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.

吞吐量的提高是认知无线电网络中的关键问题之一。为了提高认知无线电网络的吞吐量，提出了一种基于隐马尔可夫模型的信道状态预测方案。与仅依靠信道感测的常规方案不同，所提出的方案提供了能够与信道感测一起预测主要用户状态的附加优点，从而提高了频谱利用率和系统吞吐量。为了在网络频谱感知过程中提高可靠性，提出了一种基于动态阈值的能量检测技术，该技术考虑了噪声的不确定性和目标检测的概率。提出了基于动态阈值的能量检测技术与现有的基于固定阈值和基于动态阈值的检测方案的性能对比分析。分析表明，在检测概率和虚警概率上，提出的检测方案比现有检测方案表现更好。结果表明，基于隐马尔可夫模型的预测方案使认知无线电网络的吞吐量比现有方案更为有效。