Hidden Markov and Markov switching model for primary user channel state prediction in cognitive radio

Abstract

The most important challenge of the spectrum sensing in cognitive radio (CR) is to find a way to share the licensed spectrum without interfering with the licensed user transmission. Predicting the near future of the licensed or primary user (PU) channel state can solve this problem. Many studies have investigated the primary user channel state prediction in recent literature, in this study we introduce new approach for PU channel state prediction in time domain based on hidden Markov model (HMM) and Markov switching model (MSM). In our new approach we use a time series to capture the primary user channel state detection sequence (PU channel “idle” or “occupied”). Then, we fed this time series as an observation sequence into HMM and MSM algorithm to predict the switching time between the two states, “idle and occupied” before it happens so that the secondary user (SU) can adjust its transmission strategies accordingly. The experimental results show that all HMM and MSM perform very well for PU channel state prediction. It has also shown in a simulation comparison between HMM and MSM algorithm that MSM algorithm can preform without need for training process and provide a smoother prediction with low computational complexity than HMM.