Dissertation > Excellent graduate degree dissertation topics show

The Wideband Spectrum Detection Based on Compressed Sensing

Author: ChenXiaoFang
Tutor: ZhuCuiTao
School: Central South University for Nationalities
Course: Communication and Information System
Keywords: Cognitive Radio Network spectrum detection compressive sensing sparse bayesian learning
CLC: TN925
Type: Master's thesis
Year: 2012
Downloads: 65
Quote: 0
Read: Download Dissertation

Abstract


Cognitive radio is a new technology which is able to automatically sense theenvironment and detect the spectrum holes,which significantly improve the spectrumutilization. Cognitive radio is one of the hot research field of wirelesscommunications.A novel collaborative spectrum detection based on backtracking blind sparsitymatching pursuit algorithm is proposed for sparse signals with unkown sparsitywhich the cognitive radio users received.The algorithm could control the rapidityand accuracy of spectrum detection by choosing the candidate setautomatically,adopting staged changing process which estimates sparsity andbacktracking mechanism which obtains the global optimal support sets; andchoosing the optimal collaborative users although SNR estimate.The experimentalresults show that the algorithm is superior to other algorithms in the same testconditions,and detection probability about the collaboration detection of selectiveobject is increased by about25%than nonselective object.For the spectrum detection, the key job of the cognitive radio users is not toreconstruct the signal entirely,but is to estimate the presence or absence of theprimary users.So in the paper we adopt the fast sparse bayesian learning to completespectrum detection tasks.The unknown variables are endowed with the hyperparameters about following the certain prior conditional distribution. we update thehyper parameters and select the basis function through the fast algorithm,the numberof basis function is increasing from one to another until it has obtioned the relevantvector,so observation matrix contains only basis functions which exist in the currentmodel. The algorithm eliminated the complex matrix inversion process andimproved the speed.Meanwhile,the posterior distribution of the unknown signal obeythe Student-distribution,which is more sparse than the Gaussian distribution. Theprimary users’ information described with three parameters about the spectrumlocation,the mean and covariance in the posterior distribution.These three parameterswere fused to complete the spectrum detection. The simulation results show that themethod saves the resources and reduces the computational complexity withoutreconstructing the signal.

Related Dissertations

  1. Research on Spectrum Detection Technology from Compressed Sensing,TN925
  2. The Key Technology of Wideband Compressed Spectrum Sensing in Cognitive Radio Network,TN925
  3. Dynamic Channelization Techniques for Broadband Signal Reconnaissance,TN971.1
  4. Research on Spectrum Detection Algorithm of Cognitive Radio System,TN925
  5. Design and Research of Brain-computer Interface System Based on Steady-state Visual Evoked Potential,TP11
  6. Research on Blind Spectrum Sensing Algorithms in Cognitive Radio,TN925
  7. The Research of Spectrum Detection Based on Signal Sample Autocorrelation,TN911.6
  8. Research on Spectrum Detection Methods of the Active Anti-interference Receiver,TN851
  9. The Mathematical Model of Interaction among the Flora in Row Milk and the Detection of Milk Quality,O242.1
  10. Research of Spread Spectrum Steganalysis Method,TP309
  11. Performance-throughput for Spectrum Sensing Based on Relay Cooperation,TN925
  12. Research on Key Techniques of Cognitive Ultra Wideband,TN925
  13. Fission multi-parameter measuring system neutron detector calibration,O572.212
  14. The Simulation and Analysis of Cooperative Spectrum Sensing in Cognitive Radio,TN92
  15. The Research on Spectrum Sensing in Wireless Network,TN98
  16. The Spectrum Allocation in Cognitive Mobile Communication System Based on Spectrum Holes Classification,TN929.5
  17. AM, PSK and FM signal detection in cognitive radio spectrum,TN92
  18. A Study of Spectrum Detection Technology Based on Cognitive Radio Network,TN98
  19. Research on Wide-band Spectrum Compressive Sensing Algorithm in Cognitive Radio Network,TN92
  20. Applications of Filter Banks Based Multicarrier Techniques in Cognitive Radio System,TN925
  21. Research of Uncertain Data Processing Method Based on Sparse Bayesian Learning,TP274

CLC: > Industrial Technology > Radio electronics, telecommunications technology > Wireless communications > Radio relay communications,microwave communications
© 2012 www.DissertationTopic.Net  Mobile