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Spectrum Sensing

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Spectrum Sensing Marjan Hadian Outline Cognitive Cycle Enrgy Detection Matched filter cyclostationary feature detector Interference Temperature Spectral Estimation ... – PowerPoint PPT presentation

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Title: Spectrum Sensing


1
  • Spectrum Sensing
  • Marjan Hadian

2
Outline
  • Cognitive Cycle
  • Enrgy Detection
  • Matched filter
  • cyclostationary feature detector
  • Interference Temperature
  • Spectral Estimation
  • Hidden node problem
  • Cooperative detection
  • detection methods
  • log-likelihood combining
  • weighted gain combining

3
Cognitive Cycle
  • Mitola calls cognitive radio cycle cognitive
    radio continually observes the environment,
    orients itself, creates plans, decides, and then
    acts

4
  • Spectrum Sensing
  • A cognitive radio monitors the available spectral
    bands,captures their information, and detects the
    spectrum holes.
  • frequencies usage.
  • mode identification.

5
(No Transcript)
6
  • Enrgy Detection
  • Where T calculated from
  • most important problem of this, is which one
    called SNR wall. This problem comes from
    uncertainty.
  • SNR wall is a minimum SNR below which signal
    cannot be detected and formulas no longer holds

7
  • Matched filter
  • it maximizes SNR. For implementation of matched
    filter cognitive radio has a priori knowledge of
    modulation type, pulse shaping.
  • cyclostationary feature detector
  • The main advantage of the spectral correlation
    function is that it differentiates the noise
    energy from modulated signal energy.

8
Interference Temperature
  • As additional interfering signals appear the
    noise floor increases and then unlicensed devices
    could use that particular band as long as their
    energy is under mention noise floor

  • where
    Joules per Kelvin

9
Spectral Estimation
  • parametric spectral estimation
  • Non-parametric spectral estimation
  • Periodogram Spectral Estimator (PSE)
  • Blackman-Tukey Spectral Estimator (BTSE)
  • Minimum Variance Spectral Estimator (MVSE)
  • Multi taper Method (MTM)
  • Filter Bank Spectral Estimator (FBSE)

10
Hidden node problem
  • Traditional detection problem (a) Receiver
    uncertainty and (b) shadowing uncertainty5

11
Cooperative detection
  • prevent the hidden terminal problem also mitigate
    the multipath fading and shadowing effect
  • Information from multiple SUs are incorporated
    for primary user detection.
  • Implementation
  • Centralized manner
  • distributed manner

12
How SU provide its observation to other nodes?!
  • This transmission can overlap to the air
    interfaces already present in the environment, so
    it can change the nature of observations and make
    new problems. In order to solve this problem
    several solutions suggested
  • two distinct networks are deployed separately
  • the sensor network for cooperative spectrum
    sensing and the operational network for data
    transmission. This method implemented in central
    manner5
  • Sharing the analysis model in an off-line method
    when in the environment no SUs is observing the
    radio scene1

13
  • Without consideration of exchanging method, we
    assume that the observation of SU i is due to its
    position and to the state of radio source, but
    not to the observation of other SU j and .Thus we
    assume that, independent measurements for each
    SUs is presented either in a centralized or
    distributed manner. Now we review two detection
    methods
  • log-likelihood combining
  • weighted gain combining

14
  • log-likelihood combining
  • Assume that is the vector of SUs
    energy detector output, then we can write
    likelihood ratio test(LRT) as
  • weighted gain combining
  • where
    and

15
  • Thanks for your attention.
  • Questions?
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