Primary User Channel State Prediction Based on Markov Switching Model

The most important challenge of the spectrum sensing is to find a way to share the licensed spectrum without interfering with the licensed users transmission. Therefore, predicting the licensed or primary users (PU) channel occupancy status has been investigated extensively in recent years, this study introduce a novel approach for predicting the PU channel state based on Markov model. In this approach we model the detected primary user channel state, which can be represented by two states; PU channel idle or occupied as a time series changing switching over the time between two Gaussian distribution according to the detection sequence. Then we fed this time series into the Markov model to predict these changes before they happen so that the secondary user (SU) can adjust their transmission strategies accordingly .The experimental results show the efficiency of the new approach for predicting the PU channel occupancy status. KEYWORD: Channel state prediction; Channel occupancy status; Markov model; Maximum likelihood estimation; Primary users