A cell interference modeling method based on a weighted graph convolution network

Abstract

The application relates to a cell interference modeling method based on a weighted graph convolution network and belongs to the technical field of wireless communication. The method combines wireless network related data and a graph neural network to solve a core problem: how to accurately model the inter-cell interference of all wireless access points in a region according to real-time performance data and engineering parameters of the wireless access points. In view of the problem, the application analyzes influencing factors of the inter-cell interference, and designs a weighted graph convolution network combining real-time wireless resources and a frequency band coincidence rate, so that the inter-cell interference in a multi-wireless access point scenario in a cellular network can be accurately obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to an inter-cell interference modeling method based on a weighted graph convolutional network. Background Art

[0002] Predicting and modeling interference at wireless access points is crucial and can significantly improve wireless network performance and management efficiency. First, interference prediction can increase data rates and reduce bit error rates, thereby enhancing network performance. Optimizing spectrum utilization is another major benefit. Interference prediction allows for efficient spectrum allocation and dynamic spectrum management, avoiding resource waste and conflicts. Improving user experience is crucial. By reducing dropped calls and improving service quality, users enjoy more stable and high-quality communication services. Furthermore, interference prediction enhances network reliability by preventing potential issues, rapidly responding to faults, and providing efficient diagnostic and recovery measures. Energy savings are also a key benefit. Optimizing power control can reduce energy consumption in wireless access points and terminal devices, thereby reducing operating costs. Modeling wireless access point interference is primarily driven by a deeper understanding of interference sources and characteristics. Modeling can identify major interference sources, analyze the time-frequency characteristics, and spatial distribution of interference, providing a scientific basis for developing interference mitigation measures. Modeling can also optimize network planning and design, rationally placing wireless access points to avoid areas of high interference risk, and develop effective spectrum planning strategies. Support for dynamic resource management is another key factor. Interference modeling allows for real-time adjustment of network parameters and load balancing, optimizing network resource utilization. Modeling also enhances the effectiveness of interference mitigation technology by guiding interference mitigation strategies and optimizing algorithms, improving their efficiency and effectiveness.

[0003] With the development of big data technology and artificial intelligence, deep neural networks have emerged as a promising technology in the wireless communications field, particularly in wireless traffic prediction, network anomaly detection, and wireless network performance optimization. They can autonomously learn complex nonlinear relationships from large amounts of real-world data, further reducing system design complexity while also exhibiting a certain degree of generalization. These advantages enable deep neural networks to provide more accurate inter-cell interference modeling for wireless access points (APs) in wireless communication systems. Leveraging graph convolutional networks (GCNs), a deep neural network, for AP interference modeling offers a key advantage. GCNs can effectively handle complex interference relationships between APs, achieving accurate interference prediction by learning characteristics such as their spatial layout and interference strength. GCNs achieve efficient information aggregation through multi-layer convolution operations, dynamically updating models to adapt to changes in network topology, and supporting real-time interference management and spectrum allocation optimization. Ultimately, GCN modeling not only optimizes interference mitigation strategies, improving network performance and user experience, but also effectively utilizes spectrum resources, promoting the intelligent and sustainable development of wireless networks. Summary of the Invention

[0004] The purpose of the present invention is to provide an inter-cell interference modeling method based on a weighted graph convolutional network, which uses a weighted graph convolutional network in deep learning to solve the interference prediction problem of different wireless access points in a multi-wireless access point scenario.

[0005] To achieve the above object, the technical solution of the present invention is: a method for modeling inter-cell interference based on a weighted graph convolutional network, comprising the following steps:

[0006] Step S1: construct a cellular network scenario with multiple wireless access points; combine wireless network related data to screen and define key parameters of each wireless access point;

[0007] Step S2: constructing the graph structure data required for the interference model to accurately model the interference model;

[0008] Step S3: Construct an inter-cell interference model based on a weighted graph convolutional network. The inter-cell interference model based on a weighted graph convolutional network takes graph structure data as input and outputs inter-cell interference parameters through a multi-layer graph convolutional network with weights. The inter-cell interference model based on a weighted graph convolutional network is trained with the help of real data until convergence.

[0009] In one embodiment of the present invention, in step S1 , the key parameters of each wireless access point include engineering parameters and real-time parameters of each wireless access point.

[0010] In one embodiment of the present invention, in step S2, the graph structure data includes node features and structure features, which are calculated using engineering parameters and real-time parameters.

[0011] In one embodiment of the present invention, step S1 specifically includes the following steps:

[0012] Step S11: Define the parameters of wireless access points in the cellular network. Assume that there are M wireless access points in the cellular network scenario, and the engineering parameter D of the mth wireless access point is m Including rated resources R m , center carrier frequency signal f m and position coordinates (x m ,y m ), rated resource R m Including rated bandwidth B m and the maximum transmit power P m The real-time parameters of the mth wireless access point in time interval t include the average occupancy of the physical resource block PRB Maximum interference level of cell resource block RB and average interference level

[0013] Step S12: Determine the key parameters required for the interference model:

[0014] The cumulative interference received by any user u within the range of the wireless access point is

[0015]

[0016] in, is the distance between the user and the interfering wireless access point, h mu is the channel between the wireless access point and the user, then the interference to the wireless access point m is expressed as

[0017]

[0018] Without considering the user, the above formula shows that the interference suffered by the wireless access point depends on the parameters of other wireless access points sharing the same channel with it, including the transmission power, cell bandwidth and spatial location information of the wireless access point; therefore, through f m Ability to determine the set of co-frequency wireless access points with interference, through and R m Ability to determine real-time bandwidth of wireless access points The interference model is built in combination with other parameters in step S11.

[0019] In one embodiment of the present invention, in step S2, since the relationship between wireless access points in the cellular network can be naturally represented as a graph structure, where each wireless access point is a node and the edges connecting them can represent the spatial position relationship and interference relationship; therefore, the graph structure data of the cellular network scenario is constructed based on the key parameters in step S1, and the node characteristics of the graph structure data are the real-time bandwidth resources of the wireless access point. and the maximum transmit power P m , that is, each wireless access point node has a two-dimensional feature vector; the structural characteristics of the graph structure data are calculated by the distance between wireless access points and the frequency band overlap rate.

[0020] In one embodiment of the present invention, the structural features of the graph structure data are calculated using the distance between wireless access points and the frequency band overlap rate in the following specific manner:

[0021] Considering the real wireless communication scenario of acquiring data, the wireless access point uses the idle spectrum within the bandwidth range for random allocation. Assuming that the physical resource blocks (PRBs) used by the wireless access point at each moment obey a uniform distribution, the probability of frequency band overlap between the wireless access point m and other wireless access points with the same frequency is expressed as

[0022]

[0023] By analyzing all wireless access points in the cellular network scenario through the above formula, we can derive a frequency band overlap probability matrix at time t, which can be expressed as

[0024]

[0025] Among them, the diagonal elements are all set to 0, that is, the inter-cell interference does not take into account the internal interference of the wireless access point itself; Finally, combining the above formula with the spatial position relationship, the weighted adjacency matrix A is obtained t , where the elements Expressed as

[0026]

[0027] in, is the distance between wireless access points, μ is the path loss index; the matrix A is derived from the above formula t , and use it as the structural feature of graph structured data.

[0028] In one embodiment of the present invention, step S3 specifically includes the following steps:

[0029] Step S31: Determine the inter-cell interference model; assume that the inter-cell interference obeys a normal distribution within each hour, therefore, the average interference matrix composed of all wireless access points is combined The standard deviation of the interference is defined as The calculation formula is Further determine the interference parameters of the model output, that is From step S1, it can be seen that the interference of the wireless access point is closely related to the engineering parameters and real-time parameters of other wireless access points with the same frequency. Therefore, the predicted interference parameters are expressed as

[0030]

[0031] in, represents the bandwidth matrix allocated to the wireless access point at time t, It is a matrix composed of the maximum transmit power of all wireless access points; It is an inter-cell interference model that can calculate the average interference and interference standard deviation of wireless access point m based on the real-time frequency band range and engineering parameters of all wireless access points in the area;

[0032] Step S32: construct an inter-cell interference model based on a weighted graph convolutional network; with the inter-cell interference model as the target, the graph structure data derived in step S2 is used as the input X of the weighted graph convolutional network. t , a multi-layer weighted graph convolution is used to build an interference model, and its layered propagation law is as follows:

[0033] H(l+1) =λ(A t H (l) W (l) )

[0034] Among them, W (l) is the weight matrix of layer l, H (l) is the node feature matrix of the previous layer. When l=0, H (0) =X t ,λ(·) is the activation function. Since the inter-cell interference is not only affected by the wireless access points in the area, but there are still adjacent wireless access points with the same frequency outside the node area, the normalized adjacency matrix is ​​not used;

[0035] Step S33, design the loss function of the weighted graph convolutional network; take the graph structure data as input, pass through the multi-layer weighted graph convolutional network, and finally output the high-dimensional features through a pooling layer to obtain the interference parameters and σ t The network training process is supervised learning, using existing data sets for training, and the loss function is defined as:

[0036]

[0037] in, is the inter-cell interference model; after network training through the above formula, a reliable inter-cell interference model based on weighted graph convolutional network can be obtained.

[0038] The present invention also provides an inter-cell interference modeling system based on a weighted graph convolutional network, comprising a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0039] The present invention also provides a computer-readable storage medium on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0040] The present invention also provides an inter-cell interference modeling device based on a weighted graph convolutional network, comprising:

[0041] The first module builds a cellular network scenario with multiple wireless access points. It then filters and defines the key parameters of each wireless access point based on wireless network data.

[0042] The second module builds the graph structure data required for the interference model to accurately model the interference model;

[0043] The third module constructs a cell interference model based on a weighted graph convolution network, the cell interference model based on the weighted graph convolution network takes graph structure data as input, outputs cell interference parameters through a multi-layer graph convolution network with weights, and is trained based on real data until convergence.

[0044] Compared with the prior art, the present application has the following beneficial effects: the method of the present application combines "wireless network" related data and a graph neural network, and solves a core problem: how to accurately model the inter-cell interference of all wireless access points in an area according to real-time performance data and engineering parameters of the wireless access points. In view of this problem, the present application analyzes the influencing factors of inter-cell interference, and designs a weighted graph convolution network combining real-time wireless resources and frequency band overlap rate, uses a weighted graph convolution network in deep learning to solve the interference prediction problem of different wireless access points in a multi-wireless access point scenario, so as to accurately obtain the inter-cell interference in a multi-wireless access point scenario in a cellular network. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A multi-wireless access point scenario using cell interference modeling in the present application is shown in the figure.

[0046] Figure 2 A WGCN-based interference model architecture diagram in the present application is shown in the figure.

[0047] Figure 3 A real-time loss diagram during training of an interference model using different graph neural networks in the present application is shown in the figure.

[0048] Figure 4 An interference parameter output by an interference model of the algorithm proposed in the present application compared with different benchmark algorithms and real data is shown in the figure. DETAILED DESCRIPTION

[0049] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings.

[0050] It should be pointed out that the following detailed description is exemplary and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as generally understood by those skilled in the art to which the present application belongs.

[0051] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0052] See also Figures 1-4 As shown, the present invention provides a method for modeling inter-cell interference based on a weighted graph convolutional network, and the implementation process includes the following steps:

[0053] Step S1: construct a cellular network scenario with multiple wireless access points; combine "wireless network" related data, screen and define key parameters of each wireless access point, including engineering parameters and real-time parameters of each wireless access point.

[0054] Step S2: construct graph structure data required for the interference model; the graph structure data includes node features and structural features, which are calculated through engineering parameters and real-time parameters to accurately model the interference.

[0055] Step S3: Build an inter-cell interference model based on a weighted graph convolutional network. This model takes graph structure data as input and outputs inter-cell interference parameters through a weighted multi-layer graph convolutional network. The network model is trained using real data until convergence.

[0056] Step S1 specifically includes the following steps:

[0057] Step S11: Define the parameters of wireless access points in the cellular network. Assume that there are M wireless access points in the scenario, where the engineering parameter D of the mth wireless access point is m Including rated resources R m (Including rated bandwidth B m and the maximum transmit power P m ), center carrier frequency signal f m and position coordinates (x m ,y m ); The real-time parameters of the mth wireless access point in the time interval t include the average occupancy rate of the physical resource block (PRB) Maximum interference level of cell resource blocks (RBs) and average interference level

[0058] Step S12: Determine the key parameters required for the interference model; combined with theoretical analysis, it can be seen that the cumulative interference received by any user u within the range of the wireless access point is

[0059]

[0060] Where d is the distance between the user and the interfering wireless access point, h mu is the channel between the interfering wireless access point and the user. Then the interference to the wireless access point m can be expressed as

[0061]

[0062] Without considering the user, the above formula shows that the interference to the wireless access point depends on the parameters of other wireless access points sharing the same channel with it, including the transmission power of the wireless access point, cell bandwidth and spatial location information. Therefore, through f m Ability to determine the set of co-frequency wireless access points with interference, through and R m Ability to determine real-time bandwidth of wireless access points Combined with other parameters in S11 for interference modeling.

[0063] Step S2 is specifically implemented as follows:

[0064] Graph structure data required for model construction; Since the relationship between wireless access points in a mobile network can be naturally represented as a graph structure, where each wireless access point is a node and the edges connecting them can represent spatial position relationships and interference relationships. Therefore, a graph structure of a cellular network scenario is constructed based on the key parameters in S1, and its node characteristics are the real-time bandwidth resources of the wireless access point. and the maximum transmit power P m , that is, each wireless access point node has a two-dimensional feature vector; the structural feature is calculated by the distance between wireless access points and the frequency band overlap rate. Specifically, considering the real wireless communication scenario in which data is acquired, wireless access points use idle spectrum within the bandwidth range for random allocation. Assuming that the PRBs used by wireless access points at each moment follow a uniform distribution, the frequency band overlap probability between wireless access point m and other wireless access points with the same frequency is expressed as

[0065]

[0066] By analyzing all wireless access points in the scene through the above formula, we can derive a frequency band overlap probability matrix at time t, which can be expressed as

[0067]

[0068] The diagonal elements are all set to 0, that is, the inter-cell interference does not take into account the internal interference of the wireless access point itself. Finally, combining the above formula with the spatial position relationship, we get the weighted adjacency matrix A t , where the elements It can be expressed as

[0069]

[0070] where d m,n is the distance between wireless access points, μ is the path loss index. The matrix A is derived from the above formula t , and use it as the structural feature of graph data.

[0071] Step S3 specifically includes the following steps:

[0072] Step S31: Determine the inter-cell interference model; assume that the inter-cell interference obeys a normal distribution within each hour, therefore, the average interference matrix composed of all wireless access points is combined The standard deviation of the interference is defined as The calculation formula is Further determine the interference parameters of the model output, that is From step S1, it can be seen that the interference of the wireless access point is closely related to the engineering parameters and real-time parameters of other wireless access points with the same frequency. Therefore, the predicted interference parameters are expressed as

[0073]

[0074] in, represents the bandwidth matrix allocated to the wireless access point at time t, It is a matrix composed of the maximum transmit power of all wireless access points; It is an inter-cell interference model that can calculate the average interference and interference standard deviation of wireless access point m based on the real-time frequency band range and engineering parameters of all wireless access points in the area;

[0075] Step S32: Construct an interference model based on a weighted graph convolutional neural network; with the system model as the target, the graph structure data derived in S2 is used as the input X of the weighted graph convolutional network. t , a multi-layer weighted graph convolution is used to construct the interference model, and its layered propagation law is as follows

[0076] H (l+1) =λ(A t H (l) W (l) )

[0077] Where W is the weight matrix of layer l, H (l) is the node feature matrix of the previous layer. When l=0, H(0) =X t , λ(·) is the activation function. Since inter-cell interference is not only affected by the wireless access points within the area, but also by adjacent wireless access points with the same frequency outside the node area, the normalized adjacency matrix is ​​not used.

[0078] Step S33, design the loss function of the weighted graph convolutional network; take the graph structure data as input, pass through the multi-layer weighted graph convolutional network, and finally output the high-dimensional features through a pooling layer to obtain the interference parameters and σ t The network training process is supervised learning, using existing data sets for training, and the loss function is defined as

[0079]

[0080] in is the inter-cell interference model. After network training using the above formula, a reliable inter-cell interference model can be obtained.

[0081] In order to allow those skilled in the art to further understand the inter-cell interference modeling based on weighted graph convolutional network proposed in the present invention, a detailed description is given below in conjunction with a specific embodiment. This embodiment is implemented based on the technical solution of the present invention.

[0082] like Figure 1 FIG. 1 is a schematic diagram of a multi-wireless access point scenario using inter-cell interference modeling.

[0083] The model primarily includes multiple independent wireless access points serving different users and devices. Each access point has unique engineering parameters. Combined with real-time data collected from each access point, interference modeling is performed using the WGCN network to determine interference values ​​for each access point.

[0084] like Figure 2 The figure shows the interference model architecture diagram based on WGCN.

[0085] This algorithm uses a weighted graph convolutional neural network, comprising node features, structural features, weighted graph convolution layers, and pooling layers. It takes graph data as input, with node features representing the real-time resources and transmit power of co-frequency wireless access points, and structural features representing an adjacency matrix calculated based on engineering parameters and real-time PRB occupancy. It then outputs the average interference and interference standard deviation for all wireless access points.

[0086] like Figure 3 Figure 2 shows a schematic diagram of real-time loss during the training of the interference calculation model.

[0087] To compare the performance of interference models constructed using different graph neural network algorithms, a weighted graph convolutional network, a graph convolutional network, and a graph isomorphism network were trained on the same test dataset and the corresponding losses were plotted. All networks used mean squared error as the loss function. The horizontal axis of the graph represents the number of training rounds, and the vertical axis represents the loss. Compared to other algorithms, the proposed WGCN exhibits a gentle and stable downward trend during training and achieves the lowest loss after training convergence, achieving the best performance. Therefore, this algorithm is adopted for inter-cell interference modeling.

[0088] like Figure 4 The figure shows a comparison between the interference parameters output by the wireless access point through different interference models and the actual data.

[0089] The experimental figure shows the average interference and interference standard deviation (ISD) predicted by different interference modeling algorithms for a wireless access point in the scenario over a one-week period. The dark blue solid line in the figure represents the actual interference, while the red and yellow solid lines represent the interference output by WGCN and GCN, respectively. The interference parameters output by the proposed WGCN and GCN are close to the real data, but WGCN still outperforms GCN, and the proposed model still more accurately outputs the average interference and interference standard deviation. Furthermore, the average interference of both access points exhibits a certain periodicity. Interference values ​​are typically higher in the afternoon and evening, which often correspond to peak mobile network usage. When the load is high, access points schedule more resources, resulting in increased interference between adjacent co-frequency access points. Comparing the two parameters, the average interference varies very little over time, remaining within 4dBm for the access point in the figure, while the interference standard deviation varies more dramatically, indicating that even in conditions with good interference levels, some unavoidable high interference conditions may still occur. In summary, based on spatial location information, WGCN improves the modeling accuracy of inter-cell interference parameters by utilizing the frequency band overlap rate between wireless access points as a structural feature, enabling the algorithm to achieve better performance than other algorithms.

[0090] The present invention also provides a computer-readable storage medium on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0091] The present invention also provides an inter-cell interference modeling device based on a weighted graph convolutional network, comprising:

[0092] The first module builds a cellular network scenario with multiple wireless access points. It then filters and defines the key parameters of each wireless access point based on wireless network data.

[0093] The second module builds the graph structure data required for the interference model to accurately model the interference model;

[0094] The third module constructs an inter-cell interference model based on a weighted graph convolutional network. The inter-cell interference model based on a weighted graph convolutional network takes graph structure data as input and outputs inter-cell interference parameters through a multi-layer graph convolutional network with weights. The inter-cell interference model based on a weighted graph convolutional network is trained with the help of real data until convergence.

[0095] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other manner. Any person skilled in the art may utilize the above-disclosed technical content to modify or modify the present invention into equivalent embodiments. However, any simple modifications, equivalent variations, and modifications to the above embodiments that do not depart from the technical content of the present invention and are based on the technical essence of the present invention remain within the scope of protection of the present invention.

Claims

1. A method for modeling inter-cell interference based on weighted graph convolutional networks, characterized in that: The steps include: Step S1: construct a cellular network scenario with multiple wireless access points; screen and define key parameters of each wireless access point; Step S2: constructing graph structure data required for the interference model to model the interference model; Step S3: Construct an inter-cell interference model based on a weighted graph convolutional network. The inter-cell interference model based on the weighted graph convolutional network takes graph structure data as input and outputs inter-cell interference parameters through a multi-layer graph convolutional network with weights. The inter-cell interference model based on the weighted graph convolutional network is trained with real data until convergence. The specific implementation is as follows: Step S31, determine the inter-cell interference model; assume that the inter-cell interference obeys the normal distribution within each hour, based on the average interference matrix composed of all wireless access points The standard deviation of interference is Determine the disturbance parameter of the model's output The calculation formula is There are M wireless access points in the cellular network scenario. are the maximum interference level and average interference level of the cell resource block RB of the mth wireless access point in the cellular network scenario within the time interval t; the predicted interference parameter is expressed as in, represents the bandwidth matrix allocated to the wireless access point at time t, represents the real-time bandwidth of wireless access point m at time t, is the matrix composed of the maximum transmit power of all wireless access points, P m Indicates the maximum transmit power of wireless access point m, A t represents the adjacency matrix; is the inter-cell interference model; Step S32: construct an inter-cell interference model based on a weighted graph convolutional network; with the inter-cell interference model as the target, the graph structure data derived in step S2 is used as the input X of the weighted graph convolutional network. t , a multi-layer weighted graph convolution is used to construct the interference model. The hierarchical propagation law of the inter-cell interference model is as follows: H (l+1) =λ(A t H (l) W (l) ) Among them, W (l) is the weight matrix of layer l, H (l) is the node feature matrix of the previous layer. When l=0, H (0) =X t , λ(·) is the activation function; Step S33, design the loss function of the weighted graph convolutional network; take the graph structure data as input, pass through the multi-layer weighted graph convolutional network, and finally output the high-dimensional features through a pooling layer to obtain the interference parameters and σ t The network training process is supervised learning, using existing data sets for training, and the loss function is defined as:

2. The inter-cell interference modeling method based on weighted graph convolutional network according to claim 1 is characterized in that: In step S1 , the key parameters of each wireless access point include engineering parameters and real-time parameters of each wireless access point.

3. The inter-cell interference modeling method based on weighted graph convolutional network according to claim 2 is characterized in that: In step S2, the graph structure data includes node features and structure features, which are calculated using engineering parameters and real-time parameters.

4. The inter-cell interference modeling method based on weighted graph convolutional network according to claim 1, characterized in that: Step S1 specifically includes the following steps: Step S11: Define the parameters of wireless access points in the cellular network; the engineering parameters D of the mth wireless access point m Including rated resources R m , center carrier frequency signal f m and position coordinates (x m ,y m ), rated resource R m Including rated bandwidth B m and the maximum transmit power P m The real-time parameters of the mth wireless access point in time interval t include the average occupancy of the physical resource block PRB Step S12: Determine the key parameters required for the interference model: The cumulative interference received by any user u within the range of the wireless access point is in, is the distance between the user and the interfering wireless access point n, h nu To interfere with the channel between wireless access point n and user u, the interference experienced by wireless access point m is expressed as By f m Determine the set of co-frequency wireless access points that have interference, by and R m Determine the real-time bandwidth of a wireless access point 5. The method for modeling inter-cell interference based on a weighted graph convolutional network according to claim 4, characterized in that: In step S2, the graph structure data of the cellular network scenario is constructed based on the key parameters in step S1. The node characteristics of the graph structure data are the real-time bandwidth resources of the wireless access point. and the maximum transmit power P m ; The structural features of graph structure data are calculated by the distance between wireless access points and the frequency band overlap rate.

6. The inter-cell interference modeling method based on weighted graph convolutional network according to claim 5, characterized in that: The specific method for calculating the structural characteristics of graph structure data through the distance between wireless access points and the frequency band overlap rate is as follows: The probability of frequency band overlap between wireless access point m and other wireless access points with the same frequency is expressed as The frequency band overlap probability matrix at time t is expressed as Among them, the diagonal elements are all set to 0; the frequency band overlap probability matrix at time t is combined with the spatial position relationship to obtain the adjacency matrix A t , the adjacency matrix A t Chinese elements Expressed as in, is the distance between wireless access points, μ is the path loss index; the matrix A is derived from the above formula t , and use it as the structural feature of graph structured data.

7. A weighted graph convolutional network-based inter-cell interference modeling system, characterized in that: The method comprises a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps according to any one of claims 1 to 6 can be implemented.

8. A computer-readable storage medium storing computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 6 can be implemented.

9. A device for modeling inter-cell interference based on a weighted graph convolutional network, characterized in that: Applying the method according to claim 1, comprising: The first module builds a cellular network scenario with multiple wireless access points; screens and defines the key parameters of each wireless access point; The second module builds the graph structure data required for the interference model to model the interference model; The third module constructs an inter-cell interference model based on a weighted graph convolutional network. The inter-cell interference model based on a weighted graph convolutional network takes graph structure data as input and outputs inter-cell interference parameters through a multi-layer graph convolutional network with weights. The inter-cell interference model based on a weighted graph convolutional network is trained with the help of real data until convergence.

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