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    Chapter Where are the Things of the Internet? Precise Time of Arrival Estimation for IoT Positioning

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    Author(s)
    Xu, Wen
    Laas, Tobias
    Dammann, Armin
    Language
    English
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    Abstract
    The question how a 5G communication system will look like has been addressed intensely in numerous research projects and in standardization bodies. In the massively connected world of the “Internet of Things” (IoT), it is getting more and more important to be aware of where all these “things” are located. Mobile radio-based technologies envisaged for a 5G system will play an essential role in providing high-accuracy positioning of the “things.” In this work, we will first address the fundamental Cramér-Rao lower bound (CRLB) of time of arrival (TOA) estimation in an orthogonal frequency-division multiplexing (OFDM)-based system (such as 4G and 5G) using the pilots. The achievable performance is compared with the 3GPP LTE and potential future 5G requirements. The Ziv-Zakai lower bound (ZZLB) is also considered for TOA estimation, as it is tighter than the CRLB for medium to low signal-to-noise ratios (SNRs). We show how to optimize the waveform in order to reduce the TOA estimation error. Then, we describe some practical low-complexity maximum likelihood (ML) methods for TOA estimation with enhanced first-arriving path detection. Simulation results show that such adaptive ML methods can in some cases (e.g., line of sight) achieve a performance close to the CRLB. Finally, we will briefly discuss cooperation-based positioning, which will become increasingly important for massively connected IoT.
    URI
    https://library.oapen.org/handle/20.500.12657/49304
    Keywords
    Cramér-Rao lower bound, Ziv-Zakai lower bound, time (difference) of arrival, radio-based positioning, cooperative positioning
    DOI
    10.5772/intechopen.78063
    Publisher
    InTechOpen
    Publisher website
    https://www.intechopen.com/
    Publication date and place
    2019
    Classification
    WAP (wireless) technology
    Rights
    https://creativecommons.org/licenses/by/3.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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