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Compressed Spectrum Sensing in Cognitive Radio Network

Hashemi, Ali | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 44546 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Nasiri-Kenari, Masoumeh; Babayi-Zadeh, Masoud
  7. Abstract:
  8. In recent years, the Cognitive Radio Network has received significant attentions due to its high potential for better employment of the spectrum. One of the most important parts of this technology is the spectrum sensing that requires being fast and accurate. The conventional algorithms proposed so far encounter some fundamental challenges at low SNR regimes and in wideband sensing. On the other hand, the compressed sensing algorithms, which take advantages of the sparsity of the signal of interest and utilize measurements instead of the samples, can reduce the sampling rate and thus decrease the complexity associated with the wideband sensing. In this thesis, by exploiting the cyclostationary features, we propose a sensing algorithm based on the entropy of the cyclic spectrum of the received signal, which works quite well in low SNR regime. Then, we generalize the proposed scheme to the cooperative Multi-antenna systems and evaluate the performance of the sensing algorithm in the various channels. Our results demonstrate that the proposed algorithm outperforms the other well known schemes such as the energy and cyclostationary detectors, especially at the low SNR regime. Furthermore, by exploiting the compressed sensing algorithms and the sparsity feature of the received signal spectrum in wideband spectrum sensing, we propose a proper sensing algorithm which quite well overcomes wideband sensing challenges. Finally, by exploiting both concepts, namely the cyclostationary concept and the sparsity feature, we propose an efficient entropy based sensing algorithm to deal with the two problems mentioned, and evaluate its performance. The results show its superiority compared to the other traditional approaches
  9. Keywords:
  10. Spectrometry ; Compressive Sensing ; Low SNR Detection ; Entropy ; Cyclostationary ; Cognitive Radio

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