CN118264514A - Communication modulation identification method, device and storage medium - Google Patents
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Abstract
The invention discloses a communication modulation identification method, a device and a storage medium, wherein the method comprises the steps of receiving a radio frequency signal, and performing down-conversion and matched filtering treatment on the radio frequency signal to obtain a baseband sequence consisting of complex envelope samples; calculating each moment of a signal in the baseband sequence based on the baseband sequence; according to each moment obtained by calculation, the high-order accumulation amount of the signal is obtained; constructing characteristic parameters according to the difference of the higher-order accumulation amounts among different signals or the difference of the combination of the higher-order accumulation amounts of different orders, and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters; for two groups of QAM signals distinguished from the primary identification result, a subtractive clustering algorithm is adopted for modulation identification, and a final identification result is obtained.
Description
Technical Field
The invention relates to a communication modulation identification method, a device and a storage medium, belonging to the technical field of wireless communication.
Background
Communication signal modulation mode identification is widely applied to the military and civil fields, is a precondition of signal analysis, and provides key information for subsequent correct demodulation of signals and identification of signal communication systems.
The traditional modulation mode identification is divided into a modulation mode identification technology based on likelihood ratios (likelihoodbased, LB) and a modulation mode identification technology based on characteristics (featurebased, FB), the identification accuracy of the modulation mode identification technology based on the likelihood ratios is higher, but prior information such as probability distribution of random variables in signals is needed, in addition, the calculation complexity of the algorithm is high, and the implementation difficulty is higher in practical application. The feature-based modulation mode recognition algorithm has low calculation complexity, and a reasonable classifier can be arranged by constructing proper feature parameters, so that a good classification effect can be obtained. In recent years, with the development of deep learning, it has also been used in the field of modulation recognition because of its strong performance. The modulation recognition by deep learning has the advantages of improved accuracy, strong self-adaptability, complex signal processing and the like. However, the disadvantages of large data demand, high training and reasoning complexity, poor interpretability, and dependence on labels are also to be overcome. And the high complexity of the deep learning algorithm makes it disadvantageous for engineering implementation.
MPSK (multi-system digital phase modulation) and MQAM (multi-system quadrature amplitude modulation) are common digital modulation modes, are widely applied to the wireless communication fields of microwave communication, satellite communication and the like, and have important significance in modulation identification research of MPSK and MQAM. Part of the prior art adopts a subtractive clustering algorithm to identify 4QAM, 16QAM, 32QAM and 64QAM, but the identification accuracy of the method is greatly influenced by signal to noise ratio, and the identified QAM (quadrature amplitude modulation) has fewer types. Part of the prior art utilizes a fourth-order cumulant and a density difference discrimination method to identify 16QAM, 32QAM, 64QAM, 128QAM and 256QAM signals, however, the method needs to cluster to obtain the density value of a constellation diagram and then compare the density value, when square QAM signals are distinguished, the signal amplitude compactness is calculated to identify the 16QAM in advance, the engineering implementation of the method has larger workload and the identification accuracy of high-order QAM is not high.
In summary, in the existing modulation signal identification method, the number of identifiable signal types is small, the identification accuracy is greatly affected by factors such as signal-to-noise ratio and signal order, and the method based on deep learning has high algorithm complexity and large data demand, which is not beneficial to engineering realization.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a communication modulation identification method, a device and a storage medium, which solve the problems of few signal types, low identification accuracy and high complexity of the existing algorithm identification.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
In a first aspect, the present invention provides a communication modulation identification method, including:
Receiving a radio frequency signal, and performing down-conversion and matched filtering processing on the radio frequency signal to obtain a baseband sequence consisting of complex envelope samples;
calculating each moment of a signal in the baseband sequence based on the baseband sequence;
according to each moment obtained by calculation, the high-order accumulation amount of the signal is obtained;
Constructing characteristic parameters according to the difference of the higher-order accumulation amounts among different signals or the difference of the combination of the higher-order accumulation amounts of different orders, and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
Further, the calculating, based on the baseband sequence, each moment of the signal in the baseband sequence includes:
（1）；
Wherein M pq is the moment of each order of the signal in the sequence, q is the number of sequences taking the conjugate, x (n) is the baseband sequence, n is the number of points of the signal sequence, E is the desired value, and p is the order of the signal mixing moment.
Further, the calculating the higher-order cumulative amount of the signal according to the calculated moments of each order includes:
the higher-order cumulative amount of the signal is obtained according to the relation between the cumulative amount and each moment, and the formula is as follows:
（5） ；
Wherein, M 40 is the fourth moment and is not conjugated, M 41 is the fourth moment and is one conjugated, M 42 is the fourth moment and is two conjugated, M 20 is the second moment and is not conjugated, M 21 is the second moment and is one conjugated, M 60 is the sixth moment and is not conjugated, M 61 is the sixth moment and is one conjugated, M 63 is the sixth moment and is three conjugated, C 40 is the fourth moment and is not conjugated and is accumulated, C 41 is the fourth moment and is one conjugated and is accumulated, C 42 is the fourth moment and is two conjugated and is accumulated, C 60 is the sixth moment and is not conjugated and is accumulated, C 61 is the sixth moment and is accumulated, and C 63 is the sixth moment and is accumulated.
Further, the constructing the characteristic parameter according to the difference of the higher-order accumulation amounts or the difference of the combination of the higher-order accumulation amounts between different signals, and performing primary identification on different types of modulation signals by using the characteristic parameter includes:
constructing a characteristic parameter F 1=|C42|/|C21|2 to primarily identify signals, and dividing the signals into two main categories, namely mPSK and mQAM;
Performing in-class identification on the mPSK signal, constructing a characteristic parameter F 2=|C61|2/|C42|3 by using C 61 and C 42, and identifying three PSK modulation types by using the characteristic parameter F 2;
For the mQAM signal, the mQAM signal is divided into two groups of signals of 16QAM, 64QAM, 256QAM and 32QAM and 128QAM according to the characteristic parameter F 3=|C40|/|C42.
Further, the performing modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by using a subtractive clustering algorithm to obtain a final recognition result includes:
Setting a reasonable cluster center field radius according to the distance between adjacent points in each order QAM standard constellation diagram;
Calculating and determining a point with the maximum density value from the obtained baseband sequence as a first clustering center within a set clustering radius range;
Updating the radius of the field based on the first clustering center, recalculating the density of all points, selecting the point with the largest density value as the second clustering center, repeating the process until the density value of the kth clustering center is smaller than or equal to 0.01 times of the density value of the first clustering center, recording the clustering centers obtained by each calculation, and counting the number of the obtained clustering centers;
And comparing the calculated clustering center points with the constellation points of the known QAM standards of each order, and judging the signal to be detected as a QAM signal of a corresponding type according to the comparison result.
In a second aspect, the present invention provides a communication modulation recognition apparatus comprising:
The processing module is used for receiving the radio frequency signal, performing down-conversion and matched filtering processing on the radio frequency signal, and obtaining a baseband sequence consisting of complex envelope samples;
the first calculation module is used for calculating each moment of a signal in the baseband sequence based on the baseband sequence;
the second calculation module is used for calculating the high-order accumulation amount of the signals according to the calculated moments of each order;
the primary identification module is used for constructing characteristic parameters according to the difference of the high-order accumulation amounts among different signals or the difference of the combination of the high-order accumulation amounts of different orders and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And the modulation recognition module is used for carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
In a third aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods described in the preceding claims.
In a fourth aspect, the present invention provides a computer apparatus/device/system comprising:
a memory for storing computer programs/instructions;
A processor for executing the computer program/instructions to implement the steps of the method of any one of the preceding claims.
In a fifth aspect, the invention provides a computer program product comprising computer programs/instructions which when executed by a processor implement the steps of the method of any of the preceding claims.
Compared with the prior art, the invention has the beneficial effects that:
Compared with the existing modulation signal identification method, the invention provides a modulation signal identification method for distinguishing signals according to the difference of the higher-order accumulation amount and the number of signal clustering points among different signals based on the higher-order accumulation amount and the subtraction clustering algorithm of the signals.
Drawings
Fig. 1 is a flowchart of a communication modulation recognition method according to an embodiment of the present invention;
Fig. 2 is a flowchart two of a communication modulation recognition method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram showing a characteristic parameter F 1 according to a signal-to-noise ratio according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a variation curve of characteristic parameter F 2 of mPSK according to signal-to-noise ratio provided by an embodiment of the present invention;
Fig. 5 is a schematic diagram of a variation curve of characteristic parameter F 3 of mQAM according to signal-to-noise ratio according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a confusion matrix of recognition results under a 10dB signal-to-noise ratio provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a confusion matrix of recognition results under a 15dB signal-to-noise ratio provided by an embodiment of the present invention;
FIG. 8 is a block diagram of a C++ algorithm module provided by an embodiment of the present invention;
Fig. 9 is a schematic diagram of a software interface according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment 1, as shown in fig. 1, this embodiment describes a communication modulation identification method, including:
Receiving a radio frequency signal, and performing down-conversion and matched filtering processing on the radio frequency signal to obtain a baseband sequence consisting of complex envelope samples;
calculating each moment of a signal in the baseband sequence based on the baseband sequence;
according to each moment obtained by calculation, the high-order accumulation amount of the signal is obtained;
Constructing characteristic parameters according to the difference of the higher-order accumulation amounts among different signals or the difference of the combination of the higher-order accumulation amounts of different orders, and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
The application process of the communication modulation identification method provided by the embodiment specifically relates to the following steps:
S1: the receiving end carries out down-conversion and matched filtering on the received radio frequency signals to obtain a baseband sequence, wherein the baseband sequence consists of samples of complex envelope curves;
s2: the moments of the signal are calculated according to the following formula:
（1）；
Wherein M pq is the moment of each order of the signal in the sequence, q is the number of the sequences taking the conjugate, x (n) is the base band sequence, n is the number of points of the signal sequence, E is the expected value, and p is the order of the signal mixing moment;
S3: after each moment is calculated, the cumulative amount of the signal is obtained according to the relation between the cumulative amount and each moment. For a complex-valued stationary random process with an average value of 0, the second moment is calculated in two different ways depending on the position of the conjugate:
（2）；
Wherein, C 20 is the second moment of the signal and does not take the conjugate accumulation, C 21 is the second moment of the signal and takes a conjugate accumulation, A digital modulation signal received for a signal receiver;
similarly, the fourth order cumulative amounts can be expressed in three ways. Thus, the fourth order cumulative amount can be defined as:
（3）；
Wherein, C 40 is the fourth moment of the signal and does not take the conjugate accumulation amount, C 41 is the fourth moment of the signal and takes one conjugate accumulation amount, C 42 is the fourth moment of the signal and takes two conjugate accumulation amounts;
for a random variable w, x, y, z fourth order cumulative amount of zero mean can be expressed as:
（4）；
the above fourth-order cumulative amount and sixth-order cumulative amount can be calculated by the following equation by the high-order moment:
（5）；
Wherein, M 40 is the fourth moment and is not conjugated, M 41 is the fourth moment and is one conjugated, M 42 is the fourth moment and is two conjugated, M 20 is the second moment and is not conjugated, M 21 is the second moment and is one conjugated, M 60 is the sixth moment and is not conjugated, M 61 is the sixth moment and is one conjugated, M 63 is the sixth moment and is three conjugated, C 40 is the fourth moment and is not conjugated and is accumulated, C 41 is the fourth moment and is one conjugated and is accumulated, C 42 is the fourth moment and is two conjugated and is accumulated, C 60 is the sixth moment and is not conjugated and is accumulated, C 61 is the sixth moment and is accumulated, and C 63 is the sixth moment and is accumulated.
S4: constructing characteristic parameters according to the difference of higher-order accumulation amounts among different signals or the difference of different higher-order accumulation amounts to primarily identify different types of modulation signals;
S41: since the value of C 42 is-2E 2,-E2,-E2 (corresponding to binary phase shift keying BPSK, quadrature phase shift keying QPSK and 8 phase shift keying 8 PSK) for the mPSK signal, respectively, and the value of C 42 is-0.68E 2,-0.69E2,-0.62E2,-0.65E2,-0.6E2 (corresponding to 16qam,32qam,64qam,128qam,256 qam) for the mpak signal, respectively. C 42 of the mPSK is an integral multiple of E 2, and C 42 of the mQAM is obviously smaller than E 2, so that the characteristic parameter F 1=|C42|/|C21|2 is constructed to preliminarily identify the signals, and the signals are divided into two major categories of the mPSK and the mQAM.
S42: then, the mPSK signals are subjected to intra-class identification, C 61 of BPSK, QPSK and 8PSK are different from each other, the difference is large, the characteristic parameter F 2=|C61|2/|C42|3 is constructed by using C 61 and C 42, and the characteristic parameter is utilizedThree PSK (phase shift keying) modulation types are identified.
S43: for the mQAM signal, the mQAM signal is divided into two groups of signals of 16QAM, 64QAM, 256QAM and 32QAM and 128QAM according to the characteristic parameter F 3=|C40|/|C42.
S5: performing modulation identification on the two groups of distinguished QAM signals by using a subtractive clustering algorithm;
S51: an important premise that the signals can obtain relatively reduced constellation points through a subtractive clustering algorithm is to set a reasonable cluster center field radius. Setting corresponding field radii according to the distance between adjacent points in each order QAM standard constellation diagram;
S52: calculating a first point with the maximum density value from the received signal by setting a proper cluster radius, wherein the cluster center point is the maximum density center point in each point cluster in the constellation diagram;
S53: after the first clustering center is calculated, the radius of the field is updated, the density of all points is calculated, then the point with the largest density value in the points is taken as the second clustering center, and the method is used for calculating no new clustering center until the density value of the kth clustering center is less than or equal to 0.01 times of the density value of the 1 st clustering center point. Recording the data points after each calculation to obtain the cluster centers, and finally counting the number of the obtained cluster centers;
S54: and finally judging the signal to be detected as a signal of a corresponding type according to the calculated clustering center point number and the constellation point number of each level QAM standard.
The steps of the invention will be further described with reference to fig. 2.
S1: the moment of each order and the cumulative amount of each order of the down-converted baseband signal are calculated. Referring to fig. 3, F 1 of the mPSK signal is obviously greater than 0.6 and F 1 of the mQAM signal is far less than 0.6 according to the calculation of the characteristic parameter F 1=|C42|/|C21|2, and simulation results show that the influence of the signal-to-noise ratio change on the signal characteristic parameter F 1 is small, so that the mPSK and the mQAM can be distinguished by using 0.6 as a decision threshold of F 1;
s2: signals within the mPSK class are identified. Referring to fig. 4, it can be seen that under the condition of signal-to-noise ratio variation, characteristic parameter F 2=|C61|2/|C42|3 of the mPSK signal is relatively stable, F 2 is equal to 23.65 and is taken as decision threshold of BPSK and QPSK, and F 2 is equal to 8.1 and is taken as decision threshold of QPSK and 8 PSK. Signals of three modulation types including BPSK, QPSK and 8PSK can be distinguished according to the two selected decision thresholds;
S3: and carrying out preliminary identification on signals in the mQAM class. Referring to fig. 5, the characteristic parameter F 3 of the mqam is stable as the signal-to-noise ratio varies. F 3 values of 16QAM, 64QAM and 256QAM are all near 1, F 3 values of 32QAM and 128QAM are below 0.3, and the difference between the two values is quite obvious, so that F 3 is equal to 0.5 as a decision threshold, and square QAM (16 QAM, 64QAM and 256 QAM) and cross QAM (32 QAM and 128 QAM) can be distinguished;
S4: and further identifying the mQAM intra-class signals by using a subtractive clustering algorithm.
S41: performing timing extraction on the estimated baud rate of the down-converted signal, wherein the maximum distance between two constellation points in the MQAM signal horizontal direction is A max, the distance between two adjacent constellation points of 16QAM is A max/3, the distance between two adjacent constellation points of 32QAM is A max/5, the distance between two adjacent constellation points of 64QAM is A max/7, the distance between two adjacent constellation points of 128QAM is A max/11, and the distance between two adjacent constellation points of 256QAM is A max/15;
S42: if the grouped signals are 16QAM,64QAM and 256QAM, the signal data and the domain radius are respectively calculated by taking A max/3 and A max/7 as the domain radius of the clustering algorithm. If the grouped signals are 32QAM and 128QAM, using A max/5 as the domain radius of the clustering algorithm, and inputting the signal data and the domain radius into the algorithm for calculation;
s43: the algorithm input signal has N data points { x 1,x2,…,xN }, each of which can serve as a potential cluster center, and the density function at data point x i is defined as:
（6）；
S44: x i is the point whose density value is to be calculated, x j is the point whose distance from x i is to be calculated. k 1 is the adjustment weight set to 1.r a is the radius of the domain of a cluster center, and the data points outside the radius have less influence on the density value of the data points in the domain. If a data point has multiple adjacent data points, then the data point has a higher density value;
S45: after the density values of all the points are calculated for the first time, the point with the largest density value among the points is selected as the first cluster center. To get the next cluster center point, the density values of all data points calculated last time need to be updated according to the following formula:
（7）；
S46: x c1 is the calculated first cluster center point, and D c1 is its density value. All the point densities are updated according to the above formula. r b is an updated radius of the field, and is generally 1.25-1.5 times r a, which defines a field that significantly attenuates the density value, so that the density value of a point near the center of the last cluster is significantly reduced. After updating the density values of all the points, the point with the largest density value among the points is selected as the second cluster center. Then, according to the density value of the second clustering center, carrying out third point density updating, and selecting the next clustering center;
S47: and repeatedly executing the flow until the density value of the cluster center updated by the kth time is less than or equal to 0.01 times of the density value of the first cluster center. Recording the data points after each calculation to obtain the cluster centers, and finally counting the number of the obtained cluster centers;
Under the condition of 15dB signal-to-noise ratio, each order QAM signal randomly generates 100 groups, clustering operation is carried out according to different field radiuses, then the average value is taken, and the obtained clustering radius results are shown in the following table.
TABLE 1 number of cluster points for each square QAM under different cluster radii
| Signal type | Amax/3 | Amax/7 | Amax/15 |
| 16QAM | 16 | 16 | 19.29 |
| 64QAM | 22.03 | 64 | 92.21 |
| 256QAM | 20.33 | 90.87 | 281.55 |
TABLE 2 Cross QAM Cluster Point count Table for different cluster radii
| Signal type | Amax/5 | Amax/11 |
| 32QAM | 32 | 32.75 |
| 128QAM | 46.44 | 128.88 |
S48: and finally subdividing the mQAM signals obtained after primary classification according to the number of constellation points obtained by clustering. If the signal is a square QAM signal and the number of the cluster points is within 16 and acceptable error ranges, judging the signal as 16QAM, if the number of the cluster points obtained by calculation is within 64 and acceptable error ranges, judging the signal as 64QAM, otherwise, judging the signal as 256QAM;
S49: if the signal is a cross QAM signal and the number of cluster points calculated by the signal data and the domain radius input algorithm is within 32 and acceptable error ranges, judging the signal as 32QAM, otherwise, judging the signal as 128QAM;
S5, simulation experiment: research on identification accuracy of digital modulation signals under different signal-to-noise ratio conditions
A total of BPSK, QPSK,8PSK,16QAM,32QAM,64QAM,128QAM,256QAM digital modulation signals, each of which is 100 samples, are generated in matlab2018b (data processing software), 4096 symbols are transmitted at a time, and gaussian white noise is added. The carrier frequency of the signal is 1MHz, the sampling frequency is 10MHz, and the code element rate is 250KHz.
Fig. 6 is a confusion matrix of recognition results under the condition of 10dB signal-to-noise ratio, wherein the recognition accuracy of 256QAM is 79%, and the recognition accuracy of the rest modulation signals is 100%, and the reason for this is that the number of clusters obtained by the high-order QAM after the subtraction clustering algorithm under the condition of lower signal-to-noise ratio can be found out to have a certain degree of error according to the constellation clustering number analysis of 256QAM in table 2. Fig. 7 is a confusion matrix of the recognition result under 15dB snr, where Output Class is the type of modulation signal generated by simulation, and TARGET CLASS is the type of signal modulation for decision. The recognition accuracy of 256QAM can be obviously improved to 97%, and the overall recognition accuracy of a given signal set is 99.6%.
S6: algorithm module engineering practice verification
S61: the algorithm proposed by the present invention is implemented using c++ programming in the Qt Creator 4.11.1 (compiler). The c++ algorithm module is shown in fig. 8, where text_data is a received modulated signal, data_i and data_q are iq two-way signals respectively, complex_data is a recombined complex signal, rs is a symbol rate, fs is a sampling rate, points are constellation points calculated by a subtractive clustering algorithm, and modulation_type is an identified modulated signal type.
S62: a user interface is designed for the algorithm module. The user interface is shown in fig. 9.
S63: MPSK and MQAM modulation signal models are built in the Ubuntu operating system by GNURadioCompanion (flow chart editing platform) and the antenna is transmitted through the transmitting end of Antsdr E310 (software radio module). BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, 128QAM, 256QAM signals are sequentially transmitted at a 10MHz sample rate and 1.25MHz symbol rate in GNURadioCompanion of the desktop and received.
S64: the signal is received by the Antsdr E's 310 receiver antenna, then down-converted in GNURadioCompanion, etc., and then the modulated signal at the receiver in GNURadioCompanion is saved as a. Bin file by filesink module.
S65: and converting the binary data of the received signal into double data through matlab (data processing software) and storing the double data in a. Txt file (the double-double data conversion module in the application development framework Qt can also be used for directly processing the. Bin file), then processing the received data file in the Qt, reading IQ two-path double data, sending the data into a modulation signal identification module, obtaining a modulation signal type result and drawing a dynamic spectrogram of the signal.
S66: the recognition accuracy of the statistical modulation signal is shown in the following table.
TABLE 3 identification accuracy of modulated signals
| Signal type | Identification accuracy rate |
| BPSK | 100% |
| QPSK | 100% |
| 8PSK | 100% |
| 16QAM | 98% |
| 32QAM | 99% |
| 64QAM | 97% |
| 128QAM | 95% |
| 256QAM | 78% |
The simulation experiment and the hardware actual measurement result show that the modulation identification method adopted by the invention can realize the identification of eight modulation signals of BPSK, QPSK, 8PSK, 16QAM, 32QAM, 64QAM, 128QAM and 256QAM, and other signals except 256QAM have better identification rate under lower signal-to-noise ratio. The average recognition accuracy for a given modulated signal is over 95% at a signal-to-noise ratio of 15dB and above.
Embodiment 2, this embodiment provides a communication modulation identification apparatus, including:
The processing module is used for receiving the radio frequency signal, performing down-conversion and matched filtering processing on the radio frequency signal, and obtaining a baseband sequence consisting of complex envelope samples;
the first calculation module is used for calculating each moment of a signal in the baseband sequence based on the baseband sequence;
the second calculation module is used for calculating the high-order accumulation amount of the signals according to the calculated moments of each order;
the primary identification module is used for constructing characteristic parameters according to the difference of the high-order accumulation amounts among different signals or the difference of the combination of the high-order accumulation amounts of different orders and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And the modulation recognition module is used for carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
Embodiment 3 provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of embodiment 1.
Embodiment 4, the present embodiment provides a computer apparatus/device/system, comprising:
a memory for storing computer programs/instructions;
a processor for executing the computer program/instructions to implement the steps of the method of any one of embodiment 1.
Embodiment 5 provides a computer program product comprising a computer program/instructions which, when executed by a processor, implement the steps of the method of any of embodiment 1.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
It will be appreciated by those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the foregoing embodiments are merely for illustrating the technical solution of the present disclosure and not for limiting the scope thereof, and although the present disclosure has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the invention after reading the present disclosure, and these changes, modifications or equivalents are within the scope of the claims appended hereto.
Claims (9)
1. A communication modulation recognition method, comprising:
Receiving a radio frequency signal, and performing down-conversion and matched filtering processing on the radio frequency signal to obtain a baseband sequence consisting of complex envelope samples;
calculating each moment of a signal in the baseband sequence based on the baseband sequence;
according to each moment obtained by calculation, the high-order accumulation amount of the signal is obtained;
Constructing characteristic parameters according to the difference of the higher-order accumulation amounts among different signals or the difference of the combination of the higher-order accumulation amounts of different orders, and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
2. The communication modulation identification method according to claim 1, wherein the calculating each moment of the signal in the baseband sequence based on the baseband sequence comprises:
（1）；
Wherein M pq is the moment of each order of the signal in the sequence, q is the number of sequences taking the conjugate, x (n) is the baseband sequence, n is the number of points of the signal sequence, E is the desired value, and p is the order of the signal mixing moment.
3. The communication modulation recognition method according to claim 1, wherein the calculating the higher-order cumulative amount of the signal from the calculated respective moments comprises:
the higher-order cumulative amount of the signal is obtained according to the relation between the cumulative amount and each moment, and the formula is as follows:
（5）；
Wherein, M 40 is the fourth moment and is not conjugated, M 41 is the fourth moment and is one conjugated, M 42 is the fourth moment and is two conjugated, M 20 is the second moment and is not conjugated, M 21 is the second moment and is one conjugated, M 60 is the sixth moment and is not conjugated, M 61 is the sixth moment and is one conjugated, M 63 is the sixth moment and is three conjugated, C 40 is the fourth moment and is not conjugated and is accumulated, C 41 is the fourth moment and is one conjugated and is accumulated, C 42 is the fourth moment and is two conjugated and is accumulated, C 60 is the sixth moment and is not conjugated and is accumulated, C 61 is the sixth moment and is accumulated, and C 63 is the sixth moment and is accumulated.
4. The communication modulation recognition method according to claim 3, wherein the constructing the characteristic parameter according to the difference of the higher order cumulants or the difference of the combination of the higher order cumulants between the different signals and performing the primary recognition of the different types of the modulated signals using the characteristic parameter comprises:
constructing a characteristic parameter F 1=|C42|/|C21|2 to primarily identify signals, and dividing the signals into two main categories, namely mPSK and mQAM;
Performing in-class identification on the mPSK signal, constructing a characteristic parameter F 2=|C61|2/|C42|3 by using C 61 and C 42, and identifying three PSK modulation types by using the characteristic parameter F 2;
For the mQAM signal, the mQAM signal is divided into two groups of signals of 16QAM, 64QAM, 256QAM and 32QAM and 128QAM according to the characteristic parameter F 3=|C40|/|C42.
5. The communication modulation and identification method according to claim 1, wherein the performing modulation and identification on the two sets of QAM signals distinguished from the primary identification result by using a subtractive clustering algorithm to obtain a final identification result includes:
Setting a reasonable cluster center field radius according to the distance between adjacent points in each order QAM standard constellation diagram;
Calculating and determining a point with the maximum density value from the obtained baseband sequence as a first clustering center within a set clustering radius range;
Updating the radius of the field based on the first clustering center, recalculating the density of all points, selecting the point with the largest density value as the second clustering center, repeating the process until the density value of the kth clustering center is smaller than or equal to 0.01 times of the density value of the first clustering center, recording the clustering centers obtained by each calculation, and counting the number of the obtained clustering centers;
And comparing the calculated clustering center points with the constellation points of the known QAM standards of each order, and judging the signal to be detected as a QAM signal of a corresponding type according to the comparison result.
6. A communication modulation recognition apparatus, comprising:
The processing module is used for receiving the radio frequency signal, performing down-conversion and matched filtering processing on the radio frequency signal, and obtaining a baseband sequence consisting of complex envelope samples;
the first calculation module is used for calculating each moment of a signal in the baseband sequence based on the baseband sequence;
the second calculation module is used for calculating the high-order accumulation amount of the signals according to the calculated moments of each order;
the primary identification module is used for constructing characteristic parameters according to the difference of the high-order accumulation amounts among different signals or the difference of the combination of the high-order accumulation amounts of different orders and carrying out primary identification on different types of modulation signals by utilizing the characteristic parameters;
And the modulation recognition module is used for carrying out modulation recognition on the two groups of QAM signals distinguished from the primary recognition result by adopting a subtractive clustering algorithm to obtain a final recognition result.
7. A computer-readable storage medium having stored thereon a computer program, characterized by: the program, when executed by a processor, implements the steps of the method of any of claims 1-5.
8. A computer device, comprising:
a memory for storing computer programs/instructions;
a processor for executing the computer program/instructions to implement the steps of the method of any one of claims 1-5.
9. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of the method of any of claims 1-5.
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