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Demo: Meta2Locate: Meta Surface Enabled Indoor Localization in Dynamic Environments

Published: 16 October 2023 Publication History

Abstract

Received signal strength (RSS) fingerprint map is one of the most widely-used indoor localization approaches, but it often relies on multiple access points (AP) for data collection and suffers from frequent data updates due to dynamic wireless environments. In this work, we implement a reconfigurable-intelligent-surface (RIS) assisted indoor localization system named Meta2Locate to tackle the above issues using only one AP. In the proposed system, we deploy our self-designed RIS at 5.5GHz in an indoor environment, which can customize the propagation channels between the AP and the target. For the changing propagation environment, we design a mean maximum discrepancy weighted meta-learning approach to train a model that maps the RSS fingerprint to the location of the user, and it only needs a few data for the model update.

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References

[1]
Qinpei Luo and Boya Di. 2023. Meta Learning for Meta-Surface: A Fast Beamforming Method for RIS-Assisted Communications Adapting to Dynamic Environments. In IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 1--2.
[2]
Haobo Zhang, Hongliang Zhang, Boya Di, Kaigui Bian, Zhu Han, Chenren Xu, Daqing Zhang, and Lingyang Song. 2021. RSS Fingerprinting Based Multi-user Outdoor Localization Using Reconfigurable Intelligent Surfaces. In 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT). 167--172.

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  • (2025)Positioning in 5G Networks: Emerging Techniques, Use Cases, and ChallengesIEEE Internet of Things Journal10.1109/JIOT.2024.348782212:2(1408-1427)Online publication date: 15-Jan-2025

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    cover image ACM Conferences
    MobiHoc '23: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
    October 2023
    621 pages
    ISBN:9781450399265
    DOI:10.1145/3565287
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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    New York, NY, United States

    Publication History

    Published: 16 October 2023

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    Author Tags

    1. indoor localization
    2. meta-learning
    3. reconfigurable intelligent surface

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    • (2025)Positioning in 5G Networks: Emerging Techniques, Use Cases, and ChallengesIEEE Internet of Things Journal10.1109/JIOT.2024.348782212:2(1408-1427)Online publication date: 15-Jan-2025

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