity networks. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pages 122--134, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. H.-M. Tsai, O. K. Tonguz, C. Saraydar, T. Talty, M. Ames, and A. Macdonald. Zigbee-based intra-car wireless sensor networks: a case study. IEEE Wireless Communications, 14(6):67--77, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. G. Wang, C. Qian, K. Cui, H. Ding, H. Cai, W. Xi, J. Han, and J. Zhao. A (near) zero-cost and universal method to combat multipaths for rfid sensing. In 2019 IEEE 27th International Conference on Network Protocols (ICNP), pages 1--4. IEEE, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  51. J. Wang, L. Chang, O. Abari, and S. Keshav. Are rfid sensing systems ready for the real world? In Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, MobiSys '19, pages 366--377, New York, NY, USA, 2019. Association for Computing Machinery.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. S. Wang, S. M. Kim, and T. He. Symbol-level cross-technology communication via payload encoding. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pages 500--510. IEEE, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  53. W. Wang, X. Zheng, Y. He, and X. Guo. Adacomm: Tracing channel dynamics for reliable cross-technology communication. In 2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), pages 1--9. IEEE, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. X. Xie, E. Chai, X. Zhang, K. Sundaresan, A. Khojastepour, and S. Rangarajan. Hekaton: Efficient and practical large-scale mimo. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 304--316, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. X. Xie, X. Zhang, and K. Sundaresan. Adaptive feedback compression for mimo networks. In Proceedings of the 19th annual international conference on Mobile computing & networking, pages 477--488, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Y. Xie, Z. Li, and M. Li. Precise power delay profiling with commodity wi-fi. IEEE Transactions on Mobile Computing, 18(6):1342--1355, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. M. Yang, L.-X. Chuo, K. Suri, L. Liu, H. Zheng, and H.-S. Kim. ilps: Local positioning system with simultaneous localization and wireless communication. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pages 379--387. IEEE, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Q. Yang, X. Li, H. Yao, J. Fang, K. Tan, W. Hu, J. Zhang, and Y. Zhang. Bigstation: enabling scalable real-time signal processing in large mu-mimo systems. ACM SIGCOMM Computer Communication Review, 43(4):399--410, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. S. Yao, Y. Zhao, H. Shao, S. Liu, D. Liu, L. Su, and T. Abdelzaher. Fastdeepiot: Towards understanding and optimizing neural network execution time on mobile and embedded devices. In Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, pages 278--291, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. S. Yao, Y. Zhao, A. Zhang, L. Su, and T. Abdelzaher. Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework. In Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, pages 1--14, 2017.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. D. Zhang, J. Wang, J. Jang, J. Zhang, and S. Kumar. On the feasibility of wi-fi based material sensing. In The 25th Annual International Conference on Mobile Computing and Networking, pages 1--16, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  62. J. Zhang, X. Guo, H. Jiang, X. Zheng, and Y. He. Link quality estimation of cross-technology communication.Google ScholarGoogle Scholar
  63. X. Zhang, D. Yang, L. Shen, X. Chang, J. Huang, and G. Xing. Real-time power profiling of narrowband internet of things networks. In Proceedings of the ACM SIGCOMM 2019 Conference Posters and Demos, pages 90--92, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  64. M. Zhao, F. Adib, and D. Katabi. Emotion recognition using wireless signals. In Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking, pages 95--108, 2016.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. X. Zheng, Y. He, and X. Guo. Stripcomm: Interference-resilient cross-technology communication in coexisting environments. In IEEE INFOCOM 2018-IEEE Conference on Computer Communications, pages 171--179. IEEE, 2018.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. R. Zhou, Y. Xiong, G. Xing, L. Sun, and J. Ma. Zifi: wireless lan discovery via zigbee interference signatures. In Proceedings of the sixteenth annual international conference on Mobile computing and networking, pages 49--60, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. M. Zimmerling, W. Dargie, and J. M. Reason. Energy-efficient routing in linear wireless sensor networks. In 2007 IEEE International Conference on Mobile Adhoc and Sensor Systems, pages 1--3. IEEE, 2007.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. X-MIMO: cross-technology multi-user MIMO

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems
      November 2020
      852 pages
      ISBN:9781450375900
      DOI:10.1145/3384419

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 16 November 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate174of867submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader