CN103281141A

CN103281141A - Blind spectrum sensing method and device

Info

Publication number: CN103281141A
Application number: CN2013101991115A
Authority: CN
Inventors: 王东明; 刘瑞勋; 吴雨霏; 王向阳; 唐文锐; 黄禹淇
Original assignee: Southeast University
Current assignee: Southeast University
Priority date: 2013-05-24
Filing date: 2013-05-24
Publication date: 2013-09-04

Abstract

The invention relates to the technical field of wireless communication. The invention discloses a blind spectrum sensing method and a spectrum sensing device. The method comprises: a spectrum sensing device receives a signal on an authorized frequency band, samples and filters the received signal, calculates its autocorrelation coefficient, and constructs a covariance matrix of the signal , calculate the approximate two-norm condition number T of the covariance matrix, and judge whether there is a signal of the authorized user according to T. The method and device of the invention have the advantages of low computational complexity, no need for authorized signal features, insensitivity to noise uncertainty, etc., and simultaneously have excellent performance.

Description

A blind spectrum sensing method and device

技术领域technical field

本发明涉及认知无线电系统中的频谱感知技术，尤其涉及一种无需发送信号任何特征信息的盲频谱感知方法。The invention relates to a spectrum sensing technology in a cognitive radio system, in particular to a blind spectrum sensing method without sending any characteristic information of a signal.

背景技术Background technique

随着社会和经济的发展，人们对无线通信数据量的需求越来越大。然而，频谱资源是有限的，目前，大部分的频段已经分配给了授权用户，这导致可分配给新的无线系统的频谱十分少。而另一方面，实际测量表明，这些已经分配的授权频段并没有得到充分利用，它们大部分时间都处于空闲状态，特别是有些性能优良的频段，利用率非常低。频谱资源的稀缺性和低利用率的矛盾促使了认知无线电的提出。认知无线电的思想是认知用户通过频谱感知，进而获知频谱使用情况，选择授权用户暂时没有使用的子频段进行传输。With the development of society and economy, people's demand for wireless communication data volume is increasing. However, spectrum resources are limited. At present, most of the frequency bands have been allocated to licensed users, which results in very little spectrum that can be allocated to new wireless systems. On the other hand, actual measurements show that these allocated licensed frequency bands are not fully utilized, and they are idle most of the time, especially some frequency bands with excellent performance have a very low utilization rate. The contradiction between the scarcity and low utilization of spectrum resources has prompted the proposal of cognitive radio. The idea of cognitive radio is that cognitive users learn about spectrum usage through spectrum sensing, and select sub-frequency bands that are not used by authorized users for transmission.

可见，频谱感知是认知无线电系统的一个基本模块。常用的频谱检测方法包括：能量检测、匹配滤波检测和循环平稳检测。能量检测复杂度低，但是受到噪声的不确定性的影响，其性能恶化严重。匹配滤波性能优异，但是需要已知发送信号的特征。循环平稳检测的性能也很优异，但是复杂度较高，在实际应用时，受到一定的局限。It can be seen that spectrum sensing is a basic module of cognitive radio system. Commonly used spectrum detection methods include: energy detection, matched filter detection and cyclostationary detection. Energy detection has low complexity, but its performance is seriously deteriorated due to the influence of noise uncertainty. Matched filtering has excellent performance, but needs to know the characteristics of the transmitted signal. The performance of cyclostationary detection is also excellent, but the complexity is high, and it is limited in practical application.

根据接收信号的协方差矩阵可以判断该频段上是信号还是噪声。传统的技术方案提出了一种利用接收信号的协方差矩阵的特征值进行频谱感知，判决变量可以由协方差矩阵的特征值构成，并给出了一种判决变量构造方法，即最大最小特征值的比值。可以看出，理想情况下，只有噪声存在时，该最大最小特征值的比值为1，有信号存在时，该比值是大于1的。该方法的优点是无需发送信号的任何先验信息，也无需噪声的任何统计特性。但是，求解最大最小特征值需要复杂的特征值分解，计算复杂度非常高，工程实现非常困难。According to the covariance matrix of the received signal, it can be judged whether the frequency band is signal or noise. The traditional technical solution proposes a spectrum sensing using the eigenvalues of the covariance matrix of the received signal, the decision variable can be composed of the eigenvalues of the covariance matrix, and a method of constructing the decision variables is given, that is, the maximum and minimum eigenvalues ratio. It can be seen that ideally, when only noise exists, the ratio of the maximum and minimum eigenvalues is 1, and when there is a signal, the ratio is greater than 1. The advantage of this method is that it does not require any prior information about the transmitted signal, nor any statistical properties of the noise. However, solving the maximum and minimum eigenvalues requires complex eigenvalue decomposition, which has a very high computational complexity and is very difficult to implement in engineering.

发明内容Contents of the invention

技术问题：为了克服现有能量检测对噪声不确定性影响，本发明提出了一种利用无线信号的协方差矩阵的盲频谱感知方法及装置。Technical problem: In order to overcome the influence of existing energy detection on noise uncertainty, the present invention proposes a blind spectrum sensing method and device using the covariance matrix of wireless signals.

技术方案：一种盲频谱感知方法，包括如下步骤：Technical solution: a blind spectrum sensing method, comprising the following steps:

（1）接收待感知频段上的无线信号；(1) Receive wireless signals on the frequency band to be sensed;

（2）对接收信号进行采样滤波，计算信号的自相关系数，并构造协方差矩阵，记为R，其维度为L；(2) Sampling and filtering the received signal, calculating the autocorrelation coefficient of the signal, and constructing a covariance matrix, denoted as R, whose dimension is L;

采样滤波后的N个样本信号表示为，x(0),x(1),…,x(N-1)，所述的相关系数通过如下方法得到：选取计算窗口长度为L，从l=0,…,L-1，计算样本信号的相关系数The N sample signals after sampling and filtering are expressed as x(0), x(1),...,x(N-1), and the correlation coefficient is obtained by the following method: select the calculation window length as L, from l= 0,...,L-1, calculate the correlation coefficient of the sample signal

${λ λ}_{l l} = = \frac{11}{N N} {Σ Σ}_{n no = = 00}^{N N - - 11} x x ((n no)) {x x}^{* *} ((n no - - l l))$

其中，当n-l<0时，x(0)=0，运算符号*表示求共轭，样本个数N是大于1的正整数，窗口长度L是大于1的正整数；相应的协方差矩阵R表示为，Among them, when n-l<0, x(0)=0, the operation symbol * means to seek conjugation, the number of samples N is a positive integer greater than 1, the window length L is a positive integer greater than 1; the corresponding covariance matrix R Expressed as,

$R R = = [\begin{matrix} {λ λ}_{00} & {λ λ}_{11} & \cdot &Center Dot; \cdot \cdot \cdot &Center Dot; & {λ λ}_{L L - - 11} \\ {λ λ}_{11}^{* *} & {λ λ}_{00} & \cdot \cdot \cdot &Center Dot; \cdot \cdot & {λ λ}_{L L - - 22} \\ \cdot &Center Dot; & \cdot \cdot & \cdot &Center Dot; \\ \cdot &Center Dot; & \cdot &Center Dot; & \cdot \cdot \cdot \cdot \cdot &Center Dot; & \cdot \cdot \\ \cdot \cdot & \cdot &Center Dot; & \cdot &Center Dot; \\ {λ λ}_{L L - - 11}^{* *} & {λ λ}_{L L - - 22}^{* *} & \cdot \cdot \cdot \cdot \cdot \cdot & {λ λ}_{00} \end{matrix}] . .$

（3）计算协方差矩阵R在二范数下近似的条件数，构造判决变量，记为T；所述T的构造方法是：(3) Calculate the condition number of the covariance matrix R approximated under the two-norm, and construct the decision variable, denoted as T; the construction method of T is:

步骤i：对矩阵R的所有对角线元素的求和并除以L，记该值为A；Step i: sum all diagonal elements of matrix R and divide by L, record this value as A;

步骤ii：计算矩阵R所有元素的平方之和，然后除以L，再减去A的平方，再求开方，其结果记为B；Step ii: Calculate the sum of the squares of all elements of the matrix R, then divide by L, then subtract the square of A, and find the square root, and record the result as B;

步骤iii：计算B除以

其结果记为C；Step iii: Calculate B divided by

The result is recorded as C;

步骤iv：计算B乘以其结果记为D；Step iv: Calculate B multiplied by The result is recorded as D;

步骤v：计算T₁＝A+C，计算T₂＝A-D，如果T₂小于等于零，则对T₂赋予一个很小的数，得到：Step v: Calculate T ₁ =A+C, calculate T ₂ =AD, if T ₂ is less than or equal to zero, assign a very small number to T ₂ to get:

$T T = = \frac{{T T}_{11}}{{T T}_{22}} = = \frac{A A + + C C}{A A - - D D.} . .$

A和B的计算通过如下方法得到：The calculation of A and B is obtained by the following method:

A＝λ₀, $B = \sqrt{\frac{2}{L} Σ_{i = 1}^{L - 1} (L - i) {λ_{i}}^{2}} .$ A=λ ₀ , $B = \sqrt{\frac{2}{L} Σ_{i = 1}^{L - 1} (L - i) {λ_{i}}^{2}} .$

（4）根据判决变量T判定频谱是否空闲：当T大于预先设定的判决门限，则判定该频谱上有授权信号存在，当T小于预先设定的判决门限，则判定没有授权信号，即该频谱空闲。(4) Determine whether the spectrum is free according to the decision variable T: when T is greater than the preset decision threshold, it is determined that there is an authorized signal on the spectrum; when T is less than the preset decision threshold, it is determined that there is no authorized signal, that is, the The spectrum is free.

样本个数N根据频谱感知的周期、频谱感知的精度确定。窗口长度L，根据感知设备的计算能力、频谱感知精度以及复杂度确定。The number N of samples is determined according to the cycle of spectrum sensing and the accuracy of spectrum sensing. The window length L is determined according to the computing capability of the sensing device, spectrum sensing accuracy, and complexity.

所述的判决门限根据所要求的虚警概率或检测概率通过理论或仿真得到。The decision threshold is obtained through theory or simulation according to the required false alarm probability or detection probability.

该方法适用于多天线系统的频谱感知或多节点的协作感知。This method is suitable for spectrum sensing of multi-antenna system or cooperative sensing of multi-nodes.

一种盲频谱感知装置，包括：无线信号采样和滤波模块、自相关系数计算模块、判决变量计算模块和判决模块；A blind spectrum sensing device, comprising: a wireless signal sampling and filtering module, an autocorrelation coefficient calculation module, a decision variable calculation module, and a decision module;

所述无线信号采样和滤波模块用于获得所感知频段的无线信号；The wireless signal sampling and filtering module is used to obtain the wireless signal of the perceived frequency band;

所述自相关系数计算模块用于计算信号的自相关系数；The autocorrelation coefficient calculation module is used to calculate the autocorrelation coefficient of the signal;

所述的判决变量计算模块用于计算协方差矩阵R在二范数下近似的条件数，构造判决变量；Described decision variable calculation module is used for calculating the condition number that covariance matrix R approximates under two norms, constructs decision variable;

所述的判决模块包括比较器，用于比较判决变量与门限。The decision module includes a comparator for comparing the decision variable with the threshold.

本发明采用上述技术方案，具有以下有益效果：在判决变量计算阶段，该方法仅需要复杂度较低的加法运算和少量的乘法除法运算，相比背景技术中基于特征值的频谱感知，复杂度非常低，且对噪声的不确定性不敏感。并且，本发明还适用于多天线系统的频谱感知和协作频谱感知。The present invention adopts the above-mentioned technical scheme, which has the following beneficial effects: in the stage of calculating the decision variable, the method only needs low-complexity addition operations and a small amount of multiplication and division operations. Compared with the spectrum sensing based on eigenvalues in the background technology, the complexity Very low and insensitive to noise uncertainties. Moreover, the present invention is also applicable to spectrum sensing and cooperative spectrum sensing of a multi-antenna system.

附图说明Description of drawings

图1为本发明实施例的频谱感知方法流程图；FIG. 1 is a flowchart of a spectrum sensing method according to an embodiment of the present invention;

图2为本发明实施例的判决变量计算方法示意图；FIG. 2 is a schematic diagram of a calculation method of a decision variable according to an embodiment of the present invention;

图3为本发明实施例的频谱感知装置框图；FIG. 3 is a block diagram of a spectrum sensing device according to an embodiment of the present invention;

图4为本发明实施例的判决变量计算模块示意图；4 is a schematic diagram of a decision variable calculation module according to an embodiment of the present invention;

图5为针对无线麦克风信号，本发明实施例方法与背景技术的性能比较示意图；5 is a schematic diagram of performance comparison between the method of the embodiment of the present invention and the background technology for wireless microphone signals;

图6为针对随机调制信号，本发明实施例方法与背景技术的性能比较示意图。FIG. 6 is a schematic diagram of performance comparison between the method of the embodiment of the present invention and the background technology for random modulation signals.

具体实施方式Detailed ways

下面结合具体实施例，进一步阐明本发明，应理解这些实施例仅用于说明本发明而不用于限制本发明的范围，在阅读了本发明之后，本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of this application.

一种盲频谱感知方法，包括以下步骤：A blind spectrum sensing method, comprising the following steps:

1)接收待感知频段上的无线信号；1) Receive wireless signals on the frequency band to be sensed;

2)对接收信号进行采样滤波后，计算信号的自相关系数；2) After sampling and filtering the received signal, calculate the autocorrelation coefficient of the signal;

采样滤波后的N个样本信号表示为，x(0),x(1),…,x(N-1)。在实际实现时，协方差矩阵通常通过如下方法平滑得到。选取计算窗口长度为L，信号的自相关系数可以表示为，The N sample signals after sampling and filtering are expressed as x(0), x(1), . . . , x(N-1). In actual implementation, the covariance matrix is usually smoothed by the following method. The length of the calculation window is selected as L, and the autocorrelation coefficient of the signal can be expressed as,

$λ_{l} = \frac{1}{N} Σ_{n = 0}^{N - 1} x (n) x^{*} (n - l)$ [公式1] $λ_{l} = \frac{1}{N} Σ_{no = 0}^{N - 1} x (no) x^{*} (no - l)$ [Formula 1]

其中，l=1,2,…,L，相应的协方差矩阵记为R，Among them, l=1,2,...,L, the corresponding covariance matrix is recorded as R,

$R = [\begin{matrix} λ_{0} & λ_{1} & \cdot \cdot \cdot & λ_{L - 1} \\ λ_{1}^{*} & λ_{0} & \cdot \cdot \cdot & λ_{L - 2} \\ \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot \cdot \cdot & \cdot \\ \cdot & \cdot & \cdot \\ λ_{L - 1}^{*} & λ_{L - 2}^{*} & \cdot \cdot \cdot & λ_{0} \end{matrix}]$ [公式2] $R = [\begin{matrix} λ_{0} & λ_{1} & &Center Dot; &Center Dot; &Center Dot; & λ_{L - 1} \\ λ_{1}^{*} & λ_{0} & &Center Dot; &Center Dot; &Center Dot; & λ_{L - 2} \\ &Center Dot; & \cdot & &Center Dot; \\ \cdot & &Center Dot; & &Center Dot; &Center Dot; &Center Dot; & &Center Dot; \\ &Center Dot; & &Center Dot; & &Center Dot; \\ λ_{L - 1}^{*} & λ_{L - 2}^{*} & &Center Dot; &Center Dot; &Center Dot; & λ_{0} \end{matrix}]$ [Formula 2]

其中，样本个数N是大于1的正整数，窗口长度L是大于等于1的正整数。可以看出R为共轭对称矩阵。用r_i,j表示R的第i行j列元素，其中，i=1,…,L，j=1,…,L。Wherein, the number of samples N is a positive integer greater than 1, and the window length L is a positive integer greater than or equal to 1. It can be seen that R is a conjugate symmetric matrix. Let r _{i, j} represent the i-th row and j-column elements of R, where i=1,...,L, j=1,...,L.

我们知道，最大最小特征值的比值也被称为二范数条件数。为了避免特征值分解，我们提出利用矩阵近似的二范数条件数作为判决变量。其基本思想是，利用接收信号的协方差矩阵，直接计算其近似的在二范数下的条件数，根据该近似值作为判决变量，进而判定是否存在信号。这种方法可以完全避免矩阵的特征值分解运算。矩阵在二范数下的条件数的近似方法有很多，下面我们给出一个特定的实施例。We know that the ratio of the largest and smallest eigenvalues is also called the two-norm condition number. To avoid eigenvalue decomposition, we propose to use the bi-norm condition number of the matrix approximation as the decision variable. The basic idea is to use the covariance matrix of the received signal to directly calculate its approximate condition number under the two-norm, and use the approximate value as a decision variable to determine whether there is a signal. This method can completely avoid the eigenvalue decomposition operation of the matrix. There are many methods for approximating the condition number of a matrix under the two-norm, and we will give a specific embodiment below.

根据线性代数理论[Henry Wolkowicz,"Bounds for Eigenvalues Using Traces",Linear Algebra and Its Applications,vol.29,pp.471-506,1980.]，矩阵R在二范数下的条件数可以近似为T，表示为According to the linear algebra theory [Henry Wolkowicz, "Bounds for Eigenvalues Using Traces", Linear Algebra and Its Applications, vol.29, pp.471-506, 1980.], the condition number of the matrix R under the two norm can be approximated as T ,Expressed as

$T = \frac{A + B / {(L - 1)}^{1 / 2}}{A - B {(L - 1)}^{1 / 2}}$ [公式3] $T = \frac{A + B / {(L - 1)}^{1 / 2}}{A - B {(L - 1)}^{1 / 2}}$ [Formula 3]

其中，A＝Tr(R)/L，

运算操作Tr(·)表示求矩阵的迹。以T为判决变量，可避免矩阵特征值分解运算，复杂度大大降低。根据矩阵的迹的性质，Among them, A=Tr(R)/L,

The arithmetic operation Tr(·) means finding the trace of the matrix. Taking T as the decision variable can avoid the matrix eigenvalue decomposition operation, and the complexity is greatly reduced. According to the properties of the trace of the matrix,

$Tr Tr ((R R)) = = {Σ Σ}_{i i = = 11}^{L L} {r r}_{i i,, i i}$

$Tr Tr (({R R}^{22})) = = {Σ Σ}_{i i = = 11}^{L L} {Σ Σ}_{j j = = 11}^{L L} {r r}_{i i,, j j}^{22}$

利用上述思想，我们可以通过如下步骤3)–9)得到判决变量。Using the above ideas, we can obtain the decision variables through the following steps 3)–9).

3)频谱感知装置对矩阵R的所有对角线元素的求和并除以L，记该值为A；3) The spectrum sensing device sums all diagonal elements of the matrix R and divides it by L, and records the value as A;

4)频谱感知装置计算矩阵R所有元素的平方之和，然后除以L，再减去A的平方，再求开方，其结果记为B；4) The spectrum sensing device calculates the sum of the squares of all elements of the matrix R, then divides by L, then subtracts the square of A, and then finds the square root, and the result is recorded as B;

5)频谱感知装置计算B除以

其结果记为C；5) The spectrum sensing device calculates B divided by

The result is recorded as C;

6)频谱感知装置计算B乘以

其结果记为D；6) The spectrum sensing device calculates B multiplied by

The result is recorded as D;

7)频谱感知装置计算判决变量T₁：T₁＝A+C7) The spectrum sensing device calculates the decision variable T ₁ : T ₁ =A+C

8)频谱感知装置计算判决变量T₂：T₂＝A-D8) The spectrum sensing device calculates the decision variable T ₂ : T ₂ =AD

9)最后，计算判决门限T=T₁/T₂。当T大于预先设定的门限，则所述的频谱感知装置判定该频谱上有授权信号存在，当T小于预先设定的门限，则所述的频谱感知装置判定没有授权信号，即该频谱空闲。9) Finally, calculate the decision threshold T=T ₁ /T ₂ . When T is greater than the preset threshold, the spectrum sensing device determines that there is a licensed signal on the spectrum, and when T is less than the preset threshold, then the spectrum sensing device determines that there is no licensed signal, that is, the spectrum is idle .

作为优选方案，根据[公式2]所示的协方差矩阵R，A和B的计算可以通过如下方法快速得到：As a preferred solution, according to the covariance matrix R shown in [Formula 2], the calculation of A and B can be quickly obtained by the following method:

A＝λ₀ [公式4]A = λ ₀ [Formula 4]

$B = \sqrt{\frac{2}{L} Σ_{i = 1}^{L - 1} (L - i) {λ_{i}}^{2}}$ [公式5] $B = \sqrt{\frac{2}{L} Σ_{i = 1}^{L - 1} (L - i) {λ_{i}}^{2}}$ [Formula 5]

从上面可以看出，数值B的计算需要2(L-1)+1次乘法运算和1次开方运算，那么判决变量T的计算共需要2(L-1)+4次乘法以及两次开方运算，相比特征值分解的方法（复杂度为O(L³)），本发明方法的复杂度非常低。It can be seen from the above that the calculation of the value B requires 2(L-1)+1 multiplication operations and 1 square root operation, then the calculation of the decision variable T requires a total of 2(L-1)+4 multiplications and two The square root operation, compared with the method of eigenvalue decomposition (complexity is O(L ³ )), the complexity of the method of the present invention is very low.

上述方法还可以结合多天线感知系统（或多用户协作感知）。只需要将相关系数的计算推广到多天线感知（或多用户协作感知）系统即可。系统有K根天线（或K个协作感知用户）接收。假设第k根天线（协作用户）在第n个时刻的采样信号表示为y_k(n)，可以将它们排列构成如下的信号矢量，y₁(0),y₂(0),…,y_K(0),y₁(1),y₂(1),…,y_K(1),…,y₁(N-1),y₂(N-1),…,y_K(N-1)，该矢量长度为N×K。将上述y_k(n)构成的矢量表示成长度为N×K的矢量x(0),x(1),…,x(NK-1)，根据这个矢量也可以计算出相关系数，进而得到相应的判决变量。The above method can also be combined with a multi-antenna sensing system (or multi-user cooperative sensing). It is only necessary to extend the calculation of the correlation coefficient to a multi-antenna sensing (or multi-user cooperative sensing) system. The system has K antennas (or K cooperative sensing users) to receive. Assuming that the sampling signal of the kth antenna (cooperative user) at the nth moment is expressed as y _k (n), they can be arranged to form the following signal vector, y ₁ (0), y ₂ (0),...,y _K (0),y ₁ (1),y ₂ (1),…,y _K (1),…,y ₁ (N-1),y ₂ (N-1),…,y _K (N- 1), the length of the vector is N×K. The above-mentioned vector composed of y _k (n) is represented as a vector x(0), x(1),...,x(NK-1) of length N×K, and the correlation coefficient can also be calculated according to this vector, and then obtained corresponding decision variables.

对于分布式协作感知，也可以分别在每个感知用户采用本方法进行感知，然后将感知结果发送到数据融合中心，得到更精确的判决。For distributed cooperative sensing, each sensing user can also use this method for sensing, and then send the sensing results to the data fusion center to obtain more accurate judgments.

下面结合附图，对本发明的频谱感知方法的工作步骤进一步详细说明。The working steps of the spectrum sensing method of the present invention will be further described in detail below in conjunction with the accompanying drawings.

如图1所示，首先，频谱感知装置接收待感知频段上的无线信号，在对接收信号进行采样滤波后，计算其相关系数。然后根据协方差矩阵在二范数下的近似条件数为判决变量进行判决。当判决变量大于预先设定的门限，则判定该频谱上有授权信号存在，当判决变量小于预先设定的门限，则判定没有授权信号，即该频谱空闲。As shown in FIG. 1 , first, the spectrum sensing device receives a wireless signal on a frequency band to be sensed, and calculates its correlation coefficient after sampling and filtering the received signal. Then, the decision is made according to the approximate condition number of the covariance matrix under the two-norm as the decision variable. When the decision variable is greater than the preset threshold, it is determined that there is an authorized signal on the spectrum, and when the decision variable is smaller than the preset threshold, it is determined that there is no authorized signal, that is, the spectrum is idle.

如图2所示的实施例，根据[公式4]和[公式5]可以分别得到A和B，进而得到C和D的数值，从而得到判决变量T。In the embodiment shown in FIG. 2 , A and B can be obtained respectively according to [Formula 4] and [Formula 5], and then the values of C and D can be obtained, thereby obtaining the decision variable T.

如图3所示，本发明的频谱感知装置包括：无线信号采样和滤波模块、相关系数计算模块、判决变量计算模块和判决模块。无线信号采样和滤波模块用于获得所感知频段的无线信号；自相关系数计算模块用于计算信号的自相关系数；判决变量计算模块用于计算协方差矩阵R在二范数下近似的条件数，构造判决变量；判决模块包括比较器，用于比较判决变量与门限。其中，判决变量计算模块的实现框图如图4所示。As shown in FIG. 3 , the spectrum sensing device of the present invention includes: a wireless signal sampling and filtering module, a correlation coefficient calculation module, a decision variable calculation module and a decision module. The wireless signal sampling and filtering module is used to obtain the wireless signal of the perceived frequency band; the autocorrelation coefficient calculation module is used to calculate the autocorrelation coefficient of the signal; the decision variable calculation module is used to calculate the condition number of the covariance matrix R approximated under the two norm , to construct a decision variable; the decision module includes a comparator for comparing the decision variable with a threshold. Among them, the realization block diagram of the decision variable calculation module is shown in Fig. 4 .

下面通过实际仿真验证，由图5和图6给出本实施例与背景技术的性能对比。我们对比了无噪声不确定性下的能量检测、背景技术即最大最小特征值比值检测、在噪声不确定性下的能量检测、以及本专利技术。本专利技术以及最大最小特征值比值检测均对噪声不确定性不敏感，也就是，存在噪声不确定性与否不影响它们的性能。图中给出的是存在噪声不确定性下，本专利技术以及最大最小特征值比值检测的性能。噪声不确定性系数为0.5dB。The actual simulation is verified below, and the performance comparison between this embodiment and the background technology is given by FIG. 5 and FIG. 6 . We compared the energy detection under the noise-free uncertainty, the background technology ie the maximum-minimum eigenvalue ratio detection, the energy detection under the noise uncertainty, and the technology of this patent. Both the patented technology and the maximum-minimum eigenvalue ratio detection are insensitive to noise uncertainty, that is, the presence or absence of noise uncertainty does not affect their performance. Shown in the figure is the performance of the patented technology and the detection of the ratio of maximum and minimum eigenvalues in the presence of noise uncertainty. The noise uncertainty factor is 0.5dB.

图5的仿真结果是以无线麦克风信号的频谱检测为例，采样点数为4096点。从图中可以看出，相比无噪声不确定性下的能量检测，在检测概率为0.9时，本方法有2dB的增益，另外，本方法优于最大最小特征值比值检测，约0.2dB。需要注意的是，存在噪声不确定性时，能量检测有严重的“信噪比墙”现象，比本方法恶化7dB以上。这说明，本方法不仅计算复杂度比最大最小特征值比值检测低，性能还优于最大最小特征值的比值检测。The simulation result in Fig. 5 takes the spectrum detection of the wireless microphone signal as an example, and the number of sampling points is 4096 points. It can be seen from the figure that compared with the energy detection without noise uncertainty, when the detection probability is 0.9, this method has a gain of 2dB. In addition, this method is better than the maximum and minimum eigenvalue ratio detection, about 0.2dB. It should be noted that when there is noise uncertainty, the energy detection has a serious "signal-to-noise ratio wall" phenomenon, which is more than 7dB worse than this method. This shows that this method not only has a lower computational complexity than the ratio detection of the maximum and minimum eigenvalues, but also has better performance than the ratio detection of the maximum and minimum eigenvalues.

图6的仿真结果是以随机信号的频谱检测为例。采用QPSK信号，频率选择性Rayleigh衰落信道，等功率5径信道，单发送天线，单根接收天线，采样点数为4096点。对于随机信号，本方法略优于最大最小特征值检测，有将近0.2dB的性能增益，它们均劣于能量检测，但是仍然远好于存在噪声不确定性时的能量检测。The simulation results in Fig. 6 take spectrum detection of random signals as an example. Adopt QPSK signal, frequency selective Rayleigh fading channel, equal power 5-path channel, single transmitting antenna, single receiving antenna, and the number of sampling points is 4096 points. For random signals, this method is slightly better than the maximum and minimum eigenvalue detection, with a performance gain of nearly 0.2dB. They are both inferior to energy detection, but still far better than energy detection in the presence of noise uncertainty.

本领域普通技术人员可以理解上述方法中的全部或部分步骤可通过程序来指令相关硬件完成，所述程序可以存储于计算机可读存储介质中，如只读存储器、磁盘或光盘等。可选地，上述实施例的全部或部分步骤也可以使用一个或多个集成电路来实现。相应地，上述实施例中的各模块/单元可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。本发明不限制于任何特定形式的硬件和软件的结合。Those skilled in the art can understand that all or part of the steps in the above method can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, and the like. Optionally, all or part of the steps in the foregoing embodiments may also be implemented using one or more integrated circuits. Correspondingly, each module/unit in the foregoing embodiments may be implemented in the form of hardware, or may be implemented in the form of software function modules. The present invention is not limited to any specific combination of hardware and software.

Claims

1. a blind frequency spectrum sensing method is characterized in that, comprises the steps:

(1) receives the wireless signal for the treatment of on the perception frequency range;

(2) carry out sampling filter to received signal, calculate the auto-correlation coefficient of signal, and the structure covariance matrix, being designated as R, its dimension is L;

(3) calculate the conditional number that covariance matrix R is similar under two norms, the structure judgment variables is designated as T;

(4) judge according to judgment variables T whether frequency spectrum idle: when T greater than predefined decision threshold, then judging has authorization signal to exist on this frequency spectrum, when T less than predefined decision threshold, then judging does not have authorization signal, i.e. this frequency spectrum free time.

2. a kind of blind frequency spectrum sensing method according to claim 1, it is characterized in that: the sample signal of the N behind the sampling filter is expressed as, x (0), x (1) ... x (N-1), described coefficient correlation obtains by the following method: choosing calculation window length is L, from l=0 ... L-1, the coefficient correlation of calculating sample signal

λ_{l} = \frac{1}{N} Σ_{n = 0}^{N - 1} x (n) x^{*} (n - l)

Wherein, when n-l＜0, x (0)=0, oeprator * represents to ask conjugation, and number of samples N is the positive integer greater than 1, and length of window L is the positive integer greater than 1; Corresponding covariance matrix R is expressed as,

R = [\begin{matrix} λ_{0} & λ_{1} & \cdot \cdot \cdot & λ_{L - 1} \\ λ_{1}^{*} & λ_{0} & \cdot \cdot \cdot & λ_{L - 2} \\ \cdot & \cdot & \cdot \\ \cdot & \cdot & \cdot \cdot \cdot & \cdot \\ \cdot & \cdot & \cdot \\ λ_{L - 1}^{*} & λ_{L - 2}^{*} & \cdot \cdot \cdot & λ_{0} \end{matrix}] .

3. a kind of blind frequency spectrum sensing method according to claim 2 is characterized in that: number of samples N determines according to the cycle of frequency spectrum perception, the precision of frequency spectrum perception.

4. a kind of blind frequency spectrum sensing method according to claim 2 is characterized in that: length of window L, determine according to computing capability, frequency spectrum perception precision and the complexity of awareness apparatus.

5. a kind of blind frequency spectrum sensing method according to claim 2 is characterized in that the building method of described T is:

Step I: to the summation of all diagonal entries of matrix R and divided by L, remember that this value is A;

Step I i: square sum of compute matrix R all elements, then divided by L, deduct again A square, ask evolution again, its result is designated as B;

Step I ii: calculate B divided by

Its result is designated as C;

Step I v: calculate B and multiply by

Its result is designated as D;

Step v: calculate T ₁=A+C calculates T ₂If=A-D is T ₂Smaller or equal to zero, then to T ₂Give a very little number, obtain:

T = \frac{T_{1}}{T_{2}} = \frac{A + C}{A - D} .

6. a kind of blind frequency spectrum sensing method according to claim 5, it is characterized in that: the calculating of A and B obtains by the following method:

A＝λ ₀，

B = \sqrt{\frac{2}{L} Σ_{i = 1}^{L - 1} (L - i) {λ_{i}}^{2}} .

7. a kind of blind frequency spectrum sensing method according to claim 1, it is characterized in that: described decision threshold obtains by theory or emulation according to desired false alarm probability or detection probability.

8. a kind of blind frequency spectrum sensing method according to claim 1 is characterized in that: be applicable to the frequency spectrum perception of multiaerial system or the cooperation perception of multinode.

9. a blind frequency spectrum sensing device comprises: wireless signal samples and filtration module, auto-correlation coefficient computing module, judgment variables computing module and judging module; It is characterized in that,

Described wireless signal samples and filtration module are used for obtaining the wireless signal of institute's perception frequency range;

Described auto-correlation coefficient computing module is used for calculating the auto-correlation coefficient of signal;

Described judgment variables computing module is used for calculating the conditional number that covariance matrix R is similar under two norms, the structure judgment variables;

Described judging module comprises comparator, is used for relatively judgment variables and thresholding.