北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2011, Vol. 34 ›› Issue (2): 17-21.doi: 10.13190/jbupt.201102.17.liuq

• 论文 • 上一篇    下一篇

非理想决策传输条件下的协作频谱感知

刘全1,高俊1,陈乐生2,郭云玮1   

  1. 1海军工程大学 通信工程系, 武汉 430033; 2中国人民解放军92730部队, 海南 三亚 572000
  • 收稿日期:2010-06-08 修回日期:2010-12-19 出版日期:2011-04-30 发布日期:2011-04-28
  • 通讯作者: 刘全 E-mail:liuquan.hjgc@gmail.com
  • 基金资助:

    国家高技术研究发展计划项目(2009AAJ208, 2009AAJ116)

Cooperative Spectrum Sensing under Imperfect Decision Transmission

  • Received:2010-06-08 Revised:2010-12-19 Online:2011-04-30 Published:2011-04-28
  • Contact: Quan LIU E-mail:liuquan.hjgc@gmail.com

摘要:

现有的基于决策融合的协作频谱感知方案大多数都是以理想的通用控制信道为前提,没有考虑实际决策传输中也存在衰落及噪声干扰,对此,将感知信道和控制信道建模为Suzuki信道,提出一种基于最小错误概率决策估计和K/N决策融合的协作频谱感知方案. 数值分析和仿真结果表明,非理想决策传输条件下的协作感知性能较理想条件下有一定恶化,在控制信道信噪比较低的情况下,其性能甚至不如本地感知. 在此基础上,研究了阴影衰落的空间相关性对协作感知性能的影响. 仿真结果显示,随着次用户之间距离的增大,阴影相关性的不利影响将逐渐减小.

关键词: 认知无线电, 频谱感知, 能量检测, 决策融合, 阴影相关性

Abstract:

The existing solutions of cooperative spectrum sensing (CSS) based on decision fusion are mostly built on the assumption of ideal decision transmission without considering any fading or noise corruption in common control channels. For this, the sensing channels and control channels are extend as Suzuki characteristics. A cooperation scheme is proposed under imperfect decision transmission model, in which a detector based on minimum error probability rule is used for decision estimation. The final decision combination is accomplished with K out of N rule. As demonstrated both by simulation, there is some performance deterioration in the proposed scheme, when compared with the ideal case that assumes errorfree decision transmission. It is even inferior to the local sensing when the average signal to noise ratio of the control channels is very low. Further, the spatial correlation of shadowing effects is investigated. It is shown that the negative impacts of correlated shadowing on the CSS gets weaker with the increasing distance between each other of the secondary users.

Key words: cognitive radio, spectrum sensing, energy detection, decision fusion, correlated shadowing

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