Performance of a Hidden Markov channel occupancy model for cognitive radio

This paper investigates the effect that various training algorithms have on the performance of a primary user (PU) channel occupancy model for cognitive radio. The model assumes that PU channel occupancy can be described as a binary process. A two state Hidden Markov Model (HMM) has thus been chosen and it is shown that the performance of the model is influenced by the algorithm employed for training the model. Traditional training algorithms are compared to certain evolutionary based training algorithms in terms of the resulting prediction accuracy and convergence time achieved. The performance of the model is important since it provides secondary users (SU) with a basis upon which channel switching and future channel allocation may be performed. Further simulation results illustrate the positive effect that our model has on channel switching under both heavy and light traffic density conditions.