CN107295633A - The many primary user's localization methods clustered based on iteration - Google Patents
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Abstract
The invention discloses many primary user's localization methods clustered based on iteration, mainly include step a：Radio system architectures are set, the mean power that cognitive user nodes are perceived is obtained；Step b：Estimate primary user's emitter quantity；Step c：Many primary user's emitters are positioned with iteration cluster.The many primary user's localization methods clustered based on iteration of the present invention, it is possible to achieve the advantage of reduction algorithm complex and the accuracy of raising primary user transmitter site estimation.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a multi-master user positioning method based on iterative clustering.
Background
The number of primary user transmitters is estimated from matrix information of a mixed signal observed by a cognitive user node, and if observation noise exists in a communication system, the number of primary user transmitters can be relatively reliably estimated through Principal Component Analysis (PCA) and Singular Value Decomposition (SVD).
However, the current technology cannot be completely implemented in how to reduce the algorithm complexity and improve the accuracy of the primary user transmitter position estimation.
Disclosure of Invention
The invention aims to provide a multi-primary user positioning method based on iterative clustering to achieve the advantages of reducing algorithm complexity and improving the accuracy of position estimation of a primary user transmitter.
In order to achieve the purpose, the invention adopts the technical scheme that: the multi-master user positioning method based on iterative clustering specifically comprises the following steps:
step a: setting a radio system structure, and acquiring average power perceived by a cognitive user node;
step b: estimating the number of main user transmitters;
step c: and positioning the multi-main-user transmitter by using iterative clustering.
Further, the step a specifically includes:
the whole structure chart has M main user transmitters and N cognitive user nodes, and the position information of the main user transmitters is a vector theta1θ2....θM]T∈RM×2Is represented by the form of (a), whereini＝[xi,yi]Is the location of the ith primary user transmitter and sets the power of all transmitters to P0；
The independent signals observed from the M primary user transmitters are:
wherein, T represents a sampling period, L represents the number of samples, and a signal reaching the jth cognitive user node is:
in the formula,is from the primary useFading coefficient of propagation channel from user m to cognitive user i, ξjIs the wavelength and the gain factor of the transmitter antenna, d0The reference distance from the main user transmitter to the sensing node is shown, and ξ is set for simplifying the simulationj1 and d01 m, di(θm) From the m-th primary user transmitter (position of which is denoted by theta)mRepresenting) the distance from the ith sensing node, wherein the simulation model adopts a simple free space line-of-sight fading model, and the magnitude of a propagation power signal in the model is inversely proportional to the square of the propagation distance of the signal; n isjN is the mean observed by the sensing node as 0 and the variance as σ2(ii) a gaussian noise signal;
the form of the matrix is used to represent:
Y＝HS+n
Y＝[y1(t),y2(t)...yN(t)]T
n＝[n1(t),n2(t)...nN(t)]T(4-21)
h represents Hm,iM-1, 2,. M, i-1, 2,. N, since propagation channels between M primary user transmitters and N cognitive user nodes are independent, i.e., rank (h) -M;
let us know the positions of the cognitive user nodes in advance, so the average power perceived from the N cognitive user nodes can be represented by the following formula:
wherein ri is a power signal perceived by the ith cognitive user node,representing the noise power component.
Further, the step b specifically includes:
the estimation of the number of the main user transmitters is mainly obtained by calculating a covariance matrix of power signals received at cognitive user nodes, and the following formula can be obtained by setting the mean value of noise signals to be zero and being independent of the power signals:
R＝E[YYH]＝E[(H·S)(H·S)H]+σ2I
＝HSSHHH+σ2I (4-23)
wherein the matrix R is the covariance matrix of the matrix Y, E [ ·]Is to ask for the expectation of mathematics (.)HIs the complex conjugate of the computational equation, and the matrix R can be estimated from the sample covariance matrix:
the singular value matrix of matrix R is:
is the characteristic term quantity of the matrix R.Is a diagonal matrix with main diagonals composed of eigenvalues arranged in descending order,andthe diagonal arrays are respectively obtained by a signal matrix S and observation noise, and signals transmitted by different main user transmitters are assumedAre independent of each other, then SSHIs non-singular so its rank is M (i.e., the number of primary user transmitters) and H is column full rank, so according to matrix theory one can obtain:
rank(HSSHHH)＝M (4-26)
from the formulae (4-23) to (4-26) are:
in general, λMIs to be greater than sigma2And λ is the greater the SNR isMAnd σ2The greater the difference between them is also,
the Akaike Information Criterion (AIC), the Minimum Description Length (MDL) and the Bayesian Criterion are used to applyAnd matrixSeparately, this chapter uses the document [42 ]]The algorithm mentioned in (1).
Let λ be2≥...≥λM≥λM+1≥...≥λN> 0 is the eigenvalue of the matrix R (the largest eigenvalue is removed), then
di＝(λi-λi+1)/λi+1,i＝2,3...N-1 (4-28)
M＝index(max(d2,d3...dN-1)) (4-29)
Where, index (max (d)2,d3...dN-1) Is shown in (a)Is a vector (d)2,d3...dN-1) The subscript value of the medium-maximum element, M, is the number of primary user transmitters.
Further, the step c specifically includes:
first, the observation data r is giveni1,2.., N, to find the maximum likelihood estimate of the parameter θ vector:
wherein,representing two-dimensional position information for M primary user transmitters, r ═ r1,r2,...rN]Representing data vectors observed by N cognitive user nodes, fr(r | θ) is its probability density function;
from the formulae (4-22), the formula can be obtained:
the observed noise is divided into M equal parts and combined with the power of each primary user transmitter to obtain a formula
ri,mIs the power signal of the mth primary user transmitter perceived by the ith cognitive user node (it is assumed that only this one primary user transmitter is present here);
and then the information of the initial positions of M main users is given by the formula (4-22)m＝1,2,...,M，ri,mCan be represented by the following formula:
wherein,r iis a sample vector of a plurality of received powers perceived by the ith cognitive user node in a continuous time sliceWhen the accurate position of the primary user transmitter is very close to, the first item in the (4-33) expression is the power component of the mth primary user transmitter; the second term of equation (4-33) is the power component of the observed noise and is generally small as seen by the signal-to-noise ratio, and therefore ri,mIt can be expressed in the form of the inverse square of the distance:
then, from the power, the distance can be calculated:
is an approximate estimate of the distance from the ith cognitive user node to the mth primary user transmitter. After the distance between each group of main users and the cognitive user node is estimated, approximate positions of M main user transmitters can be calculated by utilizing a plane geometric triangulation algorithm and a clustering algorithm.
The invention relates to a multi-primary user positioning method based on iterative clustering, which mainly comprises the following steps: setting a radio system structure, and acquiring average power perceived by a cognitive user node; step b: estimating the number of main user transmitters; step c: the iterative clustering is used for positioning the multi-primary user transmitter, so that the advantages of reducing the algorithm complexity and improving the accuracy of the position estimation of the primary user transmitter can be realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a radio system for a multi-primary user location method based on iterative clustering in accordance with the present invention;
FIG. 2 is a diagram of the result of estimating the number of simulated primary users of the multi-primary user positioning method based on iterative clustering according to the present invention;
FIG. 3 is a diagram of a positioning algorithm simulation result of the iterative clustering-based multi-master user positioning method of the present invention;
FIG. 4 is a performance comparison graph of the iterative clustering algorithm and the EM algorithm at a signal-to-noise ratio of 10dB according to the multi-primary user positioning method based on iterative clustering.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The multi-master user positioning method based on iterative clustering specifically comprises the following steps:
step a: setting a radio system structure, and acquiring average power perceived by a cognitive user node;
step b: estimating the number of main user transmitters;
step c: and positioning the multi-main-user transmitter by using iterative clustering.
The step a specifically comprises the following steps:
the whole structure chart has M main user transmitters and N cognitive user nodes, and the position information of the main user transmitters is a vector theta1θ2....θM]T∈RM×2Is represented by the form of (a), whereini＝[xi,yi]Is the location of the ith primary user transmitter and sets the power of all transmitters to P0；
The independent signals observed from the M primary user transmitters are:
wherein, T represents a sampling period, L represents the number of samples, and a signal reaching the jth cognitive user node is:
in the formula,is the fading coefficient of the propagation channel from the primary user m to the cognitive user i, ξjIs the wavelength and the gain factor of the transmitter antenna, d0Indicating primary user transmitter to sensing nodeReferring to distance, for simplifying the simulation, we have set ξj1 and d01 m, di(θm) From the m-th primary user transmitter (position of which is denoted by theta)mRepresenting) the distance from the ith sensing node, wherein the simulation model adopts a simple free space line-of-sight fading model, and the magnitude of a propagation power signal in the model is inversely proportional to the square of the propagation distance of the signal; n isjN is the mean observed by the sensing node as 0 and the variance as σ2(ii) a gaussian noise signal;
the form of the matrix is used to represent:
Y＝HS+n
Y＝[y1(t),y2(t)...yN(t)]T
n＝[n1(t),n2(t)...nN(t)]T(4-21)
h represents Hm,iM-1, 2,. M, i-1, 2,. N, since propagation channels between M primary user transmitters and N cognitive user nodes are independent, i.e., rank (h) -M;
let us know the positions of the cognitive user nodes in advance, so the average power perceived from the N cognitive user nodes can be represented by the following formula:
in the formula, riIs the power signal perceived by the ith cognitive user node,representing the noise power component.
The step b specifically comprises the following steps:
the estimation of the number of the main user transmitters is mainly obtained by calculating a covariance matrix of power signals received at cognitive user nodes, and the following formula can be obtained by setting the mean value of noise signals to be zero and being independent of the power signals:
R＝E[YYH]＝E[(H·S)(H·S)H]+σ2I
＝HSSHHH+σ2I (4-23)
wherein the matrix R is the covariance matrix of the matrix Y, E [ ·]Is to ask for the expectation of mathematics (.)HIs the complex conjugate of the computational equation, and the matrix R can be estimated from the sample covariance matrix:
the singular value matrix of matrix R is:
is the characteristic term quantity of the matrix R.Is a diagonal matrix with main diagonals composed of eigenvalues arranged in descending order,andthe signals are mutually independent, and SS is determined by the signal matrix S and the observation noiseHIs non-singular so its rank is M (i.e., the number of primary user transmitters) and H is column full rank, so according to matrix theory one can obtain:
rank(HSSHHH)＝M (4-26)
from the formulae (4-23) to (4-26) are:
in general, λMIs to be greater than sigma2And λ is the greater the SNR isMAnd σ2The greater the difference between them is also,
the Akaike Information Criterion (AIC), the Minimum Description Length (MDL) and the Bayesian Criterion are used to applyAnd matrixSeparately, this chapter uses the document [42 ]]The algorithm mentioned in (1).
Let λ be2≥...≥λM≥λM+1≥...≥λN> 0 is the eigenvalue of the matrix R (the largest eigenvalue is removed), then
di＝(λi-λi+1)/λi+1,i＝2,3...N-1 (4-28)
M＝index(max(d2,d3...dN-1)) (4-29)
Where, index (max (d)2,d3...dN-1) Is a vector (d)2,d3...dN-1) The subscript value of the medium-maximum element, M, is the number of primary user transmitters.
The step c specifically comprises the following steps:
first, the observation data r is giveni1,2.., N, to find the maximum likelihood estimate of the parameter θ vector:
wherein,representing two-dimensional position information for M primary user transmitters, r ═ r1,r2,...rN]Representing data vectors observed by N cognitive user nodes, fr(r | θ) is its probability density function;
from the formulae (4-22), the formula can be obtained:
the observed noise is divided into M equal parts and combined with the power of each primary user transmitter to obtain a formula
ri,mIs the power signal of the mth primary user transmitter perceived by the ith cognitive user node (it is assumed that only this one primary user transmitter is present here);
and then the information of the initial positions of M main users is given by the formula (4-22)m＝1,2,...,M，ri,mCan be represented by the following formula:
wherein,r iis a sample vector of a plurality of received powers perceived by the ith cognitive user node in a continuous time sliceWhen the accurate position of the primary user transmitter is very close to, the first item in the (4-33) expression is the power component of the mth primary user transmitter; the second term of equation (4-33) is the power component of the observed noise and is generally small as seen by the signal-to-noise ratio, and therefore ri,mIt can be expressed in the form of the inverse square of the distance:
then, from the power, the distance can be calculated:
is an approximate estimate of the distance from the ith cognitive user node to the mth primary user transmitter. After the distance between each group of main users and the cognitive user node is estimated, approximate positions of M main user transmitters can be calculated by utilizing a plane geometric triangulation algorithm and a clustering algorithm.
In order to simulate a multi-master user positioning algorithm conveniently, 2 master user transmitters and 10-20 cognitive user nodes are assumed in a cognitive radio environment structure diagram, the master user transmitters and the cognitive user nodes are randomly distributed in a plane area of 1 kilometer × 1 kilometers, and channel parameters ξi＝1，d0＝1，P01, orThe convergence coefficient was 0.02.
As shown in fig. 2 below, the estimation of the number of primary user transmitters is given. As can be seen from the figure, it is known that the number of user nodes and the signal-to-noise ratio are several factors that affect the accuracy of the estimation of the number of primary users. When the signal-to-noise ratio is sufficiently high, the above-mentioned quantity estimation algorithm can ensure high accuracy. The final positioning result of the primary user transmitter is shown in fig. 3 (a total of 10 cognitive user nodes). Wherein, the positions of the cognitive user nodes are indicated by a symbol, the real positions of the primary users are indicated by a symbol, and the positions of the primary users estimated by a positioning algorithm are indicated by a symbol. From fig. 3 we can see that the estimated location of the primary user is very close to its real location.
In the simulation, we compare with the traditional EM-based positioning algorithm. Each positioning simulation is designed to be 2 main user transmitters, 4-20 cognitive user nodes and 100 times of simulation for each cognitive user node with fixed quantity. In fig. 4, the mean square distance positioning errors of the two algorithms are shown, and the signal-to-noise ratio is 10dB, so it can be seen from the figure that the effect of the multi-primary user positioning algorithm based on iterative clustering proposed in this chapter is due to the traditional EM-based positioning algorithm. From a theoretical point of view, it can be known that the algorithm proposed in this chapter only requires some iterative and clustering operations, while the EM-based positioning algorithm requires complex matrix operations (e.g., inverse matrix calculation), and thus the algorithm proposed in this chapter has a low operation complexity.
At least the following beneficial effects can be achieved:
the invention relates to a multi-primary user positioning method based on iterative clustering, which mainly comprises the following steps: setting a radio system structure, and acquiring average power perceived by a cognitive user node; step b: estimating the number of main user transmitters; step c: the iterative clustering is used for positioning the multi-primary user transmitter, so that the advantages of reducing the algorithm complexity and improving the accuracy of the position estimation of the primary user transmitter can be realized.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalent changes may be made to some features of the embodiments. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The multi-master user positioning method based on iterative clustering is characterized by specifically comprising the following steps:
step a: setting a radio system structure, and acquiring average power perceived by a cognitive user node;
step b: estimating the number of main user transmitters;
step c: and positioning the multi-main-user transmitter by using iterative clustering.
2. The iterative clustering-based multi-master user positioning method according to claim 1, wherein the step a specifically comprises:
the whole structure chart has M main user transmitters and N cognitive user nodes, and the position information of the main user transmitters is a vector theta1θ2....θM]T∈RM×2Is represented by the form of (a), whereini＝[xi,yi]Is the location of the ith primary user transmitter and sets the power of all transmitters to P0；
The independent signals observed from the M primary user transmitters are:
wherein, T represents a sampling period, L represents the number of samples, and a signal reaching the jth cognitive user node is:
in the formula,is the fading coefficient of the propagation channel from the primary user m to the cognitive user i, ξjIs the wavelength and the gain factor of the transmitter antenna, d0Indicating the reference distance from the primary user transmitter to the sensing node, and for simplicity of simulation we have set ξj1 and d01 m, di(θm) From the m-th primary user transmitter (position of which is denoted by theta)mRepresenting) the distance from the ith sensing node, wherein the simulation model adopts a simple free space line-of-sight fading model, and the magnitude of a propagation power signal in the model is inversely proportional to the square of the propagation distance of the signal; n isjN is the mean observed by the sensing node as 0 and the variance as σ2(ii) a gaussian noise signal;
the form of the matrix is used to represent:
Y＝HS+n
Y＝[y1(t),y2(t)...yN(t)]T
n＝[n1(t),n2(t)...nN(t)]T(4-21)
h represents Hm,iM-1, 2,. M, i-1, 2,. N, since propagation channels between M primary user transmitters and N cognitive user nodes are independent, i.e., rank (h) -M;
let us know the positions of the cognitive user nodes in advance, so the average power perceived from the N cognitive user nodes can be represented by the following formula:
in the formula, riIs the power signal perceived by the ith cognitive user node,representing the noise power component.
3. The iterative clustering-based multi-master user positioning method according to claim 1, wherein the step b specifically comprises:
the estimation of the number of the main user transmitters is mainly obtained by calculating a covariance matrix of power signals received at cognitive user nodes, and the following formula can be obtained by setting the mean value of noise signals to be zero and being independent of the power signals:
R＝E[YYH]＝E[(H·S)(H·S)H]+σ2I
＝HSSHHH+σ2I (4-23)
wherein the matrix R is the covariance matrix of the matrix Y, E [ ·]Is to ask for the expectation of mathematics (.)HIs the complex conjugate of the computational equation, and the matrix R can be estimated from the sample covariance matrix:
the singular value matrix of matrix R is:
is the characteristic term quantity of the matrix R.Is a diagonal matrix with main diagonals composed of eigenvalues arranged in descending order,andthe signals are mutually independent, and SS is determined by the signal matrix S and the observation noiseHIs non-singular so its rank is M (i.e., the number of primary user transmitters) and H is column full rank, so according to matrix theory one can obtain:
rank(HSSHHH)＝M (4-26)
from the formulae (4-23) to (4-26) are:
in general, λMIs to be greater than sigma2And λ is the greater the SNR isMAnd σ2The greater the difference between them is also,
akaike information criterionion Criterion, AIC), Minimum Description Length (MDL) and bayesian Criterion for usingAnd matrixSeparately, this chapter uses the document [42 ]]The algorithm mentioned in (1).
Let λ be2≥...≥λM≥λM+1≥...≥λN> 0 is the eigenvalue of the matrix R (the largest eigenvalue is removed), then
di＝(λi-λi+1)/λi+1,i＝2,3...N-1 (4-28)
M＝index(max(d2,d3...dN-1)) (4-29)
Where, index (max (d)2,d3...dN-1) Is a vector (d)2,d3...dN-1) The subscript value of the medium maximum element, M, is the number of primary user transmitters.
4. The iterative clustering-based multi-primary user positioning method according to claim 1, wherein the step c specifically comprises:
first, the observation data r is giveni1,2.., N, to find the maximum likelihood estimate of the parameter θ vector:
wherein,representing two-dimensional position information for M primary user transmitters, r ═ r1,r2,...rN]Representing data vectors observed by N cognitive user nodes, fr(r | θ) is its probability density function;
from the formulae (4-22), the formula can be obtained:
the observed noise is divided into M equal parts and combined with the power of each primary user transmitter to obtain a formula
ri,mIs the power signal of the mth primary user transmitter perceived by the ith cognitive user node (it is assumed that only this one primary user transmitter is present here);
and then the information of the initial positions of M main users is given by the formula (4-22)ri,mCan be represented by the following formula:
wherein,r iis a sample vector of a plurality of received powers perceived by the ith cognitive user node in a continuous time sliceWhen the accurate position of the primary user transmitter is very close to, the first item in the (4-33) expression is the power component of the mth primary user transmitter; the second term of equation (4-33) is the power component of the observed noise and is generally small as seen by the signal-to-noise ratio, and therefore ri,mIt can be expressed in the form of the inverse square of the distance:
then, from the power, the distance can be calculated:
is an approximate estimate of the distance from the ith cognitive user node to the mth primary user transmitter. After the distance between each group of main users and the cognitive user node is estimated, approximate positions of M main user transmitters can be calculated by utilizing a plane geometric triangulation algorithm and a clustering algorithm.
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| CN101808336A (en) * | 2009-04-30 | 2010-08-18 | 清华大学 | Method and system for positioning primary user in cognitive radio network |
| CN105025495A (en) * | 2014-12-03 | 2015-11-04 | 东南大学 | Cooperative sensing method for global frequency spectrum information of wireless cognitive network |
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Application publication date: 20171024 |