Fast Spectrum Sensing: A Combination of Channel Correlation and Markov Model

To utilize the valuable spectrum resources efficiently, cognitive radio is proposed, in which spectrum sensing plays an essential role. In this paper, we propose a novel spectrum sensing scheme to reduce the consumption of wide-band spectrum sensing. Firstly, all potential channels are clustered into highly correlated groups. In addition to the greedy clustering algorithm (GC) discussed in our previous work, a minimum entropy increment clustering algorithm (MEI) is also presented. In each group, only one channel needs to be detected directly, while the states of other channels are estimated according to historical states and their correlation with the detected channel. Through detecting several channels, all the channel states can be obtained, thus the time and energy consumption of sensing can be reduced. The influence of historical states on current state is modeled via Markov process while the dependence of estimated channels (EC) on detected channel (DC) is formulated by maximum a posteriori (MAP) principle. As Markov process and channel correlation may provide conflicting results, minimum entropy principle is adopted to unify their results. Tested with real-world measurement data and compared with an existing uncertainty based update (UU) algorithm, our scheme is proved to improve prediction accuracy considerably.