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					Spectrum Sensing

     Marjan Hadian




                     1
                         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

                                       2
                  Cognitive Cycle

Mitola calls cognitive radio cycle: cognitive radio continually
   observes the environment, orients itself, creates plans,
   decides, and then acts




                                                                  3
Spectrum Sensing:
A cognitive radio monitors the available spectral
   bands,captures their information, and detects the
   spectrum holes.

• frequencies usage.
• mode identification.




                                                       4
5
• 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




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• 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.



                                                        7
        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



                                                                     8
             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)




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                  Hidden node problem




Traditional detection problem: (a) Receiver uncertainty and (b) shadowing
   uncertainty[5]



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         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



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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 manner[5]


     Sharing the analysis model in an off-line method when in the
      environment no SUs is observing the radio scene[1]



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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




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• 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




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Thanks for your attention.
       Questions?




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