CN106656376B - Cooperative spectrum sensing method based on characteristic value consistency estimation - Google Patents
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Abstract
The invention discloses a cooperative spectrum sensing method based on consistent estimation of characteristic values, which comprises the following specific implementation steps: step 1: the macro base station transmits an authorized user signal. Step 2: the low power small cell base station receives the signal and takes continuous samples. And step 3: and the low-power small cell base station sends the sensing data to a data fusion center, and a receiving sample matrix is formed in the data fusion center. And 4, step 4: and the data fusion center obtains judgment statistics according to the received sample matrix. And 5: and the data fusion center compares the judgment statistic with a judgment threshold to judge whether the authorized user exists. The invention considers the space structure characteristic of the sending signal, and improves the performance of frequency spectrum sensing compared with the method of directly estimating the covariance matrix.
Description
Technical Field
The invention belongs to the technical field of wireless communication, relates to spectrum sensing in the cognitive radio technology and high-dimensional characteristic value estimation in an estimation theory, and particularly relates to a cooperative spectrum sensing method based on characteristic value consistent estimation.
Background
With the rapid development of wireless communication services and the widespread use of wireless communication devices, mobile data traffic has exhibited explosive growth. Under the condition of the shortage of spectrum resources nowadays, the traditional macro cellular network has limited expansion of network capacity, and the innovation of a new wireless network architecture becomes a necessary trend for supporting the network capacity. The dense heterogeneous network can realize multi-layer enhanced coverage and edge user performance improvement and improve system capacity by densely deploying low-power small cell base stations in a macro cell coverage range, and can increase the number of small cells by reducing the radius of the small cells and improve the space reuse rate of frequency spectrum resources. Due to the dense deployment of small cells, frequency reuse between small cells and macro cells can cause complex interference problems. The specific implementation method is that a macro cell user is used as an authorized user, a cognitive radio technology (cognitive network for short) is adopted for a small cell network which enhances coverage outside a macro cell, authorized spectrum resources of a macro base station are reused intelligently on the premise of not interfering communication of the macro cell user, the problem of cross-layer interference in the dense heterogeneous network is solved effectively, and coexistence between the macro cell and the cognitive network is achieved.
Opportunistic spectrum access of licensed spectrum and reuse of free spectrum by cognitive radio technology are achieved through spectrum sensing. Traditional spectrum sensing methods include cyclostationary feature sensing, matched filter sensing, energy sensing, and covariance matrix eigenvalue-based sensing. The spectrum sensing algorithms are single-user spectrum sensing, sensing performance is reduced due to shadow and multipath fading in an actual communication environment, and the negative effects are effectively relieved by the cooperative spectrum sensing algorithm through space diversity generated by cooperation of a plurality of sensing users. The cooperative spectrum sensing is divided into hard decision and soft decision according to different decision mechanisms, the hard decision requires each sensing user to make a local decision and report a decision result to the data fusion center, and the data fusion center obtains a final decision result; in the soft decision mechanism, each perception user reports original perception sample data or local decision statistics to the fusion center, so that more accurate perception is realized. In the soft decision, when the sample data of all the sensing users is sent to the fusion center, and the method for the fusion center to decide based on the receiving sample matrix formed by the sample data is a cooperative spectrum sensing method based on a random matrix, wherein the row number of the receiving sample matrix is the number of the cooperative sensing users, which is called as a data matrix dimension, and the column number is the sample number of each sensing user.
The traditional cooperative spectrum sensing algorithm based on the random matrix assumes that the number of samples is far larger than the dimension of the matrix, and under the traditional progressive assumption, the sample covariance matrix is the optimal estimation of the statistical covariance matrix. However, in a dense heterogeneous network, the dense arrangement of small cells enables the data matrix dimension and the sample number to be in the same order of magnitude, according to a high-dimensional estimation theory, under the generalized gradual assumption that the data matrix dimension and the sample number tend to infinity at the same time and the ratio of the data matrix dimension and the sample number tends to be constant, the sample covariance matrix cannot provide good estimation on the statistical covariance matrix, the eigenvalue distribution of the sample covariance matrix is more dispersed, the maximum eigenvalue is overestimated, the minimum eigenvalue is underestimated, and the sample eigenvalue is no longer a consistent estimation of the eigenvalue of the statistical covariance matrix. The estimation of the statistical covariance matrix is a key process of spectrum sensing algorithm derivation, and the reduction of the estimation performance directly influences the performance of spectrum sensing. At present, sensing methods for high-dimensional data are relatively few, and the existing maximum Minimum Eigenvalue spectrum sensing algorithm (OAS-MME) Based on a Covariance matrix shrinkage estimator improves the spectrum sensing performance by estimating a statistical Covariance matrix by using the shrinkage estimator, but the estimation of the high-dimensional Covariance matrix directly cannot fully utilize the space structure of a transmitted signal, and the spectrum sensing performance needs to be further improved.
Disclosure of Invention
In order to solve the estimation problem of unknown parameters related to a high-dimensional statistical covariance matrix in a Spectrum Sensing method, the invention combines high-dimensional estimation of characteristic values with Spectrum Sensing to obtain a Cooperative Spectrum Sensing (CEE-CSS) method Based on Consistent estimation of characteristic values. The method utilizes the space structure of the transmitted signal and utilizes a consistent estimator of the characteristic value under the generalized progressive assumption to estimate the characteristic value, thereby having good perception performance. The invention can be applied to co-frequency deployment of small cells and macro cells in a dense heterogeneous network, and a plurality of cognitive small cells cooperatively sense whether authorized users exist in the macro cells, thereby effectively avoiding interlayer interference and realizing coexistence of the macro cells and the cognitive networks.
In order to achieve the technical effect, the cooperative spectrum sensing method based on consistent eigenvalue estimation comprises the following specific implementation steps:
step 1: the macro base station transmits an authorized user signal.
Step 2: the low power small cell base station receives the signal and takes continuous samples.
And step 3: and the low-power small cell base station sends the sensing data to a data fusion center, and a receiving sample matrix is formed in the data fusion center.
And 4, step 4: and the data fusion center obtains judgment statistics according to the received sample matrix.
And 5: and the data fusion center compares the judgment statistic with a judgment threshold to judge whether the authorized user exists.
The invention has the advantages that:
(1) the spatial structure characteristic of the transmitted signal is considered, and the performance of spectrum sensing is improved compared with the method of directly estimating the covariance matrix;
(2) the characteristic value is estimated by using a consistent estimator of the characteristic value under the condition of high dimension of data (dense small cell deployment), and the good estimation of the consistent estimator ensures the performance of spectrum sensing;
(3) the power of the transmitted signal and noise, channel gain and other prior information are not needed to be known, and the decision statistic is only related to the eigenvalue of the sample covariance matrix, so that the method is suitable for an actual scene.
Drawings
FIG. 1: the cognitive small cell network topological graph of the embodiment of the invention;
FIG. 2: the embodiment of the invention provides a flow chart of a cooperative spectrum sensing method based on consistent estimation of characteristic values;
FIG. 3: an ROC curve graph (a slow fading Rayleigh channel, the signal-to-noise ratio (SNR) is-10 dB, and the number of cooperative small cells (M) is 16) of the relationship between the algorithm perception performance and the characteristic value separation condition (a coordinate graph);
FIG. 4: p of relation between algorithm perception performance and channel correlation coefficient in the inventiondSNR plot (fast fading Rayleigh channel, false alarm probability P)f0.1, the number of cooperative small cells M16, and the sample N50 (graph);
FIG. 5: the invention and OAS-MME algorithm perception performance contrast chart (slow fading Rayleigh channel, false alarm probability P)f0.1, the number of cooperative small cells M16) (graph).
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The invention provides a cooperative spectrum sensing method based on characteristic value consistent estimation to realize coexistence between macro cells and same-frequency small cells in a dense heterogeneous network.
The topology of the cognitive small cell network of the embodiment of the invention is shown in fig. 1, and macro cell network users are authorized users and use the allocated authorized spectrum resources. A plurality of dense low-power small cell nodes are deployed within the coverage of a macro cellular network, and a small cell base station samples and transmits a received signal to a data Fusion Center (FC). And the data fusion center obtains judgment statistic based on the received sample matrix, and judges whether the authorized user exists or not by comparing with the threshold. When the authorized user is sensed not to work, the small cell can utilize the authorized spectrum resource, meanwhile, the cognitive small cell still needs to perform spectrum sensing, and once the authorized user is sensed to appear again, the use of the spectrum is quitted in time so as to avoid interference on the authorized user of the macro cell.
Example (b):
referring to fig. 2, an embodiment of the present invention provides a cooperative spectrum sensing method based on eigenvalue consensus estimation, including: authorized user signal transmission and reception 100: the macro base station sends an authorized user signal, and the invention assumes that the authorized signal obeys zero mean power of sigmas 2A gaussian distribution of (a).
Signal reception and sampling 200: the signal is received by M small cellular base stations after passing through a stable fading channel, noise at the receiver end is assumed to be additive white Gaussian noise, the noise is independent and has the same power sigma2And the M small cellular base stations continuously sample the received signals to respectively obtain N sample data. By H0And H1Respectively representing the situation that the authorized user does not appear and appears, and the binary hypothesis model of spectrum sensing is expressed as follows:
wherein, x [ n ]]＝[x1[n],x2[n],…,xM[n]]T(N-0, 1, …, N-1) is a vector of M-dimensional samples sampled by M receivers at sampling time N. w [ n ]]The representative mean is zero and the covariance matrix is σ2IMIs expressed as independent identically distributed circularly symmetric complex Gaussian noise
h is a complex channel gain vector, and the channel gain is assumed to be constant during the sensing time. s [ n ]]For the nth sample of the grant signal, assume that the obedient mean is zero and the variance is
A gaussian distribution of (a).
The perception sample data is sent to the data fusion center 300: the small cell base station sends the perception sample data to a data fusion center, and a receiving sample matrix X with M multiplied by N dimensions is formed in the data fusion center [ X [0], X [1], …, X [ N-1] ].
A decision statistic 400 is obtained: the data fusion center calculates the decision statistic according to the expression of the decision statistic, and the decision statistic is derived according to the Nelman Pearson criterion, namely, the detection probability is maximized when the false alarm probability is a fixed value. According to the established perception model, the difference of the log-likelihood functions of the received sample matrix is
L(X)＝ln p(X|H1)-ln p(X|H0) (2)
H0The log-likelihood function in the case contains an unknown parameter σ2In the present embodiment, the formula (3) is specifically shown
H1The log-likelihood function of the case contains unknown parameters
h and σ2In the present embodiment, a specific tableIs shown as formula (4)
The calculation formula of the matrix determinant and the Woodbury matrix inversion equation can be obtained
Substituting and simplifying equations (5) and (6) into expression (4) of the log likelihood function:
since the channel gain and the signal and noise power are unknown in the actual scene, it is necessary to estimate these unknown parameters, and the expression of the estimated difference l (x) between the log-likelihood functions is rewritten as
Wherein,
is H1The estimation of the channel gain in the case,
and
is H1The estimation of the signal and noise power in the case,
is H0Estimation of the noise power in the case.
The mth true eigenvalue γ is knownmIs expressed as
Wherein KmFor the multiplicity of the mth real characteristic value, set
As a sample covariance matrix
The kth eigenvalue of (a). About
Equation (2)
Is solved as M ordered real numbers
In the signal model of the invention, the statistical covariance matrix has M-fold eigenvalues σ when an authorized user is not present2And therefore the parameter σ is unknown2The consensus estimator of (1) is:
by using
Instead of sigma2Can obtain H0Log likelihood function of the case
When the authorized user exists, the statistical covariance matrix has M-1 repeated minimum eigenvalue gamma1＝σ2And 1 maximum eigenvalue
Using maximum sample eigenvalues
Feature vector of
Estimating the true maximum eigenvalue gamma2Feature vector of
Can obtain
The log-likelihood function expression containing the unknown parameter estimator is further simplified to:
when gamma is1＝σ2And
when the two eigenvalues meet the eigenvalue separation condition, the estimation performance of the eigenvalue coincidence estimator on the eigenvalue is better, and N/M is required to be more than ξ at the momentMIt holds that ξ according to the established perceptual modelMIs represented by formula (13)
Wherein,
is a solution of
Substituting formula (13) and formula (14) into N/M > ξMThe expression in which the characteristic value separation condition is obtained is:
wherein,
representing the signal-to-noise ratio.
Will gamma1＝σ2And
substitution into formula (9) can give H1Expression (16) for the estimator of the unknown parameter in equation (12) below, and can provide a better estimate when the maximum eigenvalue and the minimum eigenvalue satisfy the separation condition in equation (15).
The decision statistic of the algorithm of the invention is obtained by substituting and simplifying the formula (11) and the formula (12) into the formula (8):
deciding whether an authorized user exists 500: data fusion center is to judge statistic TCEE-CSSComparing with threshold value gamma to judge if authorized user appears, when judging statistic TCEE-CSSIf the number of the authorized users is larger than the threshold gamma, judging that the authorized users exist, otherwise, judging that the authorized users do not exist, sensing that the authorized user can utilize the authorized spectrum resource, and expressing as
The results show that:
the perception performance of the algorithm is verified through simulation in the part, the transmitted signals obey complex Gaussian distribution with the mean value being zero in the simulation, and the power of the signals is unknown; the transmission channels of the authorization signal sending end and the M small cellular base station receiving ends are stable Rayleigh fading channels, and the channel gain is unknown; the noise assumption of the small cell base station receiver is the same and unknown. According to an evaluation model of a small cell enhancement technology in 3GPP Release 12, a macro cell range is set in simulation to cover 16 small cell base stations, and the value of the number of samples is the same order of magnitude as the number of small cells.
FIG. 3 shows the effect of eigenvalue separation conditions on the perceptual performance of the algorithm of the present invention. Considering that the channel is a slow fading Rayleigh channel, the channel gain is constant in the sensing time, and the SNR is 10log10The value of rho is-10 dB. In this case, as can be seen from equation (14), the eigenvalue separation condition is satisfied when N is greater than 32. Comparing the Receiver operating Characteristic curves (ROC) corresponding to different sample numbers shown in FIG. 3, the false alarm probability (P)fWhen the Probability of False Alarm) is 0.1, the detection probabilities (P) corresponding to N ═ 35 and N ═ 50 are determineddStability of Detection) is higher than 0.9, and when N ═ 16, P is presentd0.5, the perceived performance is the worst. Therefore, when the characteristic value separation condition is satisfied, the estimator can provide more accurate estimation, and the perception performance of the spectrum perception algorithm is better.
Fig. 4 shows the effect of channel correlation on the perceptual performance of the algorithm of the present invention. Consider a channel that is a fast fading rayleigh channel, such as a channel model when the symbol period of the signal is greater than the channel coherence time in narrow band communications, when the channel gain is random over the sensing time and there is correlation between the channels due to the close deployment of small cell base stations. And modeling the spatial correlation between the channels through a correlation matrix, and characterizing the magnitude of the correlation between the channels by using a correlation coefficient. Fixed false alarm probability P in simulationfWhen the number of cells M is equal to 16, the number of samples N is equal to 50, the symbol c represents the value of the correlation coefficient, and when c is 0.5, 0.75 and 0.95, three P strips are obtained by 5000 monte carlo simulationsd-SNR plot. From the figure4, it can be seen that the perceptual performance of the algorithm of the present invention becomes better with the increase of the signal-to-noise ratio, and the larger the correlation coefficient of the channel is, the better the perceptual performance is. For example when PdAt 0.9, the algorithm has a performance gain of greater than 3.5dB in perceived performance relative to 0.5 for c, and greater than 7dB for c at 0.95. Therefore, in the dense heterogeneous network, when the small cell base stations for cooperative sensing are closer, the correlation between channels is larger, the correlation between samples of different small cell base stations is also larger when the authorization signal exists, and the sensing performance of the algorithm is better.
FIG. 5 compares the perceptual performance of the CEE-CSS algorithm of the present invention with the existing OAS-MME algorithm of high-dimensional spectrum sensing. Fixed false alarm probability P in simulationfThe number of cells M is 16, and P of two perception algorithms is obtained by 5000 monte carlo simulationsd-SNR plot. As can be seen from fig. 5, CEE-CSS can provide better perceptual performance relative to OAS-MME. For example when PdAt 0.9, the CEE-CSS has approximately 1dB and 0.5dB performance gain at N50 and N100, respectively, relative to the OAS-MME. According to the comparison result, the algorithm estimates the characteristic value by considering the spatial structure of the transmitted signal, and has better perception performance compared with the OAS-MME algorithm which estimates the statistical covariance matrix.
The invention provides a cooperative spectrum sensing algorithm based on characteristic value consistency estimation, which is applied to a dense heterogeneous network to realize coexistence between macro cells and a cognitive network by sensing whether macro cell authorized users exist or not through cooperation of a plurality of cognitive small cells. The algorithm considers the space structure of the transmitted signal and utilizes a consistent estimator of high-dimensional time characteristic values, and has good perception performance.
Claims (1)
1. A cooperative spectrum sensing method based on consistent estimation of characteristic values comprises the following steps:
step 1: the macro base station sends an authorized user signal, the authorized user signal obeys zero-mean power of
(ii) a gaussian distribution of;
step 2: a small cellular base station receives signals and carries out continuous sampling;
the signal is received by M small cellular base stations after passing through a stable fading channel, noise at the receiver end is assumed to be additive white Gaussian noise, the noise is independent and has the same power sigma2M small cellular base stations continuously sample the received signal to obtain N sample data, and set H0And H1Respectively representing the situation that the authorized user does not appear and appears, and the binary hypothesis model of spectrum sensing is expressed as follows:
wherein, x [ n ]]＝[x1[n],x2[n],…,xM[n]]T(N-0, 1, …, N-1) is a vector of M-dimensional samples sampled by M receivers at sampling time N, w [ N [ [ N ]]The representative mean is zero and the covariance matrix is σ2IMIs expressed as independent identically distributed circularly symmetric complex Gaussian noise
h is a complex channel gain vector, s [ n ] assuming constant channel gain over sensing time]For the nth sample of the authorized user signal, the obedient mean is assumed to be zero and the variance is assumed to be
(ii) a gaussian distribution of;
and step 3: the small cell base station sends the sensing sample data to a data fusion center, and an M multiplied by N-dimensional receiving sample matrix X is formed in the data fusion center [ X [0], X [1], …, X [ N-1] ];
and 4, step 4: the data fusion center obtains judgment statistics according to the received sample matrix;
the difference between the log-likelihood functions of the received sample matrix is
L(X)＝lnp(X|H1)-lnp(X|H0) (2)
H0The log-likelihood function in the case contains an unknown parameter σ2Expressed as:
H1the log-likelihood function of the case contains unknown parameters
h and σ2Expressed as:
the calculation formula of the matrix determinant and the Woodbury matrix inversion equation can be obtained
Substituting and simplifying equations (5) and (6) into expression (4) of the log likelihood function:
the difference L (X) between the estimated log-likelihood functions is:
wherein,
is H1The estimation of the channel gain in the case,
and
is H1The estimation of the signal and noise power in the case,
is H0An estimate of the noise power in the case;
the mth true eigenvalue γ is knownmThe expression of the consensus estimator of (1) is:
wherein: kmFor the multiplicity of the mth real characteristic value, set
As a sample covariance matrix
The kth eigenvalue of (a); about
Equation (2)
Is solved as M ordered real numbers
Unknown parameter sigma2The consensus estimator of (1) is:
by using
Instead of sigma2To obtain H0Log likelihood function of the case
When the authorized user exists, the statistical covariance matrix has M-1 repeated minimum eigenvalue gamma1＝σ2And 1 maximum eigenvalue
Using maximum sample eigenvalues
Feature vector of
Estimating the true maximum eigenvalue gamma2Feature vector of
To obtain
The log-likelihood function expression containing the unknown parameter estimator is further simplified to:
when gamma is1＝σ2And
when the two characteristic values satisfy the characteristic value separation condition, the characteristic value consistency estimator pair characteristicThe estimated performance of the value is better, and N/M is required to be more than ξMIt holds that ξ according to the established perceptual modelMIs represented by formula (13)
Wherein,
is a solution of
Substituting formula (13) and formula (14) into N/M > ξMThe expression in which the characteristic value separation condition is obtained is:
wherein,
representing the signal-to-noise ratio;
will gamma1＝σ2And
substitution in formula (9) to give H1Expression (16) for the estimator of the unknown parameter in the following expression (12), and a better estimation is obtained when the maximum eigenvalue and the minimum eigenvalue satisfy the separation condition in the expression (15);
substituting and simplifying equations (11) and (12) into equation (8) to obtain a decision statistic:
and 5: the data fusion center compares the judgment statistic with a judgment threshold to judge whether the authorized user exists;
data fusion center is to judge statistic TCEE-CSSComparing with threshold value gamma to judge if authorized user appears, when judging statistic TCEE-CSSIf the number of the authorized users is larger than the threshold gamma, judging that the authorized users appear, otherwise, sensing that the authorized users do not appear, and expressing that the authorized users utilize the authorized spectrum resources as
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