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Title: Jamming a terahertz wireless link
Abstract

As the demand for bandwidth in wireless communication increases, carrier frequencies will reach the terahertz (THz) regime. One of the common preconceived notions is that, at these high frequencies, signals can radiate with high directivity which inherently provides more secure channels. Here, we describe the first study of the vulnerability of these directional links to jamming, in which we identify several features that are distinct from the usual considerations of jamming at low frequencies. We show that the receiver’s use of an envelope detector provides the jammer with the ability to thwart active attempts to adapt to their attack. In addition, a jammer can exploit the broadband nature of typical receivers to implement a beat jamming attack, which allows them to optimize the efficacy of the interference even if their broadcast is detuned from the frequency of the intended link. Our work quantifies the increasing susceptibility of broadband receivers to jamming, revealing previously unidentified vulnerabilities which must be considered in the development of future wireless systems operating above 100 GHz.

 
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Award ID(s):
1954780 1923733 1923782
NSF-PAR ID:
10381711
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
Nature Communications
Volume:
13
Issue:
1
ISSN:
2041-1723
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
    Contact: krd26@drexel.edu

    ---------------------------------------------------------------------------------------------

    Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

    --------------------------------

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    ---------------------------------------------------------------------------------------------

    All data was collected using the SDR implementation shown here: https://github.com/mainland/dragonradio/tree/iet-paper. Particularly for antenna state selection, the files developed for this paper are located in 'dragonradio/scripts/:'

    • 'ModeSelect.py': class used to defined the antenna state selection algorithm
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    Authors: Marko Jacovic, Xaime Rivas Rey, Geoffrey Mainland, Kapil R. Dandekar
    Contact: krd26@drexel.edu

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    Top-level directories and content will be described below. Detailed descriptions of experiments performed are provided in the paper.

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    classifier_training: files used for training classifiers that are integrated into SDR platform

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    ray_tracing_emulation: contains files related to Drexel area, Art Museum, and UAV Drexel area validation RTE studies.

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