认知无线电中的频谱感知技术研究
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摘要
无线通信的高速发展使得无线业务对无线电频谱资源有着巨大的需求。而固定的频谱分配政策导致了频谱利用率的低下。认知无线电作为一种极具发展潜力的新技术,能够极大的提升了频谱利用效率。对目标频谱进行准确有效的感知是认知无线电技术实现的基础和前提。
本文的研究工作基于国家自然科学基金(60672132、60872149)、国家重大专项(2009ZX03002-009-01)和华为基金(SKL-2012-01-07)中关于认知无线电的部分,以及“宽频认知无线电跳频通信平台的研究设计”项目。本文深入细致的研究了认知无线电中的频谱感知技术,主要工作及创新成果如下：
(1)本文对似然率检测(LRT)算法在低信噪比(SNR)下进行了化简,并分别在渐进与非渐进的条件下对化简后的LRT算法进行了性能的分析和性能参数的推导,在两种情况下分别推导出了化简LRT算法的性能参数的闭合表达式,并进一步提出了一种新的联合检测算法,该算法在低SNR条件下采用先能量检测后简化LRT检测的二步检测算法,在其他条件下只采用能量检测算法；最后对联合检测算法的性能进行了分析对联合检测算法的性能参数进行了推导,理论分析和仿真都证明算法性能接近LRT算法,且有较低的复杂度。
(2)认知无线网络中协方差检测算法,比如最大最小值算法(MME)和协方差全值算法(CAV),它们的虚警概率和漏测概率均采用假设认知无线电(CR)用户天线数趋向无穷的渐进方法得到性能参数的。而在实际中天线维度和采样点数均为有限的。本文中提出了一种改进的Cholesky的协方差盲检测算法,用非渐进方法推导了检测性能参数的闭合表达式。首先对接收协方差矩阵做Cholesky分解,然后利用主用户(PU)信号与噪声信号接收矩阵分解后的矩阵非对角元素的不同检测PU信号。最后根据随机矩阵理论,推导了非渐进条件下该算法性能参数的数学表达式。分析表明所提算法无需PU信号的先验信息和信道条件信息,对不确定噪声具有很强的适应能力。相对于原有的协方差盲检测算法性能有一定的提升。
(3)认知无线网络中PU信号与噪声信号自相关性存在差异,现有的协方差盲检测算法均只利用了协方差矩阵元素本身或者特征值的性质,而并没有利用到特征向量的性质。本文考虑CR配置多天线且天线之间存在相关性的场景,建立了一个相关多大线认知网络频谱感知模型,只选择信道条件最好的两个CR参与检测算法。利用接收协方差矩阵和噪声协方差矩阵特征向量的相关性的不同,提出了基于特征向量的全盲检测方案和已知天线相关矩阵的半盲检测方案,全盲检测利用不同CR的接收协方差矩阵中特征向量的相关性,而半盲的检测算法则利用接收协方差矩阵与己知的特征向量的相关性。并对两种算法进行了性能分析,推导出了盲算法的虚警概率和门限值的闭合表达式。分析表明所提的检测算法相比于同类的检测算法均有所改进。
(4)认知无线网络中的协作检测模型均假设各感知信道的噪声方差相同。但是考虑实际场景,感知信道的噪声协方差必然存在差异。而且目前大部分的认知无线网络中的频谱感知算法均假设PU为单天线,而实际中PU为多天线的场景是大量存在的。本文中考虑PU为多天线且感知信道的噪声协方差不同的场景,利用统计学中接收信号协方差矩阵的特征向量的性质和感知信道噪声协方差不同时接收协-方差矩阵特征向量的性质,以噪声协方差矩阵的主特征向量作为模板,提出了一种复杂场景下的协方差盲检测算法。分析表明该算法在感知信道噪声不同的场景下有着良好的性能。
(5)正交频分复用(OFDM)技术作为4G移动通信中的关键技术,也是最适合CR系统的无线传输技术。目前针对OFDM信号的频谱感知算法研究只利用时域分集和频域分集提升检测性能,而没有考虑利用空间分集提升检测性能。本文提出了一种认知网络中的OFDM信号协作软合并检测算法,利用空间分集提升CR系统性能,将OFDM信号特征自相关检测算法和协作检测相结合,建立了OFDM信号协作检测模型,用最大化改进偏移系数的方法对多用户的软合并权值向量进行了优化,得到了最优权值向量的闭合表达式。分析表明该优化算法相比于其他优化算法具有较低的计算复杂度。
The rapid growth in wiles communications has contributed to a huge demand on the deployment of wireless services in the frequency spectrum. However, the fixed spectrum assignment policy enforced today results in poor spectrum utilization. Cognitive radio has emerged as a promising technology to improve the spectrum utilization dramatically, and it is the prerequisite and foundation of cognitive radio technology accurately and effectively to sensing the target spectrum.
The paper is supported by Natural Science Foundation of China (60672132,60872149) and the project "the research on FH(Frequency Hopping) communication platform of wideband cognitive radio". The spectrum sensing technologies of cognitive radio are studied in detail in this paper, and the main works done by the author are listed below:
(1) For the problem of the energy detection's inability of weak signals and the likelihood ratio test (LRT)'s high computational complexity in Cognitive radio, the paper simplifies LRT algorithm under low signal-to-noise ratio and calculates the performance parameters of the simplified LRT algorithm. And then presents a spectrum joint detection algorithm based on simplified LRT, which uses energy and the simplified LRT under low signal noise ratio (SNR), and uses energy detection only under the other condition. Both theoretical analysis and simulation results show that the algorithm detects the primary user signal effectively under low SNR condition, and the algorithm has lower complexity than the well-known LRT with close performance.
(2) In cognitive wireless network, the Blind covariance detection algorithms, such as MME and CAV, have the shortcoming that the performance parameters are obtained using non-asymptotic method. To deal with this problem, the paper presented a new blind detection algorithm using Cholesky factorization. The algorithm utilizes the differences between the off-diagonal elements of PU signal and noise signal after decomposition of receiving covariance matrix to distinguish PU signal and noise. According to random matrix theory, the performance parameters are derived using non-asymptotic method. The proposed method overcomes the noise uncertainty problem and performs well without informations about the channel, primary user and noise. Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.
(3) The auto-correlation of PU signal is very different from noise in the cognitive wireless networks, and most existing covariance blind detector utilized only the nature of elements themselves or eigenvalues without the nature of eigenvectors. The cognitive relays with multiple correlated antennas in the multiuser cooperative sensing scenario are considered in this paper. Frist, a multi-user spectrum sensing model with multiple correlated antennas is established, and only two CR users with best sensing channel are selected to participate in the detection algorithm. Then a blind detection algorithm and a semi-blind detection algorithm based on eigenvector are presented utilizing the difference of auto-correlation between the primary user signal and noise signal. The blind algorithm is based on the correlation among the main eigenvectors of the receive covariance matrices from the different cognitive relays, whereas the semi-blind algorithm is based on the correlation of the main eigenvectors between the receive covariance matrix and the known covariance matrix. And the closed expression of false alarm probability and threshold are derived in the paper. Numerical simulations show that the proposed algorithms performs better than the similar detectors.
(4) Multiuser covariance blind detection algorithms in cognitive wireless network usually assume that the noise covariance is equal in different sensing channels, but In a real scenario, the noise covariance in the sensing channels must be different. Moreover, most blind detection algorithms assume that the PU has single antenna only, but there are always multi-antennas on PU in practice. The PU with multi-antennas and the sensing channels with different covariances in the multiuser cooperative sensing scenario are considered in this paper. Utilizing the main eigenvector's nature in statistical covariance theory, BN-FTM (Feature Template Matching Based Noise) algorithm,which uses the main eigenvector as the feature template, is proposed for the complex noise scenario. The BN-FTM algorithm can detect PU signal in complex noise scenario without the priori information of PU and the sensing channel. Simulations are presented to show that the proposed algorithm is capable to achieve an improvement in detection performance.
(5) As the key technology in4G mobile communications, OFDM is optimized for CR system wireless transmission technology. The research on OFDM spectrum sensing algorithm utilizes only the time diversity and frequency diversity to improve detection performance without utilizing spatial diversity. In this paper, a cooperative sensing algorithm for OFDM is proposed. this algorithm applies the OFDM feature detection algorithm to cooperative detection with utilizing the spatial diversity to enhance the system detection performance, then the system weight vector is optimized and the closed form expression of optimal weight vector is obtained using the maximum improved deflection coefficient. Theoretical analysis and simulation results demonstrate the remarkable improvement of proposed algorithm on detection performance compared with the classical cooperation detection algorithm.
本文的研究工作基于国家自然科学基金(60672132、60872149)、国家重大专项(2009ZX03002-009-01)和华为基金(SKL-2012-01-07)中关于认知无线电的部分,以及“宽频认知无线电跳频通信平台的研究设计”项目。本文深入细致的研究了认知无线电中的频谱感知技术,主要工作及创新成果如下：
(1)本文对似然率检测(LRT)算法在低信噪比(SNR)下进行了化简,并分别在渐进与非渐进的条件下对化简后的LRT算法进行了性能的分析和性能参数的推导,在两种情况下分别推导出了化简LRT算法的性能参数的闭合表达式,并进一步提出了一种新的联合检测算法,该算法在低SNR条件下采用先能量检测后简化LRT检测的二步检测算法,在其他条件下只采用能量检测算法；最后对联合检测算法的性能进行了分析对联合检测算法的性能参数进行了推导,理论分析和仿真都证明算法性能接近LRT算法,且有较低的复杂度。
(2)认知无线网络中协方差检测算法,比如最大最小值算法(MME)和协方差全值算法(CAV),它们的虚警概率和漏测概率均采用假设认知无线电(CR)用户天线数趋向无穷的渐进方法得到性能参数的。而在实际中天线维度和采样点数均为有限的。本文中提出了一种改进的Cholesky的协方差盲检测算法,用非渐进方法推导了检测性能参数的闭合表达式。首先对接收协方差矩阵做Cholesky分解,然后利用主用户(PU)信号与噪声信号接收矩阵分解后的矩阵非对角元素的不同检测PU信号。最后根据随机矩阵理论,推导了非渐进条件下该算法性能参数的数学表达式。分析表明所提算法无需PU信号的先验信息和信道条件信息,对不确定噪声具有很强的适应能力。相对于原有的协方差盲检测算法性能有一定的提升。
(3)认知无线网络中PU信号与噪声信号自相关性存在差异,现有的协方差盲检测算法均只利用了协方差矩阵元素本身或者特征值的性质,而并没有利用到特征向量的性质。本文考虑CR配置多天线且天线之间存在相关性的场景,建立了一个相关多大线认知网络频谱感知模型,只选择信道条件最好的两个CR参与检测算法。利用接收协方差矩阵和噪声协方差矩阵特征向量的相关性的不同,提出了基于特征向量的全盲检测方案和已知天线相关矩阵的半盲检测方案,全盲检测利用不同CR的接收协方差矩阵中特征向量的相关性,而半盲的检测算法则利用接收协方差矩阵与己知的特征向量的相关性。并对两种算法进行了性能分析,推导出了盲算法的虚警概率和门限值的闭合表达式。分析表明所提的检测算法相比于同类的检测算法均有所改进。
(4)认知无线网络中的协作检测模型均假设各感知信道的噪声方差相同。但是考虑实际场景,感知信道的噪声协方差必然存在差异。而且目前大部分的认知无线网络中的频谱感知算法均假设PU为单天线,而实际中PU为多天线的场景是大量存在的。本文中考虑PU为多天线且感知信道的噪声协方差不同的场景,利用统计学中接收信号协方差矩阵的特征向量的性质和感知信道噪声协方差不同时接收协-方差矩阵特征向量的性质,以噪声协方差矩阵的主特征向量作为模板,提出了一种复杂场景下的协方差盲检测算法。分析表明该算法在感知信道噪声不同的场景下有着良好的性能。
(5)正交频分复用(OFDM)技术作为4G移动通信中的关键技术,也是最适合CR系统的无线传输技术。目前针对OFDM信号的频谱感知算法研究只利用时域分集和频域分集提升检测性能,而没有考虑利用空间分集提升检测性能。本文提出了一种认知网络中的OFDM信号协作软合并检测算法,利用空间分集提升CR系统性能,将OFDM信号特征自相关检测算法和协作检测相结合,建立了OFDM信号协作检测模型,用最大化改进偏移系数的方法对多用户的软合并权值向量进行了优化,得到了最优权值向量的闭合表达式。分析表明该优化算法相比于其他优化算法具有较低的计算复杂度。
The rapid growth in wiles communications has contributed to a huge demand on the deployment of wireless services in the frequency spectrum. However, the fixed spectrum assignment policy enforced today results in poor spectrum utilization. Cognitive radio has emerged as a promising technology to improve the spectrum utilization dramatically, and it is the prerequisite and foundation of cognitive radio technology accurately and effectively to sensing the target spectrum.
The paper is supported by Natural Science Foundation of China (60672132,60872149) and the project "the research on FH(Frequency Hopping) communication platform of wideband cognitive radio". The spectrum sensing technologies of cognitive radio are studied in detail in this paper, and the main works done by the author are listed below:
(1) For the problem of the energy detection's inability of weak signals and the likelihood ratio test (LRT)'s high computational complexity in Cognitive radio, the paper simplifies LRT algorithm under low signal-to-noise ratio and calculates the performance parameters of the simplified LRT algorithm. And then presents a spectrum joint detection algorithm based on simplified LRT, which uses energy and the simplified LRT under low signal noise ratio (SNR), and uses energy detection only under the other condition. Both theoretical analysis and simulation results show that the algorithm detects the primary user signal effectively under low SNR condition, and the algorithm has lower complexity than the well-known LRT with close performance.
(2) In cognitive wireless network, the Blind covariance detection algorithms, such as MME and CAV, have the shortcoming that the performance parameters are obtained using non-asymptotic method. To deal with this problem, the paper presented a new blind detection algorithm using Cholesky factorization. The algorithm utilizes the differences between the off-diagonal elements of PU signal and noise signal after decomposition of receiving covariance matrix to distinguish PU signal and noise. According to random matrix theory, the performance parameters are derived using non-asymptotic method. The proposed method overcomes the noise uncertainty problem and performs well without informations about the channel, primary user and noise. Numerical simulation results demonstrate that the performance parameters expressions are correct and the new detector outperforms the other blind covariance detectors.
(3) The auto-correlation of PU signal is very different from noise in the cognitive wireless networks, and most existing covariance blind detector utilized only the nature of elements themselves or eigenvalues without the nature of eigenvectors. The cognitive relays with multiple correlated antennas in the multiuser cooperative sensing scenario are considered in this paper. Frist, a multi-user spectrum sensing model with multiple correlated antennas is established, and only two CR users with best sensing channel are selected to participate in the detection algorithm. Then a blind detection algorithm and a semi-blind detection algorithm based on eigenvector are presented utilizing the difference of auto-correlation between the primary user signal and noise signal. The blind algorithm is based on the correlation among the main eigenvectors of the receive covariance matrices from the different cognitive relays, whereas the semi-blind algorithm is based on the correlation of the main eigenvectors between the receive covariance matrix and the known covariance matrix. And the closed expression of false alarm probability and threshold are derived in the paper. Numerical simulations show that the proposed algorithms performs better than the similar detectors.
(4) Multiuser covariance blind detection algorithms in cognitive wireless network usually assume that the noise covariance is equal in different sensing channels, but In a real scenario, the noise covariance in the sensing channels must be different. Moreover, most blind detection algorithms assume that the PU has single antenna only, but there are always multi-antennas on PU in practice. The PU with multi-antennas and the sensing channels with different covariances in the multiuser cooperative sensing scenario are considered in this paper. Utilizing the main eigenvector's nature in statistical covariance theory, BN-FTM (Feature Template Matching Based Noise) algorithm,which uses the main eigenvector as the feature template, is proposed for the complex noise scenario. The BN-FTM algorithm can detect PU signal in complex noise scenario without the priori information of PU and the sensing channel. Simulations are presented to show that the proposed algorithm is capable to achieve an improvement in detection performance.
(5) As the key technology in4G mobile communications, OFDM is optimized for CR system wireless transmission technology. The research on OFDM spectrum sensing algorithm utilizes only the time diversity and frequency diversity to improve detection performance without utilizing spatial diversity. In this paper, a cooperative sensing algorithm for OFDM is proposed. this algorithm applies the OFDM feature detection algorithm to cooperative detection with utilizing the spatial diversity to enhance the system detection performance, then the system weight vector is optimized and the closed form expression of optimal weight vector is obtained using the maximum improved deflection coefficient. Theoretical analysis and simulation results demonstrate the remarkable improvement of proposed algorithm on detection performance compared with the classical cooperation detection algorithm.
引文
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