Cooperative Spectrum Sensing With Data Mining of Multiple Users' Historical Sensing Data

Under the case of exponentially growth of wireless services and the scarcity of spectrum resources, cognitive radio (CR) has been proposed to access licensed channels opportunistically, and thus improve spectrum utilization. In CR devices, accurate spectrum sensing is the prerequisite for opportunistic access. The current cooperative spectrum sensing still cannot 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. The simulation results show that the proposed algorithm performs the best, compared with other three typical cooperative spectrum sensing algorithms, in terms of detection probability and false alarm probability. Specifically, when the false alarm probability is 0.2, the proposed algorithm has more than 10% and 60% detection probability improvement under channel signal-to-noise ratio as 0 and -5 dB, respectively.