Dynamic Threshold Based Throughput Enhancement in Cognitive Radio Network Using Hidden Markov Model with State Prediction

Abstract

The enhancement of throughput is one of the key issues in the cognitive radio network. In this paper, a channel status prediction scheme based on hidden Markov model is proposed for enhancing the throughput of cognitive radio network. Unlike the conventional scheme which relies on channel sensing alone, the proposed scheme provides an additional advantage of being able to predict the primary user's state along with channel sensing, thus increasing spectrum utilization and system throughput. For achieving enhanced reliability in spectrum sensing process of the network, a dynamic threshold based energy detection technique considering noise uncertainty and target detection probabilities is proposed. Comparative analyses of the performance of the dynamic threshold based energy detection technique with that of existing fixed and dynamic threshold based detection schemes are presented. The analyses reveal that the proposed detection scheme performs better than the existing detection schemes with regard to probability of detection and probability of false alarm. It is shown that the hidden Markov model-based prediction scheme makes the cognitive radio network more efficient in terms of throughput than existing schemes.