An Efficient Channel Sensing Algorithm Based on Hidden Semi Markov Model and Channel Quality Predicition

Abstract

In recent years, Cognitive radio has become one of the most important emerging technologies to handle the primary user channel utilization in next generation cellular networks. The major issues in the future generation cellular networks are channel sensing and allocation for secondary user. Several optimization algorithms have been proposed in the literature for sensing the channel in future generation networks. Although, the existing algorithms provide good results, it has certain limitations such as high computational complexity in real time implementation. In order to overcome the limitation in existing algorithms and to obtain the efficient results, this study proposed a probability based channel sensing algorithm. Hidden Markov model is used as the probability calculation of primary user state and the predicted channel is validated using the proposed quality estimation method. The estimated channel is predicted using the probability of detection and probability of false alarm is used for validating the algorithms. The performance metrics used to evaluate the proposed algorithm is 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.