4. Conclusions

In this chapter, we have presented a per-frame decision-based cooperative spectrum sensing based on machine-learning classifier-based fusion approach. The simulation and numerical results have shown that the machine-learning classifier-based fusion algorithm performs same as conventional fusion rules in terms of sensing accuracy with less sensing time, overheads, and extra operations that limit achievable cooperative gain among cognitive radio users. In addition, we have also studied the problem of primary user channel state prediction in cognitive radio network and introduced Markov model and Markov Switching Model to solve this problem. We finally showed by the means of simulation that both hidden Markov model and Markov switching model perform very well in predicting the time series that capture the actual primary user channel state.
