A non-circular signal spectrum sensing method, system, device and medium

Abstract

The present invention belongs to the field of communication technology, and discloses a non-circular signal spectrum sensing method, system, device and medium, including: constructing an augmented sampling signal corresponding to a sampling signal of a cognitive user receiving end; constructing an augmented sampling covariance matrix based on the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix; setting a perception threshold value, constructing a decision model based on the perception threshold value, inputting the generalized likelihood ratio test statistic into the decision model, and outputting a spectrum state. The technical solution of the present invention makes full use of the characteristic that the compensation covariance of the non-circular signal is not 0, and can obtain a better detection result.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a non-circular signal spectrum sensing method, system, device and medium. Background Art

[0002] With the massive growth of spectrum resource demand for wireless communication services and the low utilization of spectrum resources in time and space, the scarcity of spectrum resources has brought great challenges to the design of future wireless communication systems. With the help of spectrum sensing technology, it is possible to find idle spectrum holes by real-time monitoring of the spectrum status of the authorized frequency band without interfering with the communication of the primary user, so that cognitive users can access the authorized frequency band for communication by dynamic spectrum access, thereby greatly improving the utilization of spectrum resources. The current spectrum sensing algorithms include matched filters, energy detectors, and cyclostationary feature detectors. Matched filters require prior information about authorized users, so their application in practical environments is limited. Energy detection is easily affected by noise uncertainty, resulting in very unstable performance. Cyclostationary feature detectors are not suitable for real-time detection systems due to their high computational complexity.

[0003] In recent years, the method of spectrum sensing using signal correlation at the receiving end has attracted much attention. The core idea is to use the difference in the covariance matrix of the received signal when the primary user signal exists or not to design a robust detection algorithm. Specifically, when the authorized frequency band is occupied by the primary user, the sampled signal has a strong correlation due to the presence of the primary user signal, and its covariance matrix is ​​a symmetric positive definite Hermitian matrix of any form; when the frequency band is idle, since the sampled signal only comes from noise, its covariance matrix is ​​a diagonal matrix. Based on the structural differences of the above covariance matrix, a series of detectors with good performance have been proposed in the prior art, including covariance detection methods represented by CAV (Covariance absolute value), eigenvalue detection methods represented by MME (Maximum minimum eigenvalue), local maximum power invariant test represented by John, and generalized likelihood ratio test (GLRT, Generalized likelihood ratio test) represented by AGM (Arithmetic to geometric mean). The above methods are all built on the assumption of circular signals, that is, only the standard covariance matrix of the received signal is considered. In fact, in actual communication systems, signals usually exhibit non-circular characteristics, and their compensation covariance matrix is ​​not zero. In this case, the above algorithm cannot obtain accurate detection results; that is, the existing spectrum sensing method has poor detection performance in a non-circular signal environment. Summary of the invention

[0004] The purpose of the present invention is to provide a non-circular signal spectrum sensing method, system, device and medium to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a non-circular signal spectrum sensing method, comprising:

[0006] constructing an augmented sampling signal corresponding to the sampling signal at the cognitive user receiving end;

[0007] Constructing an augmented sampling covariance matrix based on the augmented sampling signal;

[0008] constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix;

[0009] A perceptual threshold value is set, a decision model is constructed based on the perceptual threshold value, the generalized likelihood ratio test statistic is input into the decision model, and a spectrum state is output.

[0010] Optionally, the sampling signal of the cognitive user receiving end is:

[0011]

[0012] Where H0 indicates that the cognitive user does not exist, that is, the spectrum is in an idle state, H1 indicates that the cognitive user exists, that is, the spectrum is in an occupied state; x(n) represents the signal vector received by the cognitive user receiver at sampling time n, u(n) represents the signal vector sent by the primary user transmitter at sampling time n, w(n) represents the noise vector at sampling time n, σ squared represents the noise power, n=1,2,···,N is the number of sampling points, represents the channel gain coefficient matrix.

[0013] Optionally, the calculation formula of the augmented sampling covariance matrix is:

[0014] x (n) = [x Τ (n),x Η (n)] Τ

[0015]

[0016] In the formula, is the augmented sampled signal matrix, W is the augmented sampling covariance matrix, the superscript (·) * Indicates the conjugation symbol; superscript (·) T Indicates the transposition symbol; superscript (·) Η represents the conjugate transpose symbol; x (n) is represented as the augmented vector corresponding to x(n); x Τ(n) represents the transpose of x(n), x Η (n) represents the conjugate transpose of x(n); and denote the sampling standard covariance matrix and the sampling compensation covariance matrix respectively.

[0017] Optionally, a generalized likelihood ratio test statistic is constructed based on the determinant and rank of the augmented sampling covariance matrix, and the specific calculation formula is:

[0018]

[0019] Where T is the generalized likelihood ratio test statistic, | W | and tr( W ) are the determinant and rank of the augmented sampling covariance matrix, respectively.

[0020] Optionally, the perception threshold value constructing decision model is specifically:

[0021]

[0022] Where T is the generalized likelihood ratio test statistic, H0 means that there is no cognitive user, that is, the spectrum is idle; H1 means that there is a cognitive user, that is, the spectrum is occupied; η is the perception threshold;

[0023] Optionally, the processing process of constructing the decision model based on the perception threshold value is:

[0024] The generalized likelihood ratio test statistic is input into the decision model. When the generalized likelihood ratio test statistic is less than the perception threshold value, the output spectrum is in an occupied state; when the generalized likelihood ratio test statistic is greater than the perception threshold value, the output spectrum is in an idle state.

[0025] A non-circular signal spectrum sensing system, comprising:

[0026] A signal acquisition module, used to construct an augmented sampling signal corresponding to the sampling signal of the cognitive user receiving end;

[0027] A statistics calculation module, used for constructing an augmented sampling covariance matrix according to the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix;

[0028] The spectrum sensing module is used to set a sensing threshold value, construct a decision model based on the sensing threshold value, input the generalized likelihood ratio test statistic into the decision model, and output a spectrum state.

[0029] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform a non-circular signal spectrum sensing method.

[0030] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the non-circular signal spectrum sensing method is implemented.

[0031] The technical effects of the present invention are:

[0032] The spectrum sensing method provided by the present invention comprises: constructing an augmented sampling signal corresponding to a sampling signal of a cognitive user receiving end; constructing an augmented sampling covariance matrix based on the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix; setting a sensing threshold value, constructing a decision model based on the sensing threshold value, inputting the generalized likelihood ratio test statistic into the decision model, and outputting a spectrum state. The present invention makes full use of the characteristic that the compensation covariance of a non-circular signal is not 0, so that better detection performance can be achieved than the existing sensing algorithm in a multi-antenna scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0035] Figure 1 Schematic diagram of empirical probability distribution of NC-AGM and AGM in an embodiment of the present invention;

[0036] Figure 2 is a performance comparison chart of various detection algorithms in the embodiments of the present invention;

[0037] Figure 3 This is a spectrum sensing flow chart in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0039] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0040] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those of ordinary skill in the art generally understood by the present invention. Although the present invention describes only preferred methods, any method similar or equivalent to that described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0041] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0042] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0043] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0044] Embodiment 1

[0045] like Figure 1 - Figure 3 As shown, this embodiment provides a non-circular signal spectrum sensing method, including: constructing an augmented sampling signal corresponding to the sampling signal of the cognitive user receiving end; constructing an augmented sampling covariance matrix based on the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix; setting a perception threshold value, constructing a decision model based on the perception threshold value, inputting the generalized likelihood ratio test statistic into the decision model, and outputting a spectrum state.

[0046] In order to make up for the shortcomings of the existing algorithms in the detection performance of non-circular signal environment, this embodiment extends the classic AGM method to the case of non-circular signals under the framework of GLRT, and proposes a NC-AGM (Non-circulararithmetic to geometric mean) algorithm, which specifically includes:

[0047] Step 1: The cognitive radio system obtains the sampled signal of the cognitive user receiving end, constructs the corresponding augmented sampled signal, and calculates the augmented sampled covariance matrix;

[0048] Step 2: Calculate the determinant and rank of the augmented sampling covariance matrix to construct the generalized likelihood ratio test statistic T;

[0049] Step 3: Compare the test statistic T with the preset perception threshold to determine whether the target spectrum is occupied or idle. If the test statistic is less than the threshold, the spectrum is considered occupied, otherwise, the spectrum is considered idle, thus completing the detection;

[0050] This embodiment makes full use of the characteristic that the non-circular signal compensation covariance is not 0, and has higher detection accuracy in a multi-antenna scenario.

[0051] The sampled signal described in step 1 can be expressed as:

[0052]

[0053] Where H0 indicates that the cognitive user does not exist, that is, the spectrum is in an idle state, H1 indicates that the cognitive user exists, that is, the spectrum is in an occupied state; x(n) represents the signal vector received by the cognitive user receiving end at sampling time n, u(n) represents the signal vector sent by the primary user transmitting end at sampling time n, w(n) represents the noise vector at sampling time n, which obeys zero mean, and the covariance matrix σ 2 I M Complex Gaussian distribution, n = 1, 2, ···, N is the number of sampling points. In addition,

[0054] x(n)=[x1(n),x2(n),···,x M (n)] Τ

[0055] w(n)=[w1(n),w2(n),···,w M (n)] Τ

[0056] u(n)=[u1(n),u2(n),···,u P (n)] Τ

[0057] Where M and P represent the number of antennas configured at the cognitive user receiving end and the primary user transmitting end, respectively. Τ represents the transpose symbol; represents the channel gain coefficient matrix; x m (n) and w m (n) represents the received signal and noise vector of the mth receiving antenna at sampling time n; u i (n) represents the i-th primary user signal, which obeys the non-circular complex Gaussian distribution, that is, u i (n)~CN(0,γ i ), where the signal power γ i =E(|u i | 2 ).in addition, κ i represents the signal non-circularity, φ i ∈[-π,π) represents the non-circular phase of the signal.

[0058] The augmented sampling signal and augmented sampling covariance matrix described in step 1 are respectively expressed as:

[0059] x (n) = [x Τ (n),x Η (n)] Τ

[0060]

[0061] In the formula, is the augmented sampled signal matrix, W is the augmented sampling covariance matrix, the superscript (·) * Indicates the conjugation symbol; superscript (·) T Indicates the transposition symbol; superscript (·) Η represents the conjugate transpose symbol; x (n) is represented as the augmented vector corresponding to x(n); x Τ (n) represents the transpose of x(n), x Η (n) represents the conjugate transpose of x(n); and denote the sampling standard covariance matrix and the sampling compensation covariance matrix respectively.

[0062] The generalized likelihood ratio test statistic described in Step 2 has the following form:

[0063]

[0064] Where T is the generalized likelihood ratio test statistic, | W | and tr( W) are the determinant and rank of the augmented sampling covariance matrix, respectively.

[0065] The perception process described in step 3 is implemented by the following decision model:

[0066]

[0067] Where η is the perception threshold, that is, T>η means that the assumption H0 is true and the spectrum is idle, otherwise it means that the assumption H1 is true and the spectrum is occupied.

[0068] The present embodiment is described in detail below through simulation experiments.

[0069] Simulation parameter setting: The simulation experiment assumes a Rayleigh fading channel, that is, the column components of the channel gain matrix H obey an independent and identically distributed standard complex Gaussian distribution. Once generated in the current Monte Carlo experiment, they remain unchanged, and will be randomly generated again in the next Monte Carlo experiment. Without loss of generality, assume that the noise power σ = 1. Under the H0 assumption, the primary user signal does not exist; under the H1 assumption, the primary user signal is set as follows: signal non-circularity κ1 = ··· = κ P =1, non-circular phase φ i The uniform distribution is defined in the interval [-π,π). All simulation results are obtained by 10 4 Monte Carlo experiment.

[0070] exist Figure 1 In the figure, the empirical probability distribution curves of the NC-AGM method and the original AGM method are compared. In the simulation, it is assumed that the number of primary users P = 3, the corresponding powers are γ1 = 1dB, γ2 = 2dB, γ1 = 3dB, the number of receiving antennas M = 4, the number of sampling points N = 100, and the signal-to-noise ratio (SNR) SNR = -5dB. As can be seen from the figure, the overlapping area of ​​the probability distribution curves of the NC-AGM statistic under two different assumptions is significantly smaller than that of the AGM method, indicating that under the same simulation conditions, the proposed NC-AGM detector is significantly better than the original AGM method.

[0071] exist Figure 2 In the figure, the detection performance comparison of various perception algorithms under different signal-to-noise ratio parameters is given. The simulation parameters are set as: M = 4, N = 100, the number of primary user signals P = 2, the power γ1 = γ2 = 0 dB, the false alarm rate P f =0.1. It is not difficult to find that the performance of all detection algorithms increases with the increase of signal-to-noise ratio. Among them, the NC-AGM method has the highest detection probability because it fully utilizes the statistical characteristics of non-circular signals. The performance of the comparison algorithms that only utilize part of the statistical characteristics of non-circular signals is inferior to that of the NC-AGM algorithm.

[0072] A non-circular signal spectrum sensing system, comprising:

[0073] A signal acquisition module, used to construct an augmented sampling signal corresponding to the sampling signal of the cognitive user receiving end;

[0074] A statistics calculation module, used for constructing an augmented sampling covariance matrix according to the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix;

[0075] The spectrum sensing module is used to set a sensing threshold value, construct a decision model based on the sensing threshold value, input the generalized likelihood ratio test statistic into the decision model, and output a spectrum state.

[0076] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform a non-circular signal spectrum sensing method.

[0077] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the non-circular signal spectrum sensing method is implemented.

[0078] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A non-circular signal spectrum sensing method, characterized in that: include: constructing an augmented sampling signal corresponding to the sampling signal at the cognitive user receiving end; Constructing an augmented sampling covariance matrix based on the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix; The generalized likelihood ratio test statistic is constructed based on the determinant and rank of the augmented sampling covariance matrix. The specific calculation formula is: Where T is the generalized likelihood ratio test statistic, | W | and tr( W ) are the determinant and rank of the augmented sampling covariance matrix, respectively, and M is the number of antennas configured at the cognitive user receiving end; A perceptual threshold value is set, a decision model is constructed based on the perceptual threshold value, the generalized likelihood ratio test statistic is input into the decision model, and a spectrum state is output.

2. A non-circular signal spectrum sensing method according to claim 1, characterized in that: The sampling signal of the cognitive user receiving end is: Where H0 indicates that the cognitive user does not exist, that is, the spectrum is in an idle state, H1 indicates that the cognitive user exists, that is, the spectrum is in an occupied state; x(n) represents the signal vector received by the cognitive user receiver at sampling time n, u(n) represents the signal vector sent by the primary user transmitter at sampling time n, w(n) represents the noise vector at sampling time n, σ squared represents the noise power, n=1, 2, ···N is the sampling time, represents the channel gain coefficient matrix, M and P represent the number of antennas configured at the cognitive user receiving end and the primary user transmitting end, respectively.

3. The non-circular signal spectrum sensing method according to claim 1, characterized in that: The calculation formula of the augmented sampling covariance matrix is: In the formula, is the augmented sampled signal matrix, W is the augmented sampling covariance matrix, the superscript (·) * Indicates the conjugation symbol; superscript (·) T Indicates the transposition symbol; superscript (·) Η represents the conjugate transpose symbol; x (n) is represented as the augmented vector corresponding to x(n); x Τ (n) represents the transpose of x(n), x Η (n) represents the conjugate transpose of x(n), where n is the sampling time; and denote the sampling standard covariance matrix and the sampling compensation covariance matrix respectively.

4. The non-circular signal spectrum sensing method according to claim 1, characterized in that: The perception threshold value construction decision model is specifically: Where T is the generalized likelihood ratio test statistic, H0 means that there is no cognitive user, that is, the spectrum is idle; H1 means that there is a cognitive user, that is, the spectrum is occupied; η is the perception threshold.

5. The non-circular signal spectrum sensing method according to claim 1, characterized in that: The processing process of constructing the decision model by the perception threshold value is as follows: The generalized likelihood ratio test statistic is input into the decision model. When the generalized likelihood ratio test statistic is less than the perception threshold value, the output spectrum is in an occupied state; when the generalized likelihood ratio test statistic is greater than the perception threshold value, the output spectrum is in an idle state.

6. A non-circular signal spectrum sensing system, characterized in that: include: A signal acquisition module, used to construct an augmented sampling signal corresponding to the sampling signal of the cognitive user receiving end; A statistics calculation module, used for constructing an augmented sampling covariance matrix according to the augmented sampling signal; constructing a generalized likelihood ratio test statistic based on the determinant and rank of the augmented sampling covariance matrix; The generalized likelihood ratio test statistic is constructed based on the determinant and rank of the augmented sampling covariance matrix. The specific calculation formula is: Where T is the generalized likelihood ratio test statistic, | W | and tr( W ) are the determinant and rank of the augmented sampling covariance matrix, respectively, and M is the number of antennas configured at the cognitive user receiving end; Setting a perception threshold value, constructing a decision model based on the perception threshold value, inputting the generalized likelihood ratio test statistic into the decision model, and outputting a spectrum state; The spectrum sensing module is used to set a sensing threshold value, construct a decision model based on the sensing threshold value, input the generalized likelihood ratio test statistic into the decision model, and output a spectrum state.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform a non-circular signal spectrum sensing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: A computer program is stored therein, and when the computer program is executed by a processor, a non-circular signal spectrum sensing method as described in any one of claims 1 to 5 is implemented.

Images