Prediction of channel state for cognitive radio using higher-order hidden Markov model

Spectrum sensing detects the availability of the radio frequency spectrum, which is essential and vital to cognitive radio. Traditional techniques for spectrum sensing fail to take the latency between spectrum sensing and data transmission into consideration. However, such latency does exist in hardware implementation. Prediction can be utilized to diminish the negative effect of such latency. In this paper, this latency is illustrated, and an approach for prediction of channel state using higher-order hidden Markov model (HMM) is proposed. The predicted channel states are output together with corresponding likelihood probabilities that are helpful to subsequent decision making. Wi-Fi signals have been recorded using a latest advanced ultra-performance digital phosphor oscilloscope (DPO), which are employed to evaluate the performance of the proposed approach. Experimental results show that the proposed approach for prediction of channel state is effective. The proposed approach for prediction of channel state can be used together with traditional spectrum sensing techniques for spectrum sensing with the latency taken into consideration. And it can also be utilized to provide predictive information to upper-level modules of cognitive radio.