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Cooperative Spectrum Sensing with Data Mining of Multiple Users’ Historical Sensing Data

Cooperative Spectrum Sensing with Data Mining of
Multiple Users’ Historical Sensing Data
ABSTRACT:
                           The current cooperative spectrum sensing still can not effectively exploit the temporal correlations among sensing data, especially the correlations between the current sensing data and the historical data.
      This paper uses sticky hierarchical Dirichlet process-hidden Markov model to exploit the historical sensing data of multiple users, and classifies the historical sensing data into groups according to their latent spectrum states.
       The proposed spectrum sensing algorithm can fuse the historical sensing data into prior knowledge, which can be used to improve the accuracy in spectrum decision.
       Furthermore, a rejection process is proposed to filter out some sensing data with high uncertainty in classification, which guarantees the effectiveness of historical sensing data.

EXIXTING SYSTEM:
                 Furthermore, the  sequential spectrum sensing data are temporally correlated, which means their spectrum states may keep consistent in high probability.
      The historical sensing data will be classified according to their statistical property, and some historical sensing data that can not be classified clearly will be rejected through rejection process.
      Due to the uncertainty of spectrum sensing data, the rejection process is proposed to remove uncorrelated spectrum sensing data, in order to ensure the effectiveness of Bayesian inference.
      Historical sensing data are fused into prior knowledge using sticky HDP-HMM, where temporal correlation and statistical correlation are exploited, in order to reduce the uncertainty of current spectrum sensing and improve the accuracy of spectrum decision.
PROPOSED SYSTEM:
                 In this paper, a novel cooperative spectrum sensing algorithm has been proposed, where the sticky HDP-HMM model is adopted to exploit the historical sensing data.
      The historical sensing data has been refined through rejection process, in order to filter out some sensing data with high uncertainty.
      In the proposed cooperative spectrum sensing algorithm, the CRF-LC model could achieve the exploitation of the temporal correlations among sensing data, and thus the spectrum decision performance can be improved.
      The proposed algorithm has more than 10%, 60%, 5% detection probability improvement under false alarm probability 0.2, compared with centralized spectrum sensing algorithm, Consensus spectrum sensing algorithm and DP-based spectrum sensing algorithm, respectively.
HARDWARE REQUIREMENTS:
      Processor             -        Pentium –III
       Speed                  -        1.1 Ghz
       RAM                   -        256 MB(min)
       Hard Disk           -         20 GB
       Key Board           -        Standard Windows                                                          Keyboard
      Mouse                  -        Two or Three Button Mouse
      Monitor                -        SVGA
SOFTWARE REQUIREMENTS:
                Tool                    -        MATLAB R2012
      Operating system    -       Windows Xp, 7
REFERENCES:
          H. Ning, H. Liu, J. Ma, L.T. Yang, Y. Wan, X. Ye, and R.Huang, “From Internet to Smart World,” IEEE Access, vol.3, pp. 1994-1999, Aug. 2015.
      C. Thomes, “The National Broadband Plan: Connecting America,” Government information quarterly, vol. 28,  no. 3, pp. 435-436, Jul. 2011.
      J. Mitola III, “Cognitive Radio for Flexible Mobile Multimedia Communications,” Mobile Networks and Applications, vol. 6, no. 5, pp. 435-441, Sep. 2006.








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