Opportunistic Channel Access Algorithm Based on Hidden Semi Markov Model for Cognitive Radio Networks

Future generation cellular networks highly depend on Cognitive radio due to its capability to handle the primary user channel utilization. However, channel sensing and allocation for secondary user are the major factors to be considered. Several research works have been carried out to deal with the sensing problem. But, most of the existing algorithms results in high computational complexity in real time implementation. In recent years, Hidden Markov Model(HMM) has been considered as a powerful statistical tool for modeling generative sequences that can be characterised by an underlying process generating an observable sequence algorithms. Thus, based on the motivation of the earlier works carried out in HMM, this paper proposed a probability based channel sensing algorithm. Hidden markov model is used for probability calculation of primary user state and the predicted channel is validated using the proposed quality estimation method. The performance metrics used to evaluate the proposed algorithm is linear mean square error value and the channel is estimated using different estimators. The comparison of the proposed algorithm with the existing algorithms and performance of the proposed algorithm is better than the other algorithms