CN115225440B - CR signal modulation identification method and system based on maximum degree characteristic of graph - Google Patents
CR signal modulation identification method and system based on maximum degree characteristic of graph Download PDFInfo
- Publication number
- CN115225440B CN115225440B CN202210807005.XA CN202210807005A CN115225440B CN 115225440 B CN115225440 B CN 115225440B CN 202210807005 A CN202210807005 A CN 202210807005A CN 115225440 B CN115225440 B CN 115225440B
- Authority
- CN
- China
- Prior art keywords
- signal
- graph
- spectrum
- maximum degree
- observation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/18—Phase-modulated carrier systems, i.e. using phase-shift keying
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/32—Carrier systems characterised by combinations of two or more of the types covered by groups H04L27/02, H04L27/10, H04L27/18 or H04L27/26
- H04L27/34—Amplitude- and phase-modulated carrier systems, e.g. quadrature-amplitude modulated carrier systems
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
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. Firstly, the observation signal is preprocessed by short-time filtering to compensate the signal-to-noise ratio loss. And then windowing the magnitude spectrum, the square spectrum and the fourth-order spectrum, converting the magnitude spectrum, the square spectrum and the fourth-order spectrum into a graph domain, and converting the problem of identifying the modulation modes of the four signals into judgment of the maximum degree of the graph according to nonlinear operation of the signals. Simulation experiments show that the invention has better recognition 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 characteristics of a graph.
Background
In the current era of mobile internet, the wireless communication technology is widely applied to various industries such as daily contact, ultra-high-definition streaming media, virtual and augmented reality, artificial intelligence, blockchain, medical treatment, intelligent automobiles, intelligent home, intelligent cities and the like. However, the problem is also caused that the radio spectrum resources are non-renewable resources, and the number of wireless access devices is rapidly increased, so that the spectrum resources which are slightly scarce originally are increasingly scarce. Therefore, it is critical to reasonably allocate spectrum resources in order to achieve as many communication between wireless devices as possible under limited spectrum resource conditions. Currently, static spectrum allocation strategies are mainly used. Under the allocation policy, a manager of radio spectrum resources divides the spectrum resources into blocks, and allocates frequency bands fixedly. Thus, a particular frequency band is allocated exclusively to authorized users, and even if the frequency band is idle for some period of time, unauthorized users cannot use the frequency band. Obviously, the current static spectrum allocation scheme generates a large amount of spectrum holes, so that huge waste of spectrum resources is caused, and the current environment that the access quantity of wireless devices is rapidly increased cannot be adapted.
The system of dynamic spectrum management in Cognitive Radio (CR) is a new idea for solving the problems of unreasonable spectrum allocation and insufficient utilization rate of a wireless network in the current environment. For a CR system, the spectrum sensing technology is a premise and a basis for realizing effective work. In general, spectrum sensing can be understood in both a narrow sense and a broad sense. The task of narrow spectrum sensing is to detect spectrum holes, i.e. to detect whether the PU channel is occupied or not, essentially signal detection. In CR, a Secondary User (SU) needs to be able to quickly and accurately detect whether a Primary User (PU) is free in an authorized frequency band at any time and place for its opportunity to use. At the same time, the SU also needs to monitor the PU that may occur at any time in order to vacate the frequency band in time when the PU needs to be used. In general, generalized spectrum sensing includes identifying signal parameters such as a modulation mode, a waveform, a bandwidth, a carrier frequency and the like of a signal in addition to detecting whether an authorized frequency band of a PU signal is idle, so that fine information of the PU spectrum can be obtained more accurately, and the 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-type modulation recognition method can obtain an optimal solution, there are some inherent problems, such as strong dependence on signal and channel prior information, incapability of guaranteeing that a closed solution exists, high computational complexity, probability mismatch and the like. These lead to a great limitation on the application of LB class algorithms under uncooperative conditions. For the FB algorithm, how to extract the characteristics with strong distinguishing property, strong robustness and low calculation complexity is a 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 a CR signal modulation identification system based on the maximum degree characteristic of a graph.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the CR signal modulation identification method based on the maximum characteristic of the graph is characterized by comprising the following steps:
step 1: preprocessing the observation signal by short-time filtering;
step 2: for the processed observation signal, firstly judging whether the observation signal is a 2ASK signal by using the maximum degree of a graph generated by the amplitude spectrum of the observation signal; if not, judging whether the signal is a BPSK signal by using the maximum degree of the graph generated by the square spectrum; if not, the maximum degree of the graph generated by using 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 the step 1, the observation signal r (t) is expressed 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 superimposed after passing through an AWGN channel, and the variance is sigma 2 ；
The preprocessing process comprises the following substeps:
step 1.1: segmenting the original observation signal after discrete sampling, wherein the form is expressed as:
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 modulation signal, ω, transmitted from the transmitting end i (N) is additive white Gaussian noise, N S For the length of the signal segment, i represents the segment number;
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 band-pass filter with the following transmission characteristics is designed:
wherein k is s Is |R i (k) Maximum spectral line position of I, I·| represents modulo d s The number of points for filtering; r is R i (k) A filter of input design, the output of which is expressed as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculating R i N of' (k) s Point inverse discrete fourier transform to obtain r i ′(n)＝IDFT(R i ′(k))；
Step 1.5: each time domain signal after the segmentation and reconstruction is recombined into a new observation signal according to the original segmentation order
Further, in the step 2, the maximum degree of the graph generated by using the amplitude spectrum thereof is used to determine whether the graph is a 2ASK signal, which includes the following sub-steps:
step 2.1.1: for observation signals subjected to short-time filtering pretreatment
Fourier transformation is carried out, and a module is taken 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 expressed as:
χ W (τ)＝χ(τ)·W(τ)
wherein τ W Maximum position of |χ (τ) |, d W Is the window width;
step 2.1.3: converting the windowed amplitude spectrum into a graph domain, and calculating the maximum degree of the graph; for having N 0 Undirected simple graph G (V, E) of vertices, extraction degree matrix
The diagonal elements of (a) constitute the degree vector of the graph G +.>
Wherein d δ Degree for the delta vertex; the maximum degree of the graph G is the degree vector +.>
Maximum value d of all elements in (2) max ：
Accordingly, the maximum degree of the graph of the observed signal amplitude spectrum is calculated to be d max1 ；
Step 2.1.4: setting threshold eta based on experience 1 =3, if d max1 ＜η 1 Judging the signal as a 2ASK signal; otherwise, judging whether the BPSK signal is the maximum degree of the graph generated by using the square spectrum.
Further, in the step 2, the maximum degree of the graph generated by using the square spectrum thereof is used to determine whether the graph is a BPSK signal, which includes the following sub-steps:
step 2.2.1: square operation is carried out on the observation signals subjected to short-time filtering pretreatment, and Fourier transformation and modulo extraction are carried out to obtain square spectrums;
step 2.2.2: windowing the square spectrum of the observation signal;
step 2.2.3: converting the square spectrum after windowing into a map domain, and calculatingMaximum degree d of graph max2 ；
Step 2.2.4: setting threshold eta based on experience 2 =3, if d max2 ＜η 2 And judging the signal as a BPSK signal, otherwise judging 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.
Further, in the step 2, the maximum degree of the graph generated by using the fourth power spectrum is used to judge whether the signal is a QPSK signal or a 16QAM signal, and the method includes the following sub-steps:
step 2.3.1: performing fourth-order operation on the observation signal subjected to short-time filtering pretreatment, and performing Fourier transformation modulo to obtain a fourth-order spectrum;
step 2.3.2: windowing the fourth power spectrum of the observation signal;
step 2.3.3: converting the windowed fourth power spectrum into a map domain, and calculating the maximum degree d of the map 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 more than or equal to-7 dB, eta 3 =5; when the signal-to-noise ratio is < -7dB, eta 3 =5; if d sum ＜η 3 And judging the signal as QPSK signal, or judging the signal as 16QAM signal.
The invention also provides a CR signal modulation recognition system based on the maximum degree characteristic of the graph, which is characterized by comprising the following steps:
the pretreatment module is used for carrying out short-time filtering pretreatment on the observed signals;
the signal identification module is used for firstly judging whether the processed observation signal is a 2ASK signal by utilizing 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 the graph generated by the square spectrum; if not, the maximum degree of the graph generated by using the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
The present invention also proposes a computer-readable storage medium storing a computer program, characterized in that the computer program causes a computer to execute the CR signal modulation recognition method based on the graph maximum degree feature as described above.
The invention also proposes an electronic device, characterized by comprising: the CR signal modulation recognition method based on the graph maximum degree characteristic comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program.
The beneficial effects of the invention are as follows: 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. Firstly, the observation signal is preprocessed by short-time filtering to compensate the signal-to-noise ratio loss. And then windowing the magnitude spectrum, the square spectrum and the fourth-order spectrum, converting the magnitude spectrum, the square spectrum and the fourth-order spectrum into a graph domain, and converting the problem of identifying the modulation modes of the four signals into judgment of the maximum degree of the graph according to nonlinear operation of the signals. Simulation experiments show that the invention has better recognition capability at low signal-to-noise ratio.
Drawings
FIG. 1 is a flow chart of an identification method of the present invention.
FIG. 2 is the maximum d of the graph into which the amplitude spectrum of the observed signal is converted max Features.
FIG. 3 is the maximum d of the graph into which the square spectrum of the observed signal is converted max Features.
FIG. 4 is the maximum d of the graph converted from the fourth power spectrum of the observed signal max Features.
Fig. 5 shows recognition accuracy of four kinds of modulated signals.
Fig. 6 is an average correct recognition rate of the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings of the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The CR signal modulation recognition method based on the maximum characteristic of the graph shown in fig. 1 specifically comprises the following steps.
Step 1: and (5) filtering pretreatment.
The observed signal is pre-processed by short-time filtering before modulation identification to compensate for the loss of signal to noise ratio. The number of points for short-time filtering is generally selected to be 10-30 according to experience.
The observed signal r (t) assuming an AWGN channel can be expressed 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 superimposed after passing through an AWGN channel, and the variance is sigma 2 。
When the signal-to-noise ratio is low, the signal is submerged in the noise, and the distinguishing property of the identification features is reduced; and in the process of extracting the characteristics, nonlinear operation is often needed, which increases noise power and reduces signal-to-noise ratio of signal processing, so that filtering processing is performed before CR signal modulation and identification is performed, and influence caused by low signal-to-noise ratio environment is reduced.
The preprocessing process comprises the following substeps:
step 1.1: the original observation signal after discrete sampling is segmented by proper length, and the form can be expressed as:
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 modulation signal, ω, transmitted from the transmitting end i (N) is additive white Gaussian noise, N S Is the length of the signal segment (i.e., the number of samples of the signal segment).
Step 1.2: calculating r i N of (N) S Discrete Fourier transform of points (Discrete Fourier Transform, DFT), yielding R i (k)＝DFT[r i (n)]。
Step 1.3: a band-pass filter with the following transmission characteristics is designed:
wherein k is s Is |R i (k) Maximum spectral line position of (|·| represents modulo), d s Points for filtering. R is R i (k) The output of an input designed filter can be expressed as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculating R i N of' (k) s Point inverse discrete Fourier transform (Inverse Discrete Fourier Transform, IDFT) to obtain r i ′(n)＝IDFT(R i ′(k))。
Step 1.5: each time domain signal after the segmentation and reconstruction is recombined into a new observation signal according to the original segmentation order
Step 2: and carrying out modulation identification according to the maximum degree of the generated graph.
Firstly, judging whether the signal is a 2ASK signal or not by utilizing the maximum degree of a graph generated by an amplitude spectrum; if not, judging whether the BPSK signal is the maximum degree of the graph generated by utilizing the square spectrum; if not, the maximum degree of the graph generated by using 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: and judging whether the signal is a 2ASK signal or not by using the maximum degree of the graph generated by the amplitude spectrum.
Step 2.1.1: for observation signals subjected to short-time filtering pretreatment
FFT is performed and modulo is taken to obtain a magnitude 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 expressed as:
χ W (τ)＝χ(τ)·W(τ)
wherein τ W Maximum position of |χ (τ) |, d W Is the window width. Here, empirically, window width W d1 =4 points.
Step 2.1.3: converting the windowed amplitude spectrum into a graph domain, and calculating the maximum degree of the graph. For having N 0 Undirected simple graph G (V, E) of vertices if its degree matrix is
Extraction degree matrix->
The diagonal elements of (a) constitute the degree vector of the graph G +.>
Wherein d δ The delta vertex is the degree, i.e., the sum of the number of edges connected to the vertex. The maximum degree of the graph G is the degree vector +.>
The maximum value of all elements in (a), namely:
accordingly, the maximum degree of the graph of the observed signal amplitude spectrum is calculated to be d max1 。
Step 2.1.4: empirically setting an appropriate threshold η 1 =3. If d max1 ＜η 1 And judging as a 2ASK signal, otherwise, entering step 2.2.
Step 2.2: whether the BPSK signal is the largest or not is judged by using the maximum degree of the graph generated by the square spectrum.
Step 2.2.1: and carrying out square operation on the observation signal subjected to short-time filtering pretreatment, and carrying out FFT (fast Fourier transform) modulo obtaining a square spectrum.
Step 2.2.2: windowing the square spectrum of the observed signal, wherein the method is the same as that of the step 2.1.2, and according to experience, the window width W d1 =4 points.
Step 2.2.3: converting the windowed square spectrum to a domain, calculating the maximum degree d of the graph according to the formula provided in step 2.1.3 max2 。
Step 2.2.4: empirically setting an appropriate threshold η 2 =3. If d max2 ＜η 2 And judging the signal as the BPSK signal, otherwise, entering step 2.3.
Step 2.3: the maximum degree of the graph generated by using the fourth power spectrum is used for judging whether the signal is a QPSK signal or a 16QAM signal.
Step 2.3.1: and performing fourth-order operation on the observation signal subjected to short-time filtering pretreatment, and performing FFT (fast Fourier transform) modulo obtaining a fourth-order spectrum.
Step 2.3.2: windowing the fourth power spectrum of the observed signal, wherein the method is the same as that of the step 2.1.2, and according to experience, the window width W d2 =20 points.
Step 2.3.3: converting the windowed fourth power spectrum into a graph domain, and calculating the maximum degree d of the graph according to the 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 larger (more 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 =7. If d sum ＜η 3 And judging the signal as QPSK signal, or judging the signal as 16QAM signal.
FIGS. 2 to 4 show the maximum degree d of the graphs converted from the magnitude spectrum, the square spectrum and the fourth-order spectrum of the observed signal max Features.
For a 2ASK signal, it is a special sinusoidal signal, so its amplitude spectrum appears as a line spectrum, while the other three modulated signals have amplitude spectrums that are not line spectrums. Since the 2ASK signal has a significant single peak, it is converted into a graphic representation of the domainThe connectivity is significantly smaller than the other three modulated signals. Thus, the maximum d of the graph into which the 2ASK signal magnitude spectrum is converted max1 Significantly smaller than the other three modulated signals.
For a BPSK signal, the signal is degraded into a sine wave signal with the frequency twice that of the original signal after square, so that a line spectrum exists in the square spectrum of the BPSK signal, and the connectivity of a graph obtained by conversion is poor. Whereas for QPSK signals and 16QAM signals, the connectivity of the resulting map of its conversion is stronger, since its square spectrum is still not a line spectrum. Thus, the maximum d of the graph into which the square spectrum of the BPSK signal is converted max2 Significantly smaller than the other two modulated signals.
The four-fold frequency component of the carrier appears in the fourth power spectrum of the QPSK signal, which appears as a line spectrum, while the 16QAM signal has no line spectrum in the fourth power spectrum. Thus, the maximum d of the graph into which the fourth power spectrum of the QPSK signal is converted max3 Significantly less than the 16QAM modulated signal.
Fig. 5 and 6 show the recognition accuracy of the four modulated signals and the average accuracy of the method, respectively.
As shown in fig. 5, the recognition accuracy of the 2ASK signal and the BPSK signal by the maximum likelihood feature method is nearly 100%. The reason is that the amplitude spectrum of the 2ASK signal has obvious single peak compared with other three modulation signals, the connectivity of the graph converted by windowing is obviously smaller than that of other three signals, and under the condition of the signal-to-noise ratio variation, the maximum degree of the graph converted by the amplitude spectrum of the 2ASK signal after windowing and the difference between the other three signals are stable all the time; the BPSK signal has obvious single peak compared with the rest two modulation signals (QPSK, 16 QAM), the connectivity of the converted graph is obviously smaller than other two signals, and the maximum degree of the graph converted by the BPSK signal through the square spectrum after the windowing and the difference between the rest two signals are stable under the condition of signal-to-noise ratio variation. The recognition accuracy of the maximum degree characteristic method of the graph for QPSK signals increases along with the increase of the signal-to-noise ratio, and the recognition accuracy of the maximum degree characteristic method of the graph for 16QAM signals is always close to 100%. The reason is that the difference of the four-time spectrums of the two signals after being windowed is smaller under the condition of low signal-to-noise ratio, the connectivity difference of the graphs converted by the difference is smaller, after a certain threshold is set, the algorithm judges all the signals as 16QAM signals under the condition of low signal-to-noise ratio, and the distance of the maximum degree of the graphs converted by the four-time spectrums of the QPSK signals and the 16QAM signals after being windowed is increased along with the increase of the signal-to-noise ratio, so that the recognition accuracy of the QPSK signals by the graph maximum degree feature method is improved.
As shown in fig. 6, the average recognition accuracy of the modulated signal by the maximum degree feature method increases with the increase of the signal-to-noise ratio. When the signal-to-noise ratio is 5dB, the average recognition accuracy of the signal is over 80 percent; when the signal-to-noise ratio is 3dB, the average recognition accuracy of the signal can reach more than 98%.
In another embodiment, the present invention further provides a CR signal modulation recognition system based on graph maximum degree feature corresponding to the above CR signal modulation recognition method based on graph maximum degree feature, including:
the pretreatment module is used for carrying out short-time filtering pretreatment on the observed signals;
the signal identification module is used for firstly judging whether the processed observation signal is a 2ASK signal by utilizing 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 the graph generated by the square spectrum; if not, the maximum degree of the graph generated by the fourth power spectrum is used to judge whether the signal is QPSK signal.
In another embodiment, the present invention also proposes a computer-readable storage medium storing a computer program that causes a computer to execute the CR signal modulation identification 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 graph maximum degree characteristic is realized by the memory, the processor and the computer program stored in the memory and capable of running on 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. The 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 one or more wire-based electrical connections, 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 portable 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 will 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 solution. 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 examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the invention without departing from the principles thereof are intended to be within the scope of the invention as set forth in the following claims.
Claims (6)
1. The cognitive radio signal modulation identification method based on the maximum characteristic of the graph is characterized by comprising the following steps of:
step 1: preprocessing the observation signal by short-time filtering;
step 2: for the processed observation signal, firstly judging whether the observation signal is a 2ASK signal by using the maximum degree of a graph generated by the amplitude spectrum of the observation signal; if not, judging whether the signal is a BPSK signal by using the maximum degree of the graph generated by the square spectrum; if not, judging whether the signal is QPSK signal or 16QAM signal by using the maximum degree of the graph generated by the fourth power spectrum;
in the step 2, the maximum degree of the graph generated by the amplitude spectrum is used for judging whether the graph is a 2ASK signal, and the method comprises the following substeps:
step 2.1.1: for observation signals subjected to short-time filtering pretreatment
Fourier transform is carried out and the mode is taken to obtain the 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 expressed as:
χ W (τ)＝χ(τ)·W(τ)
wherein τ W Maximum position of |χ (τ) |, d W Is the window width;
step 2.1.3: converting the windowed amplitude spectrum into a graph domain, and calculating the maximum degree of the graph; for having N 0 Undirected simple graph G (V, E) of vertices, extraction degree matrix
The diagonal elements of (a) constitute the degree vector of the graph G +.>
Wherein d δ Degree for the delta vertex; the maximum degree of the graph G is the degree vector +.>
Maximum value d of all elements in (2) max ：
Accordingly, the maximum degree of the graph of the observed signal amplitude spectrum is calculated to be d max1 ；
Step 2.1.4: setting threshold eta based on experience 1 =3, if d max1 ＜η 1 Judging the signal as a 2ASK signal; otherwise, judging whether the signal is a BPSK signal or not by using the maximum degree of the graph generated by the square spectrum;
in the step 2, the maximum degree of the graph generated by using the square spectrum of the signal is used for judging whether the signal is a BPSK signal, and the method comprises the following substeps:
step 2.2.1: square operation is carried out on the observation signals subjected to short-time filtering pretreatment, and Fourier transformation and modulo extraction are carried out to obtain square spectrums;
step 2.2.2: windowing the square spectrum of the observation signal;
step 2.2.3: converting the square spectrum after windowing into a map domain, and calculating the maximum degree d of the map max2 ；
Step 2.2.4: setting threshold eta based on experience 2 =3, if d max2 ＜η 2 And judging the signal as a BPSK signal, otherwise judging 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.
2. The cognitive radio signal modulation identification method based on graph maximum degree features as claimed in claim 1, wherein: in the step 1, the observation signal r (t) is expressed 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 superimposed after passing through an AWGN channel, and the variance is sigma 2 ；
The preprocessing process comprises the following substeps:
step 1.1: segmenting the original observation signal after discrete sampling, wherein the form is expressed as:
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 modulation signal, ω, transmitted from the transmitting end i (N) is additive white Gaussian noise, N S For the length of the signal segment, i represents the segment number;
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 band-pass filter with the following transmission characteristics is designed:
wherein k is s Is |R i (k) Maximum spectral line position of I, I·| represents modulo d s The number of points for filtering; r is R i (k) A filter of input design, the output of which is expressed as:
R i ′(k)＝H s (k)R i (k)
step 1.4: calculating R i N of' (k) s Point inverse discrete fourier transform to obtain r i ′(n)＝IDFT(R i ′(k))；
Step 1.5: each time domain signal after the segmentation and reconstruction is recombined into a new observation signal according to the original segmentation order
3. The cognitive radio signal modulation identification method based on graph maximum degree features as claimed in claim 1, wherein: in the step 2, the maximum degree of the graph generated by using 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 fourth-order operation on the observation signal subjected to short-time filtering pretreatment, and performing Fourier transformation modulo to obtain a fourth-order spectrum;
step 2.3.2: windowing the fourth power spectrum of the observation signal;
step 2.3.3: converting the windowed fourth power spectrum into a map domain, and calculating the maximum degree d of the map 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 more than or equal to-7 dB, eta 3 =5; when the signal-to-noise ratio is < -7dB, eta 3 =5; if d sum ＜η 3 And judging the signal as QPSK signal, or judging the signal as 16QAM signal.
4. The cognitive radio signal modulation recognition system based on the maximum characteristic of the graph is characterized by comprising:
the pretreatment module is used for carrying out short-time filtering pretreatment on the observed signals;
the signal identification module is used for firstly judging whether the processed observation signal is a 2ASK signal by utilizing 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 the graph generated by the square spectrum; if not, the maximum degree of the graph generated by using the fourth power spectrum is used for judging whether the signal is QPSK signal or 16QAM signal
The signal identification module judges whether the signal is a 2ASK signal or not by using the maximum degree of the graph generated by the amplitude spectrum of the signal identification module, and the signal identification module specifically comprises the following steps:
for observation signals subjected to short-time filtering pretreatment
Fourier transform is carried out and the mode is taken to obtain the amplitude spectrum
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 expressed as:
χ W (τ)＝χ(τ)·W(τ)
wherein τ W Maximum position of |χ (τ) |, d W Is the window width;
converting the windowed amplitude spectrum into a graph domain, and calculating the maximum degree of the graph; for having N 0 Undirected simple graph G (V, E) of vertices, extraction degree matrix
The diagonal elements of (a) constitute the degree vector of the graph G +.>
Wherein d δ Degree for the delta vertex; the maximum degree of the graph G is the degree vector +.>
Maximum value dmax of all elements in (a):
accordingly, the maximum degree of the graph of the observed signal amplitude spectrum is calculated to be d max1 ；
Setting threshold eta based on experience 1 =3, if d max1 ＜η 1 Judging the signal as a 2ASK signal; otherwise, judging whether the signal is a BPSK signal or not by using the maximum degree of the graph generated by the square spectrum;
the signal identification module judges whether the signal is a BPSK signal or not by using the maximum degree of a graph generated by the square spectrum of the signal identification module, and the signal identification module specifically comprises the following steps:
square operation is carried out on the observation signals subjected to short-time filtering pretreatment, and Fourier transformation and modulo extraction are carried out to obtain square spectrums;
windowing the square spectrum of the observation signal;
converting the square spectrum after windowing into a map domain, and calculating the maximum degree d of the map max2 ；
Setting threshold eta based on experience 2 =3, if d max2 ＜η 2 And judging the signal as a BPSK signal, otherwise judging 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.
5. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the cognitive radio signal modulation recognition method based on the graph maximum degree feature according to any one of claims 1 to 3.
6. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, which processor, when executing the computer program, implements the cognitive radio signal modulation identification method based on graph maximum degree features as claimed in any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210807005.XA CN115225440B (en) | 2022-07-08 | 2022-07-08 | CR signal modulation identification method and system based on maximum degree characteristic of graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210807005.XA CN115225440B (en) | 2022-07-08 | 2022-07-08 | CR signal modulation identification method and system based on maximum degree characteristic of graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115225440A CN115225440A (en) | 2022-10-21 |
| CN115225440B true CN115225440B (en) | 2023-05-26 |
Family
ID=83610343
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210807005.XA Active CN115225440B (en) | 2022-07-08 | 2022-07-08 | CR signal modulation identification method and system based on maximum degree characteristic of graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115225440B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106130942A (en) * | 2016-07-05 | 2016-11-16 | 东南大学 | A kind of wireless communication signals Modulation Identification based on Cyclic Spectrum and method for parameter estimation |
| CN112422465A (en) * | 2019-10-09 | 2021-02-26 | 上海矢元电子有限公司 | Signal modulation identification equipment |
| CN112787964A (en) * | 2021-02-18 | 2021-05-11 | 金陵科技学院 | BPSK and QPSK signal modulation identification method based on range median domain features |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101674270B (en) * | 2009-10-16 | 2011-10-05 | 电子科技大学 | Identification method of low signal-to-noise ratio phase-shift keying modulating signal |
| US9413584B2 (en) * | 2014-04-07 | 2016-08-09 | University Of Utah Research Foundation | Blind phase-shift keying (PSK) and quadrature amplitude modulation (QAM) identification |
| US9614638B2 (en) * | 2015-07-02 | 2017-04-04 | Fujitsu Limited | Methods and systems for periodic optical filtering to identify tone modulated optical signals |
| US10749718B2 (en) * | 2017-06-29 | 2020-08-18 | Allen-Vanguard Corporation | System and method for modulation classification using signal graphs |
| CN112367282B (en) * | 2020-10-27 | 2022-08-26 | 河南科技大学 | MPSK modulation multi-symbol detection method suitable for novel smart city |
| CN113259288B (en) * | 2021-05-05 | 2023-08-08 | 青岛科技大学 | Underwater sound modulation mode identification method based on feature fusion and lightweight hybrid model |
| CN114268526B (en) * | 2021-12-21 | 2023-05-26 | 金陵科技学院 | BPSK and QPSK signal modulation identification method based on degree characteristics of graph |
-
2022
- 2022-07-08 CN CN202210807005.XA patent/CN115225440B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106130942A (en) * | 2016-07-05 | 2016-11-16 | 东南大学 | A kind of wireless communication signals Modulation Identification based on Cyclic Spectrum and method for parameter estimation |
| CN112422465A (en) * | 2019-10-09 | 2021-02-26 | 上海矢元电子有限公司 | Signal modulation identification equipment |
| CN112787964A (en) * | 2021-02-18 | 2021-05-11 | 金陵科技学院 | BPSK and QPSK signal modulation identification method based on range median domain features |
Non-Patent Citations (2)
| Title |
|---|
| M-QAM信号的调制制式识别;詹亚锋,曹志刚,马正新;通信学报(02);全文 * |
| 通信信号调制样式的自动识别;张志勇;张立民;兰天;;舰船电子工程(12);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115225440A (en) | 2022-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104468001B (en) | Signal identification method and system based on radio signal frequency spectrum feature template | |
| CN105429719B (en) | Based on power spectrum and multi-scale wavelet transformation analysis high reject signal detection method | |
| CN114268526B (en) | BPSK and QPSK signal modulation identification method based on degree characteristics of graph | |
| CN103199945B (en) | Method for identifying modulation mode of cognitive radio signal under low signal-to-noise ratio condition | |
| CN105785324A (en) | MGCSTFT-based chirp signal parameter estimation method | |
| AU2016201984A1 (en) | System and method for classifying signal modulations | |
| CN101588191B (en) | Method and device for radio signal recognition | |
| Sisi et al. | Carrier frequency and symbol rate estimation based on cyclic spectrum | |
| CN115225440B (en) | CR signal modulation identification method and system based on maximum degree characteristic of graph | |
| CN108683623A (en) | Faint OFDM sub-carrier numbers method of estimation based on accidental resonance | |
| CN115460048B (en) | MSK modulation identification method, medium and device based on code element rate | |
| CN109660475B (en) | A kind of non-cooperation phase code water sound communication signal autonomous identifying method | |
| Bao et al. | Spectrum segmentation for wideband sensing of radio signals | |
| CN115378776A (en) | MFSK modulation identification method based on cyclic spectrum parameters | |
| CN109450835B (en) | Modulation identification method for extracting communication signal characteristics based on HSCA | |
| CN111814703B (en) | HB-based signal joint feature extraction method under non-reconstruction condition | |
| CN115277322B (en) | CR signal modulation identification method and system based on graph and continuous entropy characteristics | |
| CN114006798A (en) | Signal processing method and device, electronic equipment and storage medium | |
| Li et al. | Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios | |
| CN114268393A (en) | Cognitive radio frequency spectrum sensing method based on connected component number characteristics | |
| CN102946368B (en) | The digital modulation signal recognizing method of frequency deviation and skew is contained under multidiameter fading channel | |
| CN115695119A (en) | OFDM and OCDM signal identification method and system | |
| US20120196552A1 (en) | Methods for Determining Whether a Signal Includes a Wanted Signal and Apparatuses Configured to Determine Whether a Signal Includes a Wanted Signal | |
| CN115225438B (en) | BPSK (binary phase shift keying) and QPSK (quadrature phase shift keying) signal modulation identification method and system based on piecewise linear compression quantization | |
| CN114997299B (en) | Radio frequency fingerprint identification method in resource limited environment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |