认知无线网络中硬合并协同频谱感知的融合准则优化

频谱感知是认知无线电一项基础的任务。在认知无线网络中，多个次级用户可以协同工作，对主用户进行可靠感知。如何融合多个次级用户的感知信息是实施协同感知的关键。本文围绕集中式认知无线网络中的硬合并协同感知技术展开研究，讨论了常用的k-out-of-m融合准则。与OR准则（k=1）、Half-voting准则（k=m/2）和AND准则（k=m）等特例不同，本文考虑参数k任意取值的场景，从最小化贝叶斯代价的角度，推导出了最优k值的闭合表达式。仿真结果验证了该闭合公式的有效性，并显示最优k值随判决门限、频谱非空闲与空闲先验概率比、漏检与虚警影响因子比的增大而减小，且在信噪比场景中更具应用价值。 

Optimization of Fusion Rule for Cooperative Spectrum Sensing with Hard Combination in Cognitive Radio Networks

Spectrum sensing is a fundamental task for cognitive radio. In cognitive radio networks, multiple secondary users work cooperatively to perform reliable detection of the primary user. How to fuse the sensing information from different secondary users is its key component. This paper focused on the cooperative detection with hard combination in centralized secondary networks, and discussed the popular k-out-of-m fusion rule. Different from those special cases of OR rule (k=1), Half-voting rule (k=m/2) as well as AND rule (k=m), this paper considered the scenario that k was arbitrary, and deduced a closed-form expression for the optimal value of k from minimizing the Bayesian cost point of view. Simulation results verified the closed-form expression, and demonstrated that the optimal k decreased as decision threshold, priori probability ratio of spectrum unavailability to spectrum idleness as well as impact factor ratio of missed detection to false alarm increased, and was more valuable in the scenarios with lower signal to noise ratios.