CN107071788B - Spectrum sensing method and device in cognitive wireless network - Google Patents
Spectrum sensing method and device in cognitive wireless network Download PDFInfo
- Publication number
- CN107071788B CN107071788B CN201710265365.0A CN201710265365A CN107071788B CN 107071788 B CN107071788 B CN 107071788B CN 201710265365 A CN201710265365 A CN 201710265365A CN 107071788 B CN107071788 B CN 107071788B
- Authority
- CN
- China
- Prior art keywords
- matrix
- sampling
- covariance matrix
- covariance
- coordinate point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 96
- 238000001228 spectrum Methods 0.000 title claims abstract description 53
- 230000001149 cognitive effect Effects 0.000 title claims abstract description 33
- 239000011159 matrix material Substances 0.000 claims abstract description 189
- 238000005070 sampling Methods 0.000 claims abstract description 82
- 230000008569 process Effects 0.000 claims abstract description 25
- 238000000691 measurement method Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 14
- 238000011478 gradient descent method Methods 0.000 claims description 8
- 238000000926 separation method Methods 0.000 claims description 5
- 238000001514 detection method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000005315 distribution function Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/14—Spectrum sharing arrangements between different networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a method and a device for sensing frequency spectrums in a cognitive wireless network, which comprises the steps of sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix; sampling a wireless signal to be sensed to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix; calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method, wherein the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point of the statistical manifold, and the statistical manifold is established according to Gaussian distribution; judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability. When the wireless signal to be sensed is low in signal-to-noise ratio in the using process, the wireless signal to be sensed can be sensed, and the spectrum sensing efficiency is improved.
Description
Technical Field
The invention relates to the technical field of spectrum sensing, in particular to a spectrum sensing method and device in a cognitive wireless network.
Background
With the increasing demand for wireless spectrum resources and the rapid development of wireless communication technologies, the wireless spectrum resources are increasingly strained. Cognitive radio is a key technology of wireless communication, and spectrum sensing plays an important role in the cognitive radio technology. In the process of utilizing the wireless spectrum resources, the efficiency of spectrum sensing is improved, and the utilization rate of the wireless spectrum resources is improved. The existing spectrum sensing method has high requirements on the signal-to-noise ratio, namely, the existing spectrum sensing method can sense the existence of the signal only under the condition of high signal-to-noise ratio, and cannot effectively sense whether the signal exists in the noise with low signal-to-noise ratio, so that the spectrum sensing efficiency of the existing spectrum sensing method is low.
Therefore, how to provide a spectrum sensing method and apparatus in a cognitive wireless network to solve the above technical problems becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
The invention aims to provide a frequency spectrum sensing method and a frequency spectrum sensing device in a cognitive wireless network, which can sense a wireless signal to be sensed when the signal-to-noise ratio of the wireless signal is low in the using process and improve the frequency spectrum sensing efficiency to a certain extent.
In order to solve the technical problem, the invention provides a spectrum sensing method in a cognitive wireless network, which comprises the following steps:
sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix;
sampling a wireless signal to be perceived to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix;
calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point of a statistical manifold, and the statistical manifold is established according to Gaussian distribution;
judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability.
Optionally, if there are multiple first sampling matrices, the process of obtaining a corresponding first covariance matrix according to the first sampling matrices specifically includes:
obtaining each covariance matrix corresponding to each first sampling matrix one by one according to each first sampling matrix;
and carrying out average value calculation on each covariance matrix to obtain a first covariance matrix.
Optionally, the process of calculating the average value of each covariance matrix to obtain the first covariance matrix is as follows:
and processing each covariance matrix by adopting a gradient descent method to obtain a Riemann mean matrix of each covariance matrix, and taking the Riemann mean matrix as the first covariance matrix.
Optionally, the process of calculating the average value of each covariance matrix to obtain the first covariance matrix is as follows:
and processing each covariance matrix by adopting an average value method to obtain an arithmetic mean value matrix of each covariance matrix, and taking the arithmetic mean value matrix as the first covariance matrix.
Optionally, in the spectrum sensing method in the cognitive wireless network as described above, the statistical manifold measurement method is a geodesic distance method.
Optionally, in the spectrum sensing method in the cognitive wireless network as described above, the statistical manifold measurement method is a symmetric KL split measurement method.
In order to solve the above technical problem, the present invention provides a spectrum sensing apparatus in a cognitive wireless network, where the apparatus includes:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for sampling a noise environment to obtain a first sampling matrix and obtaining a corresponding first covariance matrix according to the first sampling matrix; the device is also used for sampling the wireless signal to be sensed to obtain a second sampling matrix and obtaining a second covariance matrix according to the second sampling matrix;
the calculation module is used for calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point of a statistical manifold, and the statistical manifold is established according to Gaussian distribution;
and the judging module is used for judging whether the geometric distance is greater than a preset threshold, if so, a signal exists in the wireless signal to be perceived, otherwise, only noise exists in the wireless signal to be perceived.
The invention provides a method and a device for sensing frequency spectrums in a cognitive wireless network, which comprise the following steps: sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix; sampling a wireless signal to be sensed to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix; calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to a first coordinate point and a second coordinate point of the statistical manifold, and the statistical manifold is established according to Gaussian distribution; judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability.
The method comprises the steps of processing a noise environment to obtain a first covariance matrix of the noise environment, and processing a wireless signal to be perceived to obtain a second covariance matrix of the wireless signal to be perceived; because each covariance matrix corresponds to one coordinate point on the statistical manifold, according to the statistical manifold method in the information geometry method, the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point on the statistical manifold, then the geometric distance between the two coordinate points is calculated by using the statistical manifold metric method, when the geometric distance is more than a preset threshold, the signal exists in the wireless signal to be sensed, otherwise, only noise exists. In the using process, the invention can also sense the wireless signal to be sensed when the signal-to-noise ratio of the wireless signal is lower, and improves the spectrum sensing efficiency to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed in the prior art and the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a spectrum sensing method in a cognitive wireless network according to the present invention;
FIG. 2 is a schematic diagram of a simulation provided by the present invention;
fig. 3 is a schematic structural diagram of a spectrum sensing device in a cognitive wireless network according to the present invention.
Detailed Description
The invention provides a frequency spectrum sensing method and a frequency spectrum sensing device in a cognitive wireless network, which can sense a wireless signal to be sensed when the signal-to-noise ratio of the wireless signal is low in the using process and improve the frequency spectrum sensing efficiency to a certain extent.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a schematic flow chart of a spectrum sensing method in a cognitive wireless network according to the present invention. The method comprises the following steps:
step 10: sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix;
step 20: sampling a wireless signal to be sensed to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix;
step 30: calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to a first coordinate point and a second coordinate point of the statistical manifold, and the statistical manifold is established according to Gaussian distribution;
step 40: judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability.
Note that the noise environment is an environment containing only noise. Specifically, the noise environment is estimated (i.e., the noise environment near the sensing node is estimated), it is assumed that there are M secondary users in the cognitive wireless network, and the node sampling signal of each secondary user forms a vector matrix, for example, the vector matrix is represented by X ═ X1,x2,x3,…,xM]Is represented by the formula (I) in which xi＝[xi(1),xi(2),xi(3),…,xi(N)]TThe first sampling matrix may be represented as an N × M matrix, and the covariance calculation of the first sampling matrix yields a first covariance matrix R corresponding to the first sampling matrix1。
In the same method, a wireless signal to be sensed is sampled to obtain a second sampling matrix, and the second sampling matrix is subjected to covariance calculation to obtain a second covariance matrix R corresponding to the second sampling matrix2。
Using information geometry method to obtain first covariance matrix R1And a second covariance matrix R2The processing is performed in that the families of probability distribution functions of different types or different parameterisations correspond to a statistical manifold with a certain geometry and each point on the statistical manifold corresponds to a probability distribution function. Thus, the statistical detection problem can be translated into a geometric problem on the statistical manifold. And the geometric analysis can be carried out on the corresponding statistical manifold according to different distribution data, and a better detection effect can be obtained.
Specifically, the covariance matrix R ∈ Cn×nParameterized family of probability distribution functions S ═ { p (x | R) | R ∈ Cn×nIn which C isn×nFor an open set of n × n-dimensional vector spaces, p (x | R) is a probability density function of Gaussian distribution, therefore, according to the theory of information geometry, S can form a differentiable manifold under a certain topology, which is called a statistical manifoldThe parameter R of the shape S is a covariance matrix, so S can be a matrix manifold. A specific covariance matrix corresponds to a corresponding coordinate point on the statistical manifold S, so that a first covariance matrix obtained from a noisy environment only and a second covariance matrix obtained from a wireless signal to be sensed correspond to two different points (a first coordinate point and a second coordinate point, respectively) on the statistical manifold, respectively, and when a signal exists in the wireless signal to be sensed, it indicates that the sensed signal is stronger than the signal strength sensed in the noisy environment, so that a certain geometric distance between the coordinate point corresponding to the second covariance matrix on the statistical manifold and the coordinate point corresponding to the first covariance matrix on the statistical manifold S will exist, and the geometric distance D should be greater than a preset threshold T (also referred to as a decision threshold). When no signal (i.e. only noise) is present in the wireless signal to be sensed, the geometric distance D between the first coordinate point and the second coordinate point will be smaller than the preset threshold T. Therefore, in the invention, whether the signal exists in the wireless signal to be sensed can be judged by comparing the geometric distance D between the first coordinate point and the second coordinate point with the preset threshold T.
It should be further noted that, according to the Constant False Alarm Rate (CFAR) criterion, that is, in order to enable the utilization Rate of the idle spectrum by the cognitive user to reach a certain level, we need to limit the False Alarm probability of the CR system to a fixed value, which is called the False Alarm probability PfThe preset threshold is required according to the false alarm probability PfThe setting is performed.
Specifically, the preset threshold may be pre-calculated by the following method, and the process is as follows:
(1) simulating to generate noise, sampling the noise to obtain N '+ 1 covariance matrixes, and taking one of the covariance matrixes as a matrix R' to be detected;
(2) riemann mean R of N' covariance matrixes calculated by sampling gradient descent methoddCalculating matrix R' to be detected and Riemann mean value R by adopting statistical manifold measurement methoddThe distance D (R) between two corresponding coordinate points on the statistical manifold Sd，R＇)；
(3) Repeating the steps (1) and (2) L times, and setting the distance value D (R) obtained for the first timedR') is Ld1Distance value D (R) obtained at 2 nd timedR') is Ld2…, distance value D (R) obtained at the i-th timedR') is Ldi…, distance value D (R) obtained at the L-th timedR') is LdL(ii) a And mixing L of LdiIn descending order, i.e. first LdiHas the largest value of LdiThe value of (d) is minimal; according to false alarm probability PfObtaining the preset threshold as the L PfThe distance value corresponding to each position. Where the larger L the better, e.g. L50000, the false alarm probability Pf0.01, then the 50000PfPosition, i.e. 50000 XPfI.e. the distance value corresponding to the 500 th position, and this distance value is the preset threshold.
Specifically, when the distance value obtained according to the wireless signal to be sensed is greater than the maximum distance value among the L distance values, the signal is always present in the wireless signal to be sensed, and the false alarm probability, i.e., the corresponding fault tolerance rate, is determined, for example, when L is 50000 and the false alarm probability P isfWhen the distance value is 0.01, namely, larger than the distance value corresponding to the 500 th position, a signal exists.
It should be noted that the matrix R' to be detected and the riemann mean value R may be calculated by using a symmetric KL separation metric method or a geodesic distance method in the statistical manifold metric methoddThe distance D (R) between two corresponding coordinate points on the statistical manifold SdR'), and it is noted that the statistical manifold method employed in sensing the wireless signal to be sensed should be identical to the statistical manifold method employed in calculating the preset threshold.
Of course, the preset threshold is not limited to be calculated by the above calculation method, and may also be calculated by other calculation methods.
Of course, the false alarm probability PfThe specific numerical values of the components can be determined according to actual conditions, and the invention is not particularly limited to the specific numerical values, so that the purpose of the invention can be achieved.
Optionally, if there are a plurality of first sampling matrices, the process of obtaining a corresponding first covariance matrix according to the first sampling matrices specifically includes:
obtaining each covariance matrix corresponding to each first sampling matrix one by one according to each first sampling matrix;
and carrying out average value calculation on each covariance matrix to obtain a first covariance matrix.
Specifically, in practical applications, the noise environment may be sampled for multiple times to obtain multiple first sampling matrices, and each first sampling matrix may be subjected to covariance calculation to obtain multiple covariance matrices. And performing average calculation on the plurality of covariance matrices to obtain a representative covariance matrix, wherein the obtained covariance matrix is the first covariance matrix (i.e. the covariance matrix used for calculating the geometric distance). The noise environment is sampled for multiple times to obtain a plurality of covariance matrixes, and a first covariance matrix is obtained according to the plurality of covariance matrixes, so that the sensing accuracy can be improved.
Optionally, the process of calculating the average value of each covariance matrix to obtain the first covariance matrix is as follows:
and processing each covariance matrix by adopting a gradient descent method to obtain a Riemann mean matrix of each covariance matrix, and taking the Riemann mean matrix as a first covariance matrix.
It should be noted that the multiple covariance matrices obtained above may be processed by a gradient descent method to obtain a riemann mean matrix corresponding to the noise environment, and the riemann mean matrix is used as a first covariance matrix, that is, a covariance matrix for calculating a geometric distance by statistical manifold.
Of course, other methods may be used to process the plurality of covariance matrices and obtain the first covariance matrix. Specifically, the method is not particularly limited, and the object of the present invention can be achieved.
Optionally, the process of calculating the average value of each covariance matrix to obtain the first covariance matrix is as follows:
and processing each covariance matrix by adopting an average value method to obtain an arithmetic mean value matrix of each covariance matrix, and taking the arithmetic mean value matrix as a first covariance matrix.
Of course, in the present invention, besides the riemann mean matrix of each covariance matrix can be calculated by using the gradient descent method, the arithmetic mean matrix of each covariance matrix can be calculated by using the mean algorithm, and the arithmetic mean matrix is used as the first covariance matrix in the present application.
Optionally, as for the spectrum sensing method in the cognitive wireless network, the statistical manifold measurement method is a geodesic distance method.
Specifically, for the geometric Distance between two coordinate points on the statistical manifold S, a Geodetic Distance (GD) method may be adopted to calculate the coordinates of the first coordinate point and the second coordinate point to obtain the geometric Distance D between the two coordinate points. Of course, other statistical manifold measurement methods can be adopted to calculate the geometric distance D between the two coordinate points, and the invention does not specially limit the geometric distance D, and thus the purpose of the invention can be achieved.
Optionally, in the spectrum sensing method in the cognitive wireless network, the statistical manifold metric method is a symmetric KL Separation (SKLD) metric method.
It should be noted that, in the present invention, in addition to the geometric distance D between the first coordinate point and the second coordinate point calculated by the geodesic distance method, the geometric distance D between the first coordinate point and the second coordinate point may also be calculated by the symmetric KL separation metric method. The method for measuring the manifold statistics is particularly adopted, and the method is not specially limited in the aspect, and the purpose of the method can be achieved.
It should be further noted that, in the process of using the spectrum sensing method in the cognitive wireless network provided by the present invention, spectrum sensing is not required to be performed through a priori spectrum signal, so that the universality of the method is enhanced.
In addition, referring to fig. 2, fig. 2 is a simulation diagram provided by the present invention. In the process of simulating the wireless signal to be sensed, a geometric distance between a first coordinate point and a second coordinate point is calculated by adopting a geodesic distance method and a symmetrical KL separation degree measurement method respectively, the false alarm probability is 0.01, the number of sub-users is 5, and the number of sampling points is 500. The relationship between the detection probability and the signal-to-noise ratio is shown in fig. 2, when the signal-to-noise ratio is low (for example, -15), the signal can be perceived (whereas the spectrum sensing method in the prior art cannot perceive the signal when the signal-to-noise ratio is low), and the detection performance is rapidly improved as the signal-to-noise ratio is improved.
The invention provides a frequency spectrum sensing method in a cognitive wireless network, which comprises the following steps: sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix; sampling a wireless signal to be sensed to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix; calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to a first coordinate point and a second coordinate point of the statistical manifold, and the statistical manifold is established according to Gaussian distribution; judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability.
The method comprises the steps of processing a noise environment to obtain a first covariance matrix of the noise environment, and processing a wireless signal to be perceived to obtain a second covariance matrix of the wireless signal to be perceived; because each covariance matrix corresponds to one coordinate point on the statistical manifold, according to the statistical manifold method in the information geometry method, the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point on the statistical manifold, then the geometric distance between the two coordinate points is calculated by using the statistical manifold metric method, when the geometric distance is more than a preset threshold, the signal exists in the wireless signal to be sensed, otherwise, only noise exists. In the using process, the invention can also sense the wireless signal to be sensed when the signal-to-noise ratio of the wireless signal is lower, and improves the spectrum sensing efficiency to a certain extent.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a spectrum sensing device in a cognitive wireless network according to the present invention. On the basis of the above-described embodiment:
the device includes:
the system comprises an acquisition module 1, a processing module and a processing module, wherein the acquisition module 1 is used for sampling a noise environment to obtain a first sampling matrix and obtaining a corresponding first covariance matrix according to the first sampling matrix; the device is also used for sampling the wireless signal to be sensed to obtain a second sampling matrix and obtaining a second covariance matrix according to the second sampling matrix;
the calculation module 2 is used for calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to a first coordinate point and a second coordinate point of the statistical manifold, and the statistical manifold is established according to Gaussian distribution;
and the judging module 3 is used for judging whether the geometric distance is greater than a preset threshold, if so, a signal exists in the wireless signal to be perceived, otherwise, only noise exists in the wireless signal to be perceived.
The invention provides a frequency spectrum sensing device in a cognitive wireless network, which can sense a wireless signal to be sensed when the signal-to-noise ratio of the wireless signal is low in the using process and improve the frequency spectrum sensing efficiency to a certain extent.
In addition, for a specific description of the spectrum sensing method related to the spectrum sensing device in the cognitive wireless network provided by the present invention, please refer to the above method embodiment, which is not described herein again.
It is further noted that, in the present specification, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A method for spectrum sensing in a cognitive wireless network, the method comprising:
sampling a noise environment to obtain a first sampling matrix, and obtaining a corresponding first covariance matrix according to the first sampling matrix;
sampling a wireless signal to be perceived to obtain a second sampling matrix, and obtaining a second covariance matrix according to the second sampling matrix;
calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point of a statistical manifold, and the statistical manifold is established according to Gaussian distribution;
judging whether the geometric distance is larger than a preset threshold, if so, determining that a signal exists in the wireless signal to be sensed, otherwise, determining that only noise exists in the wireless signal to be sensed; the preset threshold is set according to the false alarm probability; wherein:
the calculation process of the preset threshold comprises the following steps:
(1) simulating to generate noise, sampling the noise to obtain N '+ 1 covariance matrixes, and taking one of the covariance matrixes as a matrix R' to be detected;
(2) riemann mean R of N' covariance matrixes calculated by sampling gradient descent methoddCalculating the matrix R' to be detected and the Riemann mean value R by adopting a statistical manifold measurement methoddThe distance D (R) between two corresponding coordinate points on the statistical manifold Sd，R＇)；
(3) Repeating the steps (1) and (2) L times, and setting the distance value D (R) obtained for the first timedR') is Ld1Distance value D (R) obtained at 2 nd timedR') is Ld2…, distance value D (R) obtained at the i-th timedR') is Ldi…, distance value D (R) obtained at the L-th timedR') is LdL(ii) a And mixing L of LdiPerforming descending arrangement; according to false alarm probability PfObtaining the preset threshold as the L PfAnd the distance value corresponding to each position is used as a preset threshold.
2. The method for spectrum sensing in a cognitive wireless network according to claim 1, wherein the first sampling matrix is a plurality of matrices, and a process of obtaining a corresponding first covariance matrix according to the first sampling matrix specifically includes:
obtaining each covariance matrix corresponding to each first sampling matrix one by one according to each first sampling matrix;
and carrying out average value calculation on each covariance matrix to obtain a first covariance matrix.
3. The spectrum sensing method in the cognitive wireless network according to claim 2, wherein the process of calculating the average value of each covariance matrix to obtain the first covariance matrix comprises:
and processing each covariance matrix by adopting a gradient descent method to obtain a Riemann mean matrix of each covariance matrix, and taking the Riemann mean matrix as the first covariance matrix.
4. The spectrum sensing method in the cognitive wireless network according to claim 2, wherein the process of calculating the average value of each covariance matrix to obtain the first covariance matrix comprises:
and processing each covariance matrix by adopting an average value method to obtain an arithmetic mean value matrix of each covariance matrix, and taking the arithmetic mean value matrix as the first covariance matrix.
5. The spectrum sensing method in the cognitive wireless network according to any one of claims 1 to 4, wherein the statistical manifold metric method is a geodesic distance method.
6. The spectrum sensing method in the cognitive wireless network according to any one of claims 1 to 4, wherein the statistical manifold metric method is a symmetric KL separation metric method.
7. An apparatus for spectrum sensing in a cognitive wireless network, the apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for sampling a noise environment to obtain a first sampling matrix and obtaining a corresponding first covariance matrix according to the first sampling matrix; the device is also used for sampling the wireless signal to be sensed to obtain a second sampling matrix and obtaining a second covariance matrix according to the second sampling matrix;
the calculation module is used for calculating the geometric distance between the first coordinate point and the second coordinate point by adopting a statistical manifold measurement method; the first covariance matrix and the second covariance matrix respectively correspond to the first coordinate point and the second coordinate point of a statistical manifold, and the statistical manifold is established according to Gaussian distribution;
the judging module is used for judging whether the geometric distance is larger than a preset threshold, if so, a signal exists in the wireless signal to be perceived, otherwise, only noise exists in the wireless signal to be perceived; the preset threshold is set according to the false alarm probability; wherein:
the calculation process of the preset threshold comprises the following steps:
(1) simulating to generate noise, sampling the noise to obtain N '+ 1 covariance matrixes, and taking one of the covariance matrixes as a matrix R' to be detected;
(2) riemann mean R of N' covariance matrixes calculated by sampling gradient descent methoddCalculating the matrix R' to be detected and the Riemann mean value R by adopting a statistical manifold measurement methoddThe distance D (R) between two corresponding coordinate points on the statistical manifold Sd，R＇)；
(3) Repeating the steps (1) and (2) L times, and setting the distance value D (R) obtained for the first timedR') is Ld1Distance value D (R) obtained at 2 nd timedR') is Ld2…, distance value D (R) obtained at the i-th timedR') is Ldi…, distance value D (R) obtained at the L-th timedR') is LdL(ii) a And mixing L of LdiPerforming descending arrangement; according to false alarm probability PfObtaining the preset threshold as the L PfAnd the distance value corresponding to each position is used as a preset threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710265365.0A CN107071788B (en) | 2017-04-21 | 2017-04-21 | Spectrum sensing method and device in cognitive wireless network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710265365.0A CN107071788B (en) | 2017-04-21 | 2017-04-21 | Spectrum sensing method and device in cognitive wireless network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107071788A CN107071788A (en) | 2017-08-18 |
| CN107071788B true CN107071788B (en) | 2020-09-11 |
Family
ID=59601263
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710265365.0A Active CN107071788B (en) | 2017-04-21 | 2017-04-21 | Spectrum sensing method and device in cognitive wireless network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107071788B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107979431B (en) * | 2017-11-28 | 2021-05-28 | 广东工业大学 | Method, device and equipment for spectrum sensing based on Riemann median |
| CN108880717A (en) * | 2018-08-17 | 2018-11-23 | 广东工业大学 | A kind of frequency spectrum sensing method of the α divergence based on information geometry |
| CN109525339B (en) * | 2018-08-21 | 2021-04-16 | 广东工业大学 | Spectrum sensing method, device, equipment and storage medium of cognitive radio |
| CN110288025A (en) * | 2019-06-25 | 2019-09-27 | 广东工业大学 | Frequency spectrum sensing method, device and equipment based on information geometry and spectral clustering |
| CN114254265B (en) * | 2021-12-20 | 2022-06-07 | 军事科学院系统工程研究院网络信息研究所 | Satellite communication interference geometric analysis method based on statistical manifold distance |
-
2017
- 2017-04-21 CN CN201710265365.0A patent/CN107071788B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 雷达目标检测的信息几何方法;赵兴刚，王首勇;《信号处理》;20150630;第631-635页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107071788A (en) | 2017-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107071788B (en) | Spectrum sensing method and device in cognitive wireless network | |
| CN107979431B (en) | Method, device and equipment for spectrum sensing based on Riemann median | |
| CN107341220B (en) | Multi-source data fusion method and device | |
| CN109685092B (en) | Clustering method, equipment, storage medium and device based on big data | |
| JP2012047724A (en) | Electromagnetic wave identification apparatus, electromagnetic wave identification method, and electromagnetic wave identification program | |
| CN109144988B (en) | Abnormal data detection method and device | |
| CN109257383B (en) | BGP anomaly detection method and system | |
| CN110083507B (en) | Key performance index classification method and device | |
| CN110224771B (en) | Spectrum sensing method and device based on BP neural network and information geometry | |
| CN110932807B (en) | Spectrum sensing method and system for MIMO (multiple input multiple output) system under non-Gaussian noise | |
| CN111626360A (en) | Method, device, equipment and storage medium for detecting fault type of boiler | |
| CN109525339B (en) | Spectrum sensing method, device, equipment and storage medium of cognitive radio | |
| CN107104744B (en) | Spectrum sensing method and device in cognitive wireless network | |
| CN108880717A (en) | A kind of frequency spectrum sensing method of the α divergence based on information geometry | |
| CN109996171B (en) | Amorphous positioning method for genetic-tabu search optimization of wireless sensor network | |
| CN106452627B (en) | A kind of noise power estimation method and device for broader frequency spectrum perception | |
| CN113378682B (en) | Millimeter wave radar fall detection method and system based on improved clustering algorithm | |
| CN115081969A (en) | Abnormal data determination method and related device | |
| CN112420066A (en) | Noise reduction method, noise reduction device, computer equipment and computer readable storage medium | |
| CN105282720B (en) | A kind of method for filtering spam short messages and device | |
| CN108133234B (en) | Sparse subset selection algorithm-based community detection method, device and equipment | |
| CN106792531B (en) | Node positioning method and device of sensor network | |
| CN116668264A (en) | Root cause analysis method, device, equipment and storage medium for alarm clustering | |
| CN110337106B (en) | SSDF attack resistant cooperative spectrum sensing method, device and system | |
| CN114417090A (en) | Data screening method and device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240115 Address after: 518063, Zone C, 3rd Floor, Building A1, Shenzhen Digital Technology Park, No. 17 Gaoxin South 7th Road, High tech Zone Community, Yuehai Street, Shenzhen City, Guangdong Province Patentee after: SHENZHEN INSWIN INTELLIGENT SYSTEM CO.,LTD. Address before: Room 201, Building A, No. 318 Outer Ring West Road, University City, Panyu District, Guangzhou City, Guangdong Province, 510000 Patentee before: Guangzhou University Town (Guangong) Science and Technology Achievement Transformation Center Effective date of registration: 20240115 Address after: Room 201, Building A, No. 318 Outer Ring West Road, University City, Panyu District, Guangzhou City, Guangdong Province, 510000 Patentee after: Guangzhou University Town (Guangong) Science and Technology Achievement Transformation Center Address before: 510062 courtyard 729, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong Patentee before: GUANGDONG University OF TECHNOLOGY |