Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part II: Spatial Beam SNRs

Existing fingerprint-based indoor localization uses either fine-grained channel state information (CSI) from the physical layer or coarse-grained received signal strength indicator (RSSI) measurements from the MAC layer. In this paper, we propose to use an intermediate channel measurement — spatial beam signal-to-noise ratios (SNRs) that are inherently available during the beam training phase as defined in the IEEE 802.11ad standard — to construct the feature space for location-and orientation-dependent fingerprinting database. We build a 60- GHz experimental platform consisting of three access points and one client using commercial-off-the-shelf routers and collect realworld beam SNR measurements in an office environment during regular office hours. Both position/orientation classification and coordinate estimation are considered using classic machine learning approaches. Comprehensive performance evaluation using real-world beam SNRs demonstrates that the classification accuracy is 99.8% if the location is only interested, while the accuracy is 98.6% for simultaneous position-and-orientations classification. Direct coordinate estimation gives an average root-mean-square error of 17.52 cm and 95% of all coordinate estimates are less than 26.90 cm away from corresponding true locations. This concept directly applies to other mmWave band (e.g., 5G) devices where beam training is also required.


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    •  NEWS   MERL Scientists Presenting 11 Papers at IEEE Global Communications Conference (GLOBECOM) 2019
      Date: December 9, 2019 - December 13, 2019
      Where: Waikoloa, Hawaii, USA
      MERL Contacts: Jianlin Guo; Kyeong Jin (K.J.) Kim; Toshiaki Koike-Akino; Rui Ma; Philip Orlik; Pu (Perry) Wang
      Research Areas: Communications, Computer Vision, Machine Learning, Signal Processing, Information Security
      • MERL Signal Processing scientists and collaborators will be presenting 11 papers at the IEEE Global Communications Conference (GLOBECOM) 2019, which is being held in Waikoloa, Hawaii from December 9-13, 2019. Topics to be presented include recent advances in power amplifier, MIMO algorithms, WiFi sensing, video casting, visible light communications, user authentication, vehicular communications, secrecy, and relay systems, including sophisticated machine learning applications. A number of these papers are a result of successful collaboration between MERL and world-leading Universities including: Osaka University, University of New South Wales, Oxford University, Princeton University, South China University of Technology, Massachusetts Institute of Technology and Aalborg University.

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  • Related Publications

  •  Koike-Akino, T., Wang, P., Pajovic, M., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach", IEEE Access, DOI: 10.1109/ACCESS.2020.2991129, April 2020.
    BibTeX TR2020-054 PDF Data
    • @article{Koike-Akino2020apr,
    • author = {Koike-Akino, Toshiaki and Wang, Pu and Pajovic, Milutin and Sun, Haijian and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach},
    • journal = {IEEE Access},
    • year = 2020,
    • month = apr,
    • doi = {10.1109/ACCESS.2020.2991129},
    • issn = {2169-3536},
    • url = {}
    • }
  •  Pajovic, M., Wang, P., Koike-Akino, T., Sun, H., Orlik, P.V., "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part I: RSS and Beam Indices", IEEE Global Communications Conference (GLOBECOM), DOI: 10.1109/GLOBECOM38437.2019.9013466, December 2019.
    BibTeX TR2019-141 PDF Data
    • @inproceedings{Pajovic2019dec,
    • author = {Pajovic, Milutin and Wang, Pu and Koike-Akino, Toshiaki and Sun, Haijian and Orlik, Philip V.},
    • title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part I: RSS and Beam Indices},
    • booktitle = {IEEE Global Communications Conference (GLOBECOM)},
    • year = 2019,
    • month = dec,
    • publisher = {IEEE},
    • doi = {10.1109/GLOBECOM38437.2019.9013466},
    • issn = {2576-6813},
    • isbn = {978-1-7281-0962-6},
    • url = {}
    • }