CN111884962A - Signal modulation type classification method and system based on convolutional neural network - Google Patents
Signal modulation type classification method and system based on convolutional neural network Download PDFInfo
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Abstract
The invention discloses a signal modulation type classification method and a system based on a convolutional neural network, wherein the method comprises the following steps: extracting modulation characteristics from a received electromagnetic signal, the modulation characteristics including an amplitude characteristic and a first phase characteristic; filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic; carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image; and classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types. Extracting modulation characteristics of the electromagnetic signals, and removing carriers of the modulation characteristics to reduce the influence of frequency offset; and converting the processed modulation characteristics into characteristic images by adopting a constellation mapping method, classifying the characteristic images by utilizing a convolutional neural network, converting modulation identification into image identification, and realizing the classification and identification of the modulation types of the electromagnetic signals in the communication system.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a signal modulation type classification method and system based on a convolutional neural network.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In the communication field, the analysis processing of the communication signal, need to discern the modulation type of the signal at first, correctly discern the modulation type and carry on the demodulation to the signal according to the modulation type subsequently; there are two methods for signal modulation identification: namely a judgment theory method based on likelihood ratio and a statistical pattern recognition method based on feature extraction; the former is sensitive to parameter deviation and model mismatch, has difficulty in forming correct hypothesis, and is difficult to determine a correct judgment threshold value; the key of the latter is the selection of characteristic parameters, and the recognition result is easily interfered by noise. Therefore, the inventor thinks that the signal modulation identification method in the communication system has the problems of low accuracy, large influence by interference such as frequency offset and the like.
Disclosure of Invention
In order to solve the problems, the invention provides a signal modulation type classification method and a signal modulation type classification system based on a convolutional neural network, which are used for extracting the modulation characteristics of electromagnetic signals, removing the carrier waves of the modulation characteristics and reducing the influence of frequency offset; and converting the processed modulation characteristics into characteristic images by adopting a constellation mapping method, classifying the characteristic images by utilizing a convolutional neural network, converting modulation identification into image identification, and realizing the classification and identification of the modulation types of the electromagnetic signals in the communication system.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a signal modulation type classification method based on a convolutional neural network, including:
extracting modulation characteristics from a received electromagnetic signal, the modulation characteristics including an amplitude characteristic and a first phase characteristic;
filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic;
carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image;
and classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
In a second aspect, the present invention provides a signal modulation type classification system based on a convolutional neural network, including:
the characteristic extraction module is used for extracting modulation characteristics from the received electromagnetic signals, wherein the modulation characteristics comprise amplitude characteristics and first phase characteristics;
the carrier processing module is used for filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic;
the constellation map mapping module is used for carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image;
and the classification module is used for classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the method adopts the convolutional neural network to automatically extract the characteristic of the characteristic picture of the modulation signal to realize the identification of the modulation type, extracts the modulation characteristic related to the signal, and eliminates the carrier component irrelevant to the modulation information by utilizing a carrier estimation algorithm in the characteristic extraction process, thereby greatly reducing the influence of frequency deviation on the extraction of the signal characteristic, effectively improving the purity of the signal modulation characteristic and extracting the difference of the signal to the maximum extent.
The method converts the modulation characteristics into the characteristic images, visually expresses the difference, converts the modulation recognition problem into the image recognition problem, and finally classifies the characteristic images by using the convolutional neural network model, thereby effectively improving the classification accuracy.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of processing a received signal according to embodiment 1 of the present invention;
fig. 2(a) is a constellation diagram of BPSK signal provided in embodiment 1 of the present invention;
fig. 2(b) is a constellation diagram of a QPSK signal according to embodiment 1 of the present invention;
fig. 2(c) is a constellation diagram of an 8PSK signal according to embodiment 1 of the present invention;
fig. 2(d) is a constellation diagram of a 16QAM signal according to embodiment 1 of the present invention;
fig. 2(e) is a constellation diagram of a 32QAM signal according to embodiment 1 of the present invention;
fig. 2(f) is a constellation diagram of a 64QAM signal according to embodiment 1 of the present invention;
FIG. 3 is a block diagram of a convolutional neural network modulation identification framework provided in embodiment 1 of the present invention;
fig. 4 is a graph of model loss in the training process of the convolutional neural network modulation recognition framework according to embodiment 1 of the present invention;
FIG. 5 is a graph of model accuracy for use in training a convolutional neural network modulation recognition framework according to embodiment 1 of the present invention;
fig. 6 is a confusion matrix of the prediction results of the convolutional neural network recognition framework provided in embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a signal modulation classification method based on a convolutional neural network, which is applied to the technical fields of wireless communication technology, communication countermeasure, electromagnetic spectrum management, and the like, and classifies 6 types of modulation signal types, such as BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, and the like, based on the convolutional neural network in the deep learning field; the method specifically comprises the following steps:
s1: extracting modulation characteristics from the received electromagnetic signal, the modulation characteristics including an amplitude characteristic and an initial phase characteristic;
s2: filtering the carrier frequency of the initial phase characteristic by adopting a carrier estimation algorithm to obtain a phase characteristic;
s3: carrying out constellation mapping on the amplitude characteristic and the phase characteristic to obtain a characteristic image;
s4: and classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
In step S1, setting parameters generated by the test signal by using a signal generator, and generating PSK and QAM signals with different modulation orders;
in the present embodiment, the signal generator employs SMW 200A; the modulation signals of different modulation orders comprise: BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, etc. 6 classes.
The antenna is used for receiving the electromagnetic signal to be modulated, the terminal adopts a signal IQ acquisition tool box to receive and sample the electromagnetic signal connected to the antenna of the PC terminal, and two paths of I/Q data of the sampled electromagnetic signal sample are stored in a file in a mat format.
In step S1, according to the modulation principle of PSK and QAM signals, the modulation characteristics related to the modulation type, i.e. amplitude characteristics a (n) and phase characteristics, are extracted from the electromagnetic signals in the received IQ samples
The method specifically comprises the following steps:
s101: the I/Q data expression of the received electromagnetic signal is:
where a (n) is the amplitude of the electromagnetic signal sample, f (n) is the instantaneous frequency,
is the initial phase; f. ofsN is 1,2,3, …, N, and the sampling time t is N/fs；
S102: the amplitude characteristics are:
s103: the phase characteristics are as follows:
s104: due to the range of the arctangent function [ -pi/2, pi/2]To do so
Is in the range of [ -pi, pi [ -pi [ ]]In the method, the positive and negative of I (n), Q (n) are determined
In quadrant iv, the specific solving formula for the phase characteristics is:
judging the positive and negative conditions of IQ sequence, and solving the equation
In step S2, since the extracted phase characteristics include carrier frequency components, the embodiment performs carrier estimation on the signal by using a carrier estimation algorithm, and filters the phase characteristics of the electromagnetic signal
The carrier frequency component in the baseband signal is used for reducing the influence of frequency deviation on modulation identification to obtain the phase information of the baseband signal
In this embodiment, the carrier estimation algorithm adopts a Rife algorithm, that is, a dual-spectrum method, and the step of implementing carrier frequency estimation according to the Rife algorithm includes:
s201: introducing an electromagnetic signal IQ sample into MATLAB, and counting the sampling rate f of the electromagnetic signal IQ samplesThe number N of FFT points is fast Fourier transformed, and Δ f is calculated as fs/N；
S202: performing N-point FFT (fast Fourier transform) on an IQ (in-phase Quadrature) sample of the electromagnetic signal to obtain a maximum value M of a frequency spectrum1And records its index value x0；
S203: according to x2＝x0Plus or minus 1 to obtain a frequency spectrum sub-maximum value M2And records its index value x2；
S204: calculating the absolute value | | of the relative deviation of the electromagnetic signal frequency and the corresponding frequency at the maximum FFT value as follows:
s205: comparison x0、x2The size of (d);
s206: according to
If x2>x0The formula is plus; if x2<x0The formula is to take the minus sign, and the frequency value x at the maximum position of the spectrum in FFT0Carrying out interpolation to obtain an accurate frequency estimation value;
s207: carrier frequency estimation by using Rife algorithm
Then, initial phase information of the baseband signal is calculated
The calculation formula is as follows:
in step S3, the amplitude characteristic a (n) and the obtained phase characteristic are subjected to constellation mapping, and then converted into a two-dimensional characteristic constellation, and the constellation calculation formula is:
where j is an imaginary unit.
As shown in fig. 2(a) -2 (f), which are constellation diagrams of six types of modulation signals, respectively, after mapping the modulation characteristic parameters of the signals into the constellation diagrams, a horizontal X axis of the constellation diagrams represents an in-phase component of the signals, and a vertical Y axis of the constellation diagrams represents an orthogonal component of the signals; the projection of a point on the X-axis is the maximum amplitude in the in-phase component, the projection of a point on the Y-axis is the maximum amplitude in the quadrature component, the length of the line connecting the point to the origin is the peak amplitude of the signal, and the angle between the line and the X-axis is the phase of the signal.
In the step S4: and constructing a modulation identification framework of the convolutional neural network model, classifying modulation types of a data set formed by the characteristic constellation diagram of the signal by using the constructed convolutional neural network model, and demodulating the electromagnetic signal according to the modulation types.
As shown in fig. 3, the convolutional neural network model constructed in the present embodiment includes 3 convolutional layers;
performing maximum pooling treatment of down-sampling after ReLu activation on the characteristic graph output by each layer of convolutional layer;
combining the features obtained by the previous convolutional layers through 3 fully-connected layers, and reducing the risk of overfitting by using a dropout layer;
and finally, the output is classified into modulation types through a softmax function.
The network model imitates a VGG network, and the performance is improved by deepening the network through repeatedly stacking 3 x 3 small convolution kernels and 2 x 2 maximum pooling layers, so that the generalization capability of the model is strong, and the model has good performance on different picture data sets.
Aiming at the training of the model, the embodiment adopts an advanced neural network application program interface Keras under a tensoflow framework, supports rapid experiments, and can rapidly convert ideas into results; constructing a CNN framework by using Keras, and when a model is compiled, adopting a category cross entropy function (category _ cross entropy) as a loss function, wherein the function is used for a multi-element classification problem; when the reverse propagation is calculated, the optimizer adopts the adaptive momentum estimation Adam, the algorithm is not easy to be trapped in local advantages, the speed is higher, and the learning effect is more effective;
in the training process, the loss change process of the training set is as shown in fig. 4, after the data of the training set and the verification set are iterated for 32 rounds, as shown in fig. 5, the accuracy of the training set is stabilized at more than 97%, and the accuracy of the verification set is stabilized at 97.6%.
In this embodiment, when the convolutional neural network CNN is used for signal modulation and classification, there is no need for any precise mathematical expression between the input signal and the output class, and as long as the convolutional network is trained by using a known pattern, the network has a mapping capability between input and output pairs, which automatically obtains the mapping capability between input and output in the learning process of training data; secondly, the convolution layer of the CNN automatically extracts features through convolution operation, so that explicit feature extraction is avoided, and learning is performed from training data implicitly; compared with other deep learning models, the CNN local connection and weight sharing characteristics reduce the complexity of the network and reduce a large number of training parameters.
In this embodiment, signal generator SMW200A is used to generate PSK and QAM signals of different modulation orders, including: BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, etc.; the verification depends on the parallel operation of a GPU of the computer, and computer software programs MATLAB and Python;
the picture data set used in the embodiment has 6480 pictures, and each of 1080 constellation diagrams of BPSK, QPSK, 8PSK, 16QAM, 32QAM, and 64 QAM; the size of the picture input into the neural network is 128 x 128 pixels; the IQ sample point used by each constellation diagram is 5000, and the size of each picture is 128 multiplied by 128 pixels; pictures of the training set, the verification set and the test set are as follows: 2: 2, and storing the data in a file with hdf5 format; the trained model is also stored in the h5 file, and in order to check the usability of the model, the test set is predicted, and the confusion matrix of the prediction result is shown in fig. 6, and the accuracy of the total is 97.38%.
Example 2
The embodiment provides a signal modulation type classification system based on a convolutional neural network, which comprises:
the characteristic extraction module is used for extracting modulation characteristics from the received electromagnetic signals, wherein the modulation characteristics comprise amplitude characteristics and first phase characteristics;
the carrier processing module is used for filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic;
the constellation map mapping module is used for carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image;
and the classification module is used for classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
It should be noted that the above modules correspond to steps S1 to S4 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.
Claims (10)
1. A signal modulation type classification method based on a convolutional neural network is characterized by comprising the following steps:
extracting modulation characteristics from a received electromagnetic signal, the modulation characteristics including an amplitude characteristic and a first phase characteristic;
filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic;
carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image;
and classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
2. The convolutional neural network-based signal modulation type classification method as claimed in claim 1, wherein the amplitude characteristic is:
the first phase characteristic is:
wherein f (n) is an instantaneous frequency,
is the initial phase; f. ofsFor the sampling frequency, the sampling time t is N/fs(ii) a I (n), Q (n) are the signal characteristics of the I path and the Q path of the electromagnetic signal respectively.
3. The convolutional neural network-based signal modulation type classification method as claimed in claim 1, wherein the carrier estimation algorithm is:
performing fast Fourier transform on the electromagnetic signal to obtain a maximum value M of a frequency spectrum1And its subscript index value x0；
According to x2＝x0Plus or minus 1 to obtain a frequency spectrum sub-maximum value M2And its subscript index value x2；
Frequency value x at spectral maximum in fast Fourier transform0Interpolation is performed according to x0、x2Calculating a frequency estimation value;
and subtracting the frequency spectrum estimation value according to the first phase characteristic to obtain a second phase characteristic.
4. The convolutional neural network-based signal modulation type classification method of claim 1, wherein the constellation map is mapped as:
wherein a (n) is an amplitude characteristic,
is the initial phase.
5. The signal modulation type classification method based on the convolutional neural network as claimed in claim 1, wherein the convolutional neural network model comprises 3 convolutional layers, and a feature map output by each convolutional layer is subjected to maximum pooling processing of downsampling after ReLu activation;
combining the characteristics obtained by the convolution layer by using 3 full-connection layers, and reducing overfitting by using a dropout layer;
the output layer performs the classification of the modulation type by means of a softmax function.
6. The signal modulation type classification method based on the convolutional neural network as claimed in claim 1, wherein in the training of the convolutional neural network model, the loss function adopts a class cross entropy function, and in the calculation of the backward propagation, an adaptive momentum estimation optimizer is adopted.
7. The method of claim 6, wherein training the convolutional neural network model to generate PSK and QAM signals of different modulation orders using a signal generator, comprises: BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64 QAM.
8. A convolutional neural network-based signal modulation type classification system, comprising:
the characteristic extraction module is used for extracting modulation characteristics from the received electromagnetic signals, wherein the modulation characteristics comprise amplitude characteristics and first phase characteristics;
the carrier processing module is used for filtering the carrier frequency of the first phase characteristic by adopting a carrier estimation algorithm to obtain a second phase characteristic;
the constellation map mapping module is used for carrying out constellation map mapping on the amplitude characteristic and the second phase characteristic to obtain a characteristic image;
and the classification module is used for classifying the characteristic images by adopting a pre-constructed convolutional neural network model to obtain modulation types to which the characteristic images belong, and demodulating the electromagnetic signals according to the modulation types.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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