Modulation signal classification method, device and equipment

Abstract

The invention provides a modulation signal classification method, a modulation signal classification device and modulation signal classification equipment. The method comprises the following steps: acquiring to-be-classified modulation signals; inputting the modulation signals to be classified into a pre-training modulation signal classification model to obtain a modulation signal classification result; due to the pre-training Mama model in the pre-training modulation signal classification model, the features of the modulation signals to be classified can be effectively extracted and processed by combining the selective state space (SSM) and the features extracted by the Mama convolution block, and the accuracy of identifying the modulation signals to be classified by the pre-training modulation signal classification model is improved; besides, by designing a specific loss function, the initial MCLDNN model and the initial Mama model can learn each other during normal training, disturbance does not need to be additionally introduced to data, and the performance of the pre-training modulation signal classification model is improved. Therefore, the method improves the utilization rate of computing resources and the generalization ability of the pre-training modulation signal classification model.

Description

Technical Field

[0001] The present invention relates to the technical field of modulation signal processing, and in particular to a method, device and equipment for classifying modulation signals. Background Art

[0002] With the rapid development of information technology, wireless communication systems have increasingly higher requirements for the utilization and management of spectrum resources. As an advanced technology based on software-defined radio, full wireless spectrum sensing technology can monitor the wireless spectrum in real time and evaluate channel quality and signal strength, providing strong support for the efficient operation of wireless communications. However, the classification of modulated signals in wireless communications, as a key link to ensure transmission quality and stability, faces the challenges of complex and changeable channel environments and noise interference. Traditional modulation signal recognition methods mainly rely on manually selected features and decision boundaries. This method is not only complex but also inefficient. Especially when the channel environment is harsh, the recognition effect is greatly reduced. Therefore, exploring an efficient, intelligent and reliable automatic modulation recognition technology to cope with the complex challenges in wireless communications has become an urgent need for current research.

[0003] In order to solve the above problems, researchers have proposed a variety of technical solutions. Among them, the model based on deep neural network (DNN) uses its ability to extract complex features in high-dimensional space to classify modulated signals and has achieved certain results. In addition, the long short-term memory network (LSTM) has also been used in automatic modulation recognition because of its advantage in capturing signal time series characteristics. In order to combine the advantages of CNN in local feature extraction and LSTM's ability to model long-term dependencies of sequence data, researchers have also constructed a CLDNN architecture to achieve a balance between performance and complexity, further improving modulation recognition performance.

[0004] Although the existing technology has improved the accuracy of modulation recognition to a certain extent, there are still many shortcomings. Although the DNN-based model performs well under specific modulation modes, its performance drops significantly in low signal-to-noise ratio environments, and it has a certain dependence on expert features, which limits its wide application. Although the LSTM-based model can capture time series features, it has limited feature mining of complex modulated signals, a single model structure, and limited adaptability to different input forms. Its performance is also limited in low signal-to-noise ratio environments. Although the CLDNN model combines the advantages of CNN and LSTM, it has limitations in model fusion strategy, outstanding problems in computing resource utilization and training efficiency, insufficient feature mining of special structures or high-dimensional modulated signals, and inflexible fusion methods. Especially on restricted devices, the computing resource utilization efficiency is low, the training time is long, and it is greatly affected by data. These problems have restricted the further development of existing technologies in the field of automatic modulation recognition. Therefore, there is an urgent need for a more efficient, intelligent and adaptable automatic modulation recognition technology to meet the complex challenges in wireless communications. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a method, device and equipment for classifying modulated signals.

[0006] The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for classifying a modulated signal, comprising:

[0008] Obtaining a modulation signal to be classified;

[0009] The modulated signal to be classified is input into the pre-trained modulated signal classification model to obtain the modulation signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on the selective state space model, and the Mamba timing layer is introduced on the basis of the Mamba convolution block, and the Mamba timing layer combines the selective SSM mechanism and the MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and timing layers, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts the modulated signal samples and the preset loss function, and is constructed based on the mutual learning strategy between the initial Mamba model and the initial MCLDNN model.

[0010] Optionally, the pre-trained MCLDNN model includes: a first convolution block, a second convolution block, a third convolution block, a fourth convolution block, a fifth convolution block, a first sequential layer, a second sequential layer, a first fully connected layer, and a second fully connected layer;

[0011] The first convolution block, the second convolution block and the third convolution block are connected in parallel, the outputs of the second convolution block and the third convolution block are connected in series with the fourth convolution block, the output of the first convolution block is connected in parallel with the output of the fourth convolution block, the first convolution block and the fourth convolution block are connected in series with the fifth convolution block, and the fifth convolution block is connected in series with the first sequential layer, the second sequential layer, the first fully connected layer and the second fully connected layer in sequence.

[0012] Optionally, the pre-trained Mamba model includes: a sixth convolution block, a seventh convolution block, an eighth convolution block, a ninth convolution block, a tenth convolution block, a first Mamba timing layer, a second Mamba timing layer, a third fully connected layer, and a fourth fully connected layer;

[0013] The sixth convolution block, the seventh convolution block and the eighth convolution block are connected in parallel, the outputs of the seventh convolution block and the eighth convolution block are connected in series with the ninth convolution block, the output of the sixth convolution block is connected in parallel with the output of the ninth convolution block, the sixth convolution block and the ninth convolution block are connected in series with the tenth convolution block, and the tenth convolution block is connected in series with the first Mamba timing layer, the second Mamba timing layer, the third fully connected layer and the fourth fully connected layer in sequence.

[0014] Optionally, the output dimensions of the first fully connected layer and the third fully connected layer are both 128, and the output dimensions of the second fully connected layer and the fourth fully connected layer are both 11; and the optimizers of the MCLDNN model and the Mamba model both use Adam.

[0015] Optionally, the preset loss function is expressed as:

[0016]

[0017] Among them, L1 represents the preset loss function, CrossEntropyLoss represents the cross entropy loss function, Represents the modulated signal sample X label Input the prediction score of the initial MCLDNN model, Represents the modulated signal sample X label Enter the prediction score of the initial Mamba model, Represents the modulated signal sample X label The real label, Represents the one-hot encoding converted from the prediction score of the initial MCLDNN model, Represents the one-hot encoding transformed according to the prediction score of the initial Mamba model.

[0018] Optionally, the training process of the pre-trained modulation signal classification model includes:

[0019] Obtaining a modulated signal sample;

[0020] Inputting the modulated signal samples into the initial modulated signal classification model for iterative training;

[0021] Using the initial modulation signal classification model that meets the preset iteration stop condition as the pre-trained modulation signal classification model;

[0022] The preset iteration stop conditions include: the number of iterations is greater than a preset iteration threshold or the preset loss function continues to converge.

[0023] Optionally, the pre-trained Mamba model is used to process the modulated signal to be classified in a low signal-to-noise ratio environment, and the nonlinear expression ability and dynamic information filtering effect of the pre-trained modulated signal classification model are enhanced through a selective SSM mechanism.

[0024] In a second aspect, the present invention provides a modulation signal classification device, comprising:

[0025] The acquisition unit is used to: acquire the modulation signal to be classified;

[0026] The classification unit is used to: input the modulated signal to be classified into the pre-trained modulated signal classification model to obtain the modulation signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on the selective state space model, and the Mamba timing layer is introduced on the basis of the Mamba convolution block, and the Mamba timing layer combines the selective SSM mechanism and the MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and timing layers, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts the modulated signal sample and the preset loss function, and is constructed based on the mutual learning strategy between the initial Mamba model and the initial MCLDNN model.

[0027] In a third aspect, the present invention provides a device for classifying modulated signals, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the device for classifying modulated signals is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for classifying modulated signals in the first aspect described above.

[0028] The present invention provides a method, device and equipment for classifying modulated signals. A method for classifying modulated signals includes: obtaining a modulated signal to be classified; inputting the modulated signal to be classified into a pre-trained modulated signal classification model to obtain a modulated signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on a selective state space model, and a Mamba timing layer is introduced on the basis of a Mamba convolution block, and the Mamba timing layer combines a selective SSM mechanism and an MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes a plurality of MCLDNN convolution blocks and a timing layer, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts a modulated signal sample and a preset loss function, and is constructed based on a mutual learning strategy between an initial Mamba model and an initial MCLDNN model. In the present invention, due to the pre-trained Mamba model in the pre-trained modulation signal classification model, by combining the features extracted by the selective state space (SSM) and the Mamba convolution block, the features of the modulation signal to be classified can be effectively extracted and processed, thereby improving the accuracy of the pre-trained modulation signal classification model in identifying the modulation signal to be classified; in addition, by designing a specific loss function, the initial Mamba model and the initial MCLDNN model can learn from each other during normal training without introducing additional disturbances to the data, thereby improving the performance of the pre-trained modulation signal classification model. Since the method of the present invention no longer relies on expert experience, and by using a more flexible fusion strategy, the generalization ability and computing resource utilization of the pre-trained modulation signal classification model are improved, and efficient training and real-time processing can be achieved.

[0029] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic diagram of a flow chart of a method for classifying a modulation signal provided by an embodiment of the present invention;

[0031] Figure 2 The structural diagram of the pre-trained MCLDNN model is shown exemplarily;

[0032] Figure 3 The structural diagram of the pre-trained Mamba model is shown exemplarily;

[0033] Figure 4 The schematic diagram of the structure of the Mamba timing layer is shown as an example;

[0034] Figure 5 The overall architecture diagram of the pre-trained modulation signal classification model is exemplarily shown;

[0035] Figure 6 A schematic diagram of the structure of a modulation signal classification device provided by an embodiment of the present invention;

[0036] Figure 7 A schematic diagram of the structure of a modulation signal classification device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0038] In order to improve the generalization ability and computing resource utilization of a pre-trained modulation signal classification model, an embodiment of the present invention provides a modulation signal classification method. Figure 1 The following is a flow chart of a method for classifying a modulation signal provided by an embodiment of the present invention. Figure 1 As shown, including:

[0039] S101. Obtain a modulated signal to be classified.

[0040] It should be noted that the specific form of the modulation signal depends on the modulation technology used. In this embodiment, the modulation signal to be classified can be obtained based on the following digital adjustment methods: 8-phase shift keying 8PSK, binary phase shift keying BPSK, continuous phase frequency shift keying CPFSK, Gaussian filter frequency shift keying GFSK, four-level pulse amplitude modulation PAM4, 16-quadrature amplitude modulation 16QAM, 64-quadrature amplitude modulation 64QAM and four-phase phase shift keying QPSK. In addition, there can be 3 analog modulation methods: double-sideband amplitude modulation AM-DSB, single-sideband amplitude modulation AM-SSB and wideband frequency modulation WBFM.

[0041] S102: Input the modulation signal to be classified into a pre-trained modulation signal classification model to obtain a modulation signal classification result.

[0042] The pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on the selective state space model, and the Mamba timing layer is introduced on the basis of the Mamba convolution block, and the Mamba timing layer combines the selective SSM mechanism and the MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and timing layers, the MCLDNN convolution blocks are used to extract local features, and the timing layers are used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts modulated signal samples and a preset loss function, and is constructed based on the mutual learning strategy between the initial Mamba model and the initial MCLDNN model.

[0043] The embodiment of the present invention provides a method, device and equipment for classifying modulated signals. Among them, a method for classifying modulated signals includes: obtaining a modulated signal to be classified; inputting the modulated signal to be classified into a pre-trained modulated signal classification model to obtain a modulated signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on a selective state space model, and a Mamba timing layer is introduced on the basis of a Mamba convolution block, and the Mamba timing layer combines a selective SSM mechanism and an MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and a timing layer, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts a modulated signal sample and a preset loss function, and is constructed based on a mutual learning strategy between an initial Mamba model and an initial MCLDNN model. In an embodiment of the present invention, the pre-trained Mamba model in the pre-trained modulation signal classification model can effectively extract and process the features of the modulation signal to be classified by combining the selective state space (SSM) with the features extracted by the Mamba convolution block, thereby improving the accuracy of the pre-trained modulation signal classification model in identifying the modulation signal to be classified; in addition, by designing a specific loss function, the initial Mamba model and the initial MCLDNN model can learn from each other during normal training without introducing additional disturbances to the data, thereby improving the performance of the pre-trained modulation signal classification model. Since the method of the present invention no longer relies on expert experience, and by using a more flexible fusion strategy, the generalization ability and computing resource utilization of the pre-trained modulation signal classification model are improved, and efficient training and real-time processing can be achieved.

[0044] To specifically illustrate the model structure of the pre-trained MCLDNN model, Figure 2 The structural diagram of the pre-trained MCLDNN model is shown as an example. Figure 2 As shown, the pre-trained MCLDNN model includes: a first convolution block, a second convolution block, a third convolution block, a fourth convolution block, a fifth convolution block, a first sequential layer, a second sequential layer, a first fully connected layer, and a second fully connected layer;

[0045] The first convolution block, the second convolution block and the third convolution block are connected in parallel, the outputs of the second convolution block and the third convolution block are connected in series with the fourth convolution block, the output of the first convolution block is connected in parallel with the output of the fourth convolution block, the first convolution block and the fourth convolution block are connected in series with the fifth convolution block, and the fifth convolution block is connected in series with the first sequential layer, the second sequential layer, the first fully connected layer and the second fully connected layer in sequence.

[0046] The specific structure of each convolution block in the pre-trained MCLDNN model is: convolution layer, batch normalization layer, ReLU activation layer. Figure 2 As shown, the number of convolution kernels of the first convolution block, the second convolution block, the third convolution block, the fourth convolution block, and the fifth convolution block are 50, 50, 50, 50, and 100 respectively, and the sizes of the convolution kernels are 2*8, 1*8, 1*8, 1*8, and 2*5 respectively. The stride is 1 and the padding is 0. The hidden layer dimension of the first LSTM (time series) layer is 128, the hidden layer dimension of the second LSTM (time series) layer is 128, the output dimension of the first fully connected layer is 128, and the output dimension of the second fully connected layer is 11. The optimizer uses Adam.

[0047] Figure 3 The schematic diagram of the structure of the pre-trained Mamba model is shown as an example. Figure 3 As shown, the pre-trained Mamba model includes: a sixth convolution block, a seventh convolution block, an eighth convolution block, a ninth convolution block, a tenth convolution block, a first Mamba timing layer, a second Mamba timing layer, a third fully connected layer, and a fourth fully connected layer;

[0048] The sixth convolution block, the seventh convolution block and the eighth convolution block are connected in parallel, the outputs of the seventh convolution block and the eighth convolution block are connected in series with the ninth convolution block, the output of the sixth convolution block is connected in parallel with the output of the ninth convolution block, the sixth convolution block and the ninth convolution block are connected in series with the tenth convolution block, and the tenth convolution block is connected in series with the first Mamba timing layer, the second Mamba timing layer, the third fully connected layer and the fourth fully connected layer in sequence.

[0049] In the pre-trained Mamba model, the specific structure of each convolution block is: convolution layer, batch normalization layer and ReLU activation layer. The number of convolution kernels of the sixth convolution block, the seventh convolution block, the eighth convolution block, the ninth convolution block and the tenth convolution block are 50, 50, 50, 50, 100 respectively, and the size of the convolution kernel is 2*8, 1*8, 1*8, 1*8, 2*5 respectively. The stride is 1 and the padding is 0; the hidden layer dimension of the first Mamba sequential layer is 128, the hidden layer dimension of the second Mamba sequential layer is 128, the output dimension of the third fully connected layer is 128, and the output dimension of the fourth fully connected layer is 11; the optimizer uses Adam.

[0050] Optionally, the output dimensions of the first fully connected layer and the third fully connected layer are both 128, and the output dimensions of the second fully connected layer and the fourth fully connected layer are both 11; and the optimizers of the MCLDNN model and the Mamba model both use Adam.

[0051] Figure 4The structural diagram of the Mamba timing layer is shown as an example. The Mamba timing layer cleverly integrates technologies such as linear projection, state space model, deep convolution, and activation function (SiLU). Through linear projection, the pre-trained Mamba model can efficiently extract the key features of the input sequence; the state space model provides it with a mechanism for dynamically selecting and updating state information, ensuring that the pre-trained Mamba model remains efficient and accurate when processing long sequences; the deep convolution layer further enhances the pre-trained Mamba model's ability to learn features of sequence data, and captures complex temporal dependencies through multi-layer convolution operations; and the introduction of the activation function gives the pre-trained Mamba model nonlinear expression capabilities, enabling it to fit more complex temporal patterns. The organic combination of these features and technologies enables the Mamba timing layer to demonstrate excellent performance and flexibility when processing temporal data.

[0052] It should be noted that the activation function used in the Mamba timing layer is SiLU. SiLU (Sigmoid Linear Unit) is an activation function that combines the characteristics of the Sigmoid function and the linear function. It has the advantages of nonlinearity, smoothness, and self-gating mechanism. It is suitable for a variety of deep learning tasks and can improve the performance and training efficiency of the model.

[0053] Optionally, the preset loss function is expressed as:

[0054]

[0055] Among them, L1 represents the preset loss function, CrossEntropyLoss represents the cross entropy loss function, Represents the modulated signal sample X label Input the prediction score of the initial MCLDNN model, Represents the modulated signal sample X label Enter the prediction score of the initial Mamba model, Represents the modulated signal sample X label The real label, Represents the one-hot encoding converted from the prediction score of the initial MCLDNN model, Represents the one-hot encoding transformed according to the prediction score of the initial Mamba model.

[0056] Optionally, the training process of the pre-trained modulation signal classification model includes:

[0057] Obtaining a modulated signal sample;

[0058] Inputting the modulated signal samples into the initial modulated signal classification model for iterative training;

[0059] Using the initial modulation signal classification model that meets the preset iteration stop condition as the pre-trained modulation signal classification model;

[0060] The preset iteration stop conditions include: the number of iterations is greater than a preset iteration threshold or the preset loss function continues to converge.

[0061] Specifically, in this embodiment, the number of iterations can be set to 200 rounds, and the learning rate can be set to 0.0003.

[0062] Figure 5 The overall architecture diagram of the pre-trained modulation signal classification model is shown as an example. Figure 5 As shown, the initial Mamba model and the initial MCLDNN model are each trained based on the cross entropy loss, and learn from each other through a preset loss function.

[0063] Optionally, the pre-trained Mamba model is used to process the modulated signal to be classified in a low signal-to-noise ratio environment, and the nonlinear expression ability and dynamic information filtering effect of the pre-trained modulated signal classification model are enhanced through a selective SSM mechanism.

[0064] It should be noted that the core of the pre-trained Mamba model lies in the structured state space model (SSM). SSM is a mechanism that allows the model to dynamically adjust its state based on the input. This selective mechanism enables the pre-trained Mamba model to effectively filter out important information when processing sequence data, while ignoring or forgetting irrelevant parts. In addition, the pre-trained Mamba model simplifies the traditional deep sequence model architecture, combining the state space model (SSM) with the multi-layer perceptron (MLP) block in the Transformer to form a simple and unified structure, namely the homogeneous stacking module. This design not only improves the flexibility of the model, but also reduces the complexity of the pre-trained Mamba model, allowing the pre-trained Mamba model to efficiently process long sequence data during both training and inference.

[0065] In summary, the initial Mamba model designed by the present invention for the field of signal modulation combines the features extracted by the selective state space (SSM) and the convolution block, and implements nonlinear transformation on the output with the help of nonlinear activation function. Each timing layer uses sequence transformation to capture the timing dependency, and combines the linear projection layer to maintain the global timing information of the input, which can effectively extract and process the features of the modulated signal to be classified, which is helpful to improve the accuracy of automatic modulation recognition.

[0066] Furthermore, the present invention constructs a pre-trained modulation signal classification model based on the initial Mamba model and the initial MCLDNN model, two randomly initialized network models. By designing a specific loss function, the above two models can learn from each other during normal training. This interactive learning method does not need to introduce additional disturbances to the data, and can make the prediction of the data different, and then train the initial modulation signal classification model through consistency constraints, and finally improve the performance of the pre-trained modulation signal classification model.

[0067] The method provided in the embodiment of the present invention can be applied to an electronic device. Specifically, the electronic device can be: a desktop computer, a portable computer, an intelligent mobile terminal, a server, etc., which is not limited in the embodiment of the present invention.

[0068] Based on the same inventive concept, an embodiment of the present invention further provides a modulation signal classification device. Figure 6 A schematic diagram of the structure of a modulation signal classification device provided by an embodiment of the present invention. Figure 6 As shown, it includes: an acquisition unit 601 and a classification unit 602;

[0069] The acquisition unit 601 is used to: acquire a modulated signal to be classified;

[0070] The classification unit 602 is used to: input the modulated signal to be classified into the pre-trained modulated signal classification model to obtain the modulation signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on the selective state space model, and the Mamba timing layer is introduced on the basis of the Mamba convolution block, and the Mamba timing layer combines the selective SSM mechanism and the MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and timing layers, the MCLDNN convolution blocks are used to extract local features, and the timing layers are used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts the modulation signal samples and the preset loss function, and is constructed based on the mutual learning strategy between the initial Mamba model and the initial MCLDNN model.

[0071] Figure 7 A structural diagram of a modulation signal classification device provided by an embodiment of the present invention includes: a processor 710, a storage medium 720 and a bus 730, wherein the storage medium 720 stores machine-readable instructions executable by the processor 710, and when the modulation signal classification device is running, the processor 710 communicates with the storage medium 720 via the bus 730, and the processor 710 executes the machine-readable instructions to execute the steps of the above method embodiment. The specific implementation method and technical effect are similar and will not be repeated here.

[0072] The storage medium may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage. Optionally, the storage medium may also be at least one storage device located away from the aforementioned processor.

[0073] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0074] The present invention also provides a computer-readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above-mentioned modulation signal classification methods are implemented.

[0075] It should be noted that the terms "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention.

[0076] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification.

[0077] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art can understand and implement other changes of the above disclosed embodiments by viewing the drawings and the disclosed content. In the description of the present invention, the term "comprising" does not exclude other components or steps, "one" or "an" does not exclude multiple situations, and the meaning of "multiple" is two or more, unless otherwise clearly and specifically limited. In addition, certain measures are recorded in different embodiments, but this does not mean that these measures cannot be combined to produce good results.

[0078] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for classifying a modulated signal, characterized in that: include: Obtaining a modulation signal to be classified; Inputting the modulation signal to be classified into a pre-trained modulation signal classification model to obtain a modulation signal classification result; The pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on a selective state space model, and a Mamba timing layer is introduced on the basis of the Mamba convolution block, and the Mamba timing layer combines the selective SSM mechanism and the MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes multiple MCLDNN convolution blocks and a timing layer, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts modulated signal samples and a preset loss function, and is constructed based on a mutual learning strategy between an initial Mamba model and an initial MCLDNN model.

2. The method for classifying modulation signals according to claim 1, characterized in that: The pre-trained MCLDNN model includes: a first convolution block, a second convolution block, a third convolution block, a fourth convolution block, a fifth convolution block, a first sequential layer, a second sequential layer, a first fully connected layer, and a second fully connected layer; The first convolution block, the second convolution block and the third convolution block are connected in parallel, the outputs of the second convolution block and the third convolution block are connected in series with the fourth convolution block, the output of the first convolution block is connected in parallel with the output of the fourth convolution block, the first convolution block and the fourth convolution block are connected in series with the fifth convolution block, and the fifth convolution block is connected in series with the first sequential layer, the second sequential layer, the first fully connected layer and the second fully connected layer in sequence.

3. The method for classifying modulation signals according to claim 2, characterized in that: The pre-trained Mamba model includes: a sixth convolution block, a seventh convolution block, an eighth convolution block, a ninth convolution block, a tenth convolution block, a first Mamba timing layer, a second Mamba timing layer, a third fully connected layer, and a fourth fully connected layer; The sixth convolution block, the seventh convolution block and the eighth convolution block are connected in parallel, the outputs of the seventh convolution block and the eighth convolution block are connected in series with the ninth convolution block, the output of the sixth convolution block is connected in parallel with the output of the ninth convolution block, the sixth convolution block and the ninth convolution block are connected in series with the tenth convolution block, and the tenth convolution block is sequentially connected in series with the first Mamba timing layer, the second Mamba timing layer, the third fully connected layer and the fourth fully connected layer.

4. The method for classifying modulation signals according to claim 3, characterized in that: The output dimensions of the first fully connected layer and the third fully connected layer are both 128, and the output dimensions of the second fully connected layer and the fourth fully connected layer are both 11; the optimizers of the MCLDNN model and the Mamba model both use Adam.

5. The method for classifying modulation signals according to claim 1, characterized in that: The preset loss function is expressed as: Among them, L1 represents the preset loss function, CrossEntropyLoss represents the cross entropy loss function, Represents the modulated signal sample X label Input the prediction score of the initial MCLDNN model, Represents the modulated signal sample X label Enter the prediction score of the initial Mamba model, Represents the modulated signal sample X label The real label, Represents the one-hot encoding converted from the prediction score of the initial MCLDNN model, Represents the one-hot encoding transformed according to the prediction score of the initial Mamba model.

6. The method for classifying modulation signals according to claim 1, characterized in that: The training process of the pre-trained modulation signal classification model includes: Obtaining a modulated signal sample; Inputting the modulated signal samples into an initial modulated signal classification model for iterative training; Using the initial modulation signal classification model that meets the preset iteration stop condition as the pre-trained modulation signal classification model; The preset iteration stop condition includes: the number of iterations is greater than a preset iteration threshold or the preset loss function continues to converge.

7. The method for classifying modulation signals according to claim 1, characterized in that: The pre-trained Mamba model is used to process the modulated signal to be classified in a low signal-to-noise ratio environment, and enhances the nonlinear expression ability and dynamic information filtering effect of the pre-trained modulated signal classification model through a selective SSM mechanism.

8. A modulation signal classification device, characterized in that: include: The acquisition unit is used to: acquire the modulation signal to be classified; The classification unit is used to: input the modulated signal to be classified into a pre-trained modulated signal classification model to obtain a modulation signal classification result; the pre-trained modulated signal classification model is provided with a pre-trained Mamba model and a pre-trained MCLDNN model connected in parallel; the pre-trained Mamba model is constructed based on a selective state space model, and a Mamba timing layer is introduced on the basis of a Mamba convolution block, and the Mamba timing layer combines a selective SSM mechanism and an MLP structure to form a homogeneous stacking module; the pre-trained MCLDNN model includes a plurality of MCLDNN convolution blocks and a timing layer, the MCLDNN convolution block is used to extract local features, and the timing layer is used to capture long sequence dependencies; the pre-trained modulated signal classification model adopts a modulated signal sample and a preset loss function, and is constructed based on a mutual learning strategy between an initial Mamba model and an initial MCLDNN model.

9. A modulation signal classification device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the modulation signal classification device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the modulation signal classification method as described in any one of claims 1-7.

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