基于线性加权数据融合的协作频谱感知优化

被引：3

作者：

刘全 ^{[1
,2
]}

高俊 ^{[2
]}

郭云玮 ^{[2
]}

刘思洋 ^{[2
]}

机构：

[1] 中国电子系统设备工程公司研究所

[2] 海军工程大学通信工程系

来源：

电子科技大学学报 | 2012年 / 05期

关键词：

Bayesian准则; 认知无线电; 数据融合; 能量检测; Neyman-Pearson准则; 频谱感知;

D O I：

暂无

中图分类号：

TN925 [无线电中继通信、微波通信];

学科分类号：

摘要：

在认知无线电网络中,协作频谱感知技术可有效地缓解本地感知场景中存在的隐藏终端等问题。为了获得更大的协作增益,该文采用基于数据融合的协作频谱感知策略,融合中心依次收集各次用户上报的本地能量检测数据,然后进行线性加权融合,并做出最终判决。重点研究了线性加权融合方案的优化,推导了各次用户分别在Neyman-Pearson(N-P)和Bayesian两种不同准则下的最优融合权重,并在Suzuki感知信道下进行了蒙特卡洛仿真和数值验证。结果表明,N-P准则下给出的两种优化加权融合方案MDC和NDC性能相近,且均比EGC、SC、MRC等常用的融合方案具有更高的协作检测概率;而Bayesian准则下推导的优化加权融合方案BAY在检测可靠性方面明显优于其他方案。

引用

页码：697 / 701+786 +786

页数：6

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