Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas
Generalized Real-Valued Weighted Covariance-Based Detection Methods for Cognitive Radio Networks With Correlated Multiple Antennas
复制标题
具有相关多天线的认知无线电网络的广义实值加权协方差检测方法
DOI：
10.1109/access.2019.2903732
复制
发表时间：
2019-03
期刊：
影响因子：
3.9
通讯作者：
Xiong Jian
中科院分区：
文献类型：
--
作者：
Chen An-Zhi;Shi Zhi-Ping;Xiong Jian
研究时变衰落信道中多天线相关接收的认知无线电网络的频谱感知问题。首先考虑了所有天线噪声方差相同的情况，提出了一种基于广义实值加权协方差的检测方法。特别是，我们在零假设下推导了GRWCD统计量的分布，这使我们能够为给定的虚警概率开发理论决策阈值。此外，我们还推导了在备择假设下GRWCD统计量的分布，从而提供了检测概率和理论接收机工作特性的数学表达式。同时，我们考虑了更一般的单天线噪声方差不等的情况，提出了一种改进的GRWCD方法以及决策阈值的理论表达式。仿真结果验证了推导结果的准确性，并表明所提出的两种方法能够比文献中几种先进的方法提供性能改进。
This paper is concerned with the spectrum sensing problem for cognitive radio networks with correlated receiving multiple antennas in the time-varying fading channel. We first consider the scenario that all the antennas have the same noise variance and present a generalized real-valued weighted-covariance-based detection (GRWCD) method. In particular, we derive the distribution of the GRWCD statistic under the null hypothesis, which allows us to develop the theoretical decision threshold for a given false alarm probability. Besides, we derive the distribution of the GRWCD statistic under the alternative hypothesis, which enables us to provide a mathematical expression for the detection probability as well as the theoretical receiver operating characteristic. Meanwhile, we consider a more general scenario of unequal per-antenna noise variances and present a modified GRWCD method as well as the theoretical expressions of the decision threshold. The simulation results are provided to verify the accuracy of the derived results and show that the proposed two methods are capable of providing performance improvement over several advanced methods in the literature.
登录
查看更多内容
DOI：
10.1007/978-1-4757-2341-0
发表时间：
1994-04
期刊：
--
影响因子：
--
作者：
H. Poor
通讯作者：
H. Poor
影响因子：
8.3
作者：
Lokesh Gahane;P. Sharma;Neeraj Varshney;T. Tsiftsis;Preetam Kumar
通讯作者：
Lokesh Gahane;P. Sharma;Neeraj Varshney;T. Tsiftsis;Preetam Kumar
影响因子：
6.8
作者：
Vladimir V. Kostylev;I. Gres
通讯作者：
Vladimir V. Kostylev;I. Gres
影响因子：
10.4
作者：
Abbas Taherpour;M. Nasiri-Kenari;S. Gazor
通讯作者：
Abbas Taherpour;M. Nasiri-Kenari;S. Gazor
在瑞利衰落通道中使用加权协方差矩阵进行频谱感知
影响因子：
6.8
作者：
Jin, Ming;Guo, Qinghua;Huang, Defeng
通讯作者：
Huang, Defeng