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Spectrum Sensing in Cognitive Radios Using Compressive Sensing and Random Sampling

Dezfouli, Milad | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 46895 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Marvasti, Farrokh
  7. Abstract:
  8. Cognitive radio (CR) can successfully deal with the growing demand and scarcity of the wireless spectrum. To exploit limited spectrum efficiently, CR technology allows unlicensed users to access licensed spectrum bands. Since licensed users have priorities to use the bands, the unlicensed users need to continuously monitor the licensed users activities to avoid interference and collisions. How to obtain reliable results of the licensed users activities is the main task for spectrum sensing. Based on the sensing results, the unlicensed users should adapt their transmit powers and access strategies to protect the licensed communications. The requirement naturally presents challenges to the implementation of CR. one of the important challenges is the sampling costs and limitations. Compressive Spectrum Sensing is being used for reducing this sampling costs. compressive sensing reconstruction methods such as BP, BPDN, LASSO using convex optimization for solving the compressive sensing problem. but in real time applications like spectrum sensing that we need to reconstruct the original signal in a very short time and this algorithms can’t operate well. greedy algorithms like OMP have less simulation time but performance degradation. our proposed algorithms have good performance and also have short simulation time. in this thesis we reconstruct the spectrum by using random sampling with IMAT method. IMAT is an iterative method with adaptive thresholding. Our proposed methods are presented in three parts. in first part by the use of information about boundaries of frequency channels and the block-sparsity of spectrum in the frequency domain we introduce a method called B-IMATCS that operates better than previous similar methods. our method is superior in both performance and simulation time. in second part cyclo-stationary detector was used for spectrum sensing. most of signals that use in communications are cyclo-stationary signals because of their natural periodicity. on the other hand white Gaussian noise is stationary process. with use of this property signal and noise could be distinguished in cyclic spectral density domain and this let us to achieve high spectrum sensing performance even in low SNRs. by given random samples of cyclo-stationary signal we reconstruct the cyclic spectral density function by IMAT algorithm. and by proposing a technique reduce the complexity from O(N4) to O(N2 log(N)) order. and finally in third part for cooperative spectrum sensing propose S-IMAT method and shows its better performance than S-OMP method
  9. Keywords:
  10. Cognitive Radio ; Compressive Sensing ; Spectrum Sensing ; Random Sampling ; Cyclostationary ; Block Sparse Signal ; Centralized Cooperative Sensing

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