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Spectrum Sensing in Cognitive Radio Networks

Taherpour, Abbas | 2009

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 39489 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nasiri Kenari, Masoumeh
  7. Abstract:
  8. In this thesis, we consider the problem of spectrum sensing in cognitive radio networks. First, the collaborative energy detectors based spectrum sensing are investigated in the case of known noise variance for two models of primary user (PU) signal, i.e. random and unknown deterministic signals. Since the derived optimum collaborative energy detector requires the signal-to-noise ratio (SNR) of secondary users (SU) and it has complex structure, the generalized likelihood ratio (GLR) detector is proposed for both models of PU signal which leads to the same decision rules for both models. Simulation results show that the performance of the proposed GLR detector is near to that of optimal detector even in the presence of quantization noise. Then, we consider spectrum sensing of a wideband frequency range in the case of unknown noise variance. To this end, we divide the frequency band into multiple subbands. We assume that at least a minimum given number of subbands are vacant of PUs. The spectrum sensing under above assumption leads to interrelated hypothesis testing problems. We derive the GLR detector for this case to identify possible spectrum holes. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch. In the next part, the spectrum sensing by using multiple antenna is studied. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the GLR detectors under these circumstances. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given noise variance. In the last part, we propose a three-stage recursive PU activity detection algorithm for a wide frequency band which updates spectrum sensing parameters. We employ a Markov model (MM) with two states per frequency subband representing the presence and absence of a PU at that subband. The proposed PU activity detector estimates the probabilities of PUs being present in different subbands, recursively. Our simulation results show that the proposed algorithm always performs better than the energy detector, and despite its simple implementation, has the same performance as the computationally complex cyclostationarity feature detector, for practical values of the SNR
  9. Keywords:
  10. Cognitive Radio ; Markov Model ; Spectrum Sensing ; Multiple Antenna System ; Unknown Noise Variance ; Energy Detector

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