Communication signal modulation identification method and system based on Angora rabbit search mechanism

Abstract

The invention discloses a communication signal modulation identification method and system based on an Angora rabbit search mechanism, and relates to the technical field of communication signal processing. The method is technically characterized by comprising the following steps: constructing a digital modulation signal data set; preprocessing data in the digital modulation signal data set to obtain a training set and a test set; based on the training set and the test set, using an Angora rabbit search mechanism to optimize and solve hyper-parameters of the support vector machine, the hyper-parameters including penalty factors and Gaussian kernels; training a support vector machine with optimal hyper-parameters by using the training set; and performing modulation identification on a communication signal to be detected by using the trained support vector machine. The method solves the problem of overfitting of the support vector machine due to improper selection of hyper-parameters, has the advantages of high recognition accuracy, high convergence speed and the like in the field of communication signal modulation recognition, can obtain a good recognition effect under a low signal-to-noise ratio, has wider application prospects and scenes, and can be suitable for solving various engineering application problems.

Description

Technical Field

[0001] The present invention relates to the technical field of communication signal processing, and in particular to a communication signal modulation recognition method and system based on an Angora rabbit search mechanism. Background Art

[0002] Modulation recognition technology is a very critical technology in the field of wireless communications. Identifying the modulation mode of wireless communication signals is a basic technology in the fields of electronic countermeasures, electronic reconnaissance, non-cooperative communications, smart antennas, and wireless spectrum management. This technology has a wide range of applications and very important value in the military or civilian fields. In recent years, with the rapid development of wireless communication technology, electronic technology, and signal processing technology, the modulation mode of wireless communication signals has become more and more complex, and the types of modulation signals have increased. With the massive increase in military and civilian wireless communication equipment, the noise interference in the wireless communication channel has increased significantly, among which Gaussian noise is the most typical noise model in the communication channel.

[0003] Generally, the modulation recognition of communication signals adopts the pattern recognition method. First, feature extraction is performed from the signal received at the receiving end, the corresponding feature parameters are extracted, and then the modulation type of the signal is determined through the pattern recognition system. There are generally two types of classifiers for determining the modulation type of the signal: the classic pattern recognition classifier and the neural network classifier. Using classic classifiers such as K-nearest neighbor classifiers, support vector machines, decision trees and other classifiers to identify the modulation type of the signal is one of the mainstream research directions in the current communication signal modulation recognition problem.

[0004] By searching for relevant literature, Li Yang et al. proposed an automatic modulation recognition technology in the article "Research on Automatic Identification Algorithm of Digital Modulation Mode of Communication Signal" published in Journal of Electronics and Information (2005, No.02, pp.197–201). The algorithm has a simple structure, small amount of calculation, is suitable for real-time calculation, and can still achieve good recognition results in low signal-to-noise ratio environments. However, this method was proposed earlier, and compared with the algorithms proposed in recent years, the accuracy of modulation recognition still needs to be improved. Wang Haibin et al. proposed a method for identifying the modulation mode of communication signals based on the instantaneous characteristics of signals and combined with decision theory in the article "Modulation Mode Recognition Method Based on Instantaneous Characteristics of Digital Communication Signals" published in Modern Electronic Technology (2019, Vol.41, No.19, pp.22-25). This method has a high recognition accuracy and a fast recognition speed when the signal-to-noise ratio is low. However, this method uses the decision theory discriminant tree as a classifier, which not only requires the selection of appropriate thresholds for each feature parameter, but also requires the use of appropriate feature parameter discrimination order in the classification algorithm. When the environment changes, this method cannot obtain a high recognition rate. Chen Fatang et al. published "Communication Signal Modulation Recognition Based on Spatiotemporal Convolutional Network" in "Telecommunication Technology" (2024, pp. 1-7), which proposed a modulation recognition algorithm based on spatiotemporal convolutional network. Compared with the traditional neural network modulation signal recognition method, it has fewer parameters and higher recognition accuracy, but its recognition accuracy is significantly reduced under low signal-to-noise ratio conditions, the applicable signal-to-noise ratio range is small, and the parameter setting process is complicated and time-sensitive, making it difficult to solve complex engineering application problems.

[0005] Existing literature shows that the communication signal modulation recognition method using neural networks as classifiers often faces problems such as complex parameter selection, the influence of initial weights, thresholds, network structure and training functions on the network training speed, training results and convergence during the network training process, and low timeliness. The process of using classical classifiers for modulation signal recognition is easily affected by environmental factors. In addition, the recognition accuracy of both methods is significantly reduced in low signal-to-noise ratio environments. To achieve the goal of high-precision modulation recognition, it is necessary to invent a feasible and effective communication signal modulation recognition method. Summary of the invention

[0006] In view of the above problems, the present invention proposes a communication signal modulation identification method and system based on Angora rabbit search mechanism.

[0007] According to one aspect of the present invention, a communication signal modulation identification method based on an Angora rabbit search mechanism is proposed, the method comprising:

[0008] Step 1: Construct a digital modulation signal data set;

[0009] Step 2: preprocess the data in the digital modulation signal data set to obtain a training set and a test set;

[0010] Step 3: Based on the training set and the test set, the hyperparameters of the support vector machine are optimized and solved using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel;

[0011] Step 4: Use the training set to train a support vector machine with optimal hyperparameters;

[0012] Step 5: Use the trained support vector machine to perform modulation recognition on the communication signal to be tested.

[0013] Furthermore, the digital modulation signal data set in step 1 is constructed as follows:

[0014] A unipolar sequence with M code elements is input, and the digital sequence is mapped into a pulse waveform through a unipolar non-return-to-zero code to obtain a digital baseband signal;

[0015] Constructing a shaping filter, wherein the shaping filter adopts a raised cosine roll-off function to perform shaping processing on the digital baseband signal;

[0016] Modulating the digital baseband signal after shaping and filtering by using different modulation methods to obtain a modulated signal;

[0017] Gaussian noise with different signal-to-noise ratios is inserted into the modulated signal to obtain a digital modulation signal dataset of the communication signal under the Gaussian noise background.

[0018] Furthermore, the step 2 of preprocessing the data in the digital modulation signal data set to obtain the training set and the test set includes:

[0019] The digital modulation signal data set of the communication signal under the Gaussian noise background is normalized and segmented, the communication signal of each modulation mode is divided into multiple data segments of equal length, and each data segment is combined with the corresponding label to obtain a digital modulation signal preprocessing data set;

[0020] Performing Hilbert transform on the digital modulation signal preprocessing data set, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency of the digital signal, and then extracting instantaneous characteristic parameters to obtain a characteristic parameter data set; the instantaneous characteristic parameters include the mean of the instantaneous amplitude envelope, the fourth-order moment compactness of the normalized center instantaneous frequency, the variance of the normalized instantaneous frequency, the standard deviation of the absolute value of the nonlinear component of the instantaneous phase of the zero-center non-weak signal segment, and the absolute value standard deviation of the instantaneous phase;

[0021] The feature parameter data is normalized to the maximum and minimum values, and the normalized feature data and its corresponding category labels are randomly shuffled to obtain the final digital modulation signal data set;

[0022] The final digital modulation signal data set is divided into a training set and a test set. The feature data and its labels accounting for α1 are divided into the training set, and the remaining data is used as the test set.

[0023] Furthermore, the optimization of the hyperparameters of the support vector machine using the Angora rabbit search mechanism described in step 3 includes:

[0024] Step 31: Use the directed acyclic graph method to construct a support vector machine and set the objective function and constraints for hyperparameter optimization;

[0025] Step 32: Initialize the position of each rabbit in the Angora rabbit population, use the inverse of the classification accuracy of the support vector machine test set as the fitness function of the Angora rabbit search mechanism, calculate the fitness value, and obtain the initial optimal position of the Angora rabbit population; wherein the position of each Angora rabbit is a hyperparameter of the support vector machine;

[0026] Step 3. Energy contraction phase: Calculate the energy of each Angora rabbit and determine whether the rabbit will enter the detour foraging phase or the random hiding phase;

[0027] Step 3 and 4: Foraging phase: Angora rabbits obtain enough food by searching for grass in the territories of random individuals in the population;

[0028] Step 35: Random hiding stage: move closer to the rabbit with the best position to avoid being hunted by natural enemies;

[0029] Step 36: Introduce a random solution generation strategy to update the random position of Angora rabbit individuals;

[0030] Step 37, calculate the fitness value of each Angora rabbit after the position is updated, and update the position of the optimal solution;

[0031] Step 38: Determine whether the maximum number of iterations has been reached. If not, return to step 33 and continue iterating; otherwise, output the optimal position.

[0032] Furthermore, the objective function in step 31 is set as follows:

[0033]

[0034] In the formula, min means minimization; w ij is the weight vector of the hyperplane of the i-th and j-th categories; C is the penalty factor; ζ ij represents the slack variable of the i-th and j-th support vector machines; L represents the total number of categories; Indicates Slack variables for the i-th and j-th samples;

[0035] The constraints are set as follows:

[0036]

[0037] Where b ij is the bias of the hyperplanes of type i and type j; A mapping function that maps data samples to high dimensions; Indicates the first samples, ω i and ω j Represents the category labels of the i-th and j-th categories.

[0038] Furthermore, step 32 specifically includes: assuming that the number of rabbits in the Angora rabbit population is N, the maximum number of iterations is T, and the lower bound R = [R1, R2, ..., R D ], upper bound U=[U1,U2,...,U D ], D is the maximum dimension of the search; let the position of the nth rabbit at the tth iteration be Where t is the current iteration number, t∈{1,2,...,T}; is the position of the nth Angora rabbit in the dth dimension at the tth iteration, then the fitness function is expressed as Where χ is the recognition accuracy of the modulation signal; the d-dimensional position of the n-th rabbit in the first generation is initialized as (R d ,U d ), d=1,2,...,D,U d is the upper bound of the d-th dimension, R d is the lower bound of the d-th dimension;

[0039] After the population initialization is completed, the position of the first-generation Angora rabbit individuals is used as the hyperparameter of the support vector machine, and the training set is input to train the support vector machine model. After the training is completed, the test set is input into the support vector machine model to obtain the recognition accuracy, and then the initial fitness value of each Angora rabbit is obtained, and the position of the Angora rabbit individual with the best fitness value in the initial population is set as the initial optimal position.

[0040] Furthermore, step three specifically includes: the energy of the rabbit is defined by the following expression:

[0041]

[0042] In the formula, E t is the energy of the prey at the tth iteration, is a random number between (0,1) selected at the tth iteration, e is the base of the natural logarithm; when |E t When |≥1, the rabbit enters the detour foraging stage, otherwise the rabbit enters the random hiding stage;

[0043] Steps 3 and 4 specifically include: The position update formula of the nth rabbit individual is:

[0044]

[0045] In the formula, is the walking distance of the rabbit, where j=1,2,...,D, is the position of a random individual in the Angora rabbit population, where is a random integer between [1,N] and and is a random number between (0,1), is a random number that follows a standard normal distribution, β1 and β2 are fixed exploration factors, exp() represents an exponential function with e as the base, The symbol for rounding up.

[0046] Furthermore, step three and five specifically include: the position update formula of the nth Angora rabbit individual is:

[0047]

[0048] In the formula, represents the location of the cave built by the nth rabbit at the tth iteration, is a random number between (0,1);

[0049] Step 36 specifically includes: the random solution generation formula for the nth Angora rabbit is:

[0050]

[0051] In the formula, is a random number between (0, 1), is the location of the random solution of the nth Angora rabbit;

[0052] According to the fitness function Calculate the fitness value of the nth Angora rabbit at a random position, and calculate the fitness value of the nth Angora rabbit after completing the position update like but

[0053] Further, step 37 specifically includes: after completing the position update, the position of the nth Angora rabbit individual in the t+1th iteration is As the hyperparameter of the support vector machine model, the training set is input to train the support vector machine model. After the training is completed, the test set is input into the support vector machine model to obtain the recognition accuracy, and then the fitness value of each Angora rabbit individual in the t+1th iteration is obtained; the position of the individual with the best fitness value in the Angora rabbit population is set as the optimal position, and the optimal position of the Angora rabbit population in the t+1th iteration is recorded; if the fitness value of the optimal position of the Angora rabbit population in the t+1th iteration is better than the fitness value of the optimal position of the Angora rabbit population in the tth iteration, then the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the t+1th iteration; otherwise, the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the tth iteration.

[0054] According to another aspect of the present invention, a communication signal modulation identification system based on an Angora rabbit search mechanism is proposed, the system comprising:

[0055] a data acquisition module configured to construct a digital modulation signal data set;

[0056] A data preprocessing module, configured to preprocess the data in the digital modulation signal data set to obtain a training set and a test set;

[0057] A hyperparameter optimization solution module, which is configured to optimize and solve the hyperparameters of the support vector machine based on the training set and the test set using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel;

[0058] A model training module configured to train a support vector machine with optimal hyperparameters using a training set;

[0059] The modulation identification module is configured to use the trained support vector machine to perform modulation identification on the communication signal to be tested.

[0060] Compared with the prior art, the beneficial technical effects of the present invention are:

[0061] 1) The present invention designs an Angora rabbit search mechanism to select the hyperparameters of a support vector machine, which solves the problem that the hyperparameters of a traditional support vector machine are difficult to select, and avoids the overfitting problem of the support vector machine due to improper hyperparameter selection. The present invention has the advantages of high recognition accuracy and fast convergence speed in the field of communication signal modulation recognition, and can also obtain good recognition effects under low signal-to-noise ratios.

[0062] 2) Compared with the existing modulation recognition methods, the designed Angora rabbit mechanism evolved support vector machine has superior classification and generalization capabilities in complex modulation recognition environments. It can significantly improve the accuracy of communication signal modulation recognition under Gaussian noise under low signal-to-noise ratio conditions, and is suitable for both high and low signal-to-noise ratio environments, and has broader application prospects and scenarios.

[0063] 3) The Angora rabbit search mechanism for support vector machine hyperparameter selection proposed in the present invention has better global convergence and convergence speed than other traditional intelligent optimization search mechanisms. It can achieve higher convergence accuracy while converging quickly, and is not prone to falling into local optimal solutions and premature maturity. The evolutionary support vector machine designed for modulated signal recognition can obtain higher recognition accuracy under the same mixed signal-to-noise ratio, and can be applied to solving various types of engineering application problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The present invention can be better understood by referring to the description given below in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification, and are used to further illustrate the preferred embodiments of the present invention and explain the principles and advantages of the present invention.

[0065] Figure 1 It is a flow chart of a communication signal modulation identification method based on Angora rabbit search mechanism described in an embodiment of the present invention.

[0066] Figure 2 It is another flow chart of a communication signal modulation identification method based on Angora rabbit search mechanism described in an embodiment of the present invention.

[0067] Figure 3 It is a convergence curve diagram of the Angora rabbit search mechanism and its comparison mechanism in the embodiment of the present invention.

[0068] Figure 4 It is a bar chart of recognition accuracy of communication signal modulation recognition using the Angora rabbit search mechanism, comparison mechanism and support vector machine automatic hyperparameter optimization in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the scheme of the present invention, exemplary implementations or embodiments of the present invention will be described below in conjunction with the accompanying drawings. Obviously, the described implementations or embodiments are only implementations or embodiments of a part of the present invention, not all of them. Based on the implementations or embodiments of the present invention, all other implementations or embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present invention.

[0070] The purpose of the present invention is to address the problem that the existing communication signal modulation recognition method has low recognition accuracy in a Gaussian noise environment, and the optimal hyperparameters of a support vector machine used as a modulation recognition classifier are difficult to determine. A data set containing characteristic parameters is obtained by a feature extraction method, and then an Angora rabbit search mechanism is designed to select the hyperparameters of the support vector machine, obtain the optimal hyperparameters of the support vector machine, and use the support vector machine with the optimal hyperparameters as a classifier to efficiently identify the communication signal modulation mode under the Gaussian noise background. In the process of constructing the data set of the modulation signal characteristic parameters, the present invention has a large coverage of the signal-to-noise ratio, covers the conditions of low signal-to-noise ratio and high signal-to-noise ratio, and has a low signal-to-noise ratio interval, which can better simulate the communication channel conditions under different engineering application scenarios. In terms of the search mechanism, the present invention designs a new intelligent optimization search mechanism, the Angora rabbit search mechanism, which has both a faster convergence speed and a higher convergence accuracy, and is not easy to fall into a local optimal solution. Compared with some traditional communication signal modulation recognition methods, the communication signal modulation recognition method based on the Angora rabbit search mechanism designed by the present invention can obtain the optimal support vector machine hyperparameters in a Gaussian noise environment, thereby obtaining a higher recognition rate.

[0071] The embodiment of the present invention proposes a communication signal modulation identification method based on the Angora rabbit search mechanism, such as Figure 1-2 As shown, the method includes:

[0072] Step 1: Construct a digital modulation signal data set;

[0073] Step 2: preprocess the data in the digital modulation signal data set to obtain a training set and a test set;

[0074] Step 3: Based on the training set and the test set, the hyperparameters of the support vector machine are optimized and solved using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel;

[0075] Step 4: Use the training set to train a support vector machine with optimal hyperparameters;

[0076] Step 5: Use the trained support vector machine to perform modulation recognition on the communication signal to be tested.

[0077] The method starts with step 1. In step 1, a digital modulation signal data set is constructed.

[0078] According to an embodiment of the present invention, a unipolar sequence with M code elements is input, and the digital sequence is mapped into a pulse waveform through a unipolar non-return-to-zero code to obtain a digital baseband signal. A shaping filter is constructed, and the shaping filter uses a raised cosine roll-off function to shape the digital baseband signal, and the expression is: in is the sampling time, δ is the roll-off coefficient, The digital baseband signal after shaping filtering is modulated using different modulation methods to obtain a modulated signal, and Gaussian noise with different signal-to-noise ratios is inserted into the modulated signal to obtain a digital modulation signal data set of the communication signal under the Gaussian noise background.

[0079] Then, step 2 is executed. In step 2, the data in the digital modulation signal data set is preprocessed to obtain a training set and a test set.

[0080] According to an embodiment of the present invention, the digital modulation signal data set of the communication signal under the background of Gaussian noise is normalized to the maximum and minimum values ​​and segmented, and the communication signal of each modulation mode is divided into multiple data segments of equal length, and each data segment is formed into a set with the corresponding label to obtain a digital modulation signal preprocessing data set. The instantaneous feature parameters of the digital modulation signal preprocessing data set are extracted to obtain a feature parameter data set. First, the digital modulation signal preprocessing data set is Hilbert transformed to extract the instantaneous amplitude A(i), instantaneous phase φ(i) and instantaneous frequency f(i) of the digital signal, where i is the data segment currently used to extract features. On the basis of obtaining the instantaneous information of the signal under the Gaussian noise environment, multiple feature statistics of the instantaneous information of the digital modulation signal can be further extracted to obtain five feature parameters that can effectively resist Gaussian noise and improve the recognition rate when identifying the modulation signal.

[0081] Characteristic parameter 1: mean value m of instantaneous amplitude envelope a , Where A(i) is the instantaneous amplitude of the communication signal, is the number of sampling points.

[0082] Characteristic parameter 2: Compactness of the fourth-order moment of the normalized central instantaneous frequency Where E represents the mathematical expectation, f cn (i) is the normalized center instantaneous frequency,

[0083] Characteristic parameter 3: variance of normalized instantaneous frequency

[0084] Characteristic parameter 4: Standard deviation σ of the absolute value of the nonlinear component of the instantaneous phase of the zero-centered non-weak signal segment ap , Among them, C is The number of non-weak signal values ​​in the sampled data. Non-weak signal means that the signal amplitude is greater than the amplitude judgment threshold level a t The signal A n (i) = A(i) / m a ,φ NL (i) is the nonlinear component of the instantaneous phase after zero-centering. When the carrier is completely synchronized, φNL (i) = φ(i) - φ0, where

[0085] Characteristic parameter 5: Absolute value standard deviation of instantaneous phase σ dp , in

[0086] By extracting the characteristic parameters, a data set containing five characteristic parameters can be obtained. The characteristic data in the data set is normalized to the maximum and minimum values, and the characteristic data and the corresponding category labels in the normalized data set are randomly shuffled to obtain a digital modulation signal data set of the communication signal under the Gaussian noise background. After the shuffling is completed, the training set and test set of the digital modulation signal data set are divided. The characteristic data and its labels accounting for α1 are divided into the training set, and the remaining data is used as the test set. The training set and the test set are respectively recorded as I1′ and I2′.

[0087] Then, step three is performed. In step three, based on the training set and the test set, the hyperparameters of the support vector machine are optimized and solved using the Angora rabbit search mechanism. The hyperparameters include a penalty factor and a Gaussian kernel. Specifically, the following are included:

[0088] Step 31: Use the directed acyclic graph method to construct a support vector machine and set the objective function and constraints for hyperparameter optimization;

[0089] Step 32: Initialize the position of each rabbit in the Angora rabbit population, use the inverse of the classification accuracy of the support vector machine test set as the fitness function of the Angora rabbit search mechanism, calculate the fitness value, and obtain the initial optimal position of the Angora rabbit population; wherein the position of each Angora rabbit is a hyperparameter of the support vector machine;

[0090] Step 3. Energy contraction phase: Calculate the energy of each Angora rabbit and determine whether the rabbit will enter the detour foraging phase or the random hiding phase;

[0091] Step 3 and 4: Foraging phase: Angora rabbits obtain enough food by searching for grass in the territories of random individuals in the population;

[0092] Step 35, random hiding stage: move closer to the rabbit with the best position to avoid being hunted by natural enemies;

[0093] Step 36: Introduce a random solution generation strategy to update the random position of Angora rabbit individuals;

[0094] Step 37, calculate the fitness value of each Angora rabbit after the position is updated, and update the position of the optimal solution;

[0095] Step 38: Determine whether the maximum number of iterations has been reached. If not, return to step 33 and continue iterating; otherwise, output the optimal position.

[0096] According to an embodiment of the present invention, first, a model for training, testing and classifying a data set using a support vector machine is constructed. Since the data set formed after feature extraction of the modulated signal is a multi-class data set, a directed acyclic graph method is used to construct a support vector machine model that can be used for multi-classification to classify the data set input to the support vector machine. Assuming that the number of features contained in the data set input to the support vector machine is k, then construct A nonlinear separable support vector machine transforms a k-class problem into A second-class problem is solved to accurately classify the data set containing k categories.

[0097] In the classification process, the support vector machine is first trained. The training set I1′ and the two hyperparameters of the support vector machine, namely the penalty factor and the Gaussian kernel parameter, are input into the support vector machine for training, where the hyperparameter of the support vector machine is the position of each Angora rabbit. The training process is equivalent to solving a quadratic programming problem, and a constrained optimization equation is constructed by introducing Lagrange multipliers. The constrained optimization equations for the i-th and j-th categories are The constraints are and in is the Lagrange multiplier of the i-th and j-th categories, C is the penalty factor, is the kernel function of the support vector machine, and K represents the kernel function symbol. Due to the lack of prior knowledge, the kernel function is selected to be a Gaussian kernel function that can be mapped to infinite dimensions, and its expression is in is the Euclidean distance between two data samples, exp() is an exponential function with the natural constant e as the base, and γ is the Gaussian kernel parameter. The training process is solved by The optimal solution To construct the optimal classification hyperplane for the i-th and j-th categories, the solution process uses the sequence minimum optimization method to search for records And the slack variables ζ of the i-th and j-th support vector machines are ij Perform grid optimization in the range [r1, r2] and use c-fold cross validation to perform cross validation. Calculate the optimal weight vectors for the i-th and j-th categories and optimal bias Then construct the decision function of the i-th and j-th categories where sgn() is a symbolic function that satisfies, for any variable u,

[0098] By permuting and combining k categories, we get There are several combinations. Each combination is trained according to the above process to obtain the support vector machine. The decision functions that adapt to different data category combinations are formed to complete the model construction of the directed acyclic graph support vector machine.

[0099] After the training is completed, the trained model is saved and the test set I2′ is input into the support vector machine for testing. Use discrete discrimination rules to determine the category to which the data sample belongs. If it satisfies If the class is the same as the class, it is the i-th class, otherwise it is the j-th class. After the test set classification is completed, the class label obtained by the support vector machine through the test is compared with the correct class label. If the two are the same, the classification is correct, otherwise the classification is wrong. The classification accuracy is the percentage of correctly classified data samples to the total number of test set data samples.

[0100] For a data set with k data categories, the category to which the data sample belongs is ω=[ω1,ω2,...,ω k ], the objective function of the constructed support vector machine is:

[0101]

[0102] In the formula, min means minimization; w ij is the weight vector of the hyperplane of the i-th and j-th categories; C is the penalty factor; ζ ij represents the slack variable of the i-th and j-th support vector machines; L represents the total number of categories; Indicates Slack variables for the i-th and j-th samples;

[0103] And satisfy the following constraints:

[0104]

[0105] where w ij is the weight vector of the hyperplane of type i and type j, b ij is the bias of the hyperplanes of type i and type j, A mapping function that maps data samples to high dimensions; Indicates the first samples, ω i and ω j Represents the category labels of the i-th and j-th categories. The optimal classification hyperplane is found through the objective function to achieve accurate classification of complex data sets.

[0106] Then, the position of each rabbit in the Angora rabbit population is initialized, the inverse of the classification accuracy of the support vector machine test set is used as the fitness function of the Angora rabbit search mechanism, the fitness value is calculated, and the initial optimal position of the Angora rabbit population is obtained.

[0107] Specifically, let the number of rabbits in the Angora rabbit population be N, the maximum number of iterations be T, and the lower bound R = [R1, R2, ..., R D ], upper bound U=[U1,U2,...,U D ], D is the maximum dimension of the search. Let the position of the nth rabbit at the tth iteration be Where t is the current iteration number, t∈{1,2,...,T}. Since the recognition accuracy is used as the measure of the modulation signal recognition accuracy, the inverse of the recognition accuracy is selected as the fitness function of the Angora rabbit search mechanism. is the position of the nth Angora rabbit in the dth dimension at the tth iteration, then the fitness function can be expressed as Where χ is the recognition accuracy of the modulation signal.

[0108] Initialize the d-dimensional position of the n-th rabbit in the first generation to (R d ,U d ), d=1,2,...,D,U d is the upper bound of the d-th dimension, R d is the lower bound of the dth dimension. After the population is initialized, the position of the first generation Angora rabbit individual is used as the hyperparameter of the support vector machine model described in step 2, and the training set is input to train the support vector machine model. After the training is completed, the test set is input into the support vector machine model to obtain the recognition accuracy, and then the initial fitness value of each Angora rabbit is obtained. The smaller the fitness value, the better the position of the individual. The position of the Angora rabbit individual with the best fitness value in the initial population is set as the initial optimal position.

[0109] Then, in the energy contraction phase, the energy of each angora rabbit is calculated to determine whether the rabbit will enter the detour foraging phase or the random hiding phase.

[0110] Specifically, in order to survive, Angora rabbits will have two activities: foraging and avoiding natural enemies. These activities are described by two stages: detour foraging and random hiding. The stage the rabbit is in is determined by the individual's energy. The rabbit's energy can be defined by the following expression: Where E t is the energy of the prey at the tth iteration, is a random number between (0,1) selected at the tth iteration, and e is the base of the natural logarithm. t When |≥1, the rabbit enters the detour foraging stage, otherwise the rabbit enters the random hiding stage.

[0111] Then, in the detour foraging stage, the Angora rabbit population conducts an exploration phase of detour foraging, in which the Angora rabbits obtain sufficient food by searching for grass in the territories of random individuals in the population.

[0112] Specifically, in the detour foraging stage, the rabbit searches for the food it needs in the distance and chooses to detour foraging in the territory of a random individual in the population. The position update formula of the nth rabbit individual is: in is the walking distance of the rabbit, where j=1,2,...,D, is the position of a random individual in the Angora rabbit population, where is a random integer between [1,N] and and is a random number between (0,1), is a random number that follows a standard normal distribution, β1 and β2 are fixed exploration factors, exp() represents an exponential function with e as the base, The symbol for rounding up.

[0113] Then, in the random hiding stage, the Angora rabbit population conducts a random hiding development phase, moving closer to the rabbits with the best position to avoid being hunted by natural enemies.

[0114] Specifically, in the random hiding stage, each rabbit will build a cave within its own territory to avoid being preyed upon by natural enemies. The position update formula of the nth Angora rabbit individual is in represents the location of the cave built by the nth rabbit at the tth iteration, and is a random number between (0,1).

[0115] Then, a random solution generation strategy is introduced to update the random positions of Angora rabbit individuals to increase the diversity of solutions within the population.

[0116] Specifically, after all Angora rabbits in the population have completed their position updates, a random solution generation strategy is introduced to simultaneously update the position of each individual in the population to increase the diversity of candidate solutions within the population. The random solution generation formula for the nth Angora rabbit is: in is a random number between (0, 1), is the location of the random solution of the nth Angora rabbit. According to the fitness function Calculate the fitness value of the nth Angora rabbit at a random position, and calculate the fitness value of the nth Angora rabbit after completing the position update The fitness value is used to evaluate whether to use a random position to replace the updated position. but

[0117] Then, the fitness value of each Angora rabbit after the position is updated is calculated, and the position of the optimal solution is updated.

[0118] Specifically, after completing the position update, the position of the nth Angora rabbit individual in the t+1th iteration is Set as the hyperparameter of the support vector machine model, input the training set to train the support vector machine model, and after the training is completed, input the test set into the support vector machine model to obtain the recognition accuracy, and then obtain the fitness value of each Angora rabbit individual in the t+1th iteration. Set the position of the individual with the best fitness value in the Angora rabbit population as the optimal position, and record the optimal position of the Angora rabbit population in the t+1th iteration. If the fitness value of the optimal position of the Angora rabbit population in the t+1th iteration is better than the fitness value of the optimal position of the Angora rabbit population in the tth iteration, then the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the t+1th iteration; otherwise, the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the tth iteration.

[0119] Then, determine whether the maximum number of iterations T is reached. If not, set t=t+1 and return to step 33 to continue iterating; otherwise, output the optimal position.

[0120] Then, step 4 is performed, in which a support vector machine with optimal hyperparameters is trained using the training set.

[0121] According to an embodiment of the present invention, the optimal position of the Angora rabbit population in the last generation is used as a hyperparameter of a support vector machine model, and a training set is input to train the support vector machine model. After the training is completed, a test set is input into the support vector machine model to obtain a final recognition accuracy rate, and the result of modulation recognition is output.

[0122] Finally, step five is executed. In step five, the trained support vector machine is used to perform modulation recognition on the communication signal to be tested.

[0123] The technical effect of the present invention is further verified through experiments.

[0124] The Angora rabbit search mechanism proposed in the present invention is abbreviated as ARBO, and the search mechanisms used for comparison are the crow search mechanism and the energy valley search mechanism, which are abbreviated as CSA and EVO, respectively. Specific parameters of some models in the simulation experiment are set as follows.

[0125] The digital modulation signal types used are 2ASK, 4ASK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, 2FSK and 4FSK. The method used in the present invention is not limited to these modulation modes. The parameters of the digital modulation signal are set as follows: Carrier frequency The carrier frequencies of 2FSK are set to and For 4FSK, in addition to the two carrier frequencies that are the same as 2FSK, there are and Sampling rate Symbol rate The sampling time T=20s, the number of sampling points for each symbol is 25; the roll-off coefficient of the shaping filter is δ=0.4; the signal-to-noise ratio range of the Gaussian noise added is between -30dB and 20dB, and it is added every 0.5dB. The characteristic parameter data set is divided into 30% as a training set and 70% as a test set, and the method of the present invention is not limited to this division method.

[0126] In order to comprehensively compare the performance of the three methods, the three methods are initialized identically, and the parameters of the Angora rabbit search mechanism are set to N = 20, T = 40, U d =100, R d =0.01, α1=0.3. For the relevant parameters of CSA, see “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm” published by Alireza Askarzadeh et al. in Computers and Structures (2016, Vol. 106, pp. 1-12); for the relevant parameters of EVO, see “Energy valley optimizer: a novel metaheuristic algorithm for global and engineering” published by Mahdi Azizi et al. in Scientific Reports (2023, Vol. 13, No. 01). The population size, maximum number of iterations, upper bound and lower bound of the two comparison mechanisms are the same as those of the Angora rabbit search mechanism, which are N=20, T=40, U d =100, R d =0.01. Each method was run independently for 30 times, and the average fitness of the 30 runs was taken to draw the fitness curve. The average modulation recognition accuracy of the 30 independent runs was taken as the final communication signal modulation recognition accuracy.

[0127] The fitness curve simulation results of the Angora rabbit search mechanism and its comparison mechanism under the same initial value conditions are as follows Figure 3 The average of 30 independent runs is shown in Figure 2. The average recognition accuracy obtained by 30 independent runs of the Angora rabbit search mechanism and its comparison mechanism for hyperparameter optimization and support vector machine automatic hyperparameter optimization is shown in Figure 2. Figure 4 shown.

[0128] Depend on Figure 3 It can be seen that under the same initial value conditions, compared with EVO and CSA, ARBO has the advantages of fast convergence speed and high convergence accuracy, which fully demonstrates that ARBO has better convergence.

[0129] Depend on Figure 4 It can be seen that under the same communication signal digital modulation signal data set under the background of Gaussian noise, the average accuracy of communication signal modulation recognition obtained by the method of optimizing the hyperparameters of the support vector machine using the intelligent optimization search mechanism is significantly better than the average recognition accuracy obtained by the automatic hyperparameter optimization of the support vector machine, and the average modulation recognition accuracy obtained by optimizing the hyperparameters of the support vector machine using the Angora rabbit search mechanism is the best among the four methods. Therefore, it can be concluded that the communication signal modulation recognition method combining the Angora rabbit search mechanism with the support vector machine is a reasonable and accurate communication signal modulation recognition method. This method can obtain high-accuracy recognition results for multiple types of digital signals in the field of communication signal modulation recognition under Gaussian noise. Since the Gaussian noise added when constructing the digital modulation signal data set of the communication signal under the background of Gaussian noise covers both high signal-to-noise ratio and low signal-to-noise ratio, this modulation recognition method is suitable for Gaussian noise environments with different signal-to-noise ratios, and has broad application prospects and strong robustness.

[0130] Another embodiment of the present invention provides a communication signal modulation identification system based on an Angora rabbit search mechanism, the system comprising:

[0131] a data acquisition module configured to construct a digital modulation signal data set;

[0132] A data preprocessing module, configured to preprocess the data in the digital modulation signal data set to obtain a training set and a test set;

[0133] A hyperparameter optimization solution module, which is configured to optimize and solve the hyperparameters of the support vector machine based on the training set and the test set using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel;

[0134] A model training module configured to train a support vector machine with optimal hyperparameters using a training set;

[0135] The modulation identification module is configured to use the trained support vector machine to perform modulation identification on the communication signal to be tested.

[0136] The function of a communication signal modulation identification system based on an Angora rabbit search mechanism described in an embodiment of the present invention can be described by the aforementioned communication signal modulation identification method based on an Angora rabbit search mechanism. Therefore, for the parts not described in detail in the system embodiment, please refer to the above method embodiment and will not be repeated here.

[0137] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments are contemplated within the scope of the invention thus described. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A communication signal modulation identification method based on Angora rabbit search mechanism, characterized in that: include: Step 1: Construct a digital modulation signal data set; Step 2: preprocess the data in the digital modulation signal data set to obtain a training set and a test set; Step 3: Based on the training set and the test set, the hyperparameters of the support vector machine are optimized and solved using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel; Step 4: Use the training set to train a support vector machine with optimal hyperparameters; Step 5: Use the trained support vector machine to perform modulation recognition on the communication signal to be tested.

2. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 1, characterized in that: The digital modulation signal data set described in step 1 is constructed as follows: A unipolar sequence with M code elements is input, and the digital sequence is mapped into a pulse waveform through a unipolar non-return-to-zero code to obtain a digital baseband signal; Constructing a shaping filter, wherein the shaping filter adopts a raised cosine roll-off function to perform shaping processing on the digital baseband signal; Modulating the digital baseband signal after shaping and filtering by using different modulation methods to obtain a modulated signal; Gaussian noise with different signal-to-noise ratios is inserted into the modulated signal to obtain a digital modulation signal dataset of the communication signal under the Gaussian noise background.

3. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 2, characterized in that: The step 2 of preprocessing the data in the digital modulation signal data set to obtain the training set and the test set includes: The digital modulation signal data set of the communication signal under the Gaussian noise background is normalized and segmented, the communication signal of each modulation mode is divided into multiple data segments of equal length, and each data segment is combined with the corresponding label to obtain a digital modulation signal preprocessing data set; Performing Hilbert transform on the digital modulation signal preprocessing data set, extracting the instantaneous amplitude, instantaneous phase and instantaneous frequency of the digital signal, and then extracting instantaneous characteristic parameters to obtain a characteristic parameter data set; the instantaneous characteristic parameters include the mean of the instantaneous amplitude envelope, the fourth-order moment compactness of the normalized center instantaneous frequency, the variance of the normalized instantaneous frequency, the standard deviation of the absolute value of the nonlinear component of the instantaneous phase of the zero-center non-weak signal segment, and the absolute value standard deviation of the instantaneous phase; The feature parameter data is normalized to the maximum and minimum values, and the normalized feature data and its corresponding category labels are randomly shuffled to obtain the final digital modulation signal data set; The final digital modulation signal data set is divided into a training set and a test set. The feature data and its labels accounting for α1 are divided into the training set, and the remaining data is used as the test set.

4. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 3, characterized in that: The optimization of the support vector machine hyperparameters using the Angora rabbit search mechanism described in step 3 includes: Step 31: Use the directed acyclic graph method to construct a support vector machine and set the objective function and constraints for hyperparameter optimization; Step 32: Initialize the position of each rabbit in the Angora rabbit population, use the inverse of the classification accuracy of the support vector machine test set as the fitness function of the Angora rabbit search mechanism, calculate the fitness value, and obtain the initial optimal position of the Angora rabbit population; wherein the position of each Angora rabbit is a hyperparameter of the support vector machine; Step 3. Energy contraction phase: Calculate the energy of each Angora rabbit and determine whether the rabbit will enter the detour foraging phase or the random hiding phase; Step 3 and 4: Foraging phase: Angora rabbits obtain enough food by searching for grass in the territories of random individuals in the population; Step 35, random hiding stage: move closer to the rabbit with the best position to avoid being hunted by natural enemies; Step 36: Introduce a random solution generation strategy to update the random position of Angora rabbit individuals; Step 37, calculate the fitness value of each Angora rabbit after the position is updated, and update the position of the optimal solution; Step 38: Determine whether the maximum number of iterations has been reached. If not, return to step 33 and continue iterating; otherwise, output the optimal position.

5. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 4, characterized in that: The objective function described in step 3.1 is set as follows: In the formula, min means minimization; w ij is the weight vector of the hyperplane of the i-th and j-th categories; C is the penalty factor; ζ ij represents the slack variable of the i-th and j-th support vector machines; L represents the total number of categories; Indicates Slack variables for the i-th and j-th samples; The constraints are set as follows: Where b ij is the bias of the hyperplanes of type i and type j; A mapping function that maps data samples to high dimensions; Indicates the first samples, ω i and ω j Represents the category labels of the i-th and j-th categories.

6. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 5, characterized in that: Step 32 specifically includes: assuming that the number of rabbits in the Angora rabbit population is N, the maximum number of iterations is T, and the lower bound R = [R1, R2, ..., R D ], upper bound U=[U1,U2,...,U D ], D is the maximum dimension of the search; let the position of the nth rabbit at the tth iteration be Where t is the current iteration number, t∈{1,2,...,T}; is the position of the nth Angora rabbit in the dth dimension at the tth iteration, then the fitness function is expressed as Where χ is the recognition accuracy of the modulation signal; the d-dimensional position of the n-th rabbit in the first generation is initialized as (R d ,U d ), d=1,2,...,D,U d is the upper bound of the d-th dimension, R d is the lower bound of the d-th dimension; After the population initialization is completed, the position of the first-generation Angora rabbit individuals is used as the hyperparameter of the support vector machine, and the training set is input to train the support vector machine model. After the training is completed, the test set is input into the support vector machine model to obtain the recognition accuracy, and then the initial fitness value of each Angora rabbit is obtained, and the position of the Angora rabbit individual with the best fitness value in the initial population is set as the initial optimal position.

7. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 6, characterized in that: Step 33 specifically includes: The energy of the rabbit is defined by the following expression: In the formula, E t is the energy of the prey at the tth iteration, is a random number between (0,1) selected at the tth iteration, e is the base of the natural logarithm; when |E t When |≥1, the rabbit enters the detour foraging stage, otherwise the rabbit enters the random hiding stage; Steps 3 and 4 specifically include: The position update formula of the nth rabbit individual is: In the formula, is the walking distance of the rabbit, where j=1,2,...,D, is the position of a random individual in the Angora rabbit population, where is a random integer between [1,N] and and is a random number between (0,1), is a random number that follows a standard normal distribution, β1 and β2 are fixed exploration factors, exp() represents an exponential function with e as the base, The round-up symbol.

8. The communication signal modulation identification method based on Angora rabbit search mechanism according to claim 7 is characterized in that: Steps 3 and 5 specifically include: The position update formula of the nth Angora rabbit individual is: In the formula, represents the location of the cave built by the nth rabbit at the tth iteration, is a random number between (0,1); Step 36 specifically includes: the random solution generation formula for the nth Angora rabbit is: In the formula, is a random number between (0,1), is the location of the random solution of the nth Angora rabbit; According to the fitness function Calculate the fitness value of the nth Angora rabbit at a random position, and calculate the fitness value of the nth Angora rabbit after completing the position update like but 9. A communication signal modulation identification method based on Angora rabbit search mechanism according to claim 8, characterized in that: Step 37 specifically includes: after completing the position update, the position of the nth Angora rabbit individual in the t+1th iteration is As the hyperparameter of the support vector machine model, the training set is input to train the support vector machine model. After the training is completed, the test set is input into the support vector machine model to obtain the recognition accuracy, and then the fitness value of each Angora rabbit individual in the t+1th iteration is obtained; the position of the individual with the best fitness value in the Angora rabbit population is set as the optimal position, and the optimal position of the Angora rabbit population in the t+1th iteration is recorded; if the fitness value of the optimal position of the Angora rabbit population in the t+1th iteration is better than the fitness value of the optimal position of the Angora rabbit population in the tth iteration, then the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the t+1th iteration; otherwise, the optimal position of the Angora rabbit population in the t+1th iteration is equal to the optimal position of the Angora rabbit population in the tth iteration.

10. A communication signal modulation identification system based on Angora rabbit search mechanism, characterized in that: include: a data acquisition module configured to construct a digital modulation signal data set; A data preprocessing module, configured to preprocess the data in the digital modulation signal data set to obtain a training set and a test set; A hyperparameter optimization solution module, which is configured to optimize and solve the hyperparameters of the support vector machine based on the training set and the test set using the Angora rabbit search mechanism, wherein the hyperparameters include a penalty factor and a Gaussian kernel; A model training module configured to train a support vector machine with optimal hyperparameters using a training set; The modulation identification module is configured to use the trained support vector machine to perform modulation identification on the communication signal to be tested.

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