TR2020-054
Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach
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- "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
- @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 = {https://www.merl.com/publications/TR2020-054}
- }
,
- "Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: A Deep Learning Approach", IEEE Access, DOI: 10.1109/ACCESS.2020.2991129, April 2020.
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MERL Contacts:
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Research Areas:
Communications, Machine Learning, Optimization, Signal Processing
Abstract:
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. In this paper, we propose to use a mid grained intermediate-level channel measurement — spatial beam signal-to-noise ratios (SNRs) that are inherently available and defined in the IEEE 802.11ad/ay standards — to construct the fingerprinting database. These intermediate channel measurements are further utilized by a deep learning approach for multiple purposes: 1) location-only classification; 2) simultaneous locationand orientation classification; and 3) direct coordinate estimation. Furthermore, the effectiveness of the framework is thoroughly validated by an in-house experimental platform consisting of 3 access points using commercial-off-the-shelf millimeter-wave WiFi routers. The results show a 100% accuracy if the location is only interested, about 99% for simultaneous location-and orientations classification, and an averaged root mean-square error (RMSE) of 11.1 cm and an average median error of 9.5 cm for direct coordinate estimate, greater than 2-fold improvements over the RMSE of 28.7 cm and median error of 23.6 cm for RSSI-like single SNR-based localization
Related News & Events
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NEWS MERL Researchers gave a Tutorial Talk on Quantum Machine Learning for Sensing and Communications at IEEE GLOBECOM Date: December 8, 2022
MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal ProcessingBrief- On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).
The IEEE GLOBECOM is a highly anticipated event for researchers and industry professionals in the field of communications. Organized by the IEEE Communications Society, the flagship conference is known for its focus on driving innovation in all aspects of the field. Each year, over 3,000 scientific researchers submit proposals for program sessions at the annual conference. The theme of this year's conference was "Accelerating the Digital Transformation through Smart Communications," and featured a comprehensive technical program with 13 symposia, various tutorials and workshops.
- On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).
Related Research Highlights
Related Publications
- @inproceedings{Wang2020dec,
- author = {Wang, Pu and Koike-Akino, Toshiaki and Orlik, Philip V.},
- title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi: NLOS Propagation},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2020,
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/GLOBECOM42002.2020.9348144},
- issn = {2576-6813},
- isbn = {978-1-7281-8298-8},
- url = {https://www.merl.com/publications/TR2020-159}
- }
- @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 = {https://www.merl.com/publications/TR2019-141}
- }
- @inproceedings{Wang2019dec2,
- author = {Wang, Pu and Pajovic, Milutin and Koike-Akino, Toshiaki and Sun, Haijian and Orlik, Philip V.},
- title = {Fingerprinting-Based Indoor Localization with Commercial MMWave WiFi - Part II: Spatial Beam SNRs},
- booktitle = {IEEE Global Communications Conference (GLOBECOM)},
- year = 2019,
- month = dec,
- publisher = {IEEE},
- doi = {10.1109/GLOBECOM38437.2019.9014103},
- issn = {2576-6813},
- isbn = {978-1-7281-0962-6},
- url = {https://www.merl.com/publications/TR2019-138}
- }