A CR spectrum sensing method, device and storage medium based on group sum power spectrum HVG transformation

Abstract

The present invention discloses a CR spectrum sensing method, device and storage medium based on group summation power spectrum HVG transformation. The device and storage medium first perform power spectrum on the observed signal; then group the power spectrum signal and sum each group to obtain a new sequence; then normalize and quantize the new sequence, perform HVG transformation, thereby obtaining the corresponding adjacency matrix A, then calculate the degree matrix D and the Laplace matrix L according to the adjacency matrix A, and finally use the sum of the eigenvalues ​​of the Laplace matrix as the detection statistic, set the decision threshold, and compare the detection statistic with it, thereby judging whether the observed signal is a BPSK modulation signal or a common noise signal. The present invention improves the detection performance at low signal-to-noise ratio, reduces the number of samples involved in graph conversion, and reduces the computational complexity.

Description

Technical Field

[0001] The present invention relates to a CR spectrum sensing method, a device and a storage medium based on group sum power spectrum HVG transformation, and belongs to the technical field of signal processing. Background Art

[0002] In recent years, with the rapid development of wireless communication technology, the number of wireless devices has increased rapidly, and the problem of spectrum resource exhaustion has become increasingly serious. The main reason for the above problem is that the static spectrum allocation strategy leads to a waste of spectrum resources and is unable to meet the growing demand for high-speed data transmission.

[0003] In this context, cognitive radio (CR) technology has emerged and is considered a feasible solution to the spectrum shortage problem. CR technology introduces the concept of dynamic spectrum access (DSA), which replaces the traditional static spectrum allocation method and can coordinate the use of spectrum by primary users (PU) and secondary users (SU). CR technology gives SU the ability to sense and effectively utilize idle frequency bands, which means that even if PU does not use a specific frequency band during a period of time, SU can make full use of it while minimizing potential interference to PU. In short, CR technology provides an effective solution to improve the efficiency of spectrum resource utilization and has brought revolutionary changes to the field of wireless communications. Spectrum sensing is the primary link of cognitive radio, so the research on spectrum sensing algorithms has always been a hot topic.

[0004] At present, the existing spectrum sensing detection algorithms can be divided into two categories: detection algorithms based on regular domain and irregular domain signal processing. Commonly used regular domain spectrum sensing algorithms include: energy detection, matched filter and cyclostationary feature detection algorithm, etc. Commonly used irregular domain spectrum sensing algorithms mainly include: quantization graph, visibility graph transformation method, etc.

[0005] The problems existing in various algorithms are as follows:

[0006] (1) Various noise or background interference. As the electromagnetic environment becomes more and more complex, signals are easily interfered by various noises.

[0007] (2) Poor performance at low signal-to-noise ratios. Currently, the signal-to-noise ratio range applicable to most algorithms is not wide enough, and the ROC curve at low signal-to-noise ratios is poor.

[0008] (3) The computational complexity is too high. Most algorithms based on visibility graphs need to normalize, quantize, and transform each sample in the process of converting a random sequence into a visibility graph. Therefore, the more samples involved in the visibility graph transformation, the higher the computational complexity.

[0009] Therefore, those skilled in the art are in urgent need of improving the method of CR signal spectrum detection to improve the detection performance. Summary of the invention

[0010] Purpose: In order to overcome the problems of high computational complexity and poor performance of the current visible graph detection algorithm at low signal-to-noise ratio in the prior art, the present invention provides a CR spectrum sensing method, device and storage medium based on group summed power spectrum HVG transformation.

[0011] Technical solution: To solve the above technical problems, the technical solution adopted by the present invention is:

[0012] In a first aspect, a CR spectrum sensing method based on group sum power spectrum HVG transformation specifically includes:

[0013] Step 1: Obtain the observed signal of cognitive radio, perform frequency domain conversion on the observed signal and take the modulus square to obtain the power spectrum, group the power spectrum and sum each group to obtain a new sequence.

[0014] Step 2: Perform HVG transformation on the new sequence to obtain the adjacency matrix .

[0015] Step 3: Through the adjacency matrix Calculate degree matrix , according to the degree matrix Compute the Laplacian matrix , and the Laplace matrix The sum of the eigenvalues ​​of As a detection feature, the observation signal is detected based on the detection feature to confirm whether the observation signal exists.

[0016] As a preferred solution, the observation signal of the cognitive radio is a CR modulated signal superimposed with Gaussian white noise, and the CR modulated signal adopts binary phase shift keying for signal modulation.

[0017] As a preferred solution, the power spectrum of the observed signal The calculation formula is as follows:

[0018]

[0019] in: is the discrete form of the observed signal, is an imaginary unit, is the circumference of a circle, is a natural constant, is the number of observed signal samples.

[0020] As a preferred solution, the power spectrum is grouped and each group is summed to obtain a new sequence, specifically including:

[0021] The power spectrum samples The elements in the sequence into a group, a total of Group, among which, L s = [ N / λ ] .

[0022] Sum each group to get a new sequence , .

[0023] As a preferred solution, the step 2 specifically includes:

[0024] Step 2.1: For the new sequence Normalization is performed, and the normalized discrete sequence .

[0025] in,

[0026] In the formula, , .

[0027] Step 2.2: Use uniform quantization to normalize the discrete sequence Quantize, set the quantization level to , and obtain the quantized sequence .

[0028] in,

[0029] In the formula, .

[0030] Step 2.3: Convert the quantized sequence The image is obtained by HVG conversion , according to the figure Get the adjacency matrix of the graph .in, represents a node set, Represents an edge set.

[0031] As a preferred solution, the step 3 specifically includes:

[0032] Step 3.1: Based on the adjacency matrix of the graph Calculate degree matrix , the degree matrix Elements The calculation formula is as follows:

[0033]

[0034] in, is the degree matrix No. diagonal elements, is the adjacency matrix of the graph No. Line Elements of a column.

[0035] Step 3.2: According to the degree matrix Compute the Laplacian matrix , the Laplacian matrix The calculation formula is as follows:

[0036]

[0037] Step 3.3: Calculate the Laplacian matrix The eigenvalue of And sum to get the detection feature , the detected features Compared with the detection threshold, when the detection feature When the detection threshold is less than or equal to the detection threshold, the observed signal of cognitive radio exists. When it is greater than the detection threshold, the observed signal of cognitive radio does not exist.

[0038] As a preferred solution, the frequency domain conversion operation adopts fast Fourier transform.

[0039] In a second aspect, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements a CR spectrum sensing method based on group sum power spectrum HVG transformation as described in any one of the first aspects.

[0040] According to a third aspect, a computer device includes:

[0041] Memory, used to store instructions.

[0042] The processor is used to execute the instructions so that the computer device performs the operations of the CR spectrum sensing method based on group summation power spectrum HVG transformation as described in any one of the first aspects.

[0043] Beneficial effect: The present invention provides a CR spectrum sensing method, device and storage medium based on group summation power spectrum HVG transformation, firstly, the power spectrum of the observed signal is made; then the power spectrum signal is grouped and each group is summed to obtain a new sequence; then the new sequence is normalized and quantized, and HVG transformation is performed to obtain the corresponding adjacency matrix , then according to the adjacency matrix Calculate degree matrix With the Laplacian matrix Finally, the sum of the eigenvalues ​​of the Laplace matrix is ​​used as the detection statistic, and the decision threshold is set. The detection statistic is compared with it to determine whether the observed signal is a BPSK modulated signal or an ordinary noise signal.

[0044] The present invention detects cognitive radio signals under low signal-to-noise ratio. This paper adopts the graph domain signal detection method in the irregular domain, converts the observed signal into a visual graph, obtains its Laplace matrix, and uses the sum of the Laplace matrix eigenvalues ​​as the detection feature to detect the signal and noise. This method has the following advantages:

[0045] (1) The sum of the eigenvalues ​​of the Laplace matrix is ​​selected as the detection statistic, which improves the detection performance at low signal-to-noise ratio.

[0046] (2) The power spectrum group sum sequence is used as the input of the horizontal visible graph transformation, which reduces the number of samples involved in the graph transformation and reduces the computational complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the CR spectrum sensing method of the present invention.

[0048] Figure 2 The observed signal is and The graph structure obtained by HVG transformation after a series of processing under the assumption. Figure 2 (a) Visible structure, Figure 2 (b) Visible structure.

[0049] Figure 3 is and Assumptions, comparison of the average values ​​of the sum of eigenvalues ​​obtained after 1000 simulations.

[0050] Figure 4 It is a curve diagram of detection probability and false alarm probability of the algorithm of the present invention. Figure 4 (a) is the algorithm detection probability, Figure 4 (b) is the false alarm probability of the algorithm.

[0051] Figure 5 It is a comparative diagram of the receiver operating characteristic curve (ROC) of the method of the present invention and other comparative methods. DETAILED DESCRIPTION

[0052] The following is a clear and complete description of the technical solutions in the examples of the present invention in conjunction with the accompanying drawings in the examples of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the protection scope of the present invention.

[0053] The present invention will be further described below in conjunction with specific embodiments.

[0054] Embodiment 1:

[0055] This embodiment introduces a CR spectrum sensing method based on group sum power spectrum HVG transformation, such as Figure 1 As shown, the following steps are included:

[0056] Step 1: Obtain the observation signal of cognitive radio, perform Fast Fourier Transform (FFT) operation on the observation signal and square the modulus to obtain the power spectrum, group the power spectrum and sum each group to obtain a new sequence.

[0057] Step 2: Perform HVG (Horizontal Visibility Graph) transformation on the new sequence to obtain the adjacency matrix .

[0058] Step 3: Through the adjacency matrix Calculate degree matrix , according to the degree matrix Compute the Laplacian matrix , and the Laplace matrix The sum of the eigenvalues ​​of As a detection feature, the observed signal is detected to detect the existence of the signal.

[0059] Furthermore, the observation signal of the cognitive radio is a CR modulated signal superimposed with Gaussian white noise. For the convenience of description, the commonly used Binary Phase Shift Keying (BPSK) is selected for signal modulation.

[0060] Furthermore, the step 1 specifically includes:

[0061] Assume that the observed signal is contaminated by Additive White Gaussian Noise (AWGN) It can be expressed as:

[0062]

[0063] In the formula, is the signal component, is the noise component.

[0064] After sampling, the observed signal is obtained in discrete form It can be expressed as:

[0065]

[0066] In the formula, is the signal sampling amount, is the noise sampling amount, is the number of observed signal samples.

[0067] Therefore, spectrum sensing can be reduced to the following hypothesis testing problem:

[0068]

[0069] In the formula, The spectrum is not occupied (i.e., there is no signal in the channel); The frequency spectrum is occupied (i.e., there is a signal in the channel).

[0070] Observe the power spectrum of the signal It can be expressed as:

[0071]

[0072] in: is the discrete form of the observed signal, is an imaginary unit, is the circumference of a circle, is a natural constant.

[0073] The power spectrum samples The elements in the sequence into a group, a total of L s = [ N / λ ] Group, sum each group to get a new sequence .

[0074]

[0075] Furthermore, in step 2, the new sequence Do HVG transformation to get the adjacency matrix , specifically including:

[0076] Step 2.1: Normalization: For the new sequence Perform normalization, that is:

[0077] Defining Discrete Sequences The maximum and minimum values ​​are:

[0078]

[0079] Then the normalized discrete sequence for:

[0080]

[0081] in: .

[0082] Step 2.2: Quantization: Before transforming into a horizontal visual graph, quantization can be performed to achieve graph conversion. Here, uniform quantization is used to normalize the discrete sequence. Quantize, set the quantization level to The quantized sequence , which can be expressed as:

[0083]

[0084] in: .

[0085] Step 2.3: Convert the quantized sequence Convert to graph via HVG The graph consists of a set of nodes and edge sets The number of nodes is determined by the dimension of the observed signal. Given, according to the figure Get the adjacency matrix of the graph .

[0086] Further, in step 3, the observed signal is detected to determine the existence of the signal, specifically including:

[0087] Step 3.1: Based on the adjacency matrix of the graph Calculate the corresponding degree matrix And the Laplacian matrix ,Right now:

[0088]

[0089] In the formula, is the degree matrix No. diagonal elements, representing the adjacency matrix No. The sum of all elements in the row.

[0090]

[0091] Step 3.2: Define the sum of the eigenvalues ​​of the Laplacian matrix , calculate the Laplacian matrix The eigenvalue of And sum to get By comparing with a specific threshold, it is determined that the observed signal belongs to Assume or Assume that the existence of the signal is detected.

[0092]

[0093] In the formula, is the Laplace matrix The number of columns.

[0094] Embodiment 2:

[0095] This embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, a CR spectrum sensing method based on group sum power spectrum HVG transformation as described in any one of Embodiments 1 is implemented.

[0096] Example 3

[0097] This embodiment introduces a computer device, including:

[0098] Memory, used to store instructions.

[0099] The processor is used to execute the instructions so that the computer device performs the operations of the CR spectrum sensing method based on group sum power spectrum HVG transformation as described in any one of Embodiment 1.

[0100] Embodiment 4:

[0101] This embodiment introduces verification data of a CR spectrum sensing method based on group sum power spectrum HVG transformation, which specifically includes:

[0102] Figure 2 It is the graph structure obtained by performing a series of HVG transformation on the observed signal. (a) The structure of the visible graph of the observed signal under the assumption that (b) is Assume that the structure of the visible graph of the observed signal is and The visual structure of The assumed visibility graph is denser with fewer scattered points, while The assumed visual pattern distribution is relatively dispersed.

[0103] Figure 3 is and Assume that, after 1000 simulations, the average value of the sum of the eigenvalues ​​is compared. It can be seen from observation that: The sum of the eigenvalues ​​of is not affected by the signal-to-noise ratio and remains stable around 800. The sum of the eigenvalues ​​decreases with the increase of the signal-to-noise ratio until it gradually stabilizes around 520.

[0104] Figure 4 The detection probability and false alarm probability curves of the algorithm of the present invention are shown in Figure 2. (a) is the detection probability curve. When , the detection probability reaches 98.8%. (b) is the false alarm probability curve. When the algorithm of the present invention calculates the detection probability, the false alarm probability needs to be controlled below 0.01.

[0105] Figure 5 This is a comparative diagram of the ROC curves of the method of the present invention and the energy detection method, the cyclostationary detection method, the second largest eigenvalue method of the quantization graph, the differential spectrum + VG method, and the aggregate operator quantization graph method. In order to make full use of the data and avoid experimental contingency, the simulation conditions are: 1000 simulations are performed, and the signal-to-noise ratio is set . Figure 5 The ROC curve areas of different detection methods are shown, which shows that the ROC curve of the algorithm of the invention is better than other methods, and the operation time of the algorithm is greatly reduced. Therefore, the present invention can better detect BPSK modulated signals, and the detection accuracy is greatly improved.

[0106] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0108] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0110] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A CR spectrum sensing method based on group sum power spectrum HVG transformation, characterized by: Specifically include: Step 1: Obtain the observed signal of cognitive radio, perform frequency domain conversion on the observed signal and square the modulus to obtain the power spectrum, group the power spectrum and sum each group to obtain a new sequence; Step 2: Perform HVG transformation on the new sequence to obtain the adjacency matrix A; Step 3: Calculate the degree matrix D through the adjacency matrix A, calculate the Laplace matrix L according to the degree matrix D, and use the sum S of the eigenvalues ​​of the Laplace matrix L as the detection feature. Detect the observed signal according to the detection feature to confirm whether the observed signal exists; The power spectrum R(k) of the observed signal is calculated as follows: Where: r(n) is the discrete form of the observed signal, j is the imaginary unit, π is the circumference of a circle, e is a natural constant, and N is the number of observed signal samples; The step of grouping the power spectrum and summing each group to obtain a new sequence specifically includes: The elements in the power spectrum sample R(k) are divided into groups of λ in order, and a total of L s Group, among which L s =[N / λ]; Sum each group to get a new sequence Z(l), Z(l) = {Z(1), Z(2), ... Z(L s )},1≤l≤L s ; The step 2 specifically includes: Step 2.1: Normalize the new sequence Z(l) to obtain the normalized discrete sequence U(l); in, In the formula, Step 2.2: Use uniform quantization to quantize the normalized discrete sequence U(l), set the quantization level to M, and obtain the quantized sequence Q(l); in, Where, μ = 1, 2, ... M; Step 2.3: The quantized sequence Q(l) is transformed into a graph G(V,E) through HVG, and the adjacency matrix A of the graph is obtained according to the graph G(V,E); wherein V represents the node set and E represents the edge set; The step 3 specifically includes: Step 3.1: Calculate the degree matrix D according to the adjacency matrix A of the graph. The element D of the degree matrix D is ii The calculation formula is as follows: Among them, D ii is the i-th diagonal element of the degree matrix D, A ij is the element in the i-th row and j-th column of the adjacency matrix A of the graph; Step 3.2: Calculate the Laplace matrix L according to the degree matrix D. The calculation formula of the Laplace matrix L is as follows: L = DA; Step 3.3: Calculate the eigenvalues ​​γ of the Laplacian matrix L k The detection feature S is obtained by summing them up, and the detection feature S is compared with the detection threshold. When the detection feature S is less than or equal to the detection threshold, the observation signal of the cognitive radio exists. When the detection feature S is greater than the detection threshold, the observation signal of the cognitive radio does not exist.

2. The CR spectrum sensing method based on group sum power spectrum HVG transformation according to claim 1 is characterized in that: The observation signal of the cognitive radio is a CR modulated signal superimposed with Gaussian white noise, and the CR modulated signal is modulated by binary phase shift keying.

3. The CR spectrum sensing method based on group sum power spectrum HVG transformation according to claim 1 or 2, characterized in that: The frequency domain conversion operation adopts fast Fourier transform.

4. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, a CR spectrum sensing method based on group sum power spectrum HVG transformation as claimed in any one of claims 1 to 3 is implemented.

5. A computer device, characterized in that: include: A memory for storing instructions; The processor is used to execute the instructions so that the computer device performs the operation of the CR spectrum sensing method based on group summation power spectrum HVG transformation as described in any one of claims 1 to 3.

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