MIMO system modulation type identification method and system under communication interference

Abstract

The invention relates to an MIMO system modulation type identification method and system under communication interference, and belongs to the technical field of signal identification, and the method comprises the steps: carrying out the filtering, denoising and whitening preprocessing of a received MIMO signal; a five-dimensional fourth-order mutual cumulant tensor is constructed by using the preprocessed signal, and a separation matrix is estimated through parallel factorization, so that suppressing interference reconstruction separation is realized; constructing a three-dimensional tensor again for the separated and suppressed signal, and performing dimensionality reduction and feature extraction on the tensor by using a generalized tensor discriminant analysis algorithm; and performing Tucker decomposition on the tensor after dimension reduction to establish a kernel tensor and a factor matrix, and identifying the similarity of the kernel tensors of different modulation types according to the Euclidean distance criterion to complete MIMO signal modulation type identification. Simulation results show that the method has good recognition accuracy under the condition of low signal to interference ratio, and prior information such as channel coefficients and noise distribution is not needed.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication signal modulation type identification in the field of electromagnetic spectrum monitoring, and in particular relates to a method and system for identifying the modulation type of a multiple input multiple output (MIMO) signal under communication interference. Background Art

[0002] Electromagnetic spectrum monitoring is an important prerequisite for ensuring the order of the electromagnetic spectrum, maintaining the legitimate rights and interests of authorized users, and investigating and punishing illegal users' illegal use of frequencies. With the update and iteration of information technology, electromagnetic spectrum monitoring services have become more and more diverse. In recent years, the application scope of multiple input multiple output (MIMO) systems has continued to increase, making MIMO system cognition gradually become an important business of electromagnetic spectrum monitoring. However, with the frequent occurrence of illegal frequency use cases such as black broadcasting, fake base stations, eavesdropping devices, jammers, and radio cheating, malicious electromagnetic interference types continue to emerge, bringing many challenges to MIMO system cognition. Modulation type recognition is an important part of MIMO system cognition. How to accurately identify the modulation type of MIMO signals under malicious interference conditions is an important prerequisite for achieving deep cognition of MIMO systems.

[0003] Many research results have been achieved in the recognition of MIMO signal modulation types. The existing technologies can be roughly divided into two categories: decision-theoretic methods and pattern recognition-based methods. The recognition method based on decision theory is mainly based on the basic principles of maximum likelihood ratio and probability theory. As a test theory of multiple hypotheses, the main idea is to statistically analyze signal characteristics through probability theory and other related mathematical theories. Usually, decision thresholds and thresholds are set to achieve signal recognition (Nandi AK, Azzouz EE. Algorithms for automatic modulation recognition of communication signals [J]. IEEE Transactions on communications, 1998, 46 (4): 431-436.). However, there are still problems of complex theoretical derivation and large amount of calculation in practical applications. The method based on pattern recognition is to extract the signal modulation mode features and identify them by building a classifier. At present, classifiers based on neural networks are usually used, which have the advantages of strong generalization ability, high efficiency and excellent performance. For example, signal modulation mode recognition based on deep learning (Xiao W, Luo Z, Hu Q. A review of research on signal modulation recognition based on deep learning [J]. Electronics, 2022, 11 (17): 2764.), modulation mode recognition based on hierarchical deep neural network (Karra K, Kuzdeba S, Petersen J. Modulation recognition using hierarchical deep neural networks [C] / / 2017 IEEE international symposium on dynamic spectrum access networks (DySPAN). IEEE, 2017: 1-3.), etc. Due to the complexity of the spectrum environment and the influence of interference noise on the modulation recognition task, exploring effective methods for modulation mode recognition in interference and noisy environments has become an increasingly important proposition. For example, exploring the modulation recognition accuracy of convolutional networks under adversarial interference (Lin Y, Zhao H, Ma X, et al. Adversarial attacks in modulation recognition with convolutional neural networks[J].IEEE Transactions on Reliability, 2020,70(1):389-401.), modulation recognition under low signal-to-noise ratio based on deep convolutional network and transfer learning (Jiang K, Zhang J, Wu H, et al. A novel digital modulation recognition algorithm based on deep convolutional neural network [J]. Applied Sciences, 2020, 10 (3): 1166.), modulation recognition under channel interference based on multimodal feature fusion (Zhang X, Li T, Gong P, et al. Modulation recognition of communication signals based on multimodal feature fusion [J]. Sensors, 2022, 22 (17): 6539.), etc. .

[0004] The current modulation recognition technology in the context of non-cooperative communication usually studies the impact of signal-to-noise ratio changes on recognition accuracy under channel noise such as additive Gaussian noise. There are few studies on recognition methods in the presence of hostile interference (such as suppression interference, deceptive interference, etc.), which is of practical significance. In addition, the currently popular modulation recognition algorithms based on pattern recognition mostly rely on classifiers based on neural networks, and the features usually extracted as input to the classifier are basically one-dimensional feature vectors or two-dimensional feature matrices. The features of multiple signal attributes (such as time, space, and frequency domains) are usually extracted separately or fused, and the multi-attribute and multi-modal characteristics of the data are not actually considered comprehensively.

[0005] Through the above analysis, the problems and defects of the prior art are as follows:

[0006] Most of the current modulation recognition technologies do not consider the existence of a strong suppression interference environment, but only consider the impact of noise interference. In addition, the existing suppression interference suppression requires a complex processing flow, which occupies more spectrum resources and consumes more energy. At the same time, the existing modulation type recognition technology often uses low-dimensional features to characterize signal type differences, without considering the multi-dimensional and multi-attribute characteristics of the signal, resulting in limited recognition performance.

[0007] The difficulty of solving the above problems and defects is: in order to save resource overhead, a method combining interference suppression and modulation method is adopted. By utilizing the statistical characteristics of the signal, interference suppression based on tensor decomposition is performed, and tensor analysis is used to characterize the multi-dimensional and multi-attribute characteristics of the modulated signal, so as to achieve efficient, robust and effective MIMO signal modulation type identification.

[0008] The significance of solving the above problems and defects is that accurate identification of MIMO signal modulation type under suppression interference is an important prerequisite for implementing electromagnetic interference investigation and maintaining order in the electromagnetic spectrum. Summary of the invention

[0009] The technical problems to be solved by the present invention are:

[0010] In order to avoid the shortcomings of the prior art, the present invention provides a method and system for identifying the modulation type of a MIMO system under communication interference, which effectively suppresses suppression interference by utilizing tensor decomposition, and significantly improves the modulation type recognition performance by constructing multi-attribute features using tensor analysis, thereby providing technical support for real-time and efficient monitoring of the electromagnetic spectrum.

[0011] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0012] A method for identifying modulation type of a MIMO system under communication interference, characterized by comprising:

[0013] S1: Filter, denoise and whiten the received MIMO signal;

[0014] S2: Perform tensor representation on the preprocessed signal and construct a five-dimensional fourth-order cross-cumulant tensor; estimate the separation matrix through parallel factor decomposition based on the fourth-order cross-cumulant tensor to achieve suppression interference reconstruction separation;

[0015] S3: reconstruct the three-dimensional tensor of the separated and suppressed signal, and use the generalized tensor discriminant analysis algorithm to reduce the dimension and extract features of the tensor;

[0016] S4: Based on Tucker decomposition, the kernel tensor and factor matrix are established for the reduced-dimensional tensor. The similarity of the kernel tensors of different modulation types is identified according to the Euclidean distance criterion to complete the MIMO signal modulation type recognition.

[0017] A further technical solution of the present invention: Step S2 comprises:

[0018] The whitened signal matrix is ​​segmented and obtained n=[z n1,z n2,...,z n k ],n i =N / K,i=1,2,3,...K;

[0019] For each signal matrix, the fourth-order cross-cumulant tensor X is calculated according to the fourth-order cross-cumulant formula. k ∈R M×M×M×M , k = 1, 2, 3...K, we get K tensors of order M×M×M×M, which are concatenated into a 5-dimensional tensor X∈R M×M×M×M×K ;

[0020] Perform a high-order singular value decomposition on the tensor X, select the rank R = M, and construct the factor matrix of the initial parallel factor decomposition CP;

[0021] Use alternating least squares method to perform CP decomposition on tensor X, and get the first N factor matrices P1, P2, ..., P N ;

[0022] Select P1, P2, ..., P N , the i-th column of the N matrices constructs an M×N feature matrix T, i=1,2,3…,M, and performs singular value decomposition on the feature matrix T; take the eigenvector corresponding to the maximum singular value of the left singular matrix as the actual estimated value of the i-th column of the mixing matrix H1, and obtain the final separation matrix Then the signal matrix after separation is

[0023] A further technical solution of the present invention: Step S3 comprises:

[0024] After each communication signal is separated and interference suppressed, db4 wavelet is used to perform wavelet decomposition at different scales, and C is calculated for the signal at each scale after decomposition through a sliding window. 20 ,C 40 ,C 60 Three high-order cumulants are used to construct a three-dimensional tensor sample of wavelet decomposition scale × high-order cumulants × time domain sequence;

[0025] The communication signals of different channels are processed as above to construct a tensor to obtain B samples, and construct χ={χ1,χ2,...χ B A 3D sample set with known category labels, belonging to C categories, each with B C samples; and there are u n ,n=1,2,3,is the projection matrix of the nth dimension, calculate the total average tensor and the average tensor within each class c=1,2...C;According to the threshold value Q of the nth dimension n Determine the dimension P of the feature subspace after projection n ,n=1,2,3;

[0026] Compute the n-th norm divergence difference matrix of the original tensor sample set right Perform feature decomposition and select the first P n The characteristic matrix composed of the eigenvectors corresponding to the eigenvalues ​​is used as the initial projection matrix of the nth mode; the original tensor sample set is projected using the initial projection matrix to find the suboptimal solution set y of the nth mode i n , thereby constructing the inter-class discrete matrix of the suboptimal solution set and the intra-class discrete matrix And get the total divergence difference matrix of the module Among them, ∈ is obtained by experiment; the total divergence difference matrix is ​​decomposed by eigenvalues, and the first P n The eigenvectors corresponding to the largest eigenvalues ​​form the n-th mode projection matrix, and the inter-class discreteness of the tensor set y after projection is calculated. and intra-class dispersion Calculate the total divergence difference for each iteration separately:

[0027] Repeat the above operation for each module n, n = 1, 2...N until the total divergence difference T vd If the difference between the iterations is less than a given value δ, the process terminates.

[0028] A further technical solution of the present invention: Step S4 comprises:

[0029] After projecting the set of tensors with known class labels using these projection matrices, calculate the average tensor within each class:

[0030]

[0031] Use the ALS algorithm to perform Tucker decomposition on the average tensor within each category to obtain the core tensor core of each category c ,c=1,2,...,C, and the corresponding factor matrix U ci , i=1,2,...N, for the unlabeled tensor sample t constructed after denoising separation and GTDA dimensionality reduction, its multiple core tensors are calculated by multiplying the factor matrix transpose of each category tensor, and the Euclidean distances between the multiple core tensors and the core tensors of each category are compared and the sample type with the smallest distance is taken as the estimated type of the tensor.

[0032] A modulation type identification system for a MIMO system under communication interference, characterized by comprising:

[0033] The signal preprocessing module is used to process the observed signal using digital filters and whitening operations to provide conditions for tensor decomposition;

[0034] Interference reconstruction and separation module, used to construct tensors for processed signals and perform parallel factor decomposition to reconstruct and separate suppressed interference;

[0035] Tensor dimension reduction and feature extraction module, used to construct feature tensors for reconstructed signals, and use generalized tensor decision analysis for dimension reduction and feature extraction;

[0036] The modulation type recognition module is used to perform Tucker decomposition on the tensor after dimensionality reduction, construct the core tensor and factor matrix, and use the Euclidean distance between the core tensors to perform modulation type recognition.

[0037] A computer system, characterized in that it comprises: one or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned method.

[0038] A computer-readable storage medium is characterized by storing computer-executable instructions, which are used to implement the above method when executed.

[0039] A computer program product, characterized by comprising computer executable instructions, wherein the instructions are used to implement the above method when executed.

[0040] The beneficial effects of the present invention are:

[0041] The present invention provides a method for identifying the modulation type of a MIMO system under communication interference. The method can utilize the high-order characteristics of the signal to more efficiently perform a blind source separation algorithm, realize the processing of suppression interference, and construct a tensor form of signal samples in multiple dimensions to better characterize the multi-attribute characteristics of the signal, thereby facilitating the improvement of recognition performance. At the same time, the method uses a GTDA+Tucker decomposition algorithm to analyze the similarity between core tensors, which has low computational cost and short computational time, and still has a certain robustness when there are fewer training samples and a low signal-to-noise ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings are only for the purpose of illustrating specific embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0043] Figure 1 It is a flow chart of a method for studying modulation type identification of a MIMO system under communication interference provided by an embodiment of the present invention.

[0044] Figure 2 It is a system structure block diagram of a method for identifying modulation types of a MIMO system under communication interference provided by an embodiment of the present invention.

[0045] Figure 3 It is a schematic diagram of the accuracy of identifying the modulation mode of channel noise changes under communication interference provided by an embodiment of the present invention.

[0046] Figure 4 It is a schematic diagram of the accuracy of identifying the modulation mode of interference power change under communication interference provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0048] like Figure 1 As shown, the method for identifying the modulation type of a MIMO system under communication interference provided by an embodiment of the present invention includes the following steps:

[0049] S101, performing filtering, denoising and whitening preprocessing on the received MIMO signal;

[0050] S102, constructing a five-dimensional fourth-order cross-cumulant tensor using the preprocessed signal, and estimating a separation matrix through parallel factor decomposition to achieve suppression interference reconstruction separation;

[0051] S103, reconstructing a three-dimensional tensor for the separated and suppressed signal, and performing dimensionality reduction and feature extraction on the tensor using a generalized tensor discriminant analysis algorithm;

[0052] S104, using Tucker decomposition to establish a core tensor and a factor matrix for the tensor after dimensionality reduction, identifying the similarity of core tensors of different modulation types based on the Euclidean distance criterion, and completing the MIMO signal modulation type recognition.

[0053] like Figure 2 As shown, the modulation mode identification system of the MIMO system under communication interference provided by the embodiment of the present invention includes:

[0054] The signal preprocessing module is used to process the observed signal using digital filters and whitening operations to provide conditions for tensor decomposition;

[0055] Interference reconstruction and separation module, used to construct tensors for processed signals and perform parallel factor decomposition to reconstruct and separate suppressed interference;

[0056] Tensor dimension reduction and feature extraction module, used to construct feature tensors for reconstructed signals, and use generalized tensor decision analysis for dimension reduction and feature extraction;

[0057] The modulation type recognition module is used to perform Tucker decomposition on the tensor after dimensionality reduction, construct the core tensor and factor matrix, and use the Euclidean distance between the core tensors to perform modulation type recognition.

[0058] The present invention will be further described below in conjunction with the embodiments.

[0059] Example 1

[0060] An embodiment of the present invention provides a method for identifying a modulation type of a MIMO system under communication interference, comprising the following steps:

[0061] The first step is data preprocessing, which involves filtering, denoising and whitening the received MIMO signal.

[0062] The multi-channel noisy mixed signal observed by the MIMO system is filtered using a 40th-order FIR filter. The main purpose of this step is to reduce the Gaussianity of the signal, and then the signal is whitened. First, the M-channel instantaneous linear mixed signal passing through the noisy channel is filtered (here the number of observed signals is equal to the number of source signals), and the denoised mixed signal x(n)[x1 n,...,x M n] T ,n=1,2,3...N, N is the number of sampling points, then calculate the covariance matrix of the mixed signal

[0063] C x =E{x(n)x T (n)}

[0064] Perform eigendecomposition on the covariance matrix:

[0065] C x =EDE T

[0066] Compute the whitening matrix:

[0067] V=ED 1 / 2 E T

[0068] Get the whitened signal:

[0069]

[0070] The second step is to use the preprocessed signal to construct a five-dimensional fourth-order cross-cumulant tensor, and estimate the separation matrix through parallel factor decomposition to achieve the repressive interference reconstruction separation; including:

[0071] The signal matrix after whitening is divided into K segments, and we get n=[z n1,z n2,z n1,...,z n2],n i =N / K, i=1,2,3,...K, and then use the following formula to calculate the fourth-order cross-accumulation tensor X for each signal matrix k ∈R M×M×M×M , k=1,2,3...K, the calculation formula is:

[0072] X k =Cum[z n1,z n1,z n1,z n1]

[0073] cum a,b,c,d i,j,k,l =Ea i b j c k d l -E a i b j E c k d l -E a i c k E j d l -E a i d l E j c k

[0074] The calculated K-order M×M×M×M tensors are concatenated into a 5-dimensional tensor X∈R M×M×M×M×K , and then perform tensor decomposition initialization. First, perform high-order singular value decomposition (HOSVD) on the tensor X, select rank R = M, and construct the initial parallel factor decomposition (CP) factor matrix. Then use alternating least squares (ALS) to perform CP decomposition on the tensor X, and get the first N factor matrices P1, P2, ..., P N , the fusion method is used to estimate the final mixing matrix H1 for these N matrices. First, take P1, P2, ..., P N , the i-th column of N matrices constructs an M×N feature matrix T;

[0075] Perform singular value decomposition (SVD) on the feature matrix T:

[0076] T=SVD T

[0077] Take the eigenvector of the largest singular value corresponding to the left singular matrix as the actual estimated value of the i-th column of the mixing matrix H1, i = 1, 2, ..., N, and finally the separation matrix Then the signal matrix after separation is

[0078] The third step is to reconstruct a three-dimensional tensor for the separated and suppressed signal, and use the generalized tensor discriminant analysis algorithm to reduce the dimension and extract features of the tensor:

[0079] First, the communication signal after each channel is separated and suppressed for interference is decomposed by using db4 wavelet at different scales, and C is calculated for the signal at each scale after decomposition through a sliding window. 20 ,C 40 ,C 60 Three high-order cumulants are used to construct a three-dimensional tensor sample of wavelet decomposition scale × high-order cumulant × time domain sequence.

[0080] For B samples, χ={χ1,χ2,...χ B} is an N-dimensional sample set with known category labels. These B samples belong to C categories, and each category has B C samples. n ,n=1,2...N, is the projection matrix of the nth dimension, after projection, the subspace dimension P n ,n=1,2,...,N. Calculate the total average tensor Compute the average tensor within each class c=1,2...C.

[0081]

[0082] In the formula, Represents the bth tensor sample in the cth class sample. Through the threshold value Q n Determine the dimension of the feature subspace after projection:

[0083]

[0084] in, is the eigenvalue obtained by eigendecomposing the divergence difference matrix of the sample tensor along each mode, I n is the module dimension of the input tensor of the GTDA algorithm, and P n is the scale of the nth mode after projection, here by selecting the first P n eigenvalues ​​to make the Q value meet the requirements, thereby determining P n , and then use the full projection truncation (FPT) to obtain the initial projection matrix.

[0085] First, calculate the n-th modulus divergence difference matrix of the original tensor sample set:

[0086]

[0087] In the formula, χ c n is the input sample χ c The modulo n expansion matrix of and are the modulo n expansion matrix of the total average tensor and the modulo n expansion matrix of the total average tensor within the class corresponding to the b-th sample, and ε is obtained by experiment.

[0088] Afterwards Feature decomposition, select the first P n The characteristic matrix composed of the eigenvectors corresponding to the eigenvalues ​​is used as the initial projection matrix of the n-th module. The original tensor sample set is projected using the initialization matrix to find the suboptimal solution set of the n-th module:

[0089] y i n = x i ×1u1×2u2...× n-1 u n-1 × n+1 u n+1 ...× N u N

[0090] i=1,2...B

[0091] The matrix is ​​expanded through the modular suboptimal solution set, and then the inter-class discrete matrix and the intra-class discrete matrix are calculated by the following formulas:

[0092]

[0093] here is the total average matrix of the suboptimal solution after expansion along the modulus n, It is the total average matrix after the expansion of each type of inner edge modulus n of the suboptimal solution set.

[0094] Thus, the total divergence difference matrix of the module is obtained ∈ is obtained from the experiment, the total divergence difference matrix is ​​decomposed into features, and the first P n The eigenvector corresponding to the largest eigenvalue is used as the n-th mode projection matrix, and then for the tensor set y obtained after projection, the inter-class discreteness is calculated as:

[0095]

[0096] The calculation of intra-class dispersion is:

[0097]

[0098] The “||·||” in the above formula represents the Frobenius norm, and the total divergence difference in each iteration is calculated as:

[0099] For each module n, n = 1, 2...N, repeat the above operation repeatedly until the difference between the total divergence difference before and after the iteration is less than a small amount δ, and the algorithm converges and terminates.

[0100] The fourth step is to use Tucker decomposition to establish the core tensor and factor matrix for the reduced dimensionality tensor, identify the similarity of the core tensors of different modulation types based on the Euclidean distance criterion, and complete the MIMO signal modulation type recognition; including:

[0101] First, after reducing the dimension of the constructed tensor in the third step, since the output of the GTDA algorithm is the iterated projection matrix, the tensor set with known category labels is projected using these projection matrices, and the average tensor within each category is calculated:

[0102]

[0103] Use the ALS algorithm to perform Turcker decomposition on the average tensor within each category (referred to as category tensor) to obtain the category core tensor core c , and the corresponding factor matrix U ci , i=1,2,...N, for the unlabeled tensor sample t constructed after denoising separation and GTDA dimensionality reduction, calculate its multiple core tensors:

[0104] core tc =t×1u1 T ×2u2 T ... N u N T ,c=1,2,...C

[0105] The calculated Euclidean distances of multiple core tensors and core tensors of each category:

[0106] dis c =||core tc -core c ||,c=1,2...C

[0107] Compare their sizes and take the sample type with the smallest distance as the estimated type of the tensor.

[0108] The technical effects of the present invention are described in detail below in conjunction with simulation.

[0109] In order to evaluate the performance of the present invention, a simulation verification is performed. A 5×4 MIMO system is used here, the suppression interference is linear frequency sweep interference, the channel is a flat fading channel, and the simulation generates four communication signals: 2PSK, 4PSK, 8QAM, and 16QAM. Each type of signal generates 2000 groups, each group of signals consists of four communication signals and one interference signal, and the number of sampling points for each signal is 10 5, respectively, at the receiving end, low signal-to-interference ratio and interference signal are mixed, and blind source separation is performed by decomposing the fourth-order mutual accumulation tensor. Then, the separated communication signal is decomposed by wavelet using db4 wavelet, and the high-order cumulants are calculated along the sliding window of the wavelet decomposition scale. A tensor sample with the size of 6×6×186 of wavelet decomposition scale×high-order cumulants×time domain sequence is constructed, and then the GTDA algorithm is used for dimensionality reduction to obtain a 3×3×83 dimensionality reduction sample. The core tensor of the average tensor within the class is calculated by Tucker decomposition, and the similarity between the core tensor of the test sample (here the size of the core tensor is specified to be 3×3×3) is used for category determination. Under different channels, the signal-to-interference ratio remains unchanged, and the classification accuracy obtained by increasing the channel noise power is as follows Figure 2 As shown in Figure 1, when the low signal-to-noise ratio channel conditions are poor, the performance of the blind source separation algorithm deteriorates sharply, and the accuracy of the back-end classification algorithm is an uncertain random value, so the algorithm is considered to be invalid at this time; increasing the interference power and keeping the channel conditions unchanged, the modulation mode recognition accuracy is as follows: Figure 3 As shown. Figure 3 It can be seen that the classification accuracy of the method of the present invention is still robust to a certain extent when the signal interference is relatively low.

[0110] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When the use is implemented in whole or in part in the form of a computer program product, the computer program product includes one or more computer instructions. When the computer program instructions are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL) or wireless (e.g., infrared, wireless, microwave, etc.) mode) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0111] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A method for identifying modulation type of MIMO system under communication interference, characterized in that: include: S1: Filter, denoise and whiten the received MIMO signal; S2: Perform tensor representation on the preprocessed signal and construct a five-dimensional fourth-order cross-cumulant tensor; estimate the separation matrix through parallel factor decomposition based on the fourth-order cross-cumulant tensor to achieve suppression interference reconstruction separation; S3: reconstruct the three-dimensional tensor of the separated and suppressed signal, and use the generalized tensor discriminant analysis algorithm to reduce the dimension and extract features of the tensor; S4: Based on Tucker decomposition, the kernel tensor and factor matrix are established for the reduced-dimensional tensor. The similarity of the kernel tensors of different modulation types is identified according to the Euclidean distance criterion to complete the MIMO signal modulation type recognition.

2. According to the method for identifying modulation type of MIMO system under communication interference according to claim 1, it is characterized in that: Step S2 includes: The signal matrix after whitening is processed segmentally to obtain zn=[zn1,zn2,...,zn k ],n i =N / K,i=1,2,3,...K; For each signal matrix, the fourth-order cross-cumulant tensor X is calculated according to the fourth-order cross-cumulant formula. k ∈R M×M×M×M , k = 1, 2, 3...K, and we get K tensors of order M×M×M×M, which are concatenated into a 5-dimensional tensor XR M×M×M×M×K ; Perform a high-order singular value decomposition on the tensor X, select the rank R = M, and construct the factor matrix of the initial parallel factor decomposition CP; Use alternating least squares method to perform CP decomposition on tensor X, and get the first N factor matrices P1, P2, ..., P N ; Select P1, P2, ..., P N , the i-th column of the N matrices constructs an M×N feature matrix T, i=1,2,3…,M, and performs singular value decomposition on the feature matrix T; take the eigenvector corresponding to the maximum singular value of the left singular matrix as the actual estimated value of the i-th column of the mixing matrix H1, and obtain the final separation matrix W=H1 -1 ; Then the separated signal matrix y = Wz.

3. According to the method for identifying modulation type of MIMO system under communication interference according to claim 1, it is characterized in that: Step S3 includes: After each communication signal is separated and interference suppressed, db4 wavelet is used to perform wavelet decomposition at different scales, and C is calculated for the signal at each scale after decomposition through a sliding window. 20 ,C 40 ,C 60 Three high-order cumulants are used to construct a three-dimensional tensor sample of wavelet decomposition scale × high-order cumulants × time domain sequence; The communication signals of different channels are processed as above to construct a tensor to obtain B samples, and construct χ={χ1,χ2,...χ B A 3D sample set with known category labels, belonging to C categories, each with B C samples; and there are u n ,n=1,2,3,is the projection matrix of the nth dimension, calculate the total average tensor and the average tensor within each class According to the threshold value Q of the nth dimension n Determine the dimension P of the feature subspace after projection n ,n=1,2,3; Compute the n-th norm divergence difference matrix of the original tensor sample set right Perform feature decomposition and select the first P n The characteristic matrix composed of the eigenvectors corresponding to the eigenvalues ​​is used as the initial projection matrix of the nth mode; the original tensor sample set is projected using the initial projection matrix to find the suboptimal solution set y of the nth mode in , thereby constructing the inter-class discrete matrix of the suboptimal solution set and the intra-class discrete matrix And get the total divergence difference matrix of the module in, ∈ Obtained from the experiment; perform eigendecomposition on the total divergence difference matrix and take the first P n The eigenvectors corresponding to the largest eigenvalues ​​form the n-th mode projection matrix, and the inter-class discreteness of the tensor set y after projection is calculated. and intra-class dispersion Calculate the total divergence difference for each iteration separately: For each module n, n ＝ 1,2...N loops repeat the above operation until the total divergence difference T vd If the difference between the iterations is less than a given value δ, the process terminates.

4. According to the method for identifying modulation type of MIMO system under communication interference according to claim 1, it is characterized in that: Step S4 includes: After projecting the set of tensors with known class labels using these projection matrices, calculate the average tensor within each class: Use the ALS algorithm to perform Tucker decomposition on the average tensor within each category to obtain the core tensor core of each category c ,c=1,2,...,C, and the corresponding factor matrix U ci , i=1,2,...N, for the unlabeled tensor sample t constructed after denoising separation and GTDA dimensionality reduction, its multiple core tensors are calculated by multiplying the factor matrix transpose of each category tensor, and the Euclidean distances between the multiple core tensors and the core tensors of each category are compared and the sample type with the smallest distance is taken as the estimated type of the tensor.

5. A modulation type identification system for a MIMO system under communication interference, characterized in that: include: The signal preprocessing module is used to process the observed signal using digital filters and whitening operations to provide conditions for tensor decomposition; Interference reconstruction and separation module, used to construct tensors for processed signals and perform parallel factor decomposition to reconstruct and separate suppressed interference; Tensor dimension reduction and feature extraction module, used to construct feature tensors for reconstructed signals, and use generalized tensor decision analysis for dimension reduction and feature extraction; The modulation type recognition module is used to perform Tucker decomposition on the tensor after dimensionality reduction, construct the core tensor and factor matrix, and use the Euclidean distance between the core tensors to identify the modulation type.

6. A computer system, characterized in that include: One or more processors, and a computer-readable storage medium for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are enabled to implement the method of claim 1.

7. A computer-readable storage medium, characterized in that Computer executable instructions are stored, and when the instructions are executed, they are used to implement the method of claim 1.

8. A computer program product, characterized in that The invention comprises computer executable instructions, which are used to implement the method of claim 1 when being executed.

Images