Collaborative Spectrum Sensing via Online Estimation of Hidden Bivariate Markov Models
Collaborative Spectrum Sensing via Online Estimation of Hidden Bivariate Markov Models
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DOI：
10.1109/twc.2016.2558506
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发表时间：
2016-08
影响因子：
10.4
通讯作者：
Yuandao Sun;B. L. Mark;Y. Ephraim
中科院分区：
文献类型：
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作者：
Yuandao Sun;B. L. Mark;Y. Ephraim
协作频谱感知通过组合来自多个二级用户的频谱感知信息来利用多用户多样性，以做出有关频谱占用的联合决策。在硬融合方案中，每个二级用户对频谱占用做出硬决策，融合中心根据融合规则通过组合各个硬决策来做出最终决策。在软融合方案中，每个二级用户向融合中心提供信号功率测量，融合中心对所有观测值的集合进行进一步处理以做出最终决定。在本文中，我们提出了基于二级用户接收到的信号的在线隐二元马尔可夫链建模的硬和软融合协作频谱感知方案。与现有的协作感知方案相比，所提出的基于模型的方案不依赖于预先计算的阈值或权重，并且实现了优越的性能。隐二元马尔可夫模型的在线估计提供了可用于提高动态频谱访问性能的预测信息。数值结果证明了所提出的协作频谱感知方案的性能和通信开销权衡。
Collaborative spectrum sensing exploits multiuser diversity by combining spectrum sensing information from multiple secondary users to make joint decisions about spectrum occupancy. In hard fusion schemes, each secondary user makes a hard decision on spectrum occupancy and a fusion center makes a final decision by combining the individual hard decisions according to a fusion rule. In soft fusion schemes, each secondary user provides a signal power measurement to the fusion center, which performs further processing on the collection of all observations to make a final decision. In this paper, we propose hard and soft fusion collaborative spectrum sensing schemes based on the online hidden bivariate Markov chain modeling of the signals received by secondary users. Compared with prior collaborative sensing schemes, the proposed model-based schemes do not rely on precomputed thresholds or weights, and achieve superior performance. The online estimation of hidden bivariate Markov models provides predictive information that can be used to improve the performance of the dynamic spectrum access. Numerical results are presented to demonstrate the performance and communication overhead tradeoffs of the proposed collaborative spectrum sensing schemes.