摘要
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。
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.
出处
《信号处理》
CSCD
北大核心
2013年第10期1416-1422,共7页
Journal of Signal Processing
基金
国家自然科学基金(61201264
61102089)
国务院侨办基金(11QZR03)
华侨大学引进高层次人才启动基金(13BS101
12BS219)
关键词
认知无线网络
协同频谱感知
融合准则优化
贝叶斯代价
Cognitive Radio Networks
Cooperative Spectrum Sensing
Fusion Rule Optimization
Bayesian Cost
