WO2021082469A1 - Modulation mode identification method and apparatus - Google Patents
Modulation mode identification method and apparatus Download PDFInfo
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- WO2021082469A1 WO2021082469A1 PCT/CN2020/096565 CN2020096565W WO2021082469A1 WO 2021082469 A1 WO2021082469 A1 WO 2021082469A1 CN 2020096565 W CN2020096565 W CN 2020096565W WO 2021082469 A1 WO2021082469 A1 WO 2021082469A1
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- 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
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- This application relates to the field of radio communication technology, and in particular to a modulation method identification method and device.
- the modulation method identification technology of the signal (especially the weak signal) has important applications in many fields.
- the purpose of the signal modulation method identification technology is to identify the modulation method of the received signal in the absence of transmitter information and channel information.
- the existing signal modulation method identification methods mainly include: calculating the cyclic spectrum of the signal to be identified, obtaining the cyclic spectrum of the signal to be identified, extracting the positions of partial peaks of the cyclic spectrum of the signal to be identified, and/or the signal to be identified Use the extracted features to identify the modulation mode of the signal to be identified by using features such as the cross-sectional information at the peak of the cyclic spectrogram.
- the extracted features are only part of the cyclic spectrogram, that is, only part of the cyclic spectrogram is used to recognize the modulation mode of the signal to be recognized. This makes the recognition accuracy of the modulation mode of the signal low.
- the purpose of the embodiments of the present application is to provide a modulation method identification method and device, so as to improve the accuracy of signal modulation identification.
- the specific technical solutions are as follows:
- an embodiment of the present application provides a modulation mode identification method, and the method includes the following steps:
- the step of determining the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram according to the frequency components and the cyclic frequency components of the signal to be identified includes:
- the step of moving the frequency spectrum of the signal to be identified to a plurality of preset frequency bands to obtain signals of multiple frequency bands corresponding to the signal to be identified includes:
- the frequency spectrum of the sub-signal to be identified is moved to multiple preset frequency bands to obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified;
- the multiple frequency band sub-signals corresponding to the sub-signal to be identified are frequency band signals corresponding to the signal to be identified in the frequency band;
- the step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal includes:
- averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
- the step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal includes:
- For each frequency band signal perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal;
- averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
- the step of determining the cyclic spectrogram of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal includes:
- the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
- the step of determining the cyclic spectrogram of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal includes:
- the step of performing superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram includes:
- the modulation mode recognition model is obtained by training in the following manner:
- sample data includes a plurality of sample signals and a label modulation mode of each sample signal
- an embodiment of the present application provides a modulation mode identification device, the device including:
- the receiving module is used to receive the signal to be identified
- the determining module is configured to determine the cyclic spectrum of the signal to be identified according to the respective frequency components and the respective cyclic frequency components of the signal to be identified, as the target cyclic spectrum;
- the recognition module is used to input the target cyclic spectrogram into a pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized, wherein the modulation mode recognition model is obtained by training a deep neural network using sample data
- the sample data includes a plurality of sample signals and the label modulation mode of each sample signal.
- the determining module includes:
- the moving sub-module is used to move the frequency spectrum of the signal to be identified to multiple preset frequency bands respectively to obtain multiple frequency band signals corresponding to the signal to be identified;
- the first determining sub-module is used to determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrum of each frequency band signal is the cycle corresponding to the frequency band where the signal of the frequency band is located.
- the superimposition sub-module is used to perform superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
- the moving sub-module is specifically configured to: perform segment processing on the signal to be identified to obtain multiple sub-signals to be identified; for each sub-signal to be identified, move the frequency spectrum of the sub-signal to be identified respectively To multiple preset frequency bands, multiple frequency band sub-signals corresponding to the sub-signals to be identified are obtained; wherein, the multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified in one frequency band are the to-be-identified sub-signals in the frequency band. Identify the frequency band signal corresponding to the signal;
- the first determining sub-module is specifically configured to: determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal; for each frequency band, multiple The cyclic spectrum of the frequency band sub-signal is averaged, and the cyclic spectrum of the frequency band signal on the frequency band is obtained.
- the first determining submodule is specifically configured to: for each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal; Frequency components and each cyclic frequency component, determine the cyclic spectrum of each frequency band sub-signal; for each frequency band, perform averaging processing on the cyclic spectrum of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrum of the frequency band signal on the frequency band Spectrogram.
- the first determining submodule is specifically configured to:
- the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
- the first determining submodule is specifically configured to:
- the superposition submodule is specifically used for:
- the device further includes: a training module for training to obtain the modulation mode recognition model;
- the training module includes:
- An obtaining sub-module configured to obtain the sample data, the sample data including a plurality of sample signals and the labeling modulation mode of each sample signal;
- the second determining sub-module is used to determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;
- the prediction sub-module is used to input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
- the third determining sub-module is used to determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeling modulation mode of each sample signal;
- the fourth determination sub-module is used to determine whether the deep neural network converges according to the loss value; if not, adjust the parameters of the deep neural network, and return to execute the input of the cyclic spectrum of each sample signal
- the preset deep neural network obtains the step of predicting the modulation mode of each sample signal; if it is, the current deep neural network is determined to be the modulation mode recognition model.
- an electronic device including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
- Memory used to store computer programs
- the processor is configured to implement any of the method steps described in the first aspect when executing the program stored in the memory.
- a computer-readable storage medium is provided, and a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, it implements any of the method steps described in the first aspect.
- a computer program which when running on a computer, causes the computer to execute any method step described in the first aspect.
- the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation mode identification model to identify the modulation mode of the signal to be identified, that is, using the Recognizing all the characteristics of the cyclic spectrogram of the signal and identifying the modulation mode of the signal to be identified, instead of using part of the characteristics of the cyclic spectrogram to identify the modulation mode of the signal to be recognized, the accuracy of the recognition of the modulation mode of the signal is improved.
- FIG. 1 is a schematic flowchart of a modulation method identification method provided by an embodiment of this application
- FIG. 2 is a schematic diagram of another flow chart of a modulation mode identification method provided by an embodiment of this application;
- FIG. 3 is a schematic diagram of a central frequency point increase provided by an embodiment of the application.
- FIG. 4 is a schematic diagram of a central frequency point reduction provided by an embodiment of the application.
- FIG. 5 is a schematic diagram of a smoothing process provided by an embodiment of this application.
- FIG. 6 is a schematic diagram of a picture superimposing process provided by an embodiment of this application.
- FIG. 7 is a schematic flowchart of a deep neural network training method provided by an embodiment of this application.
- FIG. 8 is a schematic diagram of a structure of a deep neural network provided by an embodiment of this application.
- FIG. 9 is a schematic diagram of still another flow chart of a modulation mode identification method provided by an embodiment of this application.
- FIG. 10 is a schematic structural diagram of a modulation mode identification device provided by an embodiment of this application.
- FIG. 11 is a schematic diagram of a structure of an electronic device provided by an embodiment of the application.
- Signal in cooperative mode refers to a signal whose modulation method and channel condition are known.
- Signal in non-cooperative mode Refers to a signal whose modulation method and channel condition are unknown.
- Cyclic spectrum Use frequency component as abscissa, cyclic frequency component as ordinate, or frequency component as abscissa and cyclic frequency component as ordinate to determine the cyclic spectrum of the signal.
- the cyclic spectrum is complex.
- Cyclic spectrum Take the modulus of the cyclic spectrum to obtain the cyclic spectrum.
- Step 101 Receive a signal to be identified.
- the electronic device may receive a signal with an unknown modulation mode as the signal to be recognized.
- Step 102 Determine a cyclic spectrogram of the signal to be recognized according to each frequency component and each cyclic frequency component of the signal to be recognized, as the target cyclic spectrogram.
- the electronic device after the electronic device receives the signal to be identified, it can determine the cyclic spectrum of the signal to be identified based on the frequency components and the cyclic frequency components of the signal to be identified, and the cyclic spectrum of the signal to be identified Figure as the target cycle spectrum.
- the cyclic spectrum of the signal to be identified is determined according to the frequency components and the cyclic frequency components of the signal to be identified, which can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or Using the frequency component as the abscissa and the cyclic frequency component as the ordinate, the cyclic spectrum of the signal to be identified is determined.
- Step 103 Input the target cyclic spectrogram into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized.
- the modulation recognition model is a model obtained by training a deep neural network using sample data, and the sample data includes a plurality of sample signals and a label modulation method of each sample signal.
- the electronic device may input the target cyclic spectrogram into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized.
- the modulation recognition model is a model obtained by training a deep neural network using sample data.
- the sample data may include multiple sample signals and a label modulation method for each sample signal. The specific training method of the modulation mode recognition model will be described in detail below, and the description will not be expanded here.
- the electronic device may first move the received frequency spectrum of the signal to be identified to multiple preset frequency bands.
- FIG. 2 is a schematic diagram of another flow chart of a modulation mode identification method provided by an embodiment of the present application. The method may include the following steps.
- Step 201 Receive a signal to be identified. Step 201 is consistent with step 101.
- Step 202 Move the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain multiple frequency band signals corresponding to the signal to be identified.
- the electronic device after receiving the signal to be identified, performs spectrum shift processing on the signal to be identified. Specifically, it may be: moving the received frequency spectrum of the signal to be identified to multiple preset frequency bands, that is, changing the center frequency point f c of the signal to be identified to obtain multiple frequency band signals corresponding to the signal to be identified.
- the center frequency f c of each frequency band is different.
- Step 202 as will be understood, to be the center frequency of the identification signal f c is adjusted to a predetermined center frequency f c, the identification signal to be amplitude modulated carrier signal, the frequency spectrum of the signal to be recognized without distortion to move the carrier
- the two sides of the preset center frequency point f c of the signal are to adjust the signal to be identified to the frequency band where the preset center frequency point f c is located.
- the signal obtained after the signal to be identified is adjusted to the preset frequency band is the frequency band signal.
- the electronic device may directly move the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain signals in multiple frequency bands corresponding to the signal to be identified.
- the electronic device may use the following steps to obtain multiple frequency band signals corresponding to the signal to be identified.
- moving the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain multiple frequency band signals corresponding to the signal to be identified may include:
- the electronic device may perform segmentation processing on the signal to be identified to obtain multiple sub-signals to be identified. For each sub-signal to be identified, the electronic device moves the frequency spectrum of the sub-signal to be identified to a plurality of preset frequency bands to obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified.
- the multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified in one frequency band are frequency band signals corresponding to the signal to be identified in the frequency band.
- a frequency band includes multiple frequency band sub-signals, and each frequency band sub-signal corresponds to one sub-signal to be identified, and the multiple sub-signals to be identified do not overlap, that is, multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified
- the signals do not overlap.
- Multiple sub-signals of multiple frequency bands on a frequency band correspond to multiple sub-signals to be identified, and these multiple sub-signals to be identified form the signal to be identified.
- multiple sub-signals of the frequency band constitute the frequency band signal corresponding to the signal to be identified on the frequency band.
- Step 203 Determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal.
- the electronic device performs spectrum shift processing on the signal to be identified to obtain a frequency band signal, and performs calculation cyclic spectrogram processing on the frequency band signal to obtain a cyclic spectrogram of each frequency band signal.
- it may be: determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal.
- Step 203 can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or taking the frequency component as the abscissa and the cyclic frequency component as the ordinate to determine the cyclic spectrum of each frequency band signal.
- the cyclic spectrogram of the frequency band signal is the cyclic spectrogram corresponding to the frequency band where the frequency band signal is located.
- the electronic device moves the frequency spectrum of the signal to be identified to a plurality of preset frequency bands, and after obtaining multiple frequency band signals corresponding to the signal to be identified, for each frequency band, the electronic device can be based on the frequency band on the frequency band.
- Each frequency component of the signal and each cyclic frequency component determine the cyclic spectrum of the frequency band signal.
- the electronic device moves the frequency spectrum of the signal to be identified to a plurality of preset frequency bands, and after obtaining multiple frequency band signals corresponding to the signal to be identified, for each frequency band, the electronic device can be based on the signal in the frequency band.
- Perform segmentation processing to obtain multiple frequency band sub-signals corresponding to the frequency band signal; determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal.
- the electronic device smoothes the cyclic spectrum of the sub-signal of each frequency band to obtain the cyclic spectrum of the frequency band signal in the frequency band.
- the foregoing smoothing processing may be averaging processing, that is, performing averaging processing on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
- the above steps of determining the cyclic spectrum of each frequency sub-signal according to the respective frequency components and the respective cyclic frequency components of each frequency band sub-signal can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or taking The frequency component is the abscissa, the cyclic frequency component is the ordinate, and the cyclic spectrum of each frequency band signal is drawn.
- the electronic device may first identify the signal for segmentation processing, and then perform spectrum shifting. As described above, the frequency spectrum of each sub-signal to be identified is moved to a plurality of preset frequency bands, and multiple frequency band sub-signals corresponding to each sub-signal to be identified are obtained. At this time, on each frequency band, the electronic device obtains multiple frequency band sub-signals. The electronic device can determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal. For each frequency band, the electronic device smoothes the cyclic spectrum of the sub-signal of each frequency band to obtain the cyclic spectrum of the frequency band signal in the frequency band.
- the above step of determining the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal may include:
- the electronic device can perform fast Fourier transform on the sub-signals of each frequency band to obtain the transform sub-signals corresponding to the sub-signals of each frequency band. After that, the electronic device can determine the cyclic spectrogram of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal, and use the cyclic spectrogram of each transformed sub-signal as each transformed sub-signal The cyclic spectrum of the corresponding frequency band sub-signal.
- the step of determining the cyclic spectrum of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal can be understood as taking the frequency component as the abscissa and the cyclic frequency
- the component is the ordinate, or the frequency component is the abscissa, the cyclic frequency component is the ordinate, and the cyclic spectrum of each transformed signal is determined.
- the electronic device can use the following formula to determine the cyclic spectrum of each transformed sub-signal:
- r represents the signal to be identified
- N 0 represents the signal length of the k-th transformed sub-signal corresponding to the signal to be identified
- f represents the frequency component
- ⁇ represents the cyclic frequency component
- the electronic device may use the following formula to perform averaging processing on the cyclic spectrograms of the multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signal on the frequency band:
- represents the cyclic spectrogram of the frequency band signal on the lth frequency band
- N l represents the total number of cyclic spectrograms corresponding to the lth frequency band, that is, the sub-signal of the frequency band on the lth frequency band R represents the signal to be identified
- m represents the frequency band sub-signal corresponding to the signal to be identified
- l represents the frequency band
- represents the m-th frequency band sub-signal on the l-th frequency band
- the cyclic spectrum of S r,l,m (f, ⁇ ) represents the cyclic spectrum of the m-th frequency band sub-signal on the l-th frequency band
- f represents the frequency component
- ⁇ represents the cyclic frequency component
- represents the modulus value Calculation.
- the figure includes the cyclic spectrograms of the first, second, and N l- th frequency band sub-signals on the l-th frequency band, and the above N l cyclic spectrograms are averaged to obtain the graph 5 Means shown below.
- the electronic device performs averaging processing on the cyclic spectrograms of multiple frequency band sub-signals in each frequency band, so that the glitch caused by noise in the cyclic spectrogram of the frequency band signal in each frequency band can be minimized. Reduce the influence of noise interference.
- Step 204 Perform superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
- the electronic device after obtaining the cyclic spectrogram corresponding to each frequency band, performs image superposition processing on the cyclic spectrogram corresponding to the multiple frequency bands to obtain the target cyclic spectrogram. Specifically, it can be: superimposing the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
- the electronic device may use the following formula to superimpose the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:
- represents the cyclic spectrum of the signal to be identified
- S'r (f, ⁇ ) represents the cyclic spectrum of the signal to be identified
- represents the first The cyclic spectrum corresponding to l frequency bands
- S r,l (f, ⁇ ) represents the cyclic spectrum corresponding to the l-th frequency band
- l represents the frequency band
- f represents the frequency component
- ⁇ represents the cyclic frequency component
- represents the calculation of the modulus value.
- FIG comprises a central frequency f c to be the identification signal 6 to move at 0.1f c circulation spectrum
- the signal to be recognized center frequency f c to move at 0.3f c circulation spectrum to be
- the center frequency point f c of the identification signal is moved to the cyclic spectrogram at 1.5f c , etc., and the cyclic spectrograms corresponding to these different frequency bands are superimposed, and the cyclic spectrogram after the superimposition of the pictures shown in Figure 6 is obtained.
- the electronic device can superimpose the cyclic spectrograms corresponding to multiple frequency bands, that is, the electronic device performs the superposition processing on the cyclic spectrograms after the averaging process, that is, obtains the cyclic spectrograms of multiple frequency bands.
- the processing corresponding to the maximum value of the position increases the number of peaks of the target cyclic spectrogram finally obtained, that is, increases the characteristics of the modulation mode identification. Therefore, the modulation method identification based on the target cyclic spectrum with an increased number of peaks can further improve the accuracy of the modulation method identification.
- Step 205 Input the target cyclic spectrum into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized. Step 205 is consistent with step 103.
- the electronic device may train to obtain the above-mentioned modulation mode recognition model through the following steps, which are specifically as follows.
- Step 701 Obtain sample data, where the sample data includes a plurality of sample signals and the label modulation mode of each sample signal.
- the electronic device may obtain sample data, and the sample data may include a plurality of sample signals and the labeling modulation mode of each sample signal.
- the modulation mode of the above-mentioned multiple sample signals is known, that is, the modulation mode is marked.
- the modulation mode of the a-sample signal is quadrature amplitude modulation
- the modulation mode of the b-sample signal is key shift modulation.
- Step 702 Determine a cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal.
- the electronic device may determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal. That is, taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or the frequency component as the abscissa and the cyclic frequency component as the ordinate, the cyclic spectrum of each sample signal is drawn.
- the frequency component and the cyclic frequency component of each sample signal can be arbitrarily selected within a certain range of values, for example, the value range of the frequency component is 100 Hz to 1000 MHz, and the value range of the cyclic frequency component is 2000 Hz to 2000 Hz. 10000 Hz.
- Step 703 Input the cyclic spectrogram of each sample signal into a preset deep neural network to obtain the predicted modulation mode of each sample signal.
- the electronic device can input the cyclic spectrogram into a preset deep neural network to obtain the predicted modulation mode of each sample signal.
- the above-mentioned deep neural network can choose ResNet50.
- the above-mentioned deep neural network may be ResNet50, which introduces the concept of residual error, and avoids the phenomenon of gradient dispersion and gradient disappearance in the deep neural network during the training phase.
- ResNet50 may include 1 input layer, (3+4+6+3)*3 hidden layers and one output layer, totaling 50 layers.
- the above ResNet50 can be built in Pytorch, where Pytorch is a free and open source deep neural network framework.
- the top 7 ⁇ 7 convolution kernel, 64 channels and a step size of 2,2 ⁇ 2 pooling layer rectangular box represents the input layer; the bottom rectangular box of the global average pooling layer represents the output layer; in the input layer
- Each rectangular box between the output layer and the output layer represents a hidden layer.
- Step 704 Determine the loss value identified by the modulation mode according to the predicted modulation mode and the labeled modulation mode of each sample signal.
- a cross-entropy (English: Cross-Entropy, abbreviated: CE) function can be used as the loss function to obtain the loss value J CE ( ⁇ ), as shown in the following formula:
- C represents the set of modulation methods
- c represents the c-th modulation method
- P represents the sample signal set
- p represents the sample signal
- the value of can be 0 or 1.
- ⁇ represents the parameter of the deep neural network
- O cp represents the output of the deep neural network, that is, O cp represents the probability that the predicted modulation mode is the modulation mode c.
- the mean square error function is used to determine the loss value of modulation recognition.
- the embodiments of the present application do not make specific limitations.
- Step 705 Determine whether the deep neural network converges according to the loss value. If not, go to step 706; if yes, go to step 707.
- whether the deep neural network converges may be determined based on a preset loss threshold. Specifically, it may be: when the loss value is less than the preset loss threshold, the deep neural network is determined to converge; if the loss value is greater than or equal to the preset loss threshold, it is determined that the deep neural network has not converged.
- whether the deep neural network converges may also be determined based on a preset change threshold. Specifically: when the difference between the loss value calculated this time and the loss value calculated last time is less than the preset change threshold, the deep neural network is determined to converge; if the loss value calculated this time is different from the loss value calculated last time If the difference is greater than or equal to the preset loss threshold, it is determined that the deep neural network has not converged.
- the deep neural network determines whether the deep neural network converges based on the preset training times threshold. Specifically, it may be: when the number of training times for the deep neural network reaches the preset training times threshold, the deep neural network is determined to converge; if the number of training times for the deep neural network does not reach the preset training times threshold, it is determined that the deep neural network has not converged.
- the deep neural network determines whether the deep neural network converges based on any two or three of the preset loss threshold, the preset change threshold, and the preset training times threshold. For example, based on any two or three of the preset loss threshold and the preset training times threshold, it is determined whether the deep neural network converges. Specifically, it may be: if the loss value is less than the preset loss threshold, or the number of training times for the deep neural network reaches the preset number of training times threshold, the deep neural network is determined to converge; otherwise, it is determined that the deep neural network has not converged.
- the method for determining whether the deep neural network converges is not limited.
- Step 706 Adjust the parameters of the deep neural network, and return to step 703.
- Step 707 Determine that the current deep neural network is the modulation mode recognition model.
- the electronic device for training the modulation mode recognition model and the electronic device for recognizing the modulation mode may be located on the same physical machine, or may be located on different physical machines. There is no restriction on this.
- the modulation recognition model obtained by training in the above steps 701-707 is used.
- the modulation recognition model is trained by inputting the cyclic spectra of signals in different modulation modes into the deep neural network, so that the modulation recognition model can learn different All the characteristics of the cyclic spectrogram of the signal under the modulation mode, and then the modulation mode recognition model is based on all the characteristics of the cyclic spectrogram of the signal under different modulation modes learned, and all the characteristics of the cyclic spectrogram of the signal to be identified are processed to determine the to-be-recognized
- the modulation method of the signal improves the accuracy of modulation recognition.
- the electronic device obtains the received signal in the cooperative mode, and performs processing such as spectrum shifting, calculation of the cyclic spectrum, smoothing, and image superposition on the received signal in order to obtain the cyclic spectrum of the received signal.
- the frequency spectrum shift processing is to prepare for the image superposition.
- the electronic device inputs the cyclic spectrum of the received signal into the preset deep neural network, and after the training is completed, the parameters of the preset deep neural network are fixed to obtain the modulation mode recognition model.
- the processing operations such as spectrum shifting, calculation of cyclic spectrum, and picture superposition can refer to the description in the above-mentioned parts of Figures 1-8, which will not be repeated here, and the smoothing processing is the processing of calculating the average value described above.
- the received signal can be understood as a sample signal.
- the electronic device obtains the signal to be identified, and performs the processing of spectrum shifting, calculation of the cyclic spectrum, smoothing, and image superposition of the signal to be identified in order to obtain the cyclic spectrum of the signal to be identified, and input the cyclic spectrum of the signal to be identified to the training result
- the modulation mode recognition model is used to recognize the picture, that is, to recognize the cyclic spectrum of the signal to be recognized, and then to identify the modulation mode of the signal to be recognized.
- Fig. 9 is relatively simple. For details, please refer to the description of Figs. 1-8.
- the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method identification model to identify the modulation method of the signal to be identified, that is, using the signal to be identified To identify the modulation mode of the signal to be identified instead of using part of the characteristics of the cyclic spectrum to identify the modulation mode of the signal to be identified, which improves the accuracy of the recognition of the modulation mode of the signal.
- an embodiment of the present application also provides a modulation mode identification device. As shown in FIG. 10, the device includes:
- the receiving module 1001 is used to receive the signal to be identified
- the determining module 1002 is configured to determine the cyclic spectrum of the signal to be identified according to the frequency components and the cyclic frequency components of the signal to be identified, as the target cyclic spectrum;
- the recognition module 1003 is used to input the target cycle spectrogram into the pre-trained modulation recognition model to obtain the modulation mode of the signal to be recognized.
- the modulation recognition model is a model obtained by training a deep neural network using sample data.
- the data includes multiple sample signals and the marked modulation method of each sample signal.
- the determining module 1002 may include:
- the moving sub-module is used to move the frequency spectrum of the signal to be identified to multiple preset frequency bands respectively to obtain multiple frequency band signals corresponding to the signal to be identified;
- the first determining sub-module is used to determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrum of each frequency band signal is the cycle corresponding to the frequency band where the signal of the frequency band is located.
- the superposition sub-module is used to superimpose the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
- the moving sub-module can be specifically used to: perform segment processing on the signal to be identified to obtain multiple sub-signals to be identified; for each sub-signal to be identified, move the frequency spectrum of the sub-signal to be identified to a preset Obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified on multiple frequency bands; wherein, multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified on one frequency band are frequency band signals corresponding to the signal to be identified on the frequency band;
- the first determining sub-module can be specifically used to: determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal; for each frequency band, multiple frequency bands on the frequency band The cyclic spectrum of the sub-signal is averaged to obtain the cyclic spectrum of the frequency band signal in the frequency band.
- the first determining sub-module can be specifically used to: for each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal; according to each frequency band sub-signal Frequency components and each cyclic frequency component, determine the cyclic spectrum of each frequency band sub-signal; for each frequency band, perform averaging processing on the cyclic spectrum of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrum of the frequency band signal on the frequency band Spectrogram.
- the first determining sub-module may be specifically used to: perform fast Fourier transform on each frequency band sub-signal to obtain a transformed sub-signal corresponding to each frequency band sub-signal; according to each frequency component of each transformed sub-signal With each cyclic frequency component, the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
- the first determining submodule may be specifically used for:
- can represent the calculation of the modulus value.
- the overlay sub-module can be specifically used for:
- represents the cyclic spectrum of the signal to be identified
- represents the cyclic spectrum corresponding to the l-th frequency band
- l represents the frequency band
- n represents the number of multiple frequency bands
- f represents the frequency component
- ⁇ represents the cyclic frequency component
- can represent the calculation of the modulus value.
- the above-mentioned device may further include: a training module for training to obtain a modulation mode recognition model;
- the above-mentioned training module may include:
- the acquisition sub-module is used to acquire sample data, the sample data includes multiple sample signals and the label modulation mode of each sample signal;
- the second determining sub-module is used to determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;
- the prediction sub-module is used to input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
- the third determining sub-module is used to determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeling modulation mode of each sample signal;
- the fourth determining sub-module is used to determine whether the deep neural network has converged according to the loss value; if not, adjust the parameters of the deep neural network, and return to execute inputting the cyclic spectrum of each sample signal into the preset deep neural network to obtain The step of predicting the modulation mode of each sample signal; if yes, determine that the current deep neural network is the modulation mode recognition model.
- the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method identification model to identify the modulation method of the signal to be identified, that is, using the signal to be identified To identify the modulation mode of the signal to be identified instead of using part of the characteristics of the cyclic spectrum to identify the modulation mode of the signal to be identified, which improves the accuracy of the recognition of the modulation mode of the signal.
- an embodiment of the present application also provides an electronic device, as shown in FIG. 11, including a processor 1101, a communication interface 1102, a memory 1103, and a communication bus 1104, wherein the processor 1101, the communication interface 1102, The memories 1103 communicate with each other through the communication bus 1104;
- the memory 1103 is used to store computer programs
- the processor 1101 is configured to implement the method steps in any of the foregoing modulation mode identification method embodiments when executing the program stored in the memory 1103.
- the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method recognition model to identify the modulation method of the signal to be identified, that is, using the cyclic spectrum of the signal to be identified All the characteristics of the spectrogram identify the modulation method of the signal to be identified, instead of using part of the cyclic spectrogram to identify the modulation method of the signal to be identified, which improves the accuracy of the recognition of the signal modulation method.
- the communication bus mentioned by the aforementioned network device may be a peripheral component interconnection standard (English: Peripheral Component Interconnect, abbreviated as: PCI) bus or an extended industry standard architecture (English: Extended Industry Standard Architecture, abbreviated as: EISA) bus, etc.
- PCI Peripheral Component Interconnect
- EISA Extended Industry Standard Architecture
- the communication bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
- the communication interface is used for communication between the aforementioned network device and other devices.
- the memory may include random access memory (English: Random Access Memory, abbreviated as: RAM), and may also include non-volatile memory (English: Non-Volatile Memory, abbreviated as: NVM), for example, at least one disk storage.
- RAM Random Access Memory
- NVM Non-Volatile Memory
- the memory may also be at least one storage device located far away from the foregoing processor.
- the above-mentioned processor may be a general-purpose processor, including a central processing unit (English: Central Processing Unit, abbreviated as: CPU), a network processor (English: Network Processor, abbreviated as: NP), etc.; it may also be a digital signal processor (English: : Digital Signal Processing, abbreviation: DSP), application specific integrated circuit (English: Application Specific Integrated Circuit, abbreviation: ASIC), Field-Programmable Gate Array (English: Field-Programmable Gate Array, abbreviation: FPGA) or other programmable logic devices , Discrete gates or transistor logic devices, discrete hardware components.
- CPU Central Processing Unit
- NP Network Processor
- DSP Digital Signal Processing
- ASIC Application Specific Integrated Circuit
- FPGA Field-Programmable Gate Array
- FPGA Field-Programmable Gate Array
- the embodiments of the present application also provide a computer-readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, any step of the above modulation method identification method is implemented.
- the embodiments of the present application also provide a computer program, which when running on a computer, causes the computer to execute any step of the above-mentioned modulation mode identification method.
- the computer may be implemented in whole or in part by software, hardware, firmware, or any combination thereof.
- software it can be implemented in the form of a computer program product in whole or in part.
- the computer program product includes one or more computer instructions.
- the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
- the computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
- the computer instructions may be transmitted from a website, computer, server, or data center.
- the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrated with one or more available media.
- the usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).
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Abstract
The embodiments of the present application provide a modulation mode identification method and apparatus, relating to the technical field of radio communications. Said method comprises: an electronic device being able to receive a signal to be identified, and determine, according to frequency components and cyclic frequency components of said signal, a cyclic spectrum of said signal and use same as a target cyclic spectrum; and then, input the target cyclic spectrum into a pre-trained modulation mode identification model, so as to obtain a modulation mode of said signal. By means of the present application, the cyclic spectrum of a signal to be identified can be completely inputted into the modulation mode identification model, so as to identify the modulation mode of said signal, that is, the modulation mode of said signal is identified by means of all the features of the cyclic spectrum of said signal, rather than some features of the cyclic spectrum, so that the accuracy of signal modulation mode identification is improved.
Description
本申请要求于2019年10月28日提交中国专利局、申请号为201911028838.0发明名称为“一种调制方式识别方法及装置”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on October 28, 2019 with the application number 201911028838.0 and the invention title is "a modulation method and device for identification", the entire content of which is incorporated into this application by reference .
本申请涉及无线电通信技术领域,特别是涉及一种调制方式识别方法及装置。This application relates to the field of radio communication technology, and in particular to a modulation method identification method and device.
非合作模式下,即信号的调制方式未知的情况下,信号(特别是微弱信号)的调制方式识别技术在诸多领域都有重要的应用。信号的调制方式识别技术目的是,在缺少发送端信息以及信道信息的情况下,对接收的信号的调制方式进行识别。现有的信号的调制方式识别方法主要是:计算待识别信号的循环谱,得到该待识别信号的循环谱图,提取待识别信号的循环谱图的部分峰值的位置,和/或待识别信号的循环谱图峰值处的横截面信息等特征,利用提取的特征识别待识别信号的调制方式。In the non-cooperative mode, that is, when the modulation method of the signal is unknown, the modulation method identification technology of the signal (especially the weak signal) has important applications in many fields. The purpose of the signal modulation method identification technology is to identify the modulation method of the received signal in the absence of transmitter information and channel information. The existing signal modulation method identification methods mainly include: calculating the cyclic spectrum of the signal to be identified, obtaining the cyclic spectrum of the signal to be identified, extracting the positions of partial peaks of the cyclic spectrum of the signal to be identified, and/or the signal to be identified Use the extracted features to identify the modulation mode of the signal to be identified by using features such as the cross-sectional information at the peak of the cyclic spectrogram.
然而,使用上述调制方式识别方法对待识别信号的调制方式进行识别时,提取的特征只是循环谱图的部分特征,也就是只利用了循环谱图的部分特征,对待识别信号的调制方式进行识别。这使得信号的调制方式识别准确率较低。However, when using the above modulation recognition method to recognize the modulation mode of the signal to be identified, the extracted features are only part of the cyclic spectrogram, that is, only part of the cyclic spectrogram is used to recognize the modulation mode of the signal to be recognized. This makes the recognition accuracy of the modulation mode of the signal low.
发明内容Summary of the invention
本申请实施例的目的在于提供一种调制方式识别方法及装置,以提高信号的调制方式识别的准确率。具体技术方案如下:The purpose of the embodiments of the present application is to provide a modulation method identification method and device, so as to improve the accuracy of signal modulation identification. The specific technical solutions are as follows:
第一方面,为了达到上述目的,本申请实施例提供了一种调制方式识别方法,所述方法包括如下步骤:In the first aspect, in order to achieve the foregoing objective, an embodiment of the present application provides a modulation mode identification method, and the method includes the following steps:
接收待识别信号;Receive the signal to be identified;
根据所述待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图;Determine the cyclic spectrum of the signal to be identified according to the frequency components and the cyclic frequency components of the signal to be identified, as the target cyclic spectrum;
将所述目标循环谱图输入预先训练的调制方式识别模型,得到所述待识别信号的调制方式,其中,所述调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式。Input the target cyclic spectrogram into a pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized, wherein the modulation mode recognition model is a model obtained by training a deep neural network using sample data, and The sample data includes multiple sample signals and the label modulation mode of each sample signal.
可选的,所述根据所述待识别信号的各个频率分量和各个循环频率分量,确定所述待识别信号的循环谱图,作为目标循环谱图的步骤,包括:Optionally, the step of determining the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram according to the frequency components and the cyclic frequency components of the signal to be identified includes:
将所述待识别信号的频谱分别搬移到预设的多个频段上,得到所述待识别信号对应的多个频段信号;Moving the frequency spectrum of the signal to be identified to a plurality of preset frequency bands to obtain signals of multiple frequency bands corresponding to the signal to be identified;
根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图,每一频段信号的循环谱图为该频段信号所在频段对应的循环谱图;Determine the cyclic spectrogram of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrogram of each frequency band signal is the cyclic spectrogram corresponding to the frequency band where the signal of the frequency band is located;
对所述多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作 为目标循环谱图。Performing superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
可选的,所述将所述待识别信号的频谱分别搬移到预设的多个频段上,得到所述待识别信号对应的多个频段信号的步骤包括:Optionally, the step of moving the frequency spectrum of the signal to be identified to a plurality of preset frequency bands to obtain signals of multiple frequency bands corresponding to the signal to be identified includes:
对所述待识别信号进行分段处理,得到多个待识别子信号;Performing segmentation processing on the signal to be identified to obtain a plurality of sub-signals to be identified;
针对每一待识别子信号,将该待识别子信号的频谱分别搬移到预设的多个频段上,得到该待识别子信号对应的多个频段子信号;其中,一个频段上所述多个待识别子信号对应的多个频段子信号为该频段上所述待识别信号对应的频段信号;For each sub-signal to be identified, the frequency spectrum of the sub-signal to be identified is moved to multiple preset frequency bands to obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified; The multiple frequency band sub-signals corresponding to the sub-signal to be identified are frequency band signals corresponding to the signal to be identified in the frequency band;
所述根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图的步骤,包括:The step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal includes:
根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;Determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal;
针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。For each frequency band, averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
可选的,所述根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图的步骤,包括:Optionally, the step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal includes:
针对每一频段信号,对该频段信号进行分段处理,得到该频段信号对应的多个频段子信号;For each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal;
根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;Determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal;
针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。For each frequency band, averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
可选的,所述根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图的步骤,包括:Optionally, the step of determining the cyclic spectrogram of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal includes:
对每一频段子信号进行快速傅里叶变换,得到每一频段子信号对应的变换子信号;Fast Fourier transform is performed on each frequency band sub-signal to obtain the transformed sub-signal corresponding to each frequency band sub-signal;
根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图,作为每一变换子信号对应的频段子信号的循环谱图。According to each frequency component and each cyclic frequency component of each transformed sub-signal, the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
可选的,所述根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图的步骤,包括:Optionally, the step of determining the cyclic spectrogram of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal includes:
利用以下公式,确定每一变换子信号的循环谱图:Use the following formula to determine the cyclic spectrum of each transformed sub-signal:
其中,
表示第k个变换子信号的循环谱图,N
0表示第k个变换子信号的信号长度,f表示频率分量,α表示循环频率分量,
表示在
频率下的第k个变换子 信号,
表示
的共轭,
表示在
频率下的第k个变换子信号,|·|表示取模值计算。
among them, Represents the cyclic spectrum of the k-th transformed sub-signal, N 0 represents the signal length of the k-th transformed sub-signal, f represents the frequency component, and α represents the cyclic frequency component, Expressed in The k-th transformed sub-signal at frequency, Means The conjugate, Expressed in For the k-th transformed sub-signal under frequency, |·| represents the calculation of the modulus value.
可选的,所述对所述多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图的步骤,包括:Optionally, the step of performing superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram includes:
利用以下公式,对多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图:Use the following formula to superimpose the cyclic spectra corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:
|S′
r(f,α)|=max{|S
r,1(f,α)|,|S
r,2(f,α)|,…,|S
r,n(f,α)|};
|S′ r (f,α)|=max{|S r,1 (f,α)|,|S r,2 (f,α)|,…,|S r,n (f,α)| };
其中,|S′
r(f,α)|表示所述待识别信号的循环谱图,|S
r,l(f,α)|表示第l个频段对应的循环谱图,l表示频段,l=1,2,…n,max表示求取最大值,n表示所述多个频段的数量,f表示频率分量,α表示循环频率分量,|·|表示取模值计算。
Where |S' r (f,α)| represents the cyclic spectrum of the signal to be identified, |S r,l (f,α)| represents the cyclic spectrum corresponding to the l-th frequency band, l represents the frequency band, and l =1,2,...n, max represents the maximum value, n represents the number of the multiple frequency bands, f represents the frequency component, α represents the cyclic frequency component, and |·| represents the calculation of the modulus value.
可选的,所述调制方式识别模型通过以下方式训练得到:Optionally, the modulation mode recognition model is obtained by training in the following manner:
获取所述样本数据,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式;Acquiring the sample data, where the sample data includes a plurality of sample signals and a label modulation mode of each sample signal;
根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图;Determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;
将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式;Input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
根据所述每一样本信号的预测调制方式和标注调制方式,确定调制方式识别的损失值;Determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeled modulation mode of each sample signal;
根据所述损失值,确定所述深度神经网络是否收敛;Determine whether the deep neural network converges according to the loss value;
若否,则调整所述深度神经网络的参数,返回执行所述将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式的步骤;If not, adjust the parameters of the deep neural network and return to the step of inputting the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
若是,则确定当前深度神经网络为调制方式识别模型。If yes, it is determined that the current deep neural network is the modulation mode recognition model.
第二方面,为了达到上述目的,本申请实施例提供了一种调制方式识别装置,所述装置包括:In the second aspect, in order to achieve the foregoing objective, an embodiment of the present application provides a modulation mode identification device, the device including:
接收模块,用于接收待识别信号;The receiving module is used to receive the signal to be identified;
确定模块,用于根据所述待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图;The determining module is configured to determine the cyclic spectrum of the signal to be identified according to the respective frequency components and the respective cyclic frequency components of the signal to be identified, as the target cyclic spectrum;
识别模块,用于将所述目标循环谱图输入预先训练的调制方式识别模型,得到所述待识别信号的调制方式,其中,所述调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式。The recognition module is used to input the target cyclic spectrogram into a pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized, wherein the modulation mode recognition model is obtained by training a deep neural network using sample data In the model, the sample data includes a plurality of sample signals and the label modulation mode of each sample signal.
可选的,所述确定模块,包括:Optionally, the determining module includes:
搬移子模块,用于将所述待识别信号的频谱分别搬移到预设的多个频段上,得到所述待识别信号对应的多个频段信号;The moving sub-module is used to move the frequency spectrum of the signal to be identified to multiple preset frequency bands respectively to obtain multiple frequency band signals corresponding to the signal to be identified;
第一确定子模块,用于根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图,每一频段信号的循环谱图为该频段信号所在频段对应的循环谱图;The first determining sub-module is used to determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrum of each frequency band signal is the cycle corresponding to the frequency band where the signal of the frequency band is located. Spectrogram
叠加子模块,用于对所述多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图。The superimposition sub-module is used to perform superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
可选的,所述搬移子模块具体用于:对所述待识别信号进行分段处理,得到多个待识别子信号;针对每一待识别子信号,将该待识别子信号的频谱分别搬移到预设的多个频段上,得到该待识别子信号对应的多个频段子信号;其中,一个频段上所述多个待识别子信号对应的多个频段子信号为该频段上所述待识别信号对应的频段信号;Optionally, the moving sub-module is specifically configured to: perform segment processing on the signal to be identified to obtain multiple sub-signals to be identified; for each sub-signal to be identified, move the frequency spectrum of the sub-signal to be identified respectively To multiple preset frequency bands, multiple frequency band sub-signals corresponding to the sub-signals to be identified are obtained; wherein, the multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified in one frequency band are the to-be-identified sub-signals in the frequency band. Identify the frequency band signal corresponding to the signal;
所述第一确定子模块,具体用于:根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。The first determining sub-module is specifically configured to: determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal; for each frequency band, multiple The cyclic spectrum of the frequency band sub-signal is averaged, and the cyclic spectrum of the frequency band signal on the frequency band is obtained.
可选的,所述第一确定子模块具体用于:针对每一频段信号,对该频段信号进行分段处理,得到该频段信号对应的多个频段子信号;根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。Optionally, the first determining submodule is specifically configured to: for each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal; Frequency components and each cyclic frequency component, determine the cyclic spectrum of each frequency band sub-signal; for each frequency band, perform averaging processing on the cyclic spectrum of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrum of the frequency band signal on the frequency band Spectrogram.
可选的,所述第一确定子模块具体用于:Optionally, the first determining submodule is specifically configured to:
对每一频段子信号进行快速傅里叶变换,得到每一频段子信号对应的变换子信号;Fast Fourier transform is performed on each frequency band sub-signal to obtain the transformed sub-signal corresponding to each frequency band sub-signal;
根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图,作为每一变换子信号对应的频段子信号的循环谱图。According to each frequency component and each cyclic frequency component of each transformed sub-signal, the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
可选的,所述第一确定子模块具体用于:Optionally, the first determining submodule is specifically configured to:
利用以下公式,确定每一变换子信号的循环谱图:Use the following formula to determine the cyclic spectrum of each transformed sub-signal:
其中,
表示第k个变换子信号的循环谱图,N
0表示第k个变换子信号的信号长度,f表示频率分量,α表示循环频率分量,
表示在
频率下的第k个变换子信号,
表示
的共轭,
表示在
频率下的第k个变换子信号,|·|表示取模值计算。
among them, Represents the cyclic spectrum of the k-th transformed sub-signal, N 0 represents the signal length of the k-th transformed sub-signal, f represents the frequency component, and α represents the cyclic frequency component, Expressed in The k-th transformed sub-signal at frequency, Means The conjugate, Expressed in For the k-th transformed sub-signal under frequency, |·| represents the calculation of the modulus value.
可选的,所述叠加子模块具体用于:Optionally, the superposition submodule is specifically used for:
利用以下公式,对多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图:Use the following formula to superimpose the cyclic spectra corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:
|S′
r(f,α)|=max{|S
r,1(f,α)|,|S
r,2(f,α)|,…,|S
r,n(f,α)|};
|S′ r (f,α)|=max{|S r,1 (f,α)|,|S r,2 (f,α)|,…,|S r,n (f,α)| };
其中,|S′
r(f,α)|表示所述待识别信号的循环谱图,|S
r,l(f,α)|表示第l个频段对应的循环谱图,l表示频段,l=1,2,…n,max表示求取最大值,n表示所述多个频段的数量,f表示频率分量,α表示循环频率分量,|·|表示取模值计算。
Where |S' r (f,α)| represents the cyclic spectrum of the signal to be identified, |S r,l (f,α)| represents the cyclic spectrum corresponding to the l-th frequency band, l represents the frequency band, and l =1,2,...n, max represents the maximum value, n represents the number of the multiple frequency bands, f represents the frequency component, α represents the cyclic frequency component, and |·| represents the calculation of the modulus value.
可选的,所述装置还包括:训练模块,用于训练得到所述调制方式识别模型;Optionally, the device further includes: a training module for training to obtain the modulation mode recognition model;
所述训练模块包括:The training module includes:
获取子模块,用于获取所述样本数据,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式;An obtaining sub-module, configured to obtain the sample data, the sample data including a plurality of sample signals and the labeling modulation mode of each sample signal;
第二确定子模块,用于根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图;The second determining sub-module is used to determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;
预测子模块,用于将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式;The prediction sub-module is used to input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
第三确定子模块,用于根据所述每一样本信号的预测调制方式和标注调制方式,确定调制方式识别的损失值;The third determining sub-module is used to determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeling modulation mode of each sample signal;
第四确定子模块,用于根据所述损失值,确定所述深度神经网络是否收敛;若否,则调整所述深度神经网络的参数,返回执行所述将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式的步骤;若是,则确定当前深度神经网络为调制方式识别模型。The fourth determination sub-module is used to determine whether the deep neural network converges according to the loss value; if not, adjust the parameters of the deep neural network, and return to execute the input of the cyclic spectrum of each sample signal The preset deep neural network obtains the step of predicting the modulation mode of each sample signal; if it is, the current deep neural network is determined to be the modulation mode recognition model.
第三方面,提供了一种电子设备,包括处理器、通信接口、存储器和通信总线,其中,处理器、通信接口、存储器通过通信总线完成相互间的通信;In a third aspect, an electronic device is provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;
存储器,用于存放计算机程序;Memory, used to store computer programs;
处理器,用于执行存储器上所存放的程序时,实现第一方面所述的任一方法步骤。The processor is configured to implement any of the method steps described in the first aspect when executing the program stored in the memory.
第四方面,提供了一种计算机可读存储介质,所述计算机可读存储介质内存储有计算机程序,所述计算机程序被处理器执行时实现第一方面所述的任一方法步骤。In a fourth aspect, a computer-readable storage medium is provided, and a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, it implements any of the method steps described in the first aspect.
第五方面,提供了一种计算机程序,当其在计算机上运行时,使得计算机执行上述第一方面所述的任一方法步骤。In a fifth aspect, a computer program is provided, which when running on a computer, causes the computer to execute any method step described in the first aspect.
本申请实施例提供的一种调制方式识别方法及装置中,电子设备将待识别信号的循环谱图完整的输入至调制方式识别模型中,来识别待识别信号的调制方式,也就是,利用待识别信号的循环谱图的全部特征,识别待识别信号的调制方式,而不是利用循环谱图的部分特征识别待识别信号的调制方式,提高了信号的调制方式识别的准确率。In the modulation mode identification method and device provided by the embodiments of the present application, the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation mode identification model to identify the modulation mode of the signal to be identified, that is, using the Recognizing all the characteristics of the cyclic spectrogram of the signal and identifying the modulation mode of the signal to be identified, instead of using part of the characteristics of the cyclic spectrogram to identify the modulation mode of the signal to be recognized, the accuracy of the recognition of the modulation mode of the signal is improved.
当然,实施本申请的任一产品或方法并不一定需要同时达到以上所述的所有优点。Of course, implementing any product or method of the present application does not necessarily need to achieve all the advantages described above at the same time.
为了更清楚地说明本申请实施例或现有技术中的技术方案,下面将对实施例或现有技 术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly describe the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.
图1为本申请实施例提供的一种调制方式识别方法的一种流程示意图;FIG. 1 is a schematic flowchart of a modulation method identification method provided by an embodiment of this application;
图2为本申请实施例提供的一种调制方式识别方法的另一种流程示意图;2 is a schematic diagram of another flow chart of a modulation mode identification method provided by an embodiment of this application;
图3为本申请实施例提供的一种中心频点增加的示意图;FIG. 3 is a schematic diagram of a central frequency point increase provided by an embodiment of the application;
图4为本申请实施例提供的一种中心频点减少的示意图;FIG. 4 is a schematic diagram of a central frequency point reduction provided by an embodiment of the application;
图5为本申请实施例提供的一种平滑化处理的示意图;FIG. 5 is a schematic diagram of a smoothing process provided by an embodiment of this application;
图6为本申请实施例提供的一种图片叠加处理的示意图;FIG. 6 is a schematic diagram of a picture superimposing process provided by an embodiment of this application;
图7为本申请实施例提供的一种深度神经网络训练方法的一种流程示意图;FIG. 7 is a schematic flowchart of a deep neural network training method provided by an embodiment of this application;
图8为本申请实施例提供的一种深度神经网络的一种结构示意图;FIG. 8 is a schematic diagram of a structure of a deep neural network provided by an embodiment of this application;
图9为本申请实施例提供的一种调制方式识别方法的再一种流程示意图;FIG. 9 is a schematic diagram of still another flow chart of a modulation mode identification method provided by an embodiment of this application;
图10为本申请实施例提供的一种调制方式识别装置的结构示意图;FIG. 10 is a schematic structural diagram of a modulation mode identification device provided by an embodiment of this application;
图11为本申请实施例提供的一种电子设备的一种结构示意图。FIG. 11 is a schematic diagram of a structure of an electronic device provided by an embodiment of the application.
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例,都属于本申请保护的范围。The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.
为便于理解,下面对本申请实施例中出现的词语进行解释说明。For ease of understanding, the words appearing in the embodiments of the present application are explained below.
合作模式下的信号:指调制方式和信道状况已知的信号。Signal in cooperative mode: refers to a signal whose modulation method and channel condition are known.
非合作模式下的信号:指调制方式和信道状况未知的信号。Signal in non-cooperative mode: Refers to a signal whose modulation method and channel condition are unknown.
循环谱:以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,确定信号的循环谱。循环谱为复数。Cyclic spectrum: Use frequency component as abscissa, cyclic frequency component as ordinate, or frequency component as abscissa and cyclic frequency component as ordinate to determine the cyclic spectrum of the signal. The cyclic spectrum is complex.
循环谱图:对循环谱取模值,得到循环谱图。Cyclic spectrum: Take the modulus of the cyclic spectrum to obtain the cyclic spectrum.
下面将结合具体实施方式,对本申请实施例提供的一种调制方式识别方法进行详细的说明,如图1所示,具体步骤如下:The following will describe in detail a modulation method identification method provided in an embodiment of the present application in conjunction with specific implementations, as shown in FIG. 1, and the specific steps are as follows:
步骤101,接收待识别信号。Step 101: Receive a signal to be identified.
本申请实施例中,在进行信号的调制方式识别时,电子设备可以接收调制方式未知的信号,作为待识别信号。In the embodiment of the present application, when the modulation mode of the signal is recognized, the electronic device may receive a signal with an unknown modulation mode as the signal to be recognized.
步骤102,根据待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图。Step 102: Determine a cyclic spectrogram of the signal to be recognized according to each frequency component and each cyclic frequency component of the signal to be recognized, as the target cyclic spectrogram.
本申请实施例中,电子设备在接收到待识别信号之后,可以根据该待识别信号的各个频率分量和各个循环频率分量,确定该待识别信号的循环谱图,并将待识别信号的循环谱图作为目标循环谱图。In the embodiment of the present application, after the electronic device receives the signal to be identified, it can determine the cyclic spectrum of the signal to be identified based on the frequency components and the cyclic frequency components of the signal to be identified, and the cyclic spectrum of the signal to be identified Figure as the target cycle spectrum.
本申请实施例中,根据该待识别信号的各个频率分量和各个循环频率分量,确定该待识别信号的循环谱图,可以理解为,以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,确定该待识别信号的循环谱图。In the embodiment of the present application, the cyclic spectrum of the signal to be identified is determined according to the frequency components and the cyclic frequency components of the signal to be identified, which can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or Using the frequency component as the abscissa and the cyclic frequency component as the ordinate, the cyclic spectrum of the signal to be identified is determined.
步骤103,将目标循环谱图输入预先训练的调制方式识别模型,得到待识别信号的调制方式。Step 103: Input the target cyclic spectrogram into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized.
其中,该调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,上述样本数据包括多个样本信号以及每一样本信号的标注调制方式。Wherein, the modulation recognition model is a model obtained by training a deep neural network using sample data, and the sample data includes a plurality of sample signals and a label modulation method of each sample signal.
本申请实施例中,电子设备可以将目标循环谱图输入预先训练的调制方式识别模型,得到待识别信号的调制方式。该调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,上述样本数据可以包括多个样本信号以及每一样本信号的标注调制方式。具体地调制方式识别模型的训练方法下面会详细介绍,此处不再展开说明。In the embodiment of the present application, the electronic device may input the target cyclic spectrogram into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized. The modulation recognition model is a model obtained by training a deep neural network using sample data. The sample data may include multiple sample signals and a label modulation method for each sample signal. The specific training method of the modulation mode recognition model will be described in detail below, and the description will not be expanded here.
在一个实施例中,电子设备可以先将接收到的待识别信号的频谱搬移到预设的多个频段上。具体的参考图2,图2为本申请实施例提供的一种调制方式识别方法的另一种流程示意图,该方法可以包括以下步骤。In one embodiment, the electronic device may first move the received frequency spectrum of the signal to be identified to multiple preset frequency bands. Specifically, referring to FIG. 2, FIG. 2 is a schematic diagram of another flow chart of a modulation mode identification method provided by an embodiment of the present application. The method may include the following steps.
步骤201,接收待识别信号。步骤201与步骤101一致。Step 201: Receive a signal to be identified. Step 201 is consistent with step 101.
步骤202,将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号。Step 202: Move the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain multiple frequency band signals corresponding to the signal to be identified.
本申请实施例中,电子设备在接收到待识别信号后,对待识别信号进行频谱搬移处理。具体可以为:将接收到的待识别信号的频谱分别搬移到预设的多个频段上,也就是改变待识别信号的中心频点f
c,得到该待识别信号对应的多个频段信号。
In the embodiment of the present application, after receiving the signal to be identified, the electronic device performs spectrum shift processing on the signal to be identified. Specifically, it may be: moving the received frequency spectrum of the signal to be identified to multiple preset frequency bands, that is, changing the center frequency point f c of the signal to be identified to obtain multiple frequency band signals corresponding to the signal to be identified.
每一频段的中心频点f
c不同。上述步骤202可以理解为,将待识别信号的中心频点f
c调整为预设的中心频点f
c,把待识别信号对载波信号进行调幅,将待识别信号的频谱不失真地搬移到载波信号的预设的中心频点f
c的两边,即将待识别信号调整至预设的中心频点f
c所在的频段上。待识别信号调整至预设的频段上后得到的信号,即为频段信号。
The center frequency f c of each frequency band is different. Step 202 as will be understood, to be the center frequency of the identification signal f c is adjusted to a predetermined center frequency f c, the identification signal to be amplitude modulated carrier signal, the frequency spectrum of the signal to be recognized without distortion to move the carrier The two sides of the preset center frequency point f c of the signal are to adjust the signal to be identified to the frequency band where the preset center frequency point f c is located. The signal obtained after the signal to be identified is adjusted to the preset frequency band is the frequency band signal.
一个实施例中,上述步骤202,电子设备可以直接将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号。In one embodiment, in step 202 above, the electronic device may directly move the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain signals in multiple frequency bands corresponding to the signal to be identified.
另一个实施例中,电子设备可采用如下步骤得到待识别信号对应的多个频段信号。具体的,上述步骤202,将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号,可以包括:In another embodiment, the electronic device may use the following steps to obtain multiple frequency band signals corresponding to the signal to be identified. Specifically, in the above step 202, moving the frequency spectrum of the signal to be identified to multiple preset frequency bands to obtain multiple frequency band signals corresponding to the signal to be identified may include:
电子设备在接收到待识别信号之后,可以对该待识别信号进行分段处理,得到多个待识别子信号。针对每一待识别子信号,电子设备将该待识别子信号的频谱分别搬移到预设的多个频段上,得到该待识别子信号对应的多个频段子信号。一个频段上多个待识别子信号对应的多个频段子信号为该频段上待识别信号对应的频段信号。After receiving the signal to be identified, the electronic device may perform segmentation processing on the signal to be identified to obtain multiple sub-signals to be identified. For each sub-signal to be identified, the electronic device moves the frequency spectrum of the sub-signal to be identified to a plurality of preset frequency bands to obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified. The multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified in one frequency band are frequency band signals corresponding to the signal to be identified in the frequency band.
本申请实施例中,一个频段上包括多个频段子信号,每一频段子信号对应一个待识别 子信号,多个待识别子信号不重叠,即多个待识别子信号对应的多个频段子信号不重叠。一个频段上多个频段子信号对应多个待识别子信号,这多个待识别子信号组成待识别信号,相应的,该频段上多个频段子信号组成该频段上待识别信号对应的频段信号。In the embodiment of the present application, a frequency band includes multiple frequency band sub-signals, and each frequency band sub-signal corresponds to one sub-signal to be identified, and the multiple sub-signals to be identified do not overlap, that is, multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified The signals do not overlap. Multiple sub-signals of multiple frequency bands on a frequency band correspond to multiple sub-signals to be identified, and these multiple sub-signals to be identified form the signal to be identified. Correspondingly, multiple sub-signals of the frequency band constitute the frequency band signal corresponding to the signal to be identified on the frequency band. .
步骤203,根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图。Step 203: Determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal.
本申请实施例中,电子设备对待识别信号进行频谱搬移处理,得到频段信号,对频段信号进行计算循环谱图处理,得到每一频段信号的循环谱图。具体可以为:根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图。步骤203可以理解为,以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,确定每一频段信号的循环谱图。其中,对于每一频段信号,该频段信号的循环谱图即为该频段信号所在频段对应的循环谱图。In the embodiment of the present application, the electronic device performs spectrum shift processing on the signal to be identified to obtain a frequency band signal, and performs calculation cyclic spectrogram processing on the frequency band signal to obtain a cyclic spectrogram of each frequency band signal. Specifically, it may be: determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal. Step 203 can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or taking the frequency component as the abscissa and the cyclic frequency component as the ordinate to determine the cyclic spectrum of each frequency band signal. Among them, for each frequency band signal, the cyclic spectrogram of the frequency band signal is the cyclic spectrogram corresponding to the frequency band where the frequency band signal is located.
一个实施例中,电子设备将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号后,针对每一频段,电子设备可以根据该频段上的频段信号的各个频率分量和各个循环频率分量,确定频段信号的循环谱图。In one embodiment, the electronic device moves the frequency spectrum of the signal to be identified to a plurality of preset frequency bands, and after obtaining multiple frequency band signals corresponding to the signal to be identified, for each frequency band, the electronic device can be based on the frequency band on the frequency band. Each frequency component of the signal and each cyclic frequency component determine the cyclic spectrum of the frequency band signal.
另一个实施例中,电子设备将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号后,针对每一频段,电子设备可以根据对该频段信号进行分段处理,得到该频段信号对应的多个频段子信号;根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图。针对每一频段,电子设备对每一频段子信号的循环谱图进行平滑化处理,得到该频段上频段信号的循环谱图。In another embodiment, the electronic device moves the frequency spectrum of the signal to be identified to a plurality of preset frequency bands, and after obtaining multiple frequency band signals corresponding to the signal to be identified, for each frequency band, the electronic device can be based on the signal in the frequency band. Perform segmentation processing to obtain multiple frequency band sub-signals corresponding to the frequency band signal; determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal. For each frequency band, the electronic device smoothes the cyclic spectrum of the sub-signal of each frequency band to obtain the cyclic spectrum of the frequency band signal in the frequency band.
上述平滑化处理可以为求均值处理,也就是,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。The foregoing smoothing processing may be averaging processing, that is, performing averaging processing on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
上述根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图的步骤,可以理解为,以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,绘制每一频段信号的循环谱图。The above steps of determining the cyclic spectrum of each frequency sub-signal according to the respective frequency components and the respective cyclic frequency components of each frequency band sub-signal can be understood as taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or taking The frequency component is the abscissa, the cyclic frequency component is the ordinate, and the cyclic spectrum of each frequency band signal is drawn.
再一个实施例中,电子设备可以先识别信号进行分段处理,再进行频谱搬移。如上述将每一待识别子信号的频谱分别搬移到预设的多个频段上,得到每一待识别子信号对应的多个频段子信号。此时,每一频段上,电子设备得到多个频段子信号。电子设备可以根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图。针对每一频段,电子设备对每一频段子信号的循环谱图进行平滑化处理,得到该频段上频段信号的循环谱图。In another embodiment, the electronic device may first identify the signal for segmentation processing, and then perform spectrum shifting. As described above, the frequency spectrum of each sub-signal to be identified is moved to a plurality of preset frequency bands, and multiple frequency band sub-signals corresponding to each sub-signal to be identified are obtained. At this time, on each frequency band, the electronic device obtains multiple frequency band sub-signals. The electronic device can determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal. For each frequency band, the electronic device smoothes the cyclic spectrum of the sub-signal of each frequency band to obtain the cyclic spectrum of the frequency band signal in the frequency band.
搬移到不同频段上的信号,其对应的循环谱图是不一样的。当中心频点f
c改变时,其循环谱图也会改变。如图3所示,当中心频点f
c增加时,其对应的循环谱图的峰之间的距离也会增加;相反的,如图4所示,当中心频点f
c减少时,其对应的循环谱图的峰之间的距离也会减少。对一个频段上的多个频段子信号的循环谱图进行平滑化处理,可将循环谱图中噪声造成的毛刺尽量少。
Signals moved to different frequency bands have different corresponding cyclic spectra. When the center frequency point f c changes, its cyclic spectrum will also change. As shown in Figure 3, when the center frequency f c increases, the distance between the peaks of the corresponding cyclic spectrum will also increase; on the contrary, as shown in Figure 4, when the center frequency f c decreases, the corresponding The distance between the peaks of the cyclic spectrum will also decrease. Smoothing the cyclic spectrograms of sub-signals of multiple frequency bands in one frequency band can minimize glitches caused by noise in the cyclic spectrogram.
在一个实施例中,上述根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图的步骤,可以包括:In an embodiment, the above step of determining the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal may include:
电子设备可以对每一频段子信号进行快速傅里叶变换,得到每一频段子信号对应的变 换子信号。之后,电子设备可以根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图,将每一变换子信号的循环谱图作为每一变换子信号对应的频段子信号的循环谱图。The electronic device can perform fast Fourier transform on the sub-signals of each frequency band to obtain the transform sub-signals corresponding to the sub-signals of each frequency band. After that, the electronic device can determine the cyclic spectrogram of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal, and use the cyclic spectrogram of each transformed sub-signal as each transformed sub-signal The cyclic spectrum of the corresponding frequency band sub-signal.
本申请实施例中,根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图的步骤,可以理解为,以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,确定每一变换信号的循环谱图。In the embodiment of this application, the step of determining the cyclic spectrum of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal can be understood as taking the frequency component as the abscissa and the cyclic frequency The component is the ordinate, or the frequency component is the abscissa, the cyclic frequency component is the ordinate, and the cyclic spectrum of each transformed signal is determined.
在一个实施例中,电子设备可以利用以下公式,确定每一变换子信号的循环谱图:In one embodiment, the electronic device can use the following formula to determine the cyclic spectrum of each transformed sub-signal:
其中,
表示第k个变换子信号的循环谱图,
表示第k个变换子信号的循环谱,r表示待识别信号,N
0表示待识别信号对应的第k个变换子信号的信号长度,f表示频率分量,α表示循环频率分量,
表示在
频率下的第k个变换子信号,
表示
的共轭,
表示在
频率下的第k个变换子信号,|·|表示取模值计算。
among them, Represents the cyclic spectrum of the k-th transformed sub-signal, Represents the cyclic spectrum of the k-th transformed sub-signal, r represents the signal to be identified, N 0 represents the signal length of the k-th transformed sub-signal corresponding to the signal to be identified, f represents the frequency component, and α represents the cyclic frequency component, Expressed in The k-th transformed sub-signal at frequency, Means The conjugate, Expressed in For the k-th transformed sub-signal under frequency, |·| represents the calculation of the modulus value.
在一个实施例中,针对每一频段,电子设备可以利用以下公式对该频段上的多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图:In an embodiment, for each frequency band, the electronic device may use the following formula to perform averaging processing on the cyclic spectrograms of the multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signal on the frequency band:
其中,|S
r,l(f,α)|表示第l个频段上频段信号的循环谱图,N
l表示第l个频段对应的循环谱图的总数,即第l个频段上频段子信号的个数,r表示待识别信号,m表示待识别信号对应的频段子信号,l表示频段,|S
r,l,m(f,α)|表示第l个频段上第m个频段子信号的循环谱图,S
r,l,m(f,α)表示第l个频段上第m个频段子信号的循环谱,f表示频率分量,α表示循环频率分量,|·|表示取模值计算。
Among them, |S r,l (f,α)| represents the cyclic spectrogram of the frequency band signal on the lth frequency band, N l represents the total number of cyclic spectrograms corresponding to the lth frequency band, that is, the sub-signal of the frequency band on the lth frequency band R represents the signal to be identified, m represents the frequency band sub-signal corresponding to the signal to be identified, l represents the frequency band, |S r,l,m (f,α)| represents the m-th frequency band sub-signal on the l-th frequency band The cyclic spectrum of S r,l,m (f,α) represents the cyclic spectrum of the m-th frequency band sub-signal on the l-th frequency band, f represents the frequency component, α represents the cyclic frequency component, and |·| represents the modulus value Calculation.
如图5所示,图中包括第l个频段上的第1个、第2个以及第N
l个频段子信号的循环谱图,将上述N
l个循环谱图进行求均值处理,得到图5下方所示均值。基于此,电子设备通过针对每一频段,对该频段上的多个频段子信号的循环谱图进行求均值处理,可以使得每一频段上频段信号的循环谱图中噪声造成的毛刺尽量减少,降低了噪声干扰的影响。
As shown in Figure 5, the figure includes the cyclic spectrograms of the first, second, and N l- th frequency band sub-signals on the l-th frequency band, and the above N l cyclic spectrograms are averaged to obtain the graph 5 Means shown below. Based on this, the electronic device performs averaging processing on the cyclic spectrograms of multiple frequency band sub-signals in each frequency band, so that the glitch caused by noise in the cyclic spectrogram of the frequency band signal in each frequency band can be minimized. Reduce the influence of noise interference.
步骤204,对多个频段对应的循环谱图进行叠加处理,得到待识别信号的循环谱图,作为目标循环谱图。Step 204: Perform superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
本申请实施例中,电子设备在得到每一频段对应的循环谱图后,对多个频段对应的循环谱图进行图片叠加处理,得到目标循环谱图。具体可以为:对多个频段对应的循环谱图 进行叠加处理,得到待识别信号的循环谱图,作为目标循环谱图。In the embodiment of the present application, after obtaining the cyclic spectrogram corresponding to each frequency band, the electronic device performs image superposition processing on the cyclic spectrogram corresponding to the multiple frequency bands to obtain the target cyclic spectrogram. Specifically, it can be: superimposing the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
在一个实施例中,电子设备可以利用以下公式,对多个频段对应的循环谱图进行叠加处理,得到待识别信号的循环谱图,作为目标循环谱图:In one embodiment, the electronic device may use the following formula to superimpose the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:
|S′
r(f,α)|=max{|S
r,1(f,α)|,|S
r,2(f,α)|,…,|S
r,n(f,α)|};
|S′ r (f,α)|=max{|S r,1 (f,α)|,|S r,2 (f,α)|,…,|S r,n (f,α)| };
其中,|S′
r(f,α)|表示待识别信号的循环谱图,S′
r(f,α)表示待识别信号的循环谱,|S
r,l(f,α)|表示第l个频段对应的循环谱图,S
r,l(f,α)表示第l个频段对应的循环谱,l表示频段,l=1,2,…n,max可以表示求取最大值,n表示多个频段的数量,f表示频率分量,α表示循环频率分量,|·|表示取模值计算。
Among them, |S' r (f,α)| represents the cyclic spectrum of the signal to be identified, S'r (f,α) represents the cyclic spectrum of the signal to be identified, and |S r,l (f,α)| represents the first The cyclic spectrum corresponding to l frequency bands, S r,l (f,α) represents the cyclic spectrum corresponding to the l-th frequency band, l represents the frequency band, l=1, 2,...n, max can represent the maximum value, n Represents the number of multiple frequency bands, f represents the frequency component, α represents the cyclic frequency component, and |·| represents the calculation of the modulus value.
如图6所示,图中包括待识别信号的中心频点f
c搬移到0.1f
c处的循环谱图、待识别信号的中心频点f
c搬移到0.3f
c处的循环谱图、待识别信号的中心频点f
c搬移到1.5f
c处的循环谱图等等,将这些不同频段对应的循环谱图进行叠加处理,就得到了图6下方所示的图片叠加后的循环谱图,即目标循环谱图。
As shown, in FIG comprises a central frequency f c to be the identification signal 6 to move at 0.1f c circulation spectrum, the signal to be recognized center frequency f c to move at 0.3f c circulation spectrum, to be The center frequency point f c of the identification signal is moved to the cyclic spectrogram at 1.5f c , etc., and the cyclic spectrograms corresponding to these different frequency bands are superimposed, and the cyclic spectrogram after the superimposition of the pictures shown in Figure 6 is obtained. , Which is the target cycle spectrum.
基于此,电子设备可以将多个频段对应的循环谱图进行叠加,也就是说,电子设备对经过求均值处理的循环谱图,再进行叠加处理,即求取多个频段的循环谱图的对应位置最大值的处理,使得最终得到的目标循环谱图的峰值数目增加,也就是,增加了进行调制方式识别的特征。因此,基于峰值数目增加的目标循环谱图进行调制方式识别,可进一步提高了调制方式识别的准确率。Based on this, the electronic device can superimpose the cyclic spectrograms corresponding to multiple frequency bands, that is, the electronic device performs the superposition processing on the cyclic spectrograms after the averaging process, that is, obtains the cyclic spectrograms of multiple frequency bands. The processing corresponding to the maximum value of the position increases the number of peaks of the target cyclic spectrogram finally obtained, that is, increases the characteristics of the modulation mode identification. Therefore, the modulation method identification based on the target cyclic spectrum with an increased number of peaks can further improve the accuracy of the modulation method identification.
步骤205,将目标循环谱图输入预先训练的调制方式识别模型,得到待识别信号的调制方式。步骤205与步骤103一致。Step 205: Input the target cyclic spectrum into the pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized. Step 205 is consistent with step 103.
在一个实施例中,参考图7,电子设备可以通过以下步骤训练得到上述调制方式识别模型,具体如下。In an embodiment, referring to FIG. 7, the electronic device may train to obtain the above-mentioned modulation mode recognition model through the following steps, which are specifically as follows.
步骤701,获取样本数据,该样本数据包括多个样本信号以及每一样本信号的标注调制方式。Step 701: Obtain sample data, where the sample data includes a plurality of sample signals and the label modulation mode of each sample signal.
本申请实施例中,电子设备可以获取样本数据,该样本数据可以包括多个样本信号以及每一样本信号的标注调制方式。其中,上述多个样本信号的调制方式为已知,即标注调制方式,如a样本信号的调制方式为正交振幅调制,b样本信号的调制方式为键控移项调制等。In the embodiment of the present application, the electronic device may obtain sample data, and the sample data may include a plurality of sample signals and the labeling modulation mode of each sample signal. Wherein, the modulation mode of the above-mentioned multiple sample signals is known, that is, the modulation mode is marked. For example, the modulation mode of the a-sample signal is quadrature amplitude modulation, and the modulation mode of the b-sample signal is key shift modulation.
为了提高训练的训练得到调制方式识别模型识别调制方式的准确性,获取的样本数据中包括的样本信号越多越好。In order to improve the accuracy of the modulation method recognition model obtained by the training training, the more sample signals included in the acquired sample data, the better.
步骤702,根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图。Step 702: Determine a cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal.
本申请实施例中,电子设备在获取到多个样本信号之后,可以根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图。也就是,以频率分量为横坐标,循环频率分量为纵坐标,或以频率分量为横坐标,循环频率分量为纵坐标,绘制每一样本信号的循环谱图。In the embodiment of the present application, after acquiring multiple sample signals, the electronic device may determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal. That is, taking the frequency component as the abscissa and the cyclic frequency component as the ordinate, or the frequency component as the abscissa and the cyclic frequency component as the ordinate, the cyclic spectrum of each sample signal is drawn.
其中,每一样本信号的频率分量和循环频率分量可以在一定取值范围内任意取值,如频率分量的取值范围为100赫兹到1000兆赫兹,循环频率分量的取值范围为2000赫兹到10000赫兹。Among them, the frequency component and the cyclic frequency component of each sample signal can be arbitrarily selected within a certain range of values, for example, the value range of the frequency component is 100 Hz to 1000 MHz, and the value range of the cyclic frequency component is 2000 Hz to 2000 Hz. 10000 Hz.
步骤703,将每一样本信号的循环谱图分别输入预设的深度神经网络,得到每一样本信号的预测调制方式。Step 703: Input the cyclic spectrogram of each sample signal into a preset deep neural network to obtain the predicted modulation mode of each sample signal.
本申请实施例中,电子设备在确定每一样本信号的循环谱图之后,可以将循环谱图分别输入预设的深度神经网络,得到每一样本信号的预测调制方式。其中,上述深度神经网络可以选择ResNet50。In the embodiment of the present application, after determining the cyclic spectrogram of each sample signal, the electronic device can input the cyclic spectrogram into a preset deep neural network to obtain the predicted modulation mode of each sample signal. Among them, the above-mentioned deep neural network can choose ResNet50.
在一个实施例中,上述深度神经网络可以为ResNet50,该深度神经网络引入了残差的概念,避免了深度神经网络在训练阶段常出现梯度弥散和梯度消失的现象。ResNet50的结构具体可参考图8,图8中所示ResNet50可以包括1个输入层、(3+4+6+3)*3个隐藏层以及一个输出层,总计50层。此外,上述ResNet50可以在Pytorch中搭建,其中,Pytorch为免费开源的深度神经网络框架。In one embodiment, the above-mentioned deep neural network may be ResNet50, which introduces the concept of residual error, and avoids the phenomenon of gradient dispersion and gradient disappearance in the deep neural network during the training phase. For the specific structure of ResNet50, please refer to Fig. 8. The ResNet50 shown in Fig. 8 may include 1 input layer, (3+4+6+3)*3 hidden layers and one output layer, totaling 50 layers. In addition, the above ResNet50 can be built in Pytorch, where Pytorch is a free and open source deep neural network framework.
图8中,顶部的7×7卷积核,64通道且步长2,2×2池化层的矩形框表示输入层;底部的全局平均池化层的矩形框表示输出层;在输入层和输出层之间的每一矩形框表示隐藏层。其中,包括3+4+6+3=16组隐藏层,每组隐藏层包括3个隐藏层。In Figure 8, the top 7×7 convolution kernel, 64 channels and a step size of 2,2×2 pooling layer rectangular box represents the input layer; the bottom rectangular box of the global average pooling layer represents the output layer; in the input layer Each rectangular box between the output layer and the output layer represents a hidden layer. Among them, 3+4+6+3=16 groups of hidden layers are included, and each group of hidden layers includes 3 hidden layers.
步骤704,根据每一样本信号的预测调制方式和标注调制方式,确定调制方式识别的损失值。Step 704: Determine the loss value identified by the modulation mode according to the predicted modulation mode and the labeled modulation mode of each sample signal.
本申请实施例中,具体可以使用交叉熵(英文:Cross-Entropy,简称:CE)函数作为损失函数,得到损失值J
CE(Φ),详见如下公式:
In the embodiments of this application, specifically, a cross-entropy (English: Cross-Entropy, abbreviated: CE) function can be used as the loss function to obtain the loss value J CE (Φ), as shown in the following formula:
其中,C表示调制方式的集合,c表示第c种调制方式,P表示样本信号集合,p表示样本信号,
表示指示参数,
的取值可以为0或者1,例如,若样本信号p的标注调制方式为调制方式c时,
的取值为1,若样本信号p的标注调制方式不是调制方式c时,
的取值为0;Φ表示为深度神经网络的参数,O
cp表示深度神经网络的输出,即O
cp表示预测调制方式为调制方式c的概率。
Among them, C represents the set of modulation methods, c represents the c-th modulation method, P represents the sample signal set, and p represents the sample signal, Indicates the indicator parameter, The value of can be 0 or 1. For example, if the marked modulation mode of the sample signal p is modulation mode c, The value of is 1, if the marked modulation mode of the sample signal p is not the modulation mode c, The value of is 0; Φ represents the parameter of the deep neural network, O cp represents the output of the deep neural network, that is, O cp represents the probability that the predicted modulation mode is the modulation mode c.
本申请实施例中,还可以采用其他方式确定调制方式识别的损失值。例如,采用均方误差函数确定调制方式识别的损失值。对此,本申请实施例不作具体限定。In the embodiment of the present application, other methods may also be used to determine the loss value of the modulation mode identification. For example, the mean square error function is used to determine the loss value of modulation recognition. In this regard, the embodiments of the present application do not make specific limitations.
步骤705,根据损失值,确定深度神经网络是否收敛。若否,执行步骤706;若是,执行步骤707。Step 705: Determine whether the deep neural network converges according to the loss value. If not, go to step 706; if yes, go to step 707.
本申请实施例中,可以基于预设损失阈值确定深度神经网络是否收敛。具体可以为:当损失值小于预设损失阈值时,确定深度神经网络收敛;若损失值大于等于预设损失阈值,则确定深度神经网络未收敛。In the embodiment of the present application, whether the deep neural network converges may be determined based on a preset loss threshold. Specifically, it may be: when the loss value is less than the preset loss threshold, the deep neural network is determined to converge; if the loss value is greater than or equal to the preset loss threshold, it is determined that the deep neural network has not converged.
本申请实施例中,也可以基于预设变化阈值确定深度神经网络是否收敛。具体可以为:当本次计算得到损失值与上一次计算得到的损失值之差小于预设变化阈值时,确定深度神经网络收敛;若本次计算得到损失值与上一次计算得到的损失值之差大于等于预设损失值阈值,则确定深度神经网络未收敛。In the embodiment of the present application, whether the deep neural network converges may also be determined based on a preset change threshold. Specifically: when the difference between the loss value calculated this time and the loss value calculated last time is less than the preset change threshold, the deep neural network is determined to converge; if the loss value calculated this time is different from the loss value calculated last time If the difference is greater than or equal to the preset loss threshold, it is determined that the deep neural network has not converged.
本申请实施例中,还可以基于预设训练次数阈值确定深度神经网络是否收敛。具体可以为:当对深度神经网络的训练次数达到预设训练次数阈值时,确定深度神经网络收敛;若对深度神经网络的训练次数未达到预设训练次数阈值,则确定深度神经网络未收敛。In the embodiment of the present application, it is also possible to determine whether the deep neural network converges based on the preset training times threshold. Specifically, it may be: when the number of training times for the deep neural network reaches the preset training times threshold, the deep neural network is determined to converge; if the number of training times for the deep neural network does not reach the preset training times threshold, it is determined that the deep neural network has not converged.
本申请实施例中,还可以基于预设损失阈值、预设变化阈值和预设训练次数阈值中任两种或三种,确定深度神经网络是否收敛。例如,基于预设损失阈值和预设训练次数阈值中任两种或三种,确定深度神经网络是否收敛。具体可以为:若损失值小于预设损失阈值,或对深度神经网络的训练次数达到预设训练次数阈值,则确定深度神经网络收敛;否则,确定深度神经网络未收敛。In the embodiment of the present application, it is also possible to determine whether the deep neural network converges based on any two or three of the preset loss threshold, the preset change threshold, and the preset training times threshold. For example, based on any two or three of the preset loss threshold and the preset training times threshold, it is determined whether the deep neural network converges. Specifically, it may be: if the loss value is less than the preset loss threshold, or the number of training times for the deep neural network reaches the preset number of training times threshold, the deep neural network is determined to converge; otherwise, it is determined that the deep neural network has not converged.
本申请实施例中,对确定深度神经网络是否收敛的方式不做限定。In the embodiment of the present application, the method for determining whether the deep neural network converges is not limited.
步骤706,调整深度神经网络的参数,返回执行步骤703。Step 706: Adjust the parameters of the deep neural network, and return to step 703.
步骤707,确定当前深度神经网络为调制方式识别模型。Step 707: Determine that the current deep neural network is the modulation mode recognition model.
本申请实施例中,训练调制方式识别模型的电子设备和识别调制方式的电子设备可以位于同一物理机上,也可以位于不同的物理机上。对此不做限定。In the embodiments of the present application, the electronic device for training the modulation mode recognition model and the electronic device for recognizing the modulation mode may be located on the same physical machine, or may be located on different physical machines. There is no restriction on this.
本申请实施例中,采用上述步骤701-707训练得到的调制方式识别模型,调制方式识别模型利用不同调制方式下信号的循环谱图输入深度神经网络训练得到,使得调制方式识别模型能够学习到不同调制方式下信号的循环谱图的全部特征,进而调制方式识别模型基于学习到的不同调制方式下信号的循环谱图的全部特征,对待识别信号的循环谱图的全部特征进行处理,确定待识别信号的调制方式,提高了调制方式识别的准确性。In the embodiment of this application, the modulation recognition model obtained by training in the above steps 701-707 is used. The modulation recognition model is trained by inputting the cyclic spectra of signals in different modulation modes into the deep neural network, so that the modulation recognition model can learn different All the characteristics of the cyclic spectrogram of the signal under the modulation mode, and then the modulation mode recognition model is based on all the characteristics of the cyclic spectrogram of the signal under different modulation modes learned, and all the characteristics of the cyclic spectrogram of the signal to be identified are processed to determine the to-be-recognized The modulation method of the signal improves the accuracy of modulation recognition.
下面结合图9所示的调制方式识别方法的流程图,对本申请实施例提供的调制方式识别方法进行详细说明。The following describes the modulation method identification method provided in the embodiment of the present application in detail with reference to the flowchart of the modulation method identification method shown in FIG. 9.
训练阶段:Training phase:
电子设备获取合作模式下的接收信号,对接收信号依次进行频谱搬移、计算循环谱、平滑化以及图片叠加等处理后,得到接收信号的循环谱图。其中,频谱搬移处理是为图片叠加做准备的。电子设备将接收信号的循环谱图输入预设深度神经网络,训练结束后,固定预设深度神经网络的参数,得到调制方式识别模型。其中,频谱搬移、计算循环谱以及图片叠加等处理操作可参考上述图1-8部分的描述,此处不再赘述,平滑化处理即为上述求取均值的处理。接收信号可以理解为样本信号。The electronic device obtains the received signal in the cooperative mode, and performs processing such as spectrum shifting, calculation of the cyclic spectrum, smoothing, and image superposition on the received signal in order to obtain the cyclic spectrum of the received signal. Among them, the frequency spectrum shift processing is to prepare for the image superposition. The electronic device inputs the cyclic spectrum of the received signal into the preset deep neural network, and after the training is completed, the parameters of the preset deep neural network are fixed to obtain the modulation mode recognition model. Among them, the processing operations such as spectrum shifting, calculation of cyclic spectrum, and picture superposition can refer to the description in the above-mentioned parts of Figures 1-8, which will not be repeated here, and the smoothing processing is the processing of calculating the average value described above. The received signal can be understood as a sample signal.
识别阶段:Recognition stage:
电子设备获取待识别信号,对待识别信号依次进行频谱搬移、计算循环谱、平滑化、以及图片叠加等处理后,得到待识别信号的循环谱图,将待识别信号的循环谱图输入至训练得到的调制方式识别模型,以识别图片,即识别待识别信号的循环谱图,进而识别得到待识别信号的调制方式。The electronic device obtains the signal to be identified, and performs the processing of spectrum shifting, calculation of the cyclic spectrum, smoothing, and image superposition of the signal to be identified in order to obtain the cyclic spectrum of the signal to be identified, and input the cyclic spectrum of the signal to be identified to the training result The modulation mode recognition model is used to recognize the picture, that is, to recognize the cyclic spectrum of the signal to be recognized, and then to identify the modulation mode of the signal to be recognized.
图9部分的描述相对简单,具体可参考上述图1-8的描述。The description of Fig. 9 is relatively simple. For details, please refer to the description of Figs. 1-8.
本申请实施例提供的一种调制方式识别方法中,电子设备将待识别信号的循环谱图完整的输入至调制方式识别模型中,来识别待识别信号的调制方式,也就是,利用待识别信号的循环谱图的全部特征,识别待识别信号的调制方式,而不是利用循环谱图的部分特征识别待识别信号的调制方式,提高了信号的调制方式识别的准确率。In the modulation method identification method provided by the embodiments of the present application, the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method identification model to identify the modulation method of the signal to be identified, that is, using the signal to be identified To identify the modulation mode of the signal to be identified instead of using part of the characteristics of the cyclic spectrum to identify the modulation mode of the signal to be identified, which improves the accuracy of the recognition of the modulation mode of the signal.
基于相同的技术构思,本申请实施例还提供了一种调制方式识别装置,如图10所示,该装置包括:Based on the same technical concept, an embodiment of the present application also provides a modulation mode identification device. As shown in FIG. 10, the device includes:
接收模块1001,用于接收待识别信号;The receiving module 1001 is used to receive the signal to be identified;
确定模块1002,用于根据待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图;The determining module 1002 is configured to determine the cyclic spectrum of the signal to be identified according to the frequency components and the cyclic frequency components of the signal to be identified, as the target cyclic spectrum;
识别模块1003,用于将目标循环谱图输入预先训练的调制方式识别模型,得到待识别信号的调制方式,其中,调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,该样本数据包括多个样本信号以及每一样本信号的标注调制方式。The recognition module 1003 is used to input the target cycle spectrogram into the pre-trained modulation recognition model to obtain the modulation mode of the signal to be recognized. The modulation recognition model is a model obtained by training a deep neural network using sample data. The data includes multiple sample signals and the marked modulation method of each sample signal.
可选的,确定模块1002,可以包括:Optionally, the determining module 1002 may include:
搬移子模块,用于将待识别信号的频谱分别搬移到预设的多个频段上,得到待识别信号对应的多个频段信号;The moving sub-module is used to move the frequency spectrum of the signal to be identified to multiple preset frequency bands respectively to obtain multiple frequency band signals corresponding to the signal to be identified;
第一确定子模块,用于根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图,每一频段信号的循环谱图为该频段信号所在频段对应的循环谱图;The first determining sub-module is used to determine the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrum of each frequency band signal is the cycle corresponding to the frequency band where the signal of the frequency band is located. Spectrogram
叠加子模块,用于对多个频段对应的循环谱图进行叠加处理,得到待识别信号的循环谱图,作为目标循环谱图。The superposition sub-module is used to superimpose the cyclic spectrograms corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
可选的,搬移子模块具体可以用于:对待识别信号进行分段处理,得到多个待识别子信号;针对每一待识别子信号,将该待识别子信号的频谱分别搬移到预设的多个频段上,得到该待识别子信号对应的多个频段子信号;其中,一个频段上多个待识别子信号对应的多个频段子信号为该频段上待识别信号对应的频段信号;Optionally, the moving sub-module can be specifically used to: perform segment processing on the signal to be identified to obtain multiple sub-signals to be identified; for each sub-signal to be identified, move the frequency spectrum of the sub-signal to be identified to a preset Obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified on multiple frequency bands; wherein, multiple frequency band sub-signals corresponding to the multiple sub-signals to be identified on one frequency band are frequency band signals corresponding to the signal to be identified on the frequency band;
第一确定子模块,具体可以用于:根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。The first determining sub-module can be specifically used to: determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal; for each frequency band, multiple frequency bands on the frequency band The cyclic spectrum of the sub-signal is averaged to obtain the cyclic spectrum of the frequency band signal in the frequency band.
可选的,第一确定子模块,具体可以用于:针对每一频段信号,对该频段信号进行分段处理,得到该频段信号对应的多个频段子信号;根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。Optionally, the first determining sub-module can be specifically used to: for each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal; according to each frequency band sub-signal Frequency components and each cyclic frequency component, determine the cyclic spectrum of each frequency band sub-signal; for each frequency band, perform averaging processing on the cyclic spectrum of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrum of the frequency band signal on the frequency band Spectrogram.
可选的,第一确定子模块具体可以用于:对每一频段子信号进行快速傅里叶变换,得到每一频段子信号对应的变换子信号;根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图,作为每一变换子信号对应的频段子信号的循环谱图。Optionally, the first determining sub-module may be specifically used to: perform fast Fourier transform on each frequency band sub-signal to obtain a transformed sub-signal corresponding to each frequency band sub-signal; according to each frequency component of each transformed sub-signal With each cyclic frequency component, the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
可选的,第一确定子模块具体可以用于:Optionally, the first determining submodule may be specifically used for:
利用以下公式,确定每一变换子信号的循环谱图:Use the following formula to determine the cyclic spectrum of each transformed sub-signal:
其中,
表示待识别信号对应的第k个变换子信号的循环谱图,
表示第k个变换子信号的循环谱,r表示待识别信号,N
0表示待识别信号对应的第k个变换子信号的信号长度,f表示频率分量,α表示循环频率分量,
表示在
频率下的待识别信号对应的第k个变换子信号,
表示
的共轭,
表示在
频率下的待识别信号对应的第k个变换子信号,|·|可以表示取模值计算。
among them, Represents the cyclic spectrum of the k-th transformed sub-signal corresponding to the signal to be identified, Represents the cyclic spectrum of the k-th transformed sub-signal, r represents the signal to be identified, N 0 represents the signal length of the k-th transformed sub-signal corresponding to the signal to be identified, f represents the frequency component, and α represents the cyclic frequency component, Expressed in The k-th transformed sub-signal corresponding to the signal to be identified under the frequency, Means The conjugate, Expressed in For the k-th transformed sub-signal corresponding to the signal to be identified at the frequency, |·| can represent the calculation of the modulus value.
可选的,叠加子模块具体可以用于:Optionally, the overlay sub-module can be specifically used for:
利用以下公式,对多个频段对应的循环谱图进行叠加处理,得到待识别信号的循环谱图,作为目标循环谱图:Use the following formula to superimpose the cyclic spectra corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:
|S′
r(f,α)|=max{|S
r,1(f,α)|,|S
r,2(f,α)|,…,|S
r,n(f,α)|};
|S′ r (f,α)|=max{|S r,1 (f,α)|,|S r,2 (f,α)|,…,|S r,n (f,α)| };
其中,|S′
r(f,α)|表示待识别信号的循环谱图,|S
r,l(f,α)|表示第l个频段对应的循环谱图,l表示频段,l=1,2,…n,max表示求取最大值,n表示多个频段的数量,f表示频率分量,α表示循环频率分量,|·|可以表示取模值计算。
Among them, |S' r (f,α)| represents the cyclic spectrum of the signal to be identified, |S r,l (f,α)| represents the cyclic spectrum corresponding to the l-th frequency band, l represents the frequency band, and l=1 ,2,...n,max represents the maximum value, n represents the number of multiple frequency bands, f represents the frequency component, α represents the cyclic frequency component, and |·| can represent the calculation of the modulus value.
可选的,上述装置还可以包括:训练模块,用于训练得到调制方式识别模型;Optionally, the above-mentioned device may further include: a training module for training to obtain a modulation mode recognition model;
上述训练模块可以包括:The above-mentioned training module may include:
获取子模块,用于获取样本数据,样本数据包括多个样本信号以及每一样本信号的标注调制方式;The acquisition sub-module is used to acquire sample data, the sample data includes multiple sample signals and the label modulation mode of each sample signal;
第二确定子模块,用于根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图;The second determining sub-module is used to determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;
预测子模块,用于将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式;The prediction sub-module is used to input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;
第三确定子模块,用于根据每一样本信号的预测调制方式和标注调制方式,确定调制方式识别的损失值;The third determining sub-module is used to determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeling modulation mode of each sample signal;
第四确定子模块,用于根据损失值,确定深度神经网络是否收敛;若否,则调整深度神经网络的参数,返回执行将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式的步骤;若是,则确定当前深度神经网络为调制方式识别模型。The fourth determining sub-module is used to determine whether the deep neural network has converged according to the loss value; if not, adjust the parameters of the deep neural network, and return to execute inputting the cyclic spectrum of each sample signal into the preset deep neural network to obtain The step of predicting the modulation mode of each sample signal; if yes, determine that the current deep neural network is the modulation mode recognition model.
本申请实施例提供的一种调制方式识别装置中,电子设备将待识别信号的循环谱图完整的输入至调制方式识别模型中,来识别待识别信号的调制方式,也就是,利用待识别信 号的循环谱图的全部特征,识别待识别信号的调制方式,而不是利用循环谱图的部分特征识别待识别信号的调制方式,提高了信号的调制方式识别的准确率。In the modulation method identification device provided by the embodiment of the present application, the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method identification model to identify the modulation method of the signal to be identified, that is, using the signal to be identified To identify the modulation mode of the signal to be identified instead of using part of the characteristics of the cyclic spectrum to identify the modulation mode of the signal to be identified, which improves the accuracy of the recognition of the modulation mode of the signal.
基于相同的技术构思,本申请实施例还提供了一种电子设备,如图11所示,包括处理器1101、通信接口1102、存储器1103和通信总线1104,其中,处理器1101、通信接口1102、存储器1103通过通信总线1104完成相互间的通信;Based on the same technical concept, an embodiment of the present application also provides an electronic device, as shown in FIG. 11, including a processor 1101, a communication interface 1102, a memory 1103, and a communication bus 1104, wherein the processor 1101, the communication interface 1102, The memories 1103 communicate with each other through the communication bus 1104;
存储器1103,用于存放计算机程序;The memory 1103 is used to store computer programs;
处理器1101,用于执行存储器1103上所存放的程序时,实现上述任一调制方式识别方法实施例中的方法步骤。The processor 1101 is configured to implement the method steps in any of the foregoing modulation mode identification method embodiments when executing the program stored in the memory 1103.
本申请实施例提供的一种电子设备中,电子设备将待识别信号的循环谱图完整的输入至调制方式识别模型中,来识别待识别信号的调制方式,也就是,利用待识别信号的循环谱图的全部特征,识别待识别信号的调制方式,而不是利用循环谱图的部分特征识别待识别信号的调制方式,提高了信号的调制方式识别的准确率。In an electronic device provided by an embodiment of the present application, the electronic device inputs the complete cyclic spectrum of the signal to be identified into the modulation method recognition model to identify the modulation method of the signal to be identified, that is, using the cyclic spectrum of the signal to be identified All the characteristics of the spectrogram identify the modulation method of the signal to be identified, instead of using part of the cyclic spectrogram to identify the modulation method of the signal to be identified, which improves the accuracy of the recognition of the signal modulation method.
上述网络设备提到的通信总线可以是外设部件互连标准(英文:Peripheral Component Interconnect,简称:PCI)总线或扩展工业标准结构(英文:Extended Industry Standard Architecture,简称:EISA)总线等。该通信总线可以分为地址总线、数据总线、控制总线等。为便于表示,图中仅用一条粗线表示,但并不表示仅有一根总线或一种类型的总线。The communication bus mentioned by the aforementioned network device may be a peripheral component interconnection standard (English: Peripheral Component Interconnect, abbreviated as: PCI) bus or an extended industry standard architecture (English: Extended Industry Standard Architecture, abbreviated as: EISA) bus, etc. The communication bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
通信接口用于上述网络设备与其他设备之间的通信。The communication interface is used for communication between the aforementioned network device and other devices.
存储器可以包括随机存取存储器(英文:Random Access Memory,简称:RAM),也可以包括非易失性存储器(英文:Non-Volatile Memory,简称:NVM),例如至少一个磁盘存储器。可选的,存储器还可以是至少一个位于远离前述处理器的存储装置。The memory may include random access memory (English: Random Access Memory, abbreviated as: RAM), and may also include non-volatile memory (English: Non-Volatile Memory, abbreviated as: NVM), for example, at least one disk storage. Optionally, the memory may also be at least one storage device located far away from the foregoing processor.
上述的处理器可以是通用处理器,包括中央处理器(英文:Central Processing Unit,简称:CPU)、网络处理器(英文:Network Processor,简称:NP)等;还可以是数字信号处理器(英文:Digital Signal Processing,简称:DSP)、专用集成电路(英文:Application Specific Integrated Circuit,简称:ASIC)、现场可编程门阵列(英文:Field-Programmable Gate Array,简称:FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。The above-mentioned processor may be a general-purpose processor, including a central processing unit (English: Central Processing Unit, abbreviated as: CPU), a network processor (English: Network Processor, abbreviated as: NP), etc.; it may also be a digital signal processor (English: : Digital Signal Processing, abbreviation: DSP), application specific integrated circuit (English: Application Specific Integrated Circuit, abbreviation: ASIC), Field-Programmable Gate Array (English: Field-Programmable Gate Array, abbreviation: FPGA) or other programmable logic devices , Discrete gates or transistor logic devices, discrete hardware components.
基于相同的技术构思,本申请实施例还提供了一种计算机可读存储介质,计算机可读存储介质内存储有计算机程序,计算机程序被处理器执行时实现上述调制方式识别方法的任一步骤。Based on the same technical concept, the embodiments of the present application also provide a computer-readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, any step of the above modulation method identification method is implemented.
基于相同的技术构思,本申请实施例还提供了一种计算机程序,当其在计算机上运行时,使得计算机执行上述调制方式识别方法的任一步骤。Based on the same technical concept, the embodiments of the present application also provide a computer program, which when running on a computer, causes the computer to execute any step of the above-mentioned modulation mode identification method.
在上述实施例中,可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时,可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时,全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中,或者从一个计算机可读存储介质向另一个计算机可读存储介质传输,例如,所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、 数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质,(例如,软盘、硬盘、磁带)、光介质(例如,DVD)、或者半导体介质(例如固态硬盘Solid State Disk(SSD))等。In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center. Transmission to another website site, computer, server or data center via wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrated with one or more available media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).
需要说明的是,在本文中,诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来,而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply one of these entities or operations. There is any such actual relationship or order between. Moreover, the terms "include", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes those that are not explicitly listed Other elements of, or also include elements inherent to this process, method, article or equipment. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or equipment that includes the element.
本说明书中的各个实施例均采用相关的方式描述,各个实施例之间相同相似的部分互相参见即可,每个实施例重点说明的都是与其他实施例的不同之处。尤其,对于装置、电子设备、计算机可读存储介质和计算机程序实施例而言,由于其基本相似于方法实施例,所以描述的比较简单,相关之处参见方法实施例的部分说明即可。The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, electronic equipment, computer-readable storage medium, and computer program embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiments.
以上所述仅为本申请的较佳实施例而已,并非用于限定本申请的保护范围。凡在本申请的精神和原则之内所作的任何修改、等同替换、改进等,均包含在本申请的保护范围内。The foregoing descriptions are only preferred embodiments of the present application, and are not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application are all included in the protection scope of this application.
Claims (12)
- 一种调制方式识别方法,其特征在于,所述方法包括:A modulation method identification method, characterized in that the method includes:接收待识别信号;Receive the signal to be identified;根据所述待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图;Determine the cyclic spectrum of the signal to be identified according to the frequency components and the cyclic frequency components of the signal to be identified, as the target cyclic spectrum;将所述目标循环谱图输入预先训练的调制方式识别模型,得到所述待识别信号的调制方式,其中,所述调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式。Input the target cyclic spectrogram into a pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized, wherein the modulation mode recognition model is a model obtained by training a deep neural network using sample data, and The sample data includes multiple sample signals and the label modulation mode of each sample signal.
- 根据权利要求1所述的方法,其特征在于,所述根据所述待识别信号的各个频率分量和各个循环频率分量,确定所述待识别信号的循环谱图,作为目标循环谱图的步骤,包括:The method according to claim 1, wherein the step of determining the cyclic spectrum of the signal to be identified as the target cyclic spectrum according to the respective frequency components and the respective cyclic frequency components of the signal to be identified, include:将所述待识别信号的频谱分别搬移到预设的多个频段上,得到所述待识别信号对应的多个频段信号;Moving the frequency spectrum of the signal to be identified to a plurality of preset frequency bands to obtain signals of multiple frequency bands corresponding to the signal to be identified;根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图,每一频段信号的循环谱图为该频段信号所在频段对应的循环谱图;Determine the cyclic spectrogram of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal, and the cyclic spectrogram of each frequency band signal is the cyclic spectrogram corresponding to the frequency band where the signal of the frequency band is located;对所述多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图。Performing superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram.
- 根据权利要求2所述的方法,其特征在于,所述将所述待识别信号的频谱分别搬移到预设的多个频段上,得到所述待识别信号对应的多个频段信号的步骤包括:The method according to claim 2, wherein the step of moving the frequency spectrum of the signal to be identified to a plurality of preset frequency bands respectively to obtain signals of multiple frequency bands corresponding to the signal to be identified comprises:对所述待识别信号进行分段处理,得到多个待识别子信号;Performing segmentation processing on the signal to be identified to obtain a plurality of sub-signals to be identified;针对每一待识别子信号,将该待识别子信号的频谱分别搬移到预设的多个频段上,得到该待识别子信号对应的多个频段子信号;其中,一个频段上所述多个待识别子信号对应的多个频段子信号为该频段上所述待识别信号对应的频段信号;For each sub-signal to be identified, the frequency spectrum of the sub-signal to be identified is moved to multiple preset frequency bands to obtain multiple frequency band sub-signals corresponding to the sub-signal to be identified; The multiple frequency band sub-signals corresponding to the sub-signal to be identified are frequency band signals corresponding to the signal to be identified in the frequency band;所述根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图的步骤,包括:The step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal includes:根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;Determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。For each frequency band, averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
- 根据权利要求2所述的方法,其特征在于,所述根据每一频段信号的各个频率分量和各个循环频率分量,确定每一频段信号的循环谱图的步骤,包括:3. The method according to claim 2, wherein the step of determining the cyclic spectrum of each frequency band signal according to each frequency component and each cyclic frequency component of each frequency band signal comprises:针对每一频段信号,对该频段信号进行分段处理,得到该频段信号对应的多个频段子信号;For each frequency band signal, perform segmentation processing on the frequency band signal to obtain multiple frequency band sub-signals corresponding to the frequency band signal;根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图;Determine the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal;针对每一频段,对该频段上多个频段子信号的循环谱图进行求均值处理,得到该频段上频段信号的循环谱图。For each frequency band, averaging is performed on the cyclic spectrograms of multiple frequency band sub-signals on the frequency band to obtain the cyclic spectrogram of the frequency band signals on the frequency band.
- 根据权利要求3或4所述的方法,其特征在于,所述根据每一频段子信号的各个频率分量和各个循环频率分量,确定每一频段子信号的循环谱图的步骤,包括:The method according to claim 3 or 4, wherein the step of determining the cyclic spectrum of each frequency band sub-signal according to each frequency component and each cyclic frequency component of each frequency band sub-signal comprises:对每一频段子信号进行快速傅里叶变换,得到每一频段子信号对应的变换子信号;Fast Fourier transform is performed on each frequency band sub-signal to obtain the transformed sub-signal corresponding to each frequency band sub-signal;根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图,作为每一变换子信号对应的频段子信号的循环谱图。According to each frequency component and each cyclic frequency component of each transformed sub-signal, the cyclic spectrum of each transformed sub-signal is determined as the cyclic spectrum of the frequency band sub-signal corresponding to each transformed sub-signal.
- 根据权利要求5所述的方法,其特征在于,所述根据每一变换子信号的每一频率分量和每一循环频率分量,确定每一变换子信号的循环谱图的步骤,包括:The method according to claim 5, wherein the step of determining the cyclic spectrum of each transformed sub-signal according to each frequency component and each cyclic frequency component of each transformed sub-signal comprises:利用以下公式,确定每一变换子信号的循环谱图:Use the following formula to determine the cyclic spectrum of each transformed sub-signal:其中, 表示第k个变换子信号的循环谱图,N 0表示第k个变换子信号的信号长度,f表示频率分量,α表示循环频率分量, 表示在 频率下的第k个变换子信号, 表示 的共轭, 表示在 频率下的第k个变换子信号,|·|表示取模值。 among them, Represents the cyclic spectrum of the k-th transformed sub-signal, N 0 represents the signal length of the k-th transformed sub-signal, f represents the frequency component, and α represents the cyclic frequency component, Expressed in The k-th transformed sub-signal at frequency, Means The conjugate, Expressed in For the k-th transformed sub-signal at frequency, |·| represents the modulus value.
- 根据权利要求2所述的方法,其特征在于,所述对所述多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图的步骤,包括:The method according to claim 2, wherein the step of performing superposition processing on the cyclic spectrograms corresponding to the multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram comprises :利用以下公式,对多个频段对应的循环谱图进行叠加处理,得到所述待识别信号的循环谱图,作为目标循环谱图:Use the following formula to superimpose the cyclic spectra corresponding to multiple frequency bands to obtain the cyclic spectrogram of the signal to be identified as the target cyclic spectrogram:|S′ r(f,α)|=max{|S r,1(f,α)|,|S r,2(f,α)|,…,|S r,n(f,α)|}; |S′ r (f,α)|=max{|S r,1 (f,α)|,|S r,2 (f,α)|,…,|S r,n (f,α)| };其中,|S′ r(f,α)|表示所述待识别信号的循环谱图,|S r,l(f,α)|表示第l个频段对应的循环谱图,l表示频段,l=1,2,…n,max表示求取最大值,n表示所述多个频段的数量,f表示频率分量,α表示循环频率分量,|·|表示取模值。 Where |S' r (f,α)| represents the cyclic spectrum of the signal to be identified, |S r,l (f,α)| represents the cyclic spectrum corresponding to the l-th frequency band, l represents the frequency band, and l =1,2,...n, max represents the maximum value, n represents the number of the multiple frequency bands, f represents the frequency component, α represents the cyclic frequency component, and |·| represents the modulus value.
- 根据权利要求1所述的方法,其特征在于,通过以下方式训练得到所述调制方式识别模型:The method according to claim 1, wherein the modulation mode recognition model is obtained by training in the following manner:获取所述样本数据,所述样本数据包括所述多个样本信号以及每一样本信号的标注调制方式;Acquiring the sample data, where the sample data includes the multiple sample signals and the labeling modulation mode of each sample signal;根据每一样本信号的各个频率分量和各个循环频率分量,确定每一样本信号的循环谱图;Determine the cyclic spectrum of each sample signal according to each frequency component and each cyclic frequency component of each sample signal;将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制 方式;Input the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;根据所述每一样本信号的预测调制方式和标注调制方式,确定调制方式识别的损失值;Determine the loss value of the modulation mode recognition according to the predicted modulation mode and the labeled modulation mode of each sample signal;根据所述损失值,确定所述深度神经网络是否收敛;Determine whether the deep neural network converges according to the loss value;若否,则调整所述深度神经网络的参数,返回执行所述将每一样本信号的循环谱图输入预设的深度神经网络,得到每一样本信号的预测调制方式的步骤;If not, adjust the parameters of the deep neural network and return to the step of inputting the cyclic spectrogram of each sample signal into the preset deep neural network to obtain the predicted modulation mode of each sample signal;若是,则确定当前深度神经网络为调制方式识别模型。If yes, it is determined that the current deep neural network is the modulation mode recognition model.
- 一种调制方式识别装置,其特征在于,所述装置包括:A modulation mode identification device, characterized in that the device includes:接收模块,用于接收待识别信号;The receiving module is used to receive the signal to be identified;确定模块,用于根据所述待识别信号的各个频率分量和各个循环频率分量,确定待识别信号的循环谱图,作为目标循环谱图;The determining module is configured to determine the cyclic spectrum of the signal to be identified according to the respective frequency components and the respective cyclic frequency components of the signal to be identified, as the target cyclic spectrum;识别模块,用于将所述目标循环谱图输入预先训练的调制方式识别模型,得到所述待识别信号的调制方式,其中,所述调制方式识别模型为利用样本数据对深度神经网络进行训练得到的模型,所述样本数据包括多个样本信号以及每一样本信号的标注调制方式。The recognition module is used to input the target cyclic spectrogram into a pre-trained modulation mode recognition model to obtain the modulation mode of the signal to be recognized, wherein the modulation mode recognition model is obtained by training a deep neural network using sample data In the model, the sample data includes a plurality of sample signals and the label modulation mode of each sample signal.
- 一种电子设备,其特征在于,包括处理器、通信接口、存储器和通信总线,其中,所述处理器、所述通信接口、所述存储器通过所述通信总线完成相互间的通信;An electronic device, characterized by comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete mutual communication through the communication bus;所述存储器,用于存放计算机程序;The memory is used to store computer programs;所述处理器,用于执行所述存储器上所存放的程序,实现权利要求1-8任一所述的方法步骤。The processor is configured to execute the program stored in the memory to implement the method steps of any one of claims 1-8.
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质内存储有计算机程序,所述计算机程序被处理器执行时实现权利要求1-8任一所述的方法步骤。A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the method steps according to any one of claims 1-8 are realized.
- 一种计算机程序,其特征在于,所述计算机程序被处理器执行时实现权利要求1-8任一所述的方法步骤。A computer program, characterized in that, when the computer program is executed by a processor, the method steps according to any one of claims 1-8 are realized.
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