CN115225440A - CR signal modulation identification method and system based on graph maximum degree characteristic - Google Patents
CR signal modulation identification method and system based on graph maximum degree characteristic Download PDFInfo
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Abstract
The invention provides a CR signal modulation identification method and a CR signal modulation identification system based on maximum degree characteristics of a graph aiming at four modulation signals of BPSK, QPSK, 2ASK and 16 QAM. The observation signal is first pre-processed by short-time filtering to compensate the loss of signal-to-noise ratio. And then windowing the amplitude spectrum, the square spectrum and the fourth power spectrum, converting the amplitude spectrum, the square spectrum and the fourth power spectrum into an image domain, and converting the problem of identification of the modulation modes of the four signals into judgment of the maximum degree of the image according to nonlinear operation of the signals. Simulation experiments show that the method has better identification capability at low signal-to-noise ratio.
Description
Technical Field
The invention belongs to the field of signal identification and processing, and particularly relates to a CR signal modulation identification method and system based on maximum degree characteristics of a graph.
Background
In the era of mobile internet nowadays, wireless communication technology is widely applied to various industries such as daily contact, ultra-high-definition streaming media, virtual and augmented reality, artificial intelligence, block chaining, medical treatment, intelligent automobiles, intelligent homes, intelligent cities and the like. However, the radio spectrum resource is a non-renewable resource, and the number of wireless access devices is increasing rapidly, so that the originally slightly deficient spectrum resource is increasingly scarce. Therefore, it is critical to reasonably allocate spectrum resources to achieve communication between as many wireless devices as possible under the condition of limited spectrum resources. At present, a static spectrum allocation strategy is mainly adopted. Under such an allocation strategy, a manager of radio spectrum resources divides the spectrum resources into blocks and fixedly allocates frequency bands. Thus, a particular frequency band is allocated exclusively to authorized users, and unauthorized users cannot use that band even if the band is idle for some period of time. Obviously, a large number of spectrum holes are generated in the current static spectrum allocation scheme, which causes huge waste of spectrum resources and cannot adapt to the environment of rapid increase of the access number of the current wireless devices.
A dynamic spectrum management system in Cognitive Radio (CR) is a new idea for solving the problems of unreasonable distribution and insufficient utilization rate of wireless network spectrum in the current environment. For the CR system, the spectrum sensing technology is the premise and the foundation for its effective work. Generally, spectrum sensing can be understood in both narrow and broad sense. The task of narrow spectrum sensing is to detect spectrum holes, i.e. to detect whether PU channels are occupied, which is essentially signal detection. In the CR, a Secondary User (SU) needs to be able to quickly and accurately detect whether a Primary User (PU) is authorized in a frequency band free at any time and place, so as to be available for its opportunity. Meanwhile, the SU needs to monitor the PU that may be present at any time, so as to vacate the frequency band in time when the PU needs to be used. Generally speaking, the generalized spectrum sensing includes, in addition to detecting whether the authorized frequency band of the PU signal is idle, identifying signal parameters such as a modulation mode, a waveform, a bandwidth, and a carrier frequency of the signal, so that fine information of the PU spectrum can be acquired more accurately, and effectiveness and reliability of the whole system are improved.
Current modulation identification techniques can be broadly divided into two categories: one is a modulation recognition method Based on Likelihood-Based (LB), and the other is a modulation recognition method Based on Feature-Based (FB). Although the LB-based modulation identification method can obtain an optimal solution, inherent problems also exist, such as strong dependence on signal and channel prior information, incapability of guaranteeing existence of a closed solution, high computational complexity, probability mismatch, and the like. These result in the LB-like algorithm being very limited in its application in non-cooperative conditions. For the FB algorithm, how to extract the characteristics of strong distinctiveness, strong robustness and low calculation complexity is the key for realizing modulation identification.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a CR signal modulation identification method and system based on graph maximum degree characteristics.
In order to achieve the purpose, the invention adopts the following technical scheme:
the CR signal modulation identification method based on the maximum degree characteristic of the graph is characterized by comprising the following steps of:
step 1: carrying out short-time filtering pretreatment on the observation signal;
step 2: for the processed observation signal, firstly, judging whether the observation signal is a 2ASK signal or not by using the maximum degree of a graph generated by a magnitude spectrum of the observation signal; if not, judging whether the BPSK signal is generated by utilizing the maximum degree of a graph generated by the square spectrum of the BPSK signal; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, in step 1, the observation signal r (t) is represented as:
r(t)＝s(t)+ω(t)
wherein s (t) is a modulated signal sent by a sending end, ω (t) is zero-mean real Gaussian white noise superposed after passing through an AWGN channel, and the variance is σ 2 ；
The pre-processing process comprises the following sub-steps:
step 1.1: segmenting the original observation signal after discrete sampling, wherein the form of the original observation signal is represented as follows:
r i (n)＝s i (n)+ω i (n)，i(N S -1)≤n≤(i+1)(N S -1)
wherein s is i (n) is a modulated signal transmitted from a transmitting end, ω i (N) is additive white Gaussian noise, N S I represents the number of segments as the length of the signal segment;
step 1.2: calculating r i N of (N) S Discrete Fourier transform of the points to obtain R i (k)＝DFT[r i (n)]；
Step 1.3: a bandpass filter with the following transmission characteristics was designed:
wherein k is s Is | R i (k) The maximum spectral line position of | represents the modulo, d s The number of points for filtering; r is to be i (k) The input designed filter, its output is represented as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculation of R i ' (k) N s Point inverse discrete Fourier transform to obtain r i ′(n)＝IDFT(R i ′(k))；
Step 1.5: recombining each time domain signal after segmented reconstruction into a new observation signal according to the original segmentation sequence
Further, the step 2 of determining whether or not the amplitude spectrum is a 2ASK signal using the maximum degree of the map generated by the amplitude spectrum includes the substeps of:
step 2.1.1: for observation signals pre-processed by short-time filtering
Fourier transform and modulus taking are carried out to obtain an amplitude spectrum
Step 2.1.2: windowing is carried out on the amplitude spectrum of the observation signal, and the output sequence of the amplitude spectrum chi (tau) after windowing is represented as:
χ W (τ)＝χ(τ)·W(τ)
wherein, tau W Is the maximum value position of | χ (τ) |, d W Is the window width;
step 2.1.3: converting the windowed magnitude spectrum into a graph domain, and calculating the maximum degree of the graph; for having N 0 Undirected simple graph G (V, E) of each vertex, and extraction degree matrix
The diagonal elements of (A) constitute the degree vector of the graph G
Wherein d is δ Degrees for the δ -th vertex; the maximum degree of the graph G is the degree vector
Maximum value d of all elements in max ：
Accordingly, the maximum degree of the graph of the amplitude spectrum of the observation signal is calculated to be d max1 ；
Step 2.1.4: setting the threshold eta empirically 1 If d is =3 max1 ＜η 1 Judging the signal to be a 2ASK signal; otherwise, the maximum degree of the graph generated by the square spectrum is used for judging whether the BPSK signal is generated.
Further, the step 2 of determining whether the signal is a BPSK signal by using the maximum degree of a map generated from the square spectrum thereof includes the substeps of:
step 2.2.1: carrying out square operation on the observation signal subjected to short-time filtering pretreatment, and carrying out Fourier transform and modulus taking to obtain a square spectrum;
step 2.2.2: windowing the square spectrum of the observation signal;
step 2.2.3: converting the squared spectrum into graph domain, and calculating the maximum degree d of the graph max2 ；
Step 2.2.4: setting the threshold eta empirically 2 If d is =3 max2 ＜η 2 If not, the signal is judged to be a BPSK signal, otherwise, the signal is judged to be a QPSK signal or a 16QAM signal by utilizing the maximum degree of a graph generated by the fourth power spectrum of the signal.
Further, in the step 2, determining whether the signal is a QPSK signal or a 16QAM signal by using the maximum degree of the graph generated by the fourth power spectrum thereof, includes the following sub-steps:
step 2.3.1: performing a fourth power operation on the observation signal subjected to short-time filtering pretreatment, and performing Fourier transform and modulus taking to obtain a fourth power spectrum;
step 2.3.2: windowing the fourth power spectrum of the observation signal;
step 2.3.3: converting the windowed biquadratic spectrum to a graph domain, and calculating the maximum degree d of the graph max3 ；
Step 2.3.4: estimating the signal-to-noise ratio by using a graph method, and setting a threshold eta according to the signal-to-noise ratio 3 When the signal-to-noise ratio is greater than or equal to-7 dB, eta 3 =5; when the signal-to-noise ratio is less than-7 dB, eta 3 =5; if d is sum ＜η 3 If the signal is a QPSK signal, otherwise, the signal is a 16QAM signal.
The invention also provides a CR signal modulation identification system based on the graph maximum degree characteristic, which is characterized by comprising the following steps:
the preprocessing module is used for preprocessing the short-time filtering of the observation signal;
the signal identification module is used for judging whether the processed observation signal is a 2ASK signal or not by using the maximum degree of a graph generated by the amplitude spectrum of the processed observation signal; if not, judging whether the signal is a BPSK signal by using the maximum degree of a graph generated by the square spectrum of the signal; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
The present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program is configured to cause a computer to execute the CR signal modulation identification method based on the maximum degree feature of the graph as described above.
The invention also provides an electronic device, which is characterized by comprising: the CR signal modulation recognition method based on the graph maximum degree feature as described above is implemented by a memory, a processor, and a computer program stored on the memory and executable on the processor.
The invention has the beneficial effects that: the invention provides a CR signal modulation identification method and system based on graph maximum degree characteristics aiming at four modulation signals of BPSK, QPSK, 2ASK and 16 QAM. The observation signal is first pre-processed by short-time filtering to compensate the loss of signal-to-noise ratio. And then windowing the amplitude spectrum, the square spectrum and the fourth power spectrum, converting the amplitude spectrum, the square spectrum and the fourth power spectrum into an image domain, and converting the problem of identification of the modulation modes of the four signals into judgment of the maximum degree of the image according to nonlinear operation of the signals. Simulation experiments show that the method has better identification capability at low signal-to-noise ratio.
Drawings
FIG. 1 is a flow chart of the identification method of the present invention.
FIG. 2 is a maximum degree d of a graph into which the amplitude spectrum of the observation signal is converted max And (5) characterizing.
FIG. 3 is a maximum degree d of a graph into which a square spectrum of an observed signal is converted max And (5) characterizing.
FIG. 4 is an observed signalThe maximum degree d of the graph into which the fourth power spectrum is converted max And (5) characterizing.
Fig. 5 shows the recognition accuracy of four modulated signals.
Fig. 6 is the average correct recognition rate of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The CR signal modulation identification method based on the graph maximum degree feature shown in fig. 1 specifically includes the following steps.
Step 1: and (5) filtering pretreatment.
The observation signal is preprocessed by short-time filtering before modulation identification so as to compensate the loss of signal-to-noise ratio. The number of points for the short-term filtering is generally chosen from 10 to 30 empirically.
The observed signal r (t) that is assumed to have passed through the AWGN channel can be expressed as:
r(t)＝s(t)+ω(t)
wherein: s (t) is a modulated signal transmitted by a transmitting end, omega (t) is zero-mean real Gaussian white noise superposed after passing through an AWGN channel, and the variance of the zero-mean real Gaussian white noise is sigma 2 。
When the signal-to-noise ratio is low, the signal is submerged in the noise, and the distinguishing performance of the identification features is reduced; and in the process of extracting the features, nonlinear operation is often required, which increases noise power and reduces the signal-to-noise ratio of signal processing, so that before CR signal modulation and identification, filtering processing is performed to reduce the influence caused by the low signal-to-noise ratio environment.
The pre-processing process comprises the following sub-steps:
step 1.1: segmenting the original observation signal after discrete sampling according to an appropriate length, wherein the form of the original observation signal can be expressed as follows:
r i (n)＝s i (n)+ω i (n)，i(N S -1)≤n≤(i+1)(N S -1)
wherein s is i (n) is a modulated signal transmitted from the transmitting end, ω i (N) is additive white Gaussian noise, N S Is the length of the signal segment (i.e., the number of sample points of the signal segment).
Step 1.2: calculating r i N of (N) S Discrete Fourier Transform (DFT) of the point to obtain R i (k)＝DFT[r i (n)]。
Step 1.3: a bandpass filter with the following transmission characteristics was designed:
wherein k is s Is | R i (k) The maximum spectral line position of (| · | denotes modulo), d s The number of points of filtering. R is to be i (k) The output of the input designed filter can be expressed as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculating R i ' (k) N s An Inverse Discrete Fourier Transform (IDFT) is performed to obtain r i ′(n)＝IDFT(R i ′(k))。
Step 1.5: recombining each time domain signal after segmented reconstruction into a new observation signal according to the original segmentation sequence
And 2, step: and carrying out modulation identification according to the maximum degree of the generated graph.
Firstly, judging whether the amplitude spectrum is a 2ASK signal or not by using the maximum degree of a graph generated by the amplitude spectrum; if not, judging whether the signal is a BPSK signal by using the maximum degree of a graph generated by the square spectrum; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal. The method specifically comprises the following substeps:
step 2.1: whether the amplitude spectrum is a 2ASK signal is judged by the maximum degree of a graph generated by the amplitude spectrum.
Step 2.1.1: for observation signals pre-processed by short-time filtering
FFT and modulus taking are carried out to obtain an amplitude spectrum
Step 2.1.2: windowing is carried out on the amplitude spectrum of the observation signal, and the output sequence of the amplitude spectrum chi (tau) after windowing is represented as:
χ W (τ)＝χ(τ)·W(τ)
wherein, tau W Is the position of the maximum value of | χ (τ) |, d W Is the window width. Here, empirically, the window width W d1 And 4 points.
Step 2.1.3: and converting the windowed magnitude spectrum into a graph domain, and calculating the maximum degree of the graph. For having N 0 Undirected simple graph G (V, E) of a vertex if its degree matrix is
Extraction degree matrix
Diagonal elements of (a) constitute degree vectors of the graph G
Wherein, d δ Is the degree of the δ -th vertex, i.e., the sum of the number of edges connected to that vertex. The maximum degree of the graph G is the degree vector
The maximum of all elements in (a), namely:
accordingly, the maximum degree of the graph of the amplitude spectrum of the observation signal is calculated to be d max1 。
Step 2.1.4: setting an appropriate threshold η empirically 1 And =3. If d is max1 ＜η 1 If not, the signal is judged to be a 2ASK signal, otherwise, the step 2.2 is carried out.
Step 2.2: whether the signal is a BPSK signal is determined by the maximum degree of a graph generated by a square spectrum.
Step 2.2.1: and performing square operation on the observation signal subjected to short-time filtering pretreatment, and performing FFT (fast Fourier transform) modulus taking to obtain a square spectrum.
Step 2.2.2: windowing the squared spectrum of the observed signal, the method is the same as step 2.1.2, and the window width W is empirically determined d1 And 4 points.
Step 2.2.3: converting the windowed square spectrum to a map domain, and calculating the maximum degree d of the map according to the formula provided in step 2.1.3 max2 。
Step 2.2.4: setting an appropriate threshold η empirically 2 =3. If d is max2 ＜η 2 Judging the signal to be BPSK signal, otherwise entering step 2.3.
Step 2.3: and judging whether the signal is a QPSK signal or a 16QAM signal by using the maximum degree of a graph generated by the fourth power spectrum.
Step 2.3.1: and performing a fourth power operation on the observation signal subjected to short-time filtering pretreatment, and performing FFT (fast Fourier transform) modulus taking to obtain a fourth power spectrum.
Step 2.3.2: windowing is carried out on the fourth power spectrum of the observation signal, the method is the same as the step 2.1.2, and according to experience, the window width W d2 =20 dots.
Step 2.3.3: converting the windowed biquadratic spectrum to a graph domain, and calculating the maximum degree d of the graph according to a formula provided in the step 2.1.3 max3 。
Step 2.3.4: and estimating the signal-to-noise ratio by using a graph method, and setting a threshold according to the signal-to-noise ratio. When the signal-to-noise ratio is large (greater than or equal to-7 dB), eta can be set 3 =5; when the signal-to-noise ratio is small (< -7 dB), η can be set 3 And =7. If d is sum ＜η 3 Judging the signal to be a QPSK signal, otherwise, judging the signal to be a 16QAM signal.
FIGS. 2 to 4 are maximum degrees d of graphs obtained by converting the magnitude spectrum, the square spectrum and the fourth power spectrum of the observed signal into the magnitude spectrum, the square spectrum and the fourth power spectrum, respectively max And (5) characterizing.
For the 2ASK signal, it is a special sinusoidal signal, so its amplitude spectrum shows a line spectrum, and for the other three modulation signals, its amplitude spectrum is not a line spectrum. Since the 2ASK signal has a significant single peak, the graph converted into the graph domain is obviously different from the other three modulation signals, and the connectivity of the graph is obviously smaller than the other three modulation signals. Thus, the maximum d of the map into which the 2ASK signal magnitude spectrum is converted max1 Significantly less than the other three modulated signals.
For the BPSK signal, the signal is squared and reduced to a sine wave signal with the frequency twice that of the original signal, so that a line spectrum exists in the square spectrum of the BPSK signal, and the connectivity of the converted graph is poor. For QPSK and 16QAM signals, the connectivity of the transformed graph is stronger because the square spectrum is still not the line spectrum. Therefore, the maximum degree d of the graph into which the square spectrum of the BPSK signal is converted max2 Significantly less than the other two modulated signals.
Quadrupling components of the carrier appear in the quartered spectrum of the QPSK signal as a line spectrum, whereas no line spectrum exists in the quartered spectrum of the 16QAM signal. Therefore, the maximum degree d of the graph into which the fourth power spectrum of the QPSK signal is converted max3 Significantly less than a 16QAM modulated signal.
Fig. 5 and 6 show the recognition accuracy of four modulation signals and the average correct recognition rate of the method, respectively.
As shown in fig. 5, the recognition accuracy of the graph maximum degree feature method for 2ASK signals and BPSK signals is nearly 100%. The reason for this is that the amplitude spectrum of the 2ASK signal has an obvious single peak compared with the other three modulation signals, the connectivity of the graph converted after windowing is obviously smaller than that of the other three signals, and the maximum degree of the graph converted from the amplitude spectrum of the 2ASK signal after windowing and the difference between the other three signals are always stable under the condition that the signal-to-noise ratio varies; the square spectrum of BPSK signal after being windowed has obvious single peak compared with the two rest modulation signals (QPSK, 16 QAM), the connectivity of the converted graph is obviously smaller than the other two signals, and the maximum degree of the graph converted from the square spectrum of BPSK signal after being windowed and the difference between the two rest signals are stable under the condition of changing signal-to-noise ratio. The identification accuracy of the graph maximum degree characteristic method for the QPSK signal increases along with the increase of the signal-to-noise ratio, and the identification accuracy for the 16QAM signal is close to 100 percent all the time. The reason is that the difference between the biquadratic spectrums of the two signals after windowing is small under the low signal-to-noise ratio, the connectivity difference of the converted graphs is also small, after a certain threshold is set, all the signals are judged to be 16QAM signals by an algorithm under the low signal-to-noise ratio, and the distance between the maximum degrees of the graphs converted from the biquadratic spectrums of the QPSK signals and the 16QAM signals after windowing is increased along with the increase of the signal-to-noise ratio, so that the identification accuracy of the maximum degree characteristic method of the graphs on the QPSK signals is improved.
As shown in fig. 6, the average recognition accuracy of the maximum degree characterization method on the modulation signal increases with the signal-to-noise ratio. When the signal-to-noise ratio is 5dB, the average identification accuracy of the signal reaches over 80 percent; when the signal-to-noise ratio is 3dB, the average identification accuracy of the signal can reach more than 98%.
In another embodiment, the present invention further provides a CR signal modulation identification system based on a graph maximum degree feature, corresponding to the CR signal modulation identification method based on a graph maximum degree feature described above, including:
the preprocessing module is used for preprocessing the short-time filtering of the observation signal;
the signal identification module is used for judging whether the processed observation signal is a 2ASK signal or not by using the maximum degree of a graph generated by the amplitude spectrum of the observation signal; if not, judging whether the BPSK signal is generated by utilizing the maximum degree of a graph generated by the square spectrum of the BPSK signal; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is the QPSK signal or not.
In another embodiment, the present invention further provides a computer-readable storage medium storing a computer program, which causes a computer to execute the CR signal modulation recognition method based on the graph maximum degree feature as described above.
In another embodiment, the present invention further provides an electronic device, including: the CR signal modulation recognition method based on the maximum degree feature of the graph as described above is implemented by the processor when the processor executes the computer program.
In the embodiments disclosed herein, a computer storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. A computer storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Those of ordinary skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (8)
1. The CR signal modulation identification method based on the graph maximum degree characteristic is characterized by comprising the following steps of:
step 1: carrying out short-time filtering pretreatment on the observation signal;
step 2: for the processed observation signal, firstly, judging whether the observation signal is a 2ASK signal or not by using the maximum degree of a graph generated by a magnitude spectrum of the observation signal; if not, judging whether the BPSK signal is generated by utilizing the maximum degree of a graph generated by the square spectrum of the BPSK signal; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
2. A CR signal modulation recognition method based on graph maximum degree feature as claimed in claim 1, wherein: in step 1, the observation signal r (t) is expressed as:
r(t)＝s(t)+ω(t)
wherein s (t) is a modulated signal transmitted by the transmitting end, ω (t) is zero-mean white gaussian noise superimposed after passing through the AWGN channel, and its variance is σ 2 ；
The pre-treatment process comprises the following sub-steps:
step 1.1: segmenting the original observation signal after discrete sampling, wherein the form of the original observation signal is represented as follows:
r i (n)＝s i (n)+ω i (n)，i(N S -1)≤n≤(i+1)(N S -1)
wherein s is i (n) is a modulated signal transmitted from the transmitting end, ω i (N) is additive white Gaussian noise, N S I represents the number of segments as the length of the signal segment;
step 1.2: calculating r i N of (N) S Discrete Fourier transform of the points to obtain R i (k)＝DFT[r i (n)]；
Step 1.3: a bandpass filter with the following transmission characteristics was designed:
wherein k is s Is | R i (k) The maximum spectral line position of | represents the modulus, d s The number of points for filtering; r is to be i (k) The input designed filter, its output is represented as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculating R i ' (k) N s Point inverse discrete Fourier transform to obtain r i ′(n)＝IDFT(R i ′(k))；
Step 1.5: recombining each time domain signal after segmented reconstruction into a new observation signal according to the original segmentation sequence
3. A CR signal modulation recognition method based on graph maximum degree feature as claimed in claim 1, wherein: in the step 2, determining whether the amplitude spectrum is a 2ASK signal by using the maximum degree of the map generated by the amplitude spectrum includes the following substeps:
step 2.1.1: for observation signals pre-processed by short-time filtering
Fourier transform and modulus taking are carried out to obtain an amplitude spectrum
Step 2.1.2: windowing is carried out on the amplitude spectrum of the observation signal, and the output sequence of the amplitude spectrum χ (tau) after windowing is represented as:
χ W (τ)＝χ(τ)·W(τ)
wherein, tau W Is the maximum value position of | χ (τ) |, d W Is the window width;
step 2.1.3: converting the windowed magnitude spectrum into a graph domain, and calculating the maximum degree of the graph; for having N 0 Undirected simple graph G (V, E) of each vertex, and extraction degree matrix
The diagonal elements of (A) constitute the degree vector of the graph G
Wherein d is δ Degree of the δ -th vertex; the maximum degree of the graph G is the degree vector
Maximum value d of all elements in max ：
Accordingly, the maximum degree of the graph of the amplitude spectrum of the observation signal is calculated to be d max1 ；
Step 2.1.4: setting the threshold eta empirically 1 If d is =3 max1 ＜η 1 Judging the signal to be a 2ASK signal; otherwise, the maximum degree of a graph generated by the square spectrum is used for judging whether the BPSK signal is generated.
4. A CR signal modulation identifying method based on graph maximum degree feature as claimed in claim 3, wherein: in the step 2, determining whether the BPSK signal is the BPSK signal by using the maximum degree of the map generated by the square spectrum thereof includes the following substeps:
step 2.2.1: carrying out square operation on the observation signal subjected to short-time filtering pretreatment, and carrying out Fourier transform and modulus taking to obtain a square spectrum;
step 2.2.2: windowing the square spectrum of the observation signal;
step 2.2.3: converting the squared spectrum into graph domain, and calculating the maximum degree d of the graph max2 ；
Step 2.2.4: setting the threshold eta empirically 2 If d is =3 max2 ＜η 2 If not, the signal is judged to be a BPSK signal, otherwise, the signal is judged to be a QPSK signal or a 16QAM signal by utilizing the maximum degree of a graph generated by the fourth power spectrum of the signal.
5. A CR signal modulation recognition method based on graph maximum degree feature as claimed in claim 4, wherein: in the step 2, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal, and the method comprises the following substeps:
step 2.3.1: performing a quadratic operation on the observation signal subjected to short-time filtering pretreatment, and performing Fourier transform and modulus taking to obtain a quadratic spectrum;
step 2.3.2: windowing the fourth power spectrum of the observation signal;
step 2.3.3: converting the windowed biquadratic spectrum into a graph domain, and calculating the maximum degree d of the graph max3 ；
Step 2.3.4: estimating the signal-to-noise ratio by using a graph method, and setting a threshold eta according to the signal-to-noise ratio 3 When the signal-to-noise ratio is greater than or equal to-7 dB, eta 3 =5; when the signal-to-noise ratio is less than-7 dB, eta 3 =5; if d is sum ＜η 3 If the signal is a QPSK signal, otherwise, the signal is a 16QAM signal.
6. The CR signal modulation identification system based on the graph maximum degree characteristic is characterized by comprising the following components:
the preprocessing module is used for preprocessing the short-time filtering of the observation signal;
the signal identification module is used for judging whether the processed observation signal is a 2ASK signal or not by using the maximum degree of a graph generated by the amplitude spectrum of the processed observation signal; if not, judging whether the BPSK signal is generated by utilizing the maximum degree of a graph generated by the square spectrum of the BPSK signal; if not, the maximum degree of the graph generated by the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
7. A computer-readable storage medium storing a computer program for causing a computer to execute a CR signal modulation recognition method based on a graph maximum degree feature according to any one of claims 1 to 5.
8. An electronic device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the CR signal modulation recognition method according to any one of claims 1 to 5 based on the graph maximum degree feature.
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