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Towards Low-Cost, Ubiquitous High-Time Resolution Sensing for Terrestrial Spectrum

Published: 10 August 2020 Publication History
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  • Abstract

    Spectrum utilization has become increasingly fragmented and diverse, with millions of devices supporting multiple protocols and use cases. A smartphone today uses LTE, Bluetooth, and Wi-Fi simultaneously, spanning multiple GHz of spectrum. In such an environment, comprehensive knowledge of spectrum usage can help in ushering in better networked system design, unified control, and provide new ways of understanding cyber-physical interactions. A versatile spectrum sensor that can monitor all utilized bands quickly and at low cost is a crucial enabler for such applications. Such a sensor is difficult to realize due to the vast size of the terrestrial spectrum and the necessity to keep up with fleeting wireless communication signals in innumerable bands. Additionally, it needs to be inexpensive and suitable for mass deployment. We introduce a new paradigm, in which the spectrum sensor can "sweep" over a wide band of spectrum quickly and extract useful information, such as occupancy and protocol type. The system, dubbed SweepSense, is prototyped on inexpensive hardware and opens up opportunities for new applications that were previously intractable, even with expensive solutions.

    References

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    U.S. Government. CFR title 47 section 96.67 Environmental Sensing Capability.
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    A. P. Goodson. A multi-function, broad band, high dynamic range RF receiver. Technical report, OneRadio, 2017.
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    Anritsu. MS2840A Spectrum Analyzer. https:// www.anritsu.com/en-us/test-measurement/ products/ms2840a
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    Roundup of Software Defined Radios- rtl-sdr. com [online] Available: https://www.rtl-sdr.com/ roundup-software-defined-radios/
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    N200/N210 - Ettus Knowledge Base, 2020, [Online] Available: https://kb.ettus.com/N200/ N210
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    L. Shi, P. Bahl, and D. Katabi. 2015. Beyond sensing: Multi-GHz realtime spectrum analytics. In Proc. Symposium on Networked Systems Design and Implementation (NSDI).
    [7]
    H. Hassanieh, L. Shi, O. Abari, E. Hamed, and D. Katabi. 2014. GHz-Wide sensing and decoding using the sparse fourier transform. In Proc. IEEE Conference on Computer Communications (INFOCOM).
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    W.A. Gardner, A. Napolitano, and L. Paura (2006). Cyclostationarity: Half a century of research. Signal Processing, 86(4), 639--697.
    [9]
    Y. Guddeti, R. Subbaraman, M. Khazraee, A. Schulman, and D. Bharadia. Sweepsense: Sensing 5 ghz in 5 milliseconds with low-cost radios. 2019. Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI '19), 317--330.

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    • (2021)Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural NetworksSensors10.3390/s2107241421:7(2414)Online publication date: 1-Apr-2021

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    Published In

    cover image GetMobile: Mobile Computing and Communications
    GetMobile: Mobile Computing and Communications  Volume 24, Issue 1
    March 2020
    38 pages
    ISSN:2375-0529
    EISSN:2375-0537
    DOI:10.1145/3417084
    Issue’s Table of Contents
    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.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 August 2020
    Published in SIGMOBILE-GETMOBILE Volume 24, Issue 1

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    • (2021)Robust Computationally-Efficient Wireless Emitter Classification Using Autoencoders and Convolutional Neural NetworksSensors10.3390/s2107241421:7(2414)Online publication date: 1-Apr-2021

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