Signal category-based network simulation method, device, equipment, medium, and product

Abstract

The present application discloses a network simulation method, apparatus, device, medium and product based on signal categories. The method includes: for each communication signal, inputting fusion features into a signal recognition model to determine the category of the communication signal, wherein the fusion features are determined based on the temporal features and spatial features of the communication signal; constructing a model of the communication network based on a graph representation method, wherein each node in the graph represents a network device in the communication network; for each category of the communication signal, optimizing the network topology of the communication network model with the goal of meeting network performance requirements and minimizing costs; predicting and optimizing the propagation performance of each category of communication signal in the communication network. The category of the communication signal is determined using the fusion features, and in the constructed network model, the network topology and propagation performance are optimized for the category, thereby improving the accuracy and robustness of the network simulation, providing corresponding optimization solutions for various types of communication signals, and improving the communication performance of various types of communication signals.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of communication network technology, and in particular to a signal category-based network simulation method, apparatus, device, medium, and product. Background Art

[0002] The rapid development and widespread application of communications technology have placed higher demands on the deployment, optimization, and maintenance of communications networks. Against this backdrop, communications network simulation technology has become a crucial research area. Simulation technology can help researchers and engineers predict network performance and optimize network configuration before actual deployment, while also providing a practical platform for education and training. Communications network simulation typically includes modules such as network planning, device configuration, signal transmission, and performance evaluation. These modules simulate the basic operations of 5G networks, including packet transmission, network coverage, and user device access. These process conditions rely on high-performance computing platforms and accurate network models. These models must be configured based on actual network parameters, including base station layout, frequency usage, and communication signal types.

[0003] Currently, the recognition of communication signals is typically based on the statistical properties of the signal, such as power spectral density or autocorrelation function. These methods require manual setting of recognition parameters, signal preprocessing, and feature extraction. When processing signals in complex or noisy environments, they require complex algorithms and extensive computing resources, resulting in limited accuracy. In general, existing communication signal recognition methods have the following shortcomings: limited automation, often requiring manual intervention to set parameters and make adjustments, which limits the flexibility and response speed of the system. The accuracy and robustness of recognition need to be improved. Existing recognition methods perform poorly in complex communication environments, such as those with different types of communication signals, and in the presence of multipath interference and signal attenuation. They struggle to meet the demands for high-precision recognition of communication signals and are unable to provide suitable optimization solutions for different types of communication signals. Summary of the Invention

[0004] The present application provides a network simulation method, apparatus, device, medium and product based on signal category to accurately identify communication signals and improve the accuracy and robustness of network simulation optimization for various types of communication signals.

[0005] In a first aspect, an embodiment of the present application provides a network simulation method based on signal categories, comprising:

[0006] For each communication signal, inputting a fusion feature of the communication signal into a signal recognition model to determine a category of the communication signal through the signal recognition model, wherein the fusion feature is determined according to a temporal feature and a spatial feature of the communication signal;

[0007] A communication network model is constructed based on a graph representation, where each node in the graph represents a network device in the communication network;

[0008] For each category of communication signals, optimizing the network topology of the model of the communication network with the goal of meeting network performance requirements and minimizing costs;

[0009] The propagation performance of each category of communication signals in the communication network is predicted and optimized.

[0010] In a second aspect, an embodiment of the present application further provides a network simulation device based on signal categories, including:

[0011] an identification module, configured to input, for each communication signal, a fusion feature of the communication signal into a signal identification model, so as to determine a category of the communication signal through the signal identification model, wherein the fusion feature is determined based on a temporal feature and a spatial feature of the communication signal;

[0012] A building module, for building a model of a communication network based on a graph representation, where each node in the graph represents a network device in the communication network;

[0013] A topology optimization module, configured to optimize the network topology of the model of the communication network for each category of communication signals with the goal of meeting network performance requirements and minimizing costs;

[0014] The performance optimization module is used to predict and optimize the propagation performance of each category of communication signals in the communication network.

[0015] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0016] one or more processors;

[0017] a storage device for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the signal category-based network simulation method as described in the first aspect.

[0019] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the signal category-based network simulation method as described in the first aspect.

[0020] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program and / or instructions, which, when executed by a processor, implements the signal category-based network simulation method as described in any of the above embodiments.

[0021] The embodiments of the present application provide a network simulation method, apparatus, device, medium and product based on signal categories. The network simulation method based on signal categories includes: for each communication signal, inputting the fusion features of the communication signal into a signal recognition model to determine the category of the communication signal through the signal recognition model, wherein the fusion features are determined according to the temporal features and spatial features of the communication signal; constructing a model of the communication network based on a graph representation method, wherein each node in the graph represents a network device in the communication network; for each category of the communication signal, optimizing the network topology of the model of the communication network with the goal of meeting network performance requirements and minimizing costs; predicting and optimizing the propagation performance of each category of communication signal in the communication network. The category of the communication signal is determined by utilizing the fusion features, and in the network model constructed based on the graph representation method, the network topology and propagation performance are optimized for the category, thereby improving the accuracy and robustness of the network simulation, providing targeted optimization solutions for various types of communication signals, and thereby improving the communication performance of various types of communication signals in the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0023] Figure 1 A flowchart of a network simulation method based on signal categories provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a communication network model based on a graph representation provided in an embodiment of the present application;

[0025] Figure 3 A flowchart of a network simulation method based on signal categories provided in an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the structure of a network simulation device based on signal categories provided in an embodiment of the present application;

[0027] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the present application and are not intended to limit the present application. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions of the present application, not all of the structures.

[0029] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0030] It should be noted that the concepts of "first" and "second" mentioned in the embodiments of this application are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order or interdependence of the functions performed by these devices, modules, units or other objects.

[0031] In addition, the embodiments and features in the embodiments of the present application may be combined with each other unless there is any conflict.

[0032] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0033] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the relevant content of the solution.

[0034] Figure 1 This is a flowchart of a signal-classification-based network simulation method provided in an embodiment of the present application. This embodiment is applicable to situations where communication signals and communication networks are simulated. Specifically, the signal-classification-based network simulation method can be performed by a signal-classification-based network simulation device, which can be implemented in software and / or hardware and integrated into an electronic device. Electronic devices include, but are not limited to, computers, smartphones, servers, and other devices with computing capabilities.

[0035] like Figure 1 As shown, the method specifically includes the following steps:

[0036] S110. For each communication signal, input the fusion feature of the communication signal into a signal recognition model to determine the category of the communication signal through the signal recognition model, wherein the fusion feature is determined according to the temporal feature and spatial feature of the communication signal.

[0037] In this embodiment, the communication signal may refer to a modulated signal in a communication network, such as a 5G communication signal or a 6G communication signal. The category of the communication signal may refer to the modulation method of the communication signal, such as amplitude shift keying (ASK), phase shift keying (PSK), binary phase shift keying (BPSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). It may also refer to the length, importance, or format of the communication signal. It may also be divided into different categories according to frequency, format, service, etc., such as cellular mobile network signals, WIFI signals, low frequency signals, high frequency signals, amplitude modulation signals, frequency modulation signals, voice signals, or image signals. The basis for classifying communication signals can be set according to actual needs. In the communication network simulation process, the communication signal can be used as input data, and different categories of communication signals may affect the simulation results (such as network topology, network parameters, cost, etc.).

[0038] For each communication signal, fusion features can be determined based on its spatial and temporal features, and its category can be determined based on the fusion features. Spatial features can be understood as properties of the communication signal in the spatial domain (or time domain), and can include the signal's amplitude, frequency, and / or phase. When using a CNN to extract spatial features, spatial features can refer to local patterns of the communication signal in space, such as edges, texture, and / or shape. Temporal features can include the characteristics and patterns of communication signals changing over time. On this basis, the spatial and temporal features can be fused to combine the outputs of different neural networks into a unified representation for input into the signal recognition model. The feature fusion process can be implemented through feature mapping and normalization, as well as cascading (or stacking).

[0039] The signal recognition model can be a trained artificial intelligence model, such as a deep learning model, that analyzes and infers the characteristics of the input signal and outputs the inference result (i.e., the corresponding category of the signal). Its input is the fused features of the communication signal, and its output is the corresponding recognition result, i.e., the category of the communication signal.

[0040] On the basis of the above, the signal recognition model can automatically, accurately and efficiently identify the category of communication signals according to the fusion features, providing a reliable input basis for wireless network simulation modeling.

[0041] S120. Construct a model of the communication network based on a graph representation method, where each node in the graph represents a network device in the communication network.

[0042] Figure 2 A schematic diagram of a communication network model based on a graph representation is provided in an embodiment of the present application. Figure 2 As shown, a network model of a communication network is constructed using graph representation from graph theory. Each node represents a network device in the communication network, and each edge represents a connection or link between network devices. The formula is as follows: G = (V, E). Where G is the graph, V is the set of nodes, and E is the set of edges.

[0043] S130 . For each category of communication signals, optimizing the network topology of the communication network model with the goal of meeting network performance requirements and minimizing costs.

[0044] Specifically, the entire communication network is modeled and simulated. The feasibility of the proposed solution is measured using the simulation results, and the most appropriate system configuration and parameter settings are selected for application in the actual communication network. During the communication network simulation process, the input signal data can be repeatedly changed to observe and analyze the response of the constructed communication network model to the input signal and the performance of the communication network during the simulation.

[0045] The input to the communication network model includes communication signals of identified categories. During the network simulation process, network topology optimization can be performed for each category. That is, for each category, the optimal network topology is determined with the optimization objectives of maximizing network performance and minimizing cost for the communication signals of that category.

[0046] For example, for a category of communication signals (there may be one or more such categories of communication signals), its transmission area A in the communication network may involve x1 nodes, y1 edges and z1 links. In order to maximize the network performance and minimize the cost of such category of communication signals, unnecessary nodes, edges or links in the transmission area A can be removed, or some redundant links in the transmission area A can be optimized to shorten the transmission path while ensuring that the communication signal can reach the destination device; or the transmission of some heavily loaded nodes or edges in the transmission area A can be dispersed to nodes or edges with lighter loads, so that the transmission area A The load within the network is relatively balanced, avoiding the situation where some nodes are overloaded or underloaded and resulting in unreasonable resource utilization; similarly, for another category of communication signals (there may be one or more communication signals of this category), its transmission area B in the communication network may involve x2 nodes, y2 edges and z2 links. It is possible to remove unnecessary nodes, edges or links in the transmission area B, or optimize some links in the transmission area B, or disperse the transmission of some heavily loaded nodes or edges in the transmission area B to lightly loaded nodes or edges, etc., so as to maximize the network performance and minimize the cost of this category of communication signals.

[0047] It is understandable that different categories of communication signals may have overlapping transmission areas, nodes, edges or links, and there may be different or even conflicting optimization schemes for different categories of communication signals. In this case, the network topology optimization scheme within the common transmission area (the intersection of transmission areas) can be determined with the optimization goals of maximizing the network performance and minimizing the cost of communication signals; the network topology optimization scheme within the total transmission area (the union of transmission areas) can also be determined with the optimization goals of maximizing the network performance and minimizing the cost of communication signals; the network topology optimization scheme within the total transmission area can also be determined with the optimization goals of maximizing the network performance and minimizing the cost of communication signals in the entire network graph.

[0048] In one embodiment, for a class of communication signals, the specific method for optimizing the network topology may be: applying a dynamic programming algorithm to balance the goals of minimizing cost and maximizing network performance. Specifically, the cost may be minimized while meeting the network performance requirements. The objective function may be expressed as: Formula: C * =min e∈E {C(e)+Σ v∈V P(v)}, where C * is the optimal cost (the minimum cost after optimization), C(e) is the cost of edge e, and P(v) is the performance indicator of node v. Costs can include economic and resource consumption related to network devices and connections. The optimized network topology is both cost-effective and meets performance requirements.

[0049] S140: Predict and optimize the propagation performance of each category of communication signals in the communication network.

[0050] Specifically, key performance indicators (KPIs) can be set for the propagation performance of communication signals in communication networks, such as throughput (the amount of data successfully transmitted divided by the transmission time), latency (the transmission time of a packet divided by the number of packets), bit error rate (the ratio of erroneous symbols to the total number of transmitted symbols), signal coverage, network capacity, and / or data transmission rate. By predicting the throughput, latency, and / or bit error rate of each type of communication signal transmitted in the communication network, it is possible to predict the quality of propagation performance and adjust the network topology and configuration parameters accordingly to optimize propagation performance. Deep learning algorithms can be introduced to automatically analyze network conditions and provide optimization recommendations, enabling dynamic learning and adaptive adjustments.

[0051] Leveraging the self-learning capabilities of deep learning algorithms, the simulation environment can be adjusted in real time during the simulation process to adapt to changing network conditions. This system automatically extracts key features of communication signals through neural networks for high-fidelity processing, and achieves high-precision classification of communication signals through complex pattern recognition. Furthermore, it integrates advanced network behavior simulation algorithms with real-time data streams to build a communication network model that closely resembles the actual operating environment. This highly simulated environment provides a reliable platform for testing, verification, and optimization of communication networks, effectively reducing the discrepancy between simulation and real-world operations.

[0052] The embodiment of the present application provides a network simulation method based on signal categories, which uses an advanced deep learning model to automatically extract the characteristics of communication signals and perform rapid and accurate identification. This method can significantly reduce manual intervention and improve the efficiency and accuracy of identification. On this basis, for the simulation of communication networks, high-fidelity processing can be performed on communication signals, and high-precision classification of modulated signals can be achieved, especially in complex communication environments such as multipath effects and signal attenuation, which significantly improves the accuracy of signal identification. In addition, the category of communication signals is determined by fusion features. In the constructed network model, the network topology and propagation performance are optimized according to the category, the accuracy and robustness of network simulation are improved, and corresponding optimization solutions are provided for various types of communication signals, thereby improving the communication performance of various types of communication signals.

[0053] In one embodiment, S110 includes:

[0054] S1110. Input the communication signal into a first neural network and a second neural network respectively, so as to extract spatial features of the communication signal through the first neural network and extract temporal features of the communication signal through the second neural network.

[0055] In this embodiment, the signal recognition model includes two neural networks, and the communication signal can be input into the first neural network and the second neural network respectively, so as to extract the spatial features of the communication signal through the first neural network and extract the temporal features of the communication signal through the second neural network. The first neural network and the second neural network are used to extract the spatial features and temporal features of the communication signal, respectively. The first neural network is, for example, various forms of convolutional neural networks (CNN), and the second neural network is, for example, a long short-term memory network (LSTM), a recurrent neural network (RNN), or a Transformer model.

[0056] Optionally, the first neural network is a CNN, which can also determine the pattern or structure of the communication signal based on identifiable and recurring local features in the communication signal through the first neural network, thereby understanding the essence of the communication signal and providing a basis for accurately identifying the communication signal. For example, local features may include specific frequency components or waveform features. Patterns or structures may include the following: 1) Modulation characteristics: Different modulation technologies, such as Quadrature Amplitude Modulation (QAM) or Orthogonal Frequency Division Multiplexing (OFDM), will present specific patterns in the communication signal; 2) Signal waveform: The basic shape of the communication signal, such as a sine wave or a square wave, is its manifestation in the time domain; 3) Spectral distribution: The distribution of the communication signal in the frequency domain, including the position and intensity of the main frequency components.

[0057] Optionally, the second neural network is an LSTM. LSTM is a special type of RNN that is sensitive to time and can learn features and patterns in time series data, capturing temporal dependencies. This makes it advantageous for tasks such as dynamic change analysis of communication signals or time series prediction. The update formula for the LSTM unit is as follows:

[0058] f t =σ(W f ·[h t-1 , x t ]+b f )

[0059] i t =σ(W i ·[h t-1 , x t ]+b i )

[0060]

[0061] o t =σ(W o ·[h t-1 , x t ]+b o )

[0062] h t =o t ⊙tanh(C t )

[0063] Among them, f t ,i t , o t are the activation values ​​of the forget gate, input gate, and output gate, respectively. are candidate memory cells, C t is the memory cell state, h t is the hidden state output, W represents the weight matrix, W f 、W i 、W o 、W C Represent the weights of the forget gate, input gate, output gate, and memory cell state, b represents the bias vector, and b f 、b i 、b o 、b C Represent the bias vectors of the forget gate, input gate, output gate, and memory cell state respectively, [h t-1 , x t ] represents the concatenation of the hidden state of the previous moment and the input of the current moment, σ is the sigmoid activation function, tanh is the hyperbolic tangent activation function, and ⊙ represents element-by-element multiplication (Hadamard multiplication).

[0064] In this embodiment, the first neural network and the second neural network work together to extract the spatial features and temporal features of the communication signal respectively, and then combine the two features through feature fusion. The first neural network and the second neural network can share certain layers or interact in different parts of the network to achieve feature fusion and deeper analysis, thereby providing a more comprehensive signal feature representation, which can enable the signal recognition model to more effectively cope with the complexity of the communication signal and improve the accuracy of network simulation based on signal category.

[0065] Optionally, by analyzing multi-scale time series, the characteristics of communication signals at different time scales can be extracted. Multi-scale analysis can be performed using wavelet transform: Among them, C A (a, b) are the coefficients of wavelet transform, ψ A is a wavelet function, and a and b are scale and translation parameters, respectively. It should be noted that the first neural network can be used to extract spatial features of communication signals, such as edges or textures in images, and can also identify local patterns and structures in communication signals. The use of multi-scale features can enhance the feature extraction capability of the first neural network and help the first neural network better understand the complex structures and patterns in the signal by providing information at different scales. The second neural network is mainly used to process and predict long-term dependencies in time series data. The use of multi-scale features can provide the second neural network with the ability to analyze signals at different time scales, helping the second neural network accurately capture the short-term and long-term dynamic changes of communication signals.

[0066] Optionally, both the first neural network and the second neural network use multi-scale convolutional layers to respectively extract multi-scale spatial features and temporal features of the communication signal.

[0067] In this embodiment, the communication signal may refer to a converted digital signal. A multi-scale convolutional layer is used to extract multi-scale features of the communication signal. The multi-scale features extracted by the first and second neural networks complement each other and can serve as model input, providing a more comprehensive signal representation for subsequent signal recognition models and improving the model's signal recognition and classification capabilities. The multi-scale features enhance the first neural network's understanding of the signal's local patterns and support the second neural network's capture of the signal's temporal dynamics, facilitating the identification of complex patterns within the signal.

[0068] Optionally, the training process of the signal recognition model includes: for each sample signal, inputting the sample signal into the first neural network and the second neural network respectively, so as to extract the sample space features of the sample signal through the first neural network, and extracting the sample time series features of the sample signal through the second neural network; fusing the sample space features and the sample time series features to obtain the sample fusion features of the sample signal; selecting the target sample features from the sample fusion features; and training the signal recognition model based on the target sample features of each sample signal.

[0069] In this embodiment, for each sample signal, the multi-scale features extracted by the first and second neural networks are fused, and then target sample features are selected (using DBN and mutual information-based feature selection methods) to identify the feature subset most relevant to the target variable. The optimized target sample features are then used to train a signal recognition model to achieve high-precision recognition and classification of communication signals. The trained model will be deployed in an actual communication network environment to provide real-time signal analysis and network optimization decisions.

[0070] S1120: Fuse the spatial feature and the temporal feature to obtain an original fused feature of the communication signal.

[0071] The feature fusion process can be implemented based on feature mapping and normalization, as well as cascading (or stacking).

[0072] S1130, converting the original fusion features into abstract features through a deep belief network;

[0073] In this embodiment, the Deep Belief Network (DBN) is a generative model composed of multiple Restricted Boltzmann Machines (RBMs) stacked together, which is suitable for feature fusion and abstract representation of high-level features. DBN processes signals in a layer-by-layer manner, and each layer extracts features from the input data and fuses these features into higher-level abstract features. By using DBN layer-by-layer abstraction, low-level original fusion features (such as basic statistical characteristics and simple patterns of communication signals) can be converted into higher-level abstract features, thereby deeply understanding the essence of the signal and better representing the intrinsic properties and patterns of the communication signal. It provides a powerful feature representation for the recognition and classification of communication signals. Specifically, each RBM layer can be activated in the following ways: i =σ(∑ j W ij h j +b i )h j =σ(∑ i W ij v i +b j ), where v and h represent the activation values ​​of the visible layer and the hidden layer respectively, W is the weight matrix, and b is the bias term.

[0074] S1140 : Based on the mutual information between features and categories, select some features from the abstract features as fusion features of the communication signal.

[0075] In this embodiment, for abstract features, feature selection can be used to optimize the feature set and remove redundant or irrelevant features to improve the recognition performance of the signal recognition model. The selected features can be understood as representative features that are helpful in distinguishing communication signal types, or high-level abstract features that can represent the inherent properties and complex patterns of communication signals. This can achieve efficient and accurate recognition of communication signals using fewer, high-quality features.

[0076] For example, feature selection can be performed based on mutual information (MI), whereby the abstract features with the highest mutual information can be selected as target features, which helps improve the prediction accuracy of the model. Based on mutual information, the interdependence or correlation between the abstract features and the target variable can be evaluated to determine which abstract features have the greatest information sharing with the target variable (the variable being predicted or classified, i.e., the category of the communication signal), thereby selecting the most useful target features, which contain the most informative information about the target variable.

[0077] Specifically, calculate the mutual information between each abstract feature and the target variable:

[0078] Among them, I(x;y) represents the mutual information between the abstract feature x and the target variable y, p(x,y) is the joint probability distribution of the abstract feature and the target variable, and p(x) and p(y) are the marginal probability distributions of the abstract feature and the target variable, respectively.

[0079] Optionally, during the process of extracting spatial features by the first neural network, frequency domain features may also be extracted as part of the fusion feature, thereby more comprehensively analyzing the characteristics of the communication signal and further improving the accuracy of communication signal recognition. Specifically, the method of extracting frequency domain features may include at least one of the following:

[0080] Method 1: Convert the time domain signal into a frequency domain signal. For example, the time domain signal is converted into a frequency domain representation through Fast Fourier Transform (FFT), and then the frequency domain representation is analyzed using a first neural network. The convolutional layer in the first neural network can identify patterns in the frequency domain, such as the distribution of specific frequencies or specific structures in the spectrum.

[0081] Method 2: By applying convolutional layers at different scales, the first neural network can simultaneously capture the local and global features of the communication signal and analyze the behavior of the communication signal at different time scales, which may be manifested as the importance of different frequency components in the frequency domain.

[0082] Method 3: This is achieved through forward propagation. Forward propagation refers to the process in which the communication signal passes through the first neural network, with the output of each convolutional layer becoming the input of the next convolutional layer. During this process, the first neural network can learn the signal's hierarchical features, which may include patterns in the frequency domain.

[0083] For example, the forward propagation formula is: (l) =W (l) *A (l-1) +b (l) A (l) =σ(Z (l) , where W (l) Represents the convolution kernel weight of the ,th layer, b (l) Represents the bias term of the,th layer, A (l-1) is the activation output of the previous layer, Z (l) is the linear output of the current layer, and σ is the activation function, such as ReLU (σ(x)=max(0,x)).

[0084] Optionally, before the communication signal to be identified is input into the first neural network and the second neural network respectively, the communication signal can be preprocessed, for example, using a high-precision analog-to-digital converter (ADC) to convert the analog signal into a digital signal to ensure the digitization accuracy of the signal; for example, the communication signal can be processed by an adaptive filter group and multi-scale time series analysis to eliminate the effects of noise and signal attenuation and improve the quality of the communication signal.

[0085] For example, the input to the first neural network can be a communication signal acquired and digitized by an ADC. Before inputting into the first neural network, the digital signal can also be preprocessed, such as filtering and normalization, to ensure signal quality and highlight important features, thereby automatically extracting the frequency and time domain features of the communication signal using the convolutional layer of the CNN.

[0086] Exemplarily, the input to the second neural network can be a preprocessed digital signal sequence to highlight the time series characteristics of the communication signal. These sequences can be digital signals obtained by performing ADC conversion on the communication signal. The communication signal can also undergo some preprocessing, such as segmentation, normalization, differentiation, and / or feature extraction, so that the second neural network can more effectively learn and predict the time series characteristics of the signal. Segmentation can refer to dividing a continuous signal into a series of shorter time series, each containing a certain number of data points; normalization can refer to adjusting the signal's amplitude range to make it suitable for neural network processing; differentiation can refer to calculating the difference between consecutive data points to highlight signal changes; and feature extraction can refer to extracting the statistical properties or other time series characteristics of the signal. Preprocessing can minimize noise and improve the signal-to-noise ratio of the communication signal.

[0087] Communication signals may have different signal-to-noise ratios after different levels of preprocessing. Communication signals using different modulation schemes (such as 4ASK, BPSK, QPSK, OQPSK, 8PSK, 16QAM, and 32QAM) have different processing effects under noise with different signal-to-noise ratios.

[0088] Optionally, before inputting the communication signal to be identified into the first neural network and the second neural network respectively, the method further includes:

[0089] S1010, converting the communication signal into a frequency domain signal through Fourier transform;

[0090] S1020, filtering the frequency domain signal through a bandpass filter;

[0091] S1030 , converting the filtered frequency domain signal into a time domain signal through inverse Fourier transform.

[0092] Specifically, an adaptive filter bank can be established to filter communication signals for real-time noise reduction, enhancement, and frequency band selection, thereby improving signal quality. The adaptive filter bank dynamically adjusts its parameters to adapt to the characteristics of the communication signal, ensuring that useful information in the communication signal is preserved and enhanced while mitigating the effects of noise and other interfering components. This preprocessed communication signal is more suitable for feature extraction and analysis in deep learning models.

[0093] Taking the Least Mean Square (LMS) error algorithm as an example, the weight update formula of the adaptive filter is: Among them, W n is the filter weight of the nth iteration, μ is the learning rate, e n is the error signal, is the conjugate of the input signal.

[0094] In this embodiment, the adaptive filter bank can use a bandpass filter to better remove high-frequency and low-frequency noise in the communication signal. The filtering process can be expressed as: in, and denote the Fourier transform and inverse transform, respectively, and H(f) is the frequency response of the bandpass filter.

[0095] Specifically, the communication signal (analog signal, x(t)) can be converted to the frequency domain through Fourier transform, and then the frequency response H(f) of the bandpass filter is used to selectively retain the frequency components related to the signal in the frequency domain. Finally, the processed signal is converted back to the time domain through inverse Fourier transform to obtain the filtered signal y(t), thereby improving the quality of the communication signal.

[0096] Optionally, before inputting the communication signal to be identified into the first neural network and the second neural network respectively, the method further includes:

[0097] S1040, decomposing the communication signal by wavelet transform to obtain wavelet coefficients of different frequencies;

[0098] S1050, processing wavelet coefficients of different frequencies according to a threshold;

[0099] S1060: Perform inverse wavelet transform on the processed wavelet coefficients to obtain a preprocessed communication signal.

[0100] In this embodiment, in order to ensure efficient application of denoising, the following operations may be performed on the communication signal: filtered (t) = WPT -1 {WPT{x(t)}·Ψ}, where WPT and WPT -1 and WPT-1 Represent wavelet transform and inverse transform respectively, Ψ represents denoising threshold, x filtered (t) represents the preprocessed communication signal.

[0101] Specifically, through wavelet decomposition, the communication signal can be decomposed into wavelet coefficients of different frequencies. The wavelet coefficients represent the energy distribution of the communication signal at different frequencies. This process can use the following wavelet functions: Haar wavelet, Daubechies wavelet, Morlet wavelet, etc.; the wavelet coefficients are threshold processed to set the wavelet coefficients with lower energy to zero, thereby suppressing noise. Hard threshold processing or soft threshold processing can be used. Hard threshold processing can be understood as setting coefficients less than the threshold to zero, and soft threshold processing can be understood as attenuating coefficients less than the threshold; the wavelet coefficients after threshold processing are inversely transformed to obtain the denoised communication signal. The inverse transformation process can restore the original signal by linearly combining the wavelet coefficients with the wavelet basis function.

[0102] The wavelet transform denoising process described above allows for simultaneous analysis of communication signals in both the time and frequency domains, more accurately suppressing noise while preserving the signal's time-frequency characteristics. By decomposing the signal into wavelet coefficients of varying frequencies, multi-resolution analysis can be performed on the signal, suppressing noise of varying frequencies to varying degrees. In some embodiments, a threshold can be adaptively selected based on the signal's energy characteristics, allowing the wavelet transform to better adapt to the noise characteristics of different communication signals, further enhancing the denoising effect.

[0103] Optionally, for the communication signal, the frequency domain is analyzed and filtered by Fourier transform and its inverse transform, and then the time-frequency domain is denoised by wavelet transform to improve the quality of the communication signal.

[0104] In one embodiment, S1120 includes:

[0105] S11210: Normalize the spatial features and the temporal features respectively so that the spatial features and the temporal features have the same scale and distribution;

[0106] S11220: Cascade the normalized spatial features and the temporal features to obtain a fusion feature of the communication signal.

[0107] S1210: Normalize the spatial features and the temporal features respectively so that the spatial features and the temporal features have the same scale and distribution;

[0108] S1220: Concatenate the normalized spatial features and temporal features to obtain a fusion feature of the communication signal. In this embodiment, the normalization of the spatial features and temporal features can be achieved by the following formula: Among them, F is the eigenvector of spatial features or the eigenvector of temporal features, and accordingly, F norm is the eigenvector of the normalized spatial feature or the eigenvector of the temporal feature, μ is the mean of the eigenvector of the spatial feature or the mean of the eigenvector of the temporal feature, and σ is the standard deviation of the eigenvector of the spatial feature or the standard deviation of the eigenvector of the temporal feature. The concatenation of the normalized spatial feature and temporal feature can be expressed as: F concat =[F CNN ; F LSTM ], where F CNN The feature vector representing the normalized spatial features corresponding to the first neural network, F LSTM The feature vector representing the normalized time series features corresponding to the second neural network, F concat The feature vector representing the fused features after cascading, and the semicolon represents the vertical stacking of feature vectors.

[0109] Figure 3 A flowchart of a signal category-based network simulation method provided in an embodiment of the present application.

[0110] like Figure 3 As shown, the method includes:

[0111] S1. Convert the communication signal to be identified into a frequency domain signal through Fourier transform;

[0112] S2, filtering the frequency domain signal through a bandpass filter;

[0113] S3, converting the filtered frequency domain signal into a time domain signal through inverse Fourier transform;

[0114] S4. Decomposing the communication signal by wavelet transform to obtain wavelet coefficients of different frequencies;

[0115] S5. Processing the wavelet coefficients of different frequencies according to the threshold value;

[0116] S6. Perform inverse wavelet transform on the processed wavelet coefficients to obtain the pre-processed communication signal

[0117] S7, inputting the preprocessed communication signal into the first neural network and the second neural network respectively to obtain spatial features and temporal features of the communication signal;

[0118] S8, normalize the spatial features and temporal features respectively;

[0119] S9, cascading the normalized spatial features and temporal features to obtain the original fusion features of the communication signal;

[0120] S10, converting the original fusion features into abstract features through deep belief network;

[0121] S11. Select target features from abstract features based on the mutual information between features and categories;

[0122] S12. Input the target feature into a signal recognition model to determine the category of the communication signal through the signal recognition model.

[0123] The signal category-based network simulation method of this embodiment employs advanced feature extraction mechanisms, such as the multi-scale convolution kernels and pooling layers in convolutional neural networks, to achieve multi-level feature abstraction of communication signals and extract key features in the frequency and time domains. It also employs temporal dependency modeling, effectively capturing the temporal characteristics of communication signals through the gating mechanism of long short-term memory networks, including input gates, forget gates, and output gates, to address the long-term dependencies of time series data. On this basis, through feature fusion and feature selection, the features most relevant to the target variable are selected and identified through a signal recognition model, accurately determining the category of the communication signal.

[0124] On this basis, the network simulation method of this embodiment takes into account that current simulation of communication networks mainly relies on static, preset parameter configurations, lacks the ability to dynamically learn and adapt to different network conditions, and generally requires users to manually configure network parameters and topology structures, and cannot adaptively adjust; and considering that the simulation accuracy of communication networks needs to be improved, existing simulation tools often fail to fully capture network dynamics and complex scenarios when simulating actual network operations, resulting in deviations between simulation results and the real world, affecting the practical value of the simulation. To this end, by integrating advanced network behavior simulation algorithms and real-time data streams, a simulation model that is closer to the actual operating environment is constructed. This highly simulated environment provides a reliable platform for testing, verifying, and optimizing communication networks, effectively reducing the deviation between simulation and real-world operations.

[0125] In one embodiment, S130 includes:

[0126] S1310. Divide the network topology into at least two sub-networks;

[0127] S1320: For each of the sub-networks and each category of communication signals, optimize the network topology of the sub-network with the goal of meeting network performance requirements and minimizing costs.

[0128] In this embodiment, by dividing the network topology into at least two sub-networks, targeted simulation and optimization can be performed on different sub-networks. A sub-network can be understood as a sub-graph of a graph representation of a model of the entire communication network.

[0129] For example, subnetworks can be divided according to different categories of communication signals. For example, the transmission area of ​​each category of communication signal is respectively regarded as a corresponding subnetwork. For N categories of communication signals, there are N subnetworks. The advantage of doing so is that it is more conducive to targeted simulation, optimization and prediction for each category of communication signal.

[0130] Optionally, the subnetworks can also be divided according to the degree of co-reference, where the nodes in the network topology represent the network devices, and the edges represent the degree of co-reference between the network devices. The principle used in dividing the subnetworks is that the ratio of the weight of a hyperedge (an edge that allows the connection of at least two nodes) that originates from a node in the subgraph and crosses its boundary to the sum of the weights of all hyperedges originating from the node of the subgraph is less than a set threshold, and the scale of each subgraph is relatively close, and the gap is within a set range. Among them, for a category of communication signals, the weight of the hyperedge can be comprehensively determined based on the transmission performance (transmission time, transmission quality and / or transmission distance, etc.) of the communication signal of this type on the link corresponding to the hyperedge. On this basis, each device and link in a subnetwork has similar performance, different subnetworks have significantly different performance, and the complexity of different subnetworks is close, thereby improving the reliability and rationality of subnetwork simulation and optimization.

[0131] Exemplarily, optimizing the network topology of a subnetwork can be understood as removing unnecessary nodes, edges or links in the subnetwork, or optimizing some redundant links in the subnetwork to shorten the transmission path while ensuring that the communication signal can reach the destination device; it can also disperse the transmission of some heavily loaded nodes or edges in the subnetwork to nodes or edges with lighter loads, so that the load within the subnetwork is more balanced, avoiding unreasonable resource utilization caused by some nodes being overloaded or underloaded, etc. The purpose is to maximize the network performance of communication signals of various categories within the subnetwork and minimize the cost.

[0132] In one embodiment, S130 includes:

[0133] S1330. Based on a graph neural network algorithm, extract node features and edge features from the network topology of the communication network model.

[0134] Node features can be understood as network device features, such as their type or model, traffic volume, computing power, failure rate, the number of communication signals or frequency that can be carried per unit time, etc. Edge features can be understood as the characteristics of links between network devices, such as bandwidth, latency, and channel quality. The Graph Neural Network (GNN) algorithm uses neural networks to learn graph-structured data, extracting and discovering features and patterns within it. Each graph convolutional layer in the neural network learns the interactions and dependencies between nodes, aggregates information from adjacent nodes, and increases the perception range, enabling each node to capture a wide range of topological information. The final layer is a global pooling layer that aggregates features from all nodes and edges for scoring network performance and cost.

[0135] S1340 . For each category of the communication signal, determine a network performance score and a cost score corresponding to the category according to the characteristics of the node and the characteristics of the edge.

[0136] A scoring model can be trained that has performance evaluation capabilities and can learn the relationships and patterns between node and edge features and performance and cost scores. By inputting node and edge features into the scoring model, network performance and cost scores can be inferred and output. For example, the more advanced the type or model of network equipment, the greater the traffic volume, the greater the computing power, the lower the failure rate, the greater the number of communication signals or communication frequency that can be carried per unit time, the greater the link bandwidth, the lower the latency, or the better the channel quality, the higher the network performance score and the lower the cost score.

[0137] S1350: Optimize the nodes and edges of the network topology according to the network performance score and the cost score.

[0138] Among them, based on the network performance score and cost score, redundant nodes and edges in the network topology can be determined and removed; nodes and edges with heavier loads can be determined and their loads can be distributed to other nodes and edges with lighter loads to achieve load balancing and reasonable bandwidth allocation; the transmission path of communication signals can be optimized, such as reducing the number of hops, increasing link bandwidth, etc., thereby shortening the transmission path and reducing latency; the number of nodes and links can be increased, redundant paths can be established, etc., to improve the capacity and scalability of the network, meet the growing communication needs, and on this basis improve network performance and reduce costs.

[0139] It should be noted that the network topology can be divided into at least two sub-networks, and for each sub-network of the network topology, a graph neural network algorithm is used to extract node features and edge features and perform network performance scoring, cost scoring, and optimization.

[0140] In one embodiment, S140 includes: predicting and optimizing the propagation performance of the communication signal in the communication network by using random forest, gradient boosting machine (GBM) or deep deterministic policy gradient (DDPG).

[0141] Taking GBM as an example, the principles of predicting and optimizing network performance are as follows: in, is the predicted value, η is the intercept, T k (x) is a decision tree. GBM builds a strong prediction model by combining multiple weak prediction models (usually decision trees). In the context of network performance optimization, GBM can be used to predict key performance indicators such as throughput, latency, and bit error rate, and then adjust the network configuration based on these predictions to optimize performance. However, before training the GBM model, a set of training data needs to be collected and labeled. These data include the input features of the network and the corresponding performance indicators (such as throughput, latency, etc.), and the GBM model is trained using this training data. The model will learn the mapping relationship between input features and performance indicators. The finally trained GBM model can be used to predict performance indicators under new network configurations or conditions, or to evaluate the impact of different configurations on performance.

[0142] GBM inputs include features extracted from the signal processing phase and parameters obtained from the network model construction phase. These features comprehensively describe the state and configuration of the network. The output of GBM is the predicted performance indicator value. By training the GBM model to learn the relationship between input features and performance indicators, the model's predictions can be used to guide adjustments to the network configuration to optimize network performance. For example, if GBM predictions indicate that adding certain network resources will improve throughput, resource allocation strategies can be adjusted based on these predictions.

[0143] For DDPG, Q-Learning or Policy Gradient methods can be used. In addition, cloud computing platforms and distributed computing resources can also be used for analytical calculations. The DDPG algorithm learns and optimizes network operation strategies through the interaction between the intelligent agent and the simulation environment, dynamically adjusting simulation parameters to maximize network performance indicators such as throughput and reduce latency. DDPG can handle high-dimensional network configuration spaces and respond to changes in network conditions in real time, thereby achieving better network performance. During the simulation process, the DDPG algorithm uses neural networks to approximate the optimal strategy and continuously updates the strategy to adapt to dynamic changes in the network environment. Through reinforcement learning, the algorithm can self-optimize without the need for manual parameter adjustment, greatly reducing the complexity and cost of simulation. In addition, the DDPG algorithm has demonstrated significant technical advantages in dealing with nonlinear and highly interactive network performance optimization problems, including faster convergence speed and higher policy execution accuracy.

[0144] Optionally, a frequency domain smoothing method may be used during the simulation process to accurately simulate the propagation and changes of signals in an actual network, thereby narrowing the gap between simulation and actual deployment.

[0145] Optionally, the operation and maintenance platform provides a user-friendly interactive interface, allowing users to easily build, configure and debug the network.

[0146] The network simulation process of this embodiment can automatically adjust simulation parameters according to network conditions, improving the intelligence and accuracy of the simulation; it is close to the actual deployment environment and performance, and the simulation results can be directly applied to actual network optimization, providing more reliable data support for network optimization.

[0147] In one embodiment, the method further includes: S1410, simulating the propagation of communication signals in the communication network by using a simulation engine based on a network simulation technology software package or a network simulation tool.

[0148] Among them, network simulation technology software packages such as OPNET and network simulation tools such as NS-3. The node configuration and parameters in the NS-3 network simulation together define the simulated network environment, which is used to simulate and test the behavior and performance of the network under specific conditions. Use a simulation engine based on NS-3 to simulate operations in the communication network to evaluate and optimize network performance. Simulation includes building a network model, configuring network parameters and traffic patterns, and running simulations to collect performance data. By analyzing the simulation results, it is possible to identify bottlenecks in network performance, evaluate the impact of different network configurations, and optimize network design to increase throughput, reduce latency, and improve overall network efficiency. The ultimate goal of simulation is to ensure that the communication network can meet performance requirements and provide users with high-quality services.

[0149] The core of this embodiment's signal-classification-based network simulation method lies in its use of a highly integrated deep learning architecture, integrating convolutional neural networks and long-short-term memory networks to achieve efficient extraction of signal features and accurate modeling of timing dependencies. Through an end-to-end simulation optimization strategy, it provides a continuous solution from network topology planning to performance evaluation, ensuring high fidelity and real-time performance of the simulation process. Adaptive algorithms embedded within the system, such as a parameter adjustment mechanism based on deep reinforcement learning, enable immediate response to dynamic network conditions, thereby optimizing network configuration and improving overall performance.

[0150] It should be noted that the network simulation process based on signal categories and the simulation process of the communication network in the above embodiment are two complementary processes, and both jointly support the performance analysis and optimization of the communication system. The features extracted in the feature processing stage of the communication signal (such as timing features, spatial features, fusion features, abstract features and target features, etc.) can provide input data for the simulation of the communication network, and are used to define the properties and behaviors of the communication signal in the communication network simulation, such as signal strength, modulation mode, etc.; the communication network simulation can evaluate and optimize the propagation performance of the communication signal in the network based on the characteristics of the communication signal, such as signal coverage, network capacity and / or data transmission rate, etc. These evaluations can be performed based on the features obtained from the signal feature processing stage. The optimization of signal features can improve the accuracy of network simulation, and the optimization of network models can improve the efficiency and effectiveness of network operations. The feature processing of communication signals and the communication network simulation work together to provide reliable support for the design and optimization of communication systems.

[0151] Optionally, in order to take user interaction into consideration during the simulation process, an intelligent user interaction interface can be built by integrating natural language processing (NLP) and sentiment analysis technology, allowing users to interact with the system in a more natural and intuitive way. The performance evaluation system adopts a multi-objective optimization algorithm, combined with artificial intelligence-assisted decision analysis tools, to provide comprehensive and in-depth network performance prediction and optimization. A network performance evaluation system based on multi-objective optimization can be constructed, using the Pareto optimal theory to balance the trade-offs between different performance indicators and achieve comprehensive optimization of network performance.

[0152] Optionally, in terms of security, quantum encryption and blockchain technology can be used to provide multi-level security protection for data transmission and processing, ensuring the absolute security of user data and privacy.

[0153] Optionally, microservices and containerization technologies can be adopted in the system architecture. Containerization technology and continuous integration (CI) and continuous deployment (CD) processes can be used to achieve high modularity, rapid iteration, and scalability of the microservice architecture, supporting rapid iteration and elastic scaling of cloud-native applications.

[0154] Alternatively, in multimodal data processing, deep coupled networks and multi-task learning frameworks can be used to effectively fuse and analyze cross-modal information. Multimodal data fusion techniques based on DBN or multimodal deep learning frameworks can be combined with data from signal processing and machine vision to perform cross-modal feature learning.

[0155] Optionally, the network can be given adaptability and autonomy by integrating self-organizing network (Self-Organizing Network) technology, including self-configuration, self-healing and self-optimization functions, so that it can automatically respond to changes in the external environment and achieve intelligent management and optimization of network resources.

[0156] Optionally, Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies are used to achieve flexible configuration and optimization of network resources.

[0157] Optionally, a heterogeneous computing platform can be used to combine the computing power of the Central Processing Unit (CPU) and the Graphics Processing Unit (GPU), and use the Compute Unified Device Architecture (CUDA) or Open Computing Language (OpenCL) for parallel computing optimization to accelerate the training and inference process of deep learning models.

[0158] Optional, dynamic network behavior simulation: Leverage agent-based modeling (ABM) and meta-heuristic algorithms, such as genetic algorithms (GA) or particle swarm optimization (PSO), to simulate dynamic behaviors and adaptive mechanisms in the network.

[0159] Figure 4 This is a schematic diagram of the structure of a network simulation device based on signal categories provided in an embodiment of the present application. The network simulation device based on signal categories provided in this embodiment includes:

[0160] an identification module 210 for inputting, for each communication signal, a fusion feature of the communication signal into a signal recognition model to determine a category of the communication signal through the signal recognition model, wherein the fusion feature is determined based on a temporal feature and a spatial feature of the communication signal;

[0161] A construction module 220 is configured to construct a model of the communication network based on a graph representation, where each node in the graph represents a network device in the communication network;

[0162] a topology optimization module 230 for optimizing the network topology of the model of the communication network for each category of communication signals with the goal of meeting network performance requirements and minimizing costs;

[0163] The performance optimization module 240 is configured to predict and optimize the propagation performance of each category of communication signals in the communication network.

[0164] The device uses fusion features to determine the category of communication signals. In the constructed network model, it optimizes the network topology and propagation performance according to the category, improves the accuracy and robustness of network simulation, provides corresponding optimization solutions for various types of communication signals, and improves the communication performance of various types of communication signals.

[0165] Optionally, the device further includes: a fusion feature determination module, configured to:

[0166] Inputting the communication signal into a first neural network and a second neural network respectively, so as to extract spatial features of the communication signal through the first neural network and extract temporal features of the communication signal through the second neural network;

[0167] Fusing the spatial features and the temporal features to obtain original fused features of the communication signal;

[0168] Converting the original fusion features into abstract features through a deep belief network;

[0169] Based on the mutual information between features and categories, some features are selected from the abstract features as fusion features of the communication signal.

[0170] Optionally, fusing the spatial feature and the temporal feature to obtain a fused feature of the communication signal includes:

[0171] Normalizing the spatial features and the temporal features respectively so that the spatial features and the temporal features have the same scale and distribution;

[0172] The normalized spatial features and the temporal features are cascaded to obtain a fusion feature of the communication signal.

[0173] Optionally, for each category of communication signals, optimizing the network topology of the communication network model with the goal of meeting network performance requirements and minimizing costs includes:

[0174] Based on the graph neural network algorithm, the network topology of the communication network model is used to extract node features and edge features;

[0175] For each category of the communication signal, determine a network performance score and a cost score corresponding to the category according to the characteristics of the node and the characteristics of the edge;

[0176] Optimizing nodes and edges of the network topology according to the network performance score and the cost score.

[0177] Optionally, optimizing the network topology of the communication network model according to the category of each communication signal with the goal of meeting network performance requirements and minimizing costs includes:

[0178] Dividing the network topology into at least two sub-networks;

[0179] For each of the sub-networks and each category of communication signals, the network topology of the sub-network is optimized with the goal of meeting network performance requirements and minimizing costs.

[0180] Optionally, predicting and optimizing propagation performance of each category of communication signals in the communication network includes:

[0181] The propagation performance of each category of communication signals in the communication network is predicted and optimized by random forest, gradient boosting machine or deep deterministic policy gradient DDPG.

[0182] The signal category-based network simulation device provided in the embodiment of the present application can be used to execute the signal category-based network simulation method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0183] Figure 5 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided for example only and are not intended to limit the implementation of the present application as described and / or claimed herein.

[0184] like Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0185] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks and wireless networks.

[0186] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above.

[0187] In some embodiments, the methods of the above embodiments may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform any of the above-described methods in any suitable manner (e.g., by means of firmware).

[0188] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0189] Computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0190] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0191] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 10 having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device 10. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0192] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0193] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0194] An embodiment of the present application further provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements the signal category-based network simulation method as described in any of the above embodiments.

[0195] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of this application can be achieved. This is not limited herein.

[0196] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A network simulation method based on signal classification, characterized in that: include: For each communication signal, inputting a fusion feature of the communication signal into a signal recognition model to determine a category of the communication signal through the signal recognition model, wherein the fusion feature is determined according to a temporal feature and a spatial feature of the communication signal; A communication network model is constructed based on a graph representation, where each node in the graph represents a network device in the communication network; For each category of communication signals, optimizing the network topology of the model of the communication network with the goal of meeting network performance requirements and minimizing costs; predicting and optimizing the propagation performance of each class of communication signals in the communication network; The method of optimizing the network topology of the communication network model for each category of communication signals with the goal of meeting network performance requirements and minimizing costs includes: Based on the graph neural network algorithm, the network topology of the communication network model is used to extract node features and edge features; For each category of the communication signal, determine a network performance score and a cost score corresponding to the category according to the characteristics of the node and the characteristics of the edge; Optimizing nodes and edges of the network topology according to the network performance score and the cost score; The method further comprises: Inputting the communication signal into a first neural network and a second neural network respectively, so as to extract multi-scale spatial features of the communication signal through the first neural network and extract multi-scale temporal features of the communication signal through the second neural network; Fusing the spatial features and the temporal features to obtain original fused features of the communication signal; Converting the original fusion features into abstract features through a deep belief network; Selecting some features from the abstract features as fusion features of the communication signal based on the mutual information between the features and the categories; Among them, when optimizing the network topology, when different categories of communication signals have overlapping transmission areas in the communication network and there are conflicts in the optimization schemes for the different categories of communication signals, the network topology optimization scheme within the intersection of the transmission areas is determined with the maximization of the communication signal network performance and minimization of the cost at the intersection of the transmission areas as the optimization goal.

2. The method according to claim 1, characterized in that The spatial feature and the temporal feature are fused to obtain the original fused feature of the communication signal, including: Normalizing the spatial features and the temporal features respectively so that the spatial features and the temporal features have the same scale and distribution; The normalized spatial features and the temporal features are cascaded to obtain the original fusion features of the communication signal.

3. The method according to claim 1, characterized in that Predicting and optimizing the propagation performance of each category of communication signals in the communication network, including: The propagation performance of each category of communication signals in the communication network is predicted and optimized by random forest, gradient boosting machine or deep deterministic policy gradient DDPG.

4. A network simulation device based on signal category, characterized in that: include: an identification module, configured to input, for each communication signal, a fusion feature of the communication signal into a signal identification model, so as to determine a category of the communication signal through the signal identification model, wherein the fusion feature is determined based on a temporal feature and a spatial feature of the communication signal; A building module, for building a model of a communication network based on a graph representation, where each node in the graph represents a network device in the communication network; A topology optimization module, configured to optimize the network topology of the model of the communication network for each category of communication signals with the goal of meeting network performance requirements and minimizing costs; a performance optimization module, configured to predict and optimize the propagation performance of each category of communication signals in the communication network; The topology optimization module is specifically configured to extract node features and edge features from the network topology of the communication network model based on a graph neural network algorithm, determine, for each category of the communication signal, a network performance score and a cost score corresponding to the category based on the node features and the edge features, and optimize the nodes and edges of the network topology based on the network performance score and the cost score; The apparatus further includes: a fusion feature determination module, configured to input the communication signal into a first neural network and a second neural network, respectively, to extract multi-scale spatial features of the communication signal through the first neural network, to extract multi-scale temporal features of the communication signal through the second neural network, to fuse the spatial features and the temporal features to obtain original fused features of the communication signal, and to convert the original fused features into abstract features through a deep belief network; and to select some features from the abstract features as fused features of the communication signal based on mutual information between features and categories; Among them, when optimizing the network topology, when different categories of communication signals have overlapping transmission areas in the communication network and there are conflicts in the optimization schemes for the different categories of communication signals, the network topology optimization scheme within the intersection of the transmission areas is determined with the maximization of the communication signal network performance and minimization of the cost at the intersection of the transmission areas as the optimization goal.

5. An electronic device, characterized in that: include: at least one processor; a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the signal category-based network simulation method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the signal category-based network simulation method according to any one of claims 1 to 3 is implemented.

7. A computer program product comprising a computer program and / or instructions, characterized in that When the computer program and / or instructions are executed by a processor, the signal category-based network simulation method according to any one of claims 1 to 3 is implemented.

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