Lowering the SNR-Wall for Energy Detection Using Crosscorrelation

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    Abstract

    Abstract Spectrum sensing is a key enabler of cognitive radio but generally suffers from what is called a signal-to-noise ratio (SNR) wall, i.e., a minimum SNR below which it is impossible to reliably detect a signal. For energy detection, which has the advantage of not requiring knowledge of the signal, the SNR wall is caused by uncertainty in the noise level. Cross-correlation has been suggested as a possible means to obtain higher sensitivity but has received little attention in the context of noise uncertainty. The idea of cross-correlation is to have two receive paths, where each path independently processes the signal before they are combined, such that the noise added to the input signal at the individual paths is largely uncorrelated. In this paper, we mathematically quantify the SNR wall for cross-correlation, showing that it linearly scales with the amount of noise correlation. This lower noise correlation results in higher sensitivity, which is significantly better than that for autocorrelation. Equations that can be used to estimate the benefit over autocorrelation and the measurement time for a required probability of detection and false alarm are derived.
    Original languageEnglish
    Pages (from-to)3748-3757
    Number of pages2
    JournalIEEE transactions on vehicular technology
    Volume60
    Issue number8
    DOIs
    Publication statusPublished - 1 Oct 2011

    Fingerprint

    Energy Detection
    Cross-correlation
    Signal to noise ratio
    Autocorrelation
    Cognitive radio
    Time measurement
    Path
    Uncertainty
    Probability of Detection
    Cognitive Radio
    False Alarm
    Quantify
    Sensing
    Linearly

    Keywords

    • noise uncertainty
    • Dynamic spectrum access
    • IR-78308
    • Crosscorrelation
    • Spectrum Sensing
    • METIS-279167
    • energy detection
    • Cognitive radio (CR)
    • signal-to-noise (SNR) wall
    • radiometer
    • EWI-20417

    Cite this

    @article{5636d946b2644320a44f89340cf5afd2,
    title = "Lowering the SNR-Wall for Energy Detection Using Crosscorrelation",
    abstract = "Abstract Spectrum sensing is a key enabler of cognitive radio but generally suffers from what is called a signal-to-noise ratio (SNR) wall, i.e., a minimum SNR below which it is impossible to reliably detect a signal. For energy detection, which has the advantage of not requiring knowledge of the signal, the SNR wall is caused by uncertainty in the noise level. Cross-correlation has been suggested as a possible means to obtain higher sensitivity but has received little attention in the context of noise uncertainty. The idea of cross-correlation is to have two receive paths, where each path independently processes the signal before they are combined, such that the noise added to the input signal at the individual paths is largely uncorrelated. In this paper, we mathematically quantify the SNR wall for cross-correlation, showing that it linearly scales with the amount of noise correlation. This lower noise correlation results in higher sensitivity, which is significantly better than that for autocorrelation. Equations that can be used to estimate the benefit over autocorrelation and the measurement time for a required probability of detection and false alarm are derived.",
    keywords = "noise uncertainty, Dynamic spectrum access, IR-78308, Crosscorrelation, Spectrum Sensing, METIS-279167, energy detection, Cognitive radio (CR), signal-to-noise (SNR) wall, radiometer, EWI-20417",
    author = "{Oude Alink}, M.S. and Kokkeler, {Andre B.J.} and Klumperink, {Eric A.M.} and Smit, {Gerardus Johannes Maria} and Bram Nauta",
    year = "2011",
    month = "10",
    day = "1",
    doi = "10.1109/TVT.2011.2165569",
    language = "English",
    volume = "60",
    pages = "3748--3757",
    journal = "IEEE transactions on vehicular technology",
    issn = "0018-9545",
    publisher = "IEEE",
    number = "8",

    }

    TY - JOUR

    T1 - Lowering the SNR-Wall for Energy Detection Using Crosscorrelation

    AU - Oude Alink, M.S.

    AU - Kokkeler, Andre B.J.

    AU - Klumperink, Eric A.M.

    AU - Smit, Gerardus Johannes Maria

    AU - Nauta, Bram

    PY - 2011/10/1

    Y1 - 2011/10/1

    N2 - Abstract Spectrum sensing is a key enabler of cognitive radio but generally suffers from what is called a signal-to-noise ratio (SNR) wall, i.e., a minimum SNR below which it is impossible to reliably detect a signal. For energy detection, which has the advantage of not requiring knowledge of the signal, the SNR wall is caused by uncertainty in the noise level. Cross-correlation has been suggested as a possible means to obtain higher sensitivity but has received little attention in the context of noise uncertainty. The idea of cross-correlation is to have two receive paths, where each path independently processes the signal before they are combined, such that the noise added to the input signal at the individual paths is largely uncorrelated. In this paper, we mathematically quantify the SNR wall for cross-correlation, showing that it linearly scales with the amount of noise correlation. This lower noise correlation results in higher sensitivity, which is significantly better than that for autocorrelation. Equations that can be used to estimate the benefit over autocorrelation and the measurement time for a required probability of detection and false alarm are derived.

    AB - Abstract Spectrum sensing is a key enabler of cognitive radio but generally suffers from what is called a signal-to-noise ratio (SNR) wall, i.e., a minimum SNR below which it is impossible to reliably detect a signal. For energy detection, which has the advantage of not requiring knowledge of the signal, the SNR wall is caused by uncertainty in the noise level. Cross-correlation has been suggested as a possible means to obtain higher sensitivity but has received little attention in the context of noise uncertainty. The idea of cross-correlation is to have two receive paths, where each path independently processes the signal before they are combined, such that the noise added to the input signal at the individual paths is largely uncorrelated. In this paper, we mathematically quantify the SNR wall for cross-correlation, showing that it linearly scales with the amount of noise correlation. This lower noise correlation results in higher sensitivity, which is significantly better than that for autocorrelation. Equations that can be used to estimate the benefit over autocorrelation and the measurement time for a required probability of detection and false alarm are derived.

    KW - noise uncertainty

    KW - Dynamic spectrum access

    KW - IR-78308

    KW - Crosscorrelation

    KW - Spectrum Sensing

    KW - METIS-279167

    KW - energy detection

    KW - Cognitive radio (CR)

    KW - signal-to-noise (SNR) wall

    KW - radiometer

    KW - EWI-20417

    U2 - 10.1109/TVT.2011.2165569

    DO - 10.1109/TVT.2011.2165569

    M3 - Article

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    SP - 3748

    EP - 3757

    JO - IEEE transactions on vehicular technology

    JF - IEEE transactions on vehicular technology

    SN - 0018-9545

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    ER -