Uplink interference identification method and device, electronic equipment and storage medium

Abstract

The application provides an uplink interference identification method and device, electronic equipment and storage medium, and relates to the technical field of interference identification. The method comprises the following steps: constructing first node features of each node in an interference feature graph based on network information of each user terminal; the interference feature graph takes the user terminals as nodes; constructing edge weights between the nodes based on position information and uplink beam information of the user terminals; determining interference influence degrees between the nodes based on the first node features and the edge weights; and determining uplink interference terminals based on the interference influence degrees between the nodes. The application can identify uplink interference terminals, thereby providing accurate interference positioning for subsequent network interference optimization, and reducing network interference and improving network quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of interference identification, and in particular to an uplink interference identification method and device, an electronic device, and a storage medium. BACKGROUND

[0002] Currently, China Mobile 5G network adopts 2.6G same frequency networking. For areas with high site density and high traffic volume, uplink interference between macro-macro, macro-micro, and macro stations and room distribution is inevitable. The existing method for reducing 5G TDD (Time Division Duplexing) network interference includes SRS (Sounding Reference Signal) resource configuration optimization, uplink power control, and cyclic prefix, but none of the above methods can identify uplink interference and cannot optimize network interference through accurate positioning. SUMMARY

[0003] The embodiments of the present application provide an uplink interference identification method, device, electronic device, and storage medium to solve the technical problem that existing interference solutions cannot be positioned.

[0004] In a first aspect, the embodiments of the present application provide an uplink interference identification method, comprising: constructing a first node feature of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes the each user terminal as a node; constructing an edge weight between the each node based on position information and uplink beam information of the each user terminal; determining an interference influence degree between the each node based on the first node feature and the edge weight; and determining an uplink interference terminal based on the interference influence degree between the each node.

[0005] In some embodiments, the constructing an edge weight between the each node based on the position information and the uplink beam information of the each user terminal comprises: determining a spatial distance between the each user terminal based on the position information of the each user terminal; determining a beam influence degree between the each user terminal based on the uplink beam information of the each user terminal; and determining the edge weight between the each node based on the spatial distance and the beam influence degree.

[0006] In some embodiments, the determining the interference influence degree between the nodes based on the first node feature and the edge weight comprises: fusing the first node feature and the edge weight to obtain a second node feature of each node; determining a similarity matrix based on the second node feature of each node; performing spectral clustering based on the similarity matrix to obtain a plurality of interference node sets; and determining the interference influence degree between each node in each interference node set based on the edge weight.

[0007] As a possible implementation, the fusing the first node feature and the edge weight to obtain a second node feature of each node comprises: fusing the first node feature and the edge weight based on a graph convolution operation to obtain the second node feature of each node.

[0008] As a possible implementation, the determining the interference influence degree between each node in each interference node set based on the edge weight comprises: obtaining an interference level value between each node in each interference node set; and determining the interference influence degree between each node in each interference node set according to the interference level value and the edge weight.

[0009] In some embodiments, the determining the uplink interference terminal based on the interference influence degree between the nodes comprises: determining a target interference node from the nodes based on the interference influence degree between the nodes and an interference influence degree threshold; and determining the uplink interference terminal based on the target interference node.

[0010] In a second aspect, an embodiment of the present application provides an uplink interference identification device, comprising: a first construction module configured to construct a first node feature of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes the user terminals as nodes; a second construction module configured to construct an edge weight between the nodes based on position information and uplink beam information of the user terminals; a first determination module configured to determine an interference influence degree between the nodes based on the first node feature and the edge weight; and a second determination module configured to determine an uplink interference terminal based on the interference influence degree between the nodes.

[0011] In a third aspect, an embodiment of the present application provides a network device, comprising a memory, a transceiver, and a processor; the memory is configured to store a computer program; the transceiver is configured to transceive data under the control of the processor; and the processor is configured to read the computer program in the memory and perform the following operations: constructing a first node feature of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes the user terminals as nodes; constructing an edge weight between the nodes based on position information and uplink beam information of the user terminals; determining an interference influence degree between the nodes based on the first node feature and the edge weight; and determining an uplink interference terminal based on the interference influence degree between the nodes.

[0012] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory storing a computer program; the processor implements the uplink interference identification method of the first aspect when executing the program.

[0013] In a fifth aspect, an embodiment of the present application provides a non-transitory computer-readable storage medium, which stores a computer program; the computer program is executed by a processor to implement the uplink interference identification method of the first aspect.

[0014] In a sixth aspect, an embodiment of the present application provides a computer program product, comprising a computer program; the computer program is executed by a processor to implement the uplink interference identification method of the first aspect.

[0015] The uplink interference identification method, device, electronic device, and storage medium provided by the embodiments of the present application construct a first node feature of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes the user terminals as nodes; construct an edge weight between the nodes based on position information and uplink beam information of the user terminals; determine an interference influence degree between the nodes based on the first node feature and the edge weight; and determine an uplink interference terminal based on the interference influence degree between the nodes. The present application combines the network information, position information, and uplink beam information of the user terminals to construct an interference feature graph, and determines the interference influence degree between the nodes based on the interference feature graph, to identify the uplink interference terminal, thereby providing accurate interference positioning for subsequent network interference optimization, and further reducing 5G TDD network interference and improving network quality. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to make the technical solutions in the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0017] Figure 1 A flowchart of an uplink interference identification method provided by an embodiment of the present application is shown in Figure 1.

[0018] Figure 2 A flowchart of an uplink interference identification method provided by an embodiment of the present application is shown in Figure 2.

[0019] Figure 3 A structural diagram of an uplink interference identification device provided by an embodiment of the present application is shown in Figure 3.

[0020] Figure 4 A structural diagram of a network device provided by an embodiment of the present application is shown in Figure 4.

[0021] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present application is shown in Figure 5. DETAILED DESCRIPTION

[0022] In order to make the technical solutions in the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0023] In the related art, the solutions for reducing the 5G TDD network interference phenomenon include SRS resource configuration optimization, uplink power control, cyclic prefix optimization and other methods, which can improve the network interference in some scenarios. However, the above methods do not combine the new features of 5G air interface to design interference avoidance solutions, and even at the cost of spectrum efficiency, to reduce the network co-frequency interference in light load scenarios, which is not sufficient for future more and more complex 5G application scenarios.

[0024] In order to solve the above problems, the present application provides an uplink interference identification method, device, electronic device and storage medium.

[0025] Figure 1 A flowchart of an uplink interference identification method provided by an embodiment of the present application is shown in Figure 1. Figure 1 As shown in Figure 1, the method can include the following steps.

[0026] In step 101, first node features of nodes in the interference feature graph are constructed based on network information of each user terminal, wherein each user terminal is a node in the interference feature graph.

[0027] In some embodiments, each user terminal can be an uplink user terminal currently residing in the 5G network. The network information of each user terminal can be network basic data of each user terminal during the current service.

[0028] In some embodiments, the network information of each user terminal can include a user-configured SRS set, SRS resources, information available for uplink and downlink beam management, uplink adaptation, uplink channel correlation measurement, and can also include throughput, user number, beam spatial grid, measurement information of serving cells and neighboring cells, etc. The network information of each user terminal can be obtained through a network management data platform, or can be obtained through a base station data management platform.

[0029] As an example, the network information of each user terminal can include the data in Table 1 below.

[0030] Table 1 Network information of each user terminal

[0031]

[0032] In some embodiments, each user terminal can be taken as a node in the interference feature graph, and each item of data contained in the network information of each user terminal can be spliced and combined, and the node features of the corresponding nodes can be obtained through encoding conversion.

[0033] In step 102, edge weights between nodes are constructed based on position information and uplink beam information of each user terminal.

[0034] In some embodiments, the position information of each user terminal can be user latitude and longitude information. The uplink beam information of each user terminal can include SRS beam level information.

[0035] It can be understood that the position information and uplink beam information between user terminals can have a direct impact on the interference between user terminals, so the edge weights between two nodes can be determined based on the position information and uplink beam information between user terminals. That is, the edge weights between two nodes in the interference feature graph can represent the degree of association between nodes. If the edge weight is larger, it means that the association between two nodes is relatively close, and it is more likely to exist interference. If the edge weight is smaller, it means that the association between two nodes is not close, and the possibility of interference is smaller.

[0036] In some embodiments, the edge weight between each node is constructed based on the position information and the uplink beam information of each user terminal, including: determining the spatial distance between each user terminal based on the position information of each user terminal; determining the beam influence degree between each user terminal based on the uplink beam information of each user terminal; determining the edge weight between each node based on the spatial distance and the beam influence degree. Wherein, the position information of each user terminal can be the longitude and latitude information of the user, and the uplink beam information can be the SRS beam level value.

[0037] As a possible implementation, the spatial distance between each user terminal is determined based on the position information of each user terminal, which can be realized by the following formula (1).

[0038] (1);

[0039] Wherein, is the spatial distance between user terminal i and user terminal j; is the longitude of user terminal i; is the latitude of user terminal i; is the longitude of user terminal j; is the latitude of user terminal j; , which represents the difference in latitude between user terminal i and user terminal j; , which represents the difference in latitude between user terminal i and user terminal j.

[0040] As a possible implementation, the beam influence degree between each user terminal can be determined based on the uplink beam information of each user terminal, including: determining the level difference value between each user terminal based on the SRS beam level value of each user terminal; determining the beam influence degree between each user terminal based on the level difference value and the preset corresponding relationship between the level difference value and the beam influence degree. As an example, the level difference value n between user terminal i and user terminal j can be determined based on the difference between the SRS beam level value of user terminal i and the SRS beam level value of user terminal j; based on Table 2 below, the SRS beam influence degree m(i,j) corresponding to the level difference value n is determined.

[0041] Table 2 Corresponding relationship table of level difference value and beam influence degree

[0042]

[0043] As an example, the edge weight between each node can be determined based on the spatial distance and the beam influence degree, which can be realized by the following formula (2).

[0044] (2);

[0045] Wherein, Let the weight of the edge between node i and node j be denoted as ; Let be the spatial distance between user terminal i and user terminal j; The beam influence between user terminal i and user terminal j.

[0046] Step 103: Based on the features of the first node and the edge weights, determine the degree of interference between each node.

[0047] In some embodiments, a graph neural network can be used to determine the degree of interference between nodes based on the features of the first node and variable weights. The graph neural network can be trained based on interference feature map samples and the labels of the degree of interference between nodes.

[0048] In some embodiments, a symmetric similarity matrix between nodes can be determined based on the first node features and edge weights; graph clustering processing is performed based on the similarity matrix to divide each node in the interference feature graph into multiple interference node sets; and the interference influence between each node is determined based on the nodes contained in each interference node set and the edge weights between each node.

[0049] The interference influence between nodes refers to the degree of interference influence of each node on other nodes. For example, the interference influence of node i on node j is Rji, and the interference influence of node j on node i is Rij.

[0050] Step 104: Determine the uplink interference terminal based on the interference impact between each node.

[0051] In some embodiments, the interference impact levels among the nodes can be sorted, and the user terminal corresponding to the interfering node with the highest interference impact level is determined as the uplink interfering terminal. As an example, if the highest interference impact level is Rij, that is, node j has the highest interference impact on node i, then the user terminal of interfering node j is the uplink interfering terminal.

[0052] In other embodiments, a target interference node can be determined from among the nodes based on the interference impact degree between each node and an interference impact degree threshold; based on the target interference node, an uplink interference terminal can be determined. As an example, the interference impact degree between each node can be compared with an interference impact degree threshold to determine a target interference impact degree greater than the threshold; the interference node corresponding to the target interference impact degree is determined as the target interference node; and the user terminal corresponding to the target interference node is determined as the uplink interference terminal. The number of target interference nodes can be one or more.

[0053] For example, if the interference influence degree Rij of node j on node i is greater than the interference influence degree threshold, then node j is identified as the target interference node, and the user terminal corresponding to node j is identified as the uplink interference terminal.

[0054] In some embodiments, if node j is determined to be an uplink interference terminal, the cell where the uplink interference terminal is located can be optimized to accurately suppress network interference and improve network service quality.

[0055] According to the uplink interference identification method of the present invention, a first node feature is constructed for each node in the interference feature map based on the network information of each user terminal; wherein, the interference feature map uses each user terminal as a node; based on the location information and uplink beam information of each user terminal, edge weights between each node are constructed; based on the first node feature and edge weights, the interference impact degree between each node is determined; based on the interference impact degree between each node, the uplink interfering terminal is identified. This invention constructs an interference feature map by combining the network information, location information, and uplink beam information of the user terminal, and determines the interference impact degree between each node based on the interference feature map, thereby achieving the identification of uplink interfering terminals. This provides accurate interference localization for subsequent network interference optimization, thereby reducing 5G TDD network interference and improving network quality.

[0056] Figure 2 This is a second schematic flowchart of the uplink interference identification method provided in an embodiment of the present invention. Figure 2 As shown, based on the above embodiments, Figure 1 The implementation process of step 103 may include the following steps.

[0057] Step 201: The first node features and edge weights are fused to obtain the second node features of each node.

[0058] In some embodiments, the second node features of each node can be obtained by concatenating the first node features with the corresponding edge weights.

[0059] In some embodiments, attention graph neural networks can be used to fuse the features of the first node with the edge weights to obtain the second node features of each node.

[0060] In some embodiments, the first node features and edge weights can be fused based on graph convolution operations to obtain the second node features of each node.

[0061] As an implementation manner, an adjacency matrix Z can be determined based on the edge weights between the nodes, a feature matrix Y={y1, y2, …yn} can be determined based on the first node features of the nodes, yi is the first node feature of node i, and n is the total number of nodes; a degree matrix D is constructed based on the adjacency matrix Z; and a normalized Laplacian matrix L is constructed ; an order k of graph convolution is set, and the edge weights between the nodes and the first node features are fused by performing a graph convolution operation on the interference feature map, to obtain a second node feature matrix of each node .

[0062] In step 202, a similarity matrix is determined based on the second node features of the nodes.

[0063] In some embodiments, the similarity matrix can be determined by the following formula (3).

[0064] (3)

[0065] wherein, is the similarity matrix; is the second feature matrix.

[0066] In step 203, spectral clustering processing is performed based on the similarity matrix, to obtain a plurality of interference node sets.

[0067] It can be understood that each node has node features related to the serving cell and the neighboring cells, and the higher the node feature similarity between different nodes and the closer the spatial distance, the greater the possibility of interference between the nodes. Therefore, the interference feature map can be processed by graph cutting in a spectral clustering manner, so that the sum of the edge weights between different subgraphs after the graph cutting processing is as low as possible, and the sum of the edge weights within the subgraph is as high as possible. The subgraph obtained in this step is the interference node set.

[0068] In some embodiments, the nodes can be divided into s interference node sets, s is a positive integer greater than 1. The eigenvectors corresponding to the first s largest eigenvalues of the similarity matrix W are calculated, and the s eigenvectors are combined into an n×s eigenvector matrix F; each row in F is regarded as an s-dimensional vector sample, and there are n samples in total. The K-means algorithm is used to cluster the eigenvector matrix F, to obtain a matrix A, , is the i th interference node set, each interference node set includes a plurality of nodes, and the s interference node sets can be determined based on A.

[0069] The embodiment of the present application combines the graph convolution operation and the spectral clustering operation to divide the nodes with a high possibility of interference into the same interference node set, and to make the possibility of interference between the nodes in different interference node sets as low as possible, so as to improve the accuracy of subsequent interference influence degree calculation, thereby improving the ability of uplink interference identification.

[0070] In some other embodiments, the embodiment of the present application can be implemented based on the spectral clustering method of adaptive graph convolution, and the implementation process of steps 201 to 203 specifically includes the following steps.

[0071] Step S1, defining an initial iteration number r=0, determining an adjacency matrix Z based on the edge weights between the nodes, determining a feature matrix Y={y1, y2, …yn} based on the first node features of the nodes, yi being the first node feature of node i, n being the total number of nodes, constructing a degree matrix D according to the adjacency matrix Z, and constructing a normalized Laplacian matrix ;

[0072] Step S2, setting r=r+1, setting the graph convolution order k=r, and performing k-order graph convolution on the interference feature map, so as to fuse the edge weights between the nodes and the first node features, and obtain a matrix composed of the second node features of the nodes ;

[0073] Step S3, calculating a symmetrized similarity matrix W between the nodes ;

[0074] Step S4, performing spectral clustering on the similarity matrix W, i.e., calculating the eigenvectors corresponding to the first s largest eigenvalues of W, composing an n×s eigenvector matrix F from the s eigenvectors, taking each row in F as an s-dimensional vector sample, a total of n samples, clustering by inputting the K-means algorithm, and obtaining a matrix ;

[0075] Step S5, calculating the intra-set distance and the inter-set distance between the interference node sets, wherein is defined as the average of the average distances between the samples in the set, is the i-th row vector of . The calculation formula of is as follows.

[0076] (4)

[0077] wherein, is the i-th node; is the j-th node.

[0078] The average value of the distances between the centroid points of each interference set is defined as the average interference distance, and the calculation formula is as follows.

[0079] (5);

[0080] wherein, The centroid point of the interference node set i is calculated according to the following formula (6).

[0081] (6);

[0082] Step S6, return to continue to execute step S2 until and , or The loop iteration termination condition is that the distance within the interference node set does not decrease compared with the last iteration, and the distance between sets does not increase or the iteration number is greater than s, and the plurality of interference node sets are determined based on A obtained when the iteration is terminated.

[0083] In the embodiment of the application, the structure features and node features of the interference feature map are fused by the adaptive graph convolution spectral clustering algorithm, each node is divided into different interference node sets, and the accuracy of interference node set division can be further improved, so that the accuracy of interference recognition can be further improved.

[0084] Step 204, determining the interference influence degree between each node in each interference node set based on the edge weight.

[0085] The interference influence degree between each node in each interference node set refers to the interference influence degree of each node in the interference node set on each other node in the set. For example, the interference node set The interference influence degree between each node in the interference node set includes the interference influence degree of on , the interference influence degree of on , the interference influence degree of on , the interference influence degree of on , the interference influence degree of on , the interference influence degree of on , and the interference influence degree of on . , the interference impact degree of , the interference impact degree of , the interference impact degree of , the interference impact degree of , the interference impact degree of , the interference impact degree of , the interference impact degree of , .

[0086] In some embodiments, interference measurement information between each node can be acquired, and for each interference node set, the interference impact degree between each two nodes in the interference node set can be determined based on the edge weight between each two nodes in the interference node set and the corresponding interference measurement information. For example, the edge weight between node i and node j can be multiplied by the interference measurement information of node i to node j to obtain the interference impact degree of node i to node j.

[0087] In some embodiments, the interference measurement information can be an interference level value, an interference power value, etc.

[0088] In some embodiments, the implementation process of determining the interference impact degree between each two nodes in each interference node set based on the edge weight can include: acquiring the interference level value between each two nodes in each interference node set; and determining the interference impact degree between each two nodes in each interference node set according to the interference level value and the edge weight.

[0089] For example, the implementation process of determining the interference impact degree between each two nodes in each interference node set according to the interference level value and the edge weight is as follows formula (7).

[0090] (7);

[0091] wherein, is the edge weight between node i and node j; is the interference level value of node j to node i; is the interference floor; is the interference impact degree of node j to node i.

[0092] According to the uplink interference identification method provided in the embodiments of the present application, the first node feature is fused with the edge weight to obtain the second node feature of each node, the similarity matrix is determined based on the second node feature of each node, and the spectral clustering processing is performed based on the similarity matrix to obtain a plurality of interference node sets, and the interference influence degree between each node in each interference node set is determined based on the edge weight. The present application divides each node into different interference node sets through the feature fusion processing and the spectral clustering processing, and can improve the accuracy of interference identification.

[0093] The uplink interference identification device provided in the embodiments of the present application is described below, and the uplink interference identification device described below can be correspondingly referred to the uplink interference identification method described above.

[0094] Figure 3 The structural schematic diagram of the uplink interference identification device provided in the embodiments of the present application is shown in FIG. 3. Figure 3 As shown in FIG. 3, the device includes a first construction module 310, a second construction module 320, a first determination module 330 and a second determination module 340.

[0095] The first construction module 310 is configured to construct the first node feature of each node in the interference feature graph based on the network information of each user terminal; wherein the interference feature graph takes each user terminal as a node.

[0096] The second construction module 320 is configured to construct the edge weight between each node based on the position information and the uplink beam information of each user terminal.

[0097] The first determination module 330 is configured to determine the interference influence degree between each node based on the first node feature and the edge weight.

[0098] The second determination module 340 is configured to determine the uplink interference terminal based on the interference influence degree between each node.

[0099] In some embodiments, the second construction module 320 is specifically configured to determine the spatial distance between each user terminal based on the position information of each user terminal, determine the beam influence degree between each user terminal based on the uplink beam information of each user terminal, and determine the edge weight between each node based on the spatial distance and the beam influence degree.

[0100] In some embodiments, the first determination module 330 is specifically configured to fuse the first node feature with the edge weight to obtain the second node feature of each node, determine the similarity matrix based on the second node feature of each node, perform the spectral clustering processing based on the similarity matrix to obtain a plurality of interference node sets, and determine the interference influence degree between each node in each interference node set based on the edge weight.

[0101] As a possible implementation manner, the first determining module 330 is further configured to: perform fusion processing on the first node features and the edge weights based on a graph convolution operation to obtain second node features of the nodes.

[0102] As a possible implementation manner, the first determining module 330 is further configured to: obtain interference level values between the nodes in each interference node set; and determine interference influence degrees between the nodes in each interference node set according to the interference level values and the edge weights.

[0103] In some embodiments, the second determining module 340 is specifically configured to: determine target interference nodes from the nodes based on the interference influence degrees between the nodes and an interference influence degree threshold; and determine the uplink interference terminal based on the target interference nodes.

[0104] It should be noted that the above explanation and description of the embodiments of the uplink interference identification method can also apply to the uplink interference identification device of the embodiments of the present application, which will not be described here again.

[0105] The network device involved in the embodiments of the present application can be a base station, which can include a plurality of cells providing services for user terminals. According to different specific application occasions, the base station can also be referred to as an access point, or can be a device in an access network that communicates with wireless terminal devices through one or more sectors over an air interface, or other names.

[0106] Figure 4 For the structural schematic diagram of the network device provided according to the embodiments of the present application, refer to Figure 4 The embodiments of the present application also provide a network device, which can include: a memory 410, a transceiver 420, and a processor 430.

[0107] The memory 410 is configured to store a computer program; the transceiver 420 is configured to transceive data under the control of the processor 430; and the processor 430 is configured to read the computer program in the memory 410 and perform the following operations: based on network information of each user terminal, constructing first node features of nodes in an interference feature graph; wherein the interference feature graph takes each user terminal as a node; based on position information and uplink beam information of each user terminal, constructing edge weights between the nodes; based on the first node features and the edge weights, determining interference influence degrees between the nodes; and based on the interference influence degrees between the nodes, determining an uplink interference terminal.

[0108] In the above embodiments of the present application, Figure 4In particular embodiments, the bus architecture can include any number of interconnecting buses and bridges, and the various circuitry representative of the processor 430 and the memory 410, which for purposes of example, are linked together by bus 440. The bus architecture can also include various other circuitry that is well known, such as peripheral device, voltage regulators, power management circuitry, and the like, which are not further described herein for the sake of brevity. The bus interface provides an interface to the transceiver 420. The transceiver 420 can be a plurality of elements including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 430 is responsible for managing the bus architecture and general processing, including the execution of software stored in the memory 410.

[0109] Optionally, the processor 430 is further configured to determine spatial distances between the user terminals based on the position information of the user terminals; determine beam influence degrees between the user terminals based on the uplink beam information of the user terminals; and determine edge weights between the nodes based on the spatial distances and the beam influence degrees.

[0110] Optionally, the processor 430 is further configured to fuse the first node features with the edge weights to obtain second node features of the nodes; determine a similarity matrix based on the second node features of the nodes; perform spectral clustering based on the similarity matrix to obtain a plurality of interference node sets; and determine interference influence degrees between nodes in each of the interference node sets based on the edge weights.

[0111] Optionally, the processor 430 is further configured to fuse the first node features and the edge weights based on a graph convolution operation to obtain second node features of the nodes.

[0112] Optionally, the processor 440 is further configured to obtain interference level values between nodes in each of the interference node sets; and determine interference influence degrees between nodes in each of the interference node sets based on the interference level values and the edge weights.

[0113] Optionally, the processor 440 is further configured to determine target interference nodes from the nodes based on the interference influence degrees between the nodes and an interference influence degree threshold; and determine the uplink interference terminal based on the target interference nodes.

[0114] It should be noted that the network device provided by the embodiments of the present application can realize all the method steps realized by the method embodiments described above, and can achieve the same technical effects. Therefore, the same parts and beneficial effects of the method embodiments in the embodiments will not be described in detail.

[0115] Figure 5 An example of a schematic diagram of a physical structure of an electronic device is shown in Figure 5 The electronic device can include a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 can communicate with each other through the communication bus 540. The processor 510 can invoke a computer program in the memory 530 to execute the steps of the uplink interference identification method.

[0116] For example, the method includes: constructing a first node feature of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes each user terminal as a node; constructing an edge weight between each node based on position information and uplink beam information of each user terminal; determining an interference influence degree between each node based on the first node feature and the edge weight; and determining an uplink interference terminal based on the interference influence degree between each node.

[0117] In addition, the logic instructions in the memory 530 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the prior art that essentially contributes or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0118] On the other hand, the embodiments of the present application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, the computer can execute the steps of the uplink interference identification method provided by each embodiment described above.

[0119] The method comprises: constructing first node features of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes each user terminal as a node; constructing edge weights between each node based on position information and uplink beam information of each user terminal; determining interference influence degrees between each node based on the first node features and the edge weights; and determining uplink interference terminals based on the interference influence degrees between each node.

[0120] In another aspect, the embodiments of the present application further provide a processor-readable storage medium, which stores a computer program for causing a processor to perform the steps of the uplink interference identification method provided by each of the above embodiments.

[0121] For example, the method comprises: constructing first node features of each node in an interference feature graph based on network information of each user terminal; wherein the interference feature graph takes each user terminal as a node; constructing edge weights between each node based on position information and uplink beam information of each user terminal; determining interference influence degrees between each node based on the first node features and the edge weights; and determining uplink interference terminals based on the interference influence degrees between each node.

[0122] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to a magnetic storage (such as a floppy disk, a hard disk, a magnetic tape, a magneto-optical disk (MO), etc.), an optical storage (such as a CD, a DVD, a BD, a HVD, etc.), and a semiconductor memory (such as a ROM, an EPROM, an EEPROM, a NAND FLASH, a solid state disk (SSD)), etc.

[0123] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., they can be located in one place, or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the present embodiment scheme according to actual needs. Those skilled in the art can understand and implement it without creative labor.

[0124] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0125] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An uplink interference identification method, characterized by, Comprising: based on the network information of each user terminal, the first node feature of each node in the interference feature graph is constructed; wherein the interference feature graph takes the user terminal as the node; based on the position information and uplink beam information of the user terminal, the edge weight between the nodes is constructed; based on the first node feature and the edge weight, the interference influence degree between the nodes is determined; based on the interference influence degree between the nodes, the uplink interference terminal is determined; wherein the edge weight is determined by the following formula: ； wherein, is an edge weight between node i and node j; is a spatial distance between user terminal i and user terminal j; is a beam influence degree between user terminal i and user terminal j; wherein the beam influence degree is determined by the following method: based on the SRS beam level value of each user terminal, the level difference value between each user terminal is determined; based on the level difference value and the preset corresponding relationship between the level difference value and the beam influence degree, the beam influence degree between each user terminal is determined.

2. The method of claim 1, wherein, based on the position information and uplink beam information of the user terminal, the edge weight between the nodes is constructed, comprising: based on the position information of the user terminal, the spatial distance between the user terminals is determined; based on the uplink beam information of the user terminal, the beam influence degree between the user terminals is determined; based on the spatial distance and the beam influence degree, the edge weight between the nodes is determined.

3. The method of claim 1, wherein, based on the first node feature and the edge weight, the interference influence degree between the nodes is determined, comprising: the first node feature and the edge weight are fused to obtain the second node feature of the node; based on the second node feature of the node, a similarity matrix is determined; based on the similarity matrix, spectral clustering processing is performed to obtain a plurality of interference node sets; based on the edge weight, the interference influence degree between each node in each interference node set is determined.

4. The method of claim 3, wherein, based on the first node feature and the edge weight, the interference influence degree between the nodes is determined, comprising: based on the graph convolution operation, the first node feature and the edge weight are fused to obtain the second node feature of the node.

5. The method of claim 3, wherein, based on the edge weight, the interference influence degree between each node in each interference node set is determined, comprising: obtain the interference level value between each node in each interference node set; according to the interference level value and the edge weight, the interference influence degree between each node in each interference node set is determined.

6. The method according to any one of claims 1 to 5, characterized in that, based on the interference influence degree between the nodes, the uplink interference terminal is determined, comprising: based on the interference influence degree between the nodes and the interference influence degree threshold, the target interference node is determined from the nodes; based on the target interference node, the uplink interference terminal is determined.

7. An uplink interference identification device, characterized in that, Comprising: a first construction module for constructing the first node feature of each node in the interference feature graph based on the network information of each user terminal; wherein the interference feature graph takes the user terminal as the node; a second construction module for constructing the edge weight between the nodes based on the position information and uplink beam information of the user terminal; The first determining module is configured to determine the interference influence degrees between the nodes based on the first node features and the edge weights. The second determining module is configured to determine the uplink interference terminals based on the interference influence degrees between the nodes. The edge weight is determined by the following formula: ； wherein, is an edge weight between node i and node j; is a spatial distance between user terminal i and user terminal j; is a beam influence degree between user terminal i and user terminal j; The beam influence degree is determined by the following method: The level difference values between the user terminals are determined based on the SRS beam level values of the user terminals; and the beam influence degrees between the user terminals are determined based on the level difference values and a preset corresponding relationship between the level difference values and the beam influence degrees.

8. A network device, comprising: The memory, the transceiver and the processor are included. The memory is configured to store a computer program; the transceiver is configured to transceive data under the control of the processor; and the processor is configured to read the computer program in the memory and perform the following operations: The first node features of the nodes in the interference feature graph are constructed based on the network information of the user terminals; wherein the user terminals are taken as the nodes in the interference feature graph; The edge weights between the nodes are constructed based on the position information and the uplink beam information of the user terminals; The interference influence degrees between the nodes are determined based on the first node features and the edge weights; The uplink interference terminals are determined based on the interference influence degrees between the nodes; The edge weight is determined by the following formula: ； wherein, is an edge weight between node i and node j; is a spatial distance between user terminal i and user terminal j; is a beam influence degree between user terminal i and user terminal j; The beam influence degree is determined by the following method: The level difference values between the user terminals are determined based on the SRS beam level values of the user terminals; and the beam influence degrees between the user terminals are determined based on the level difference values and a preset corresponding relationship between the level difference values and the beam influence degrees.

9. An electronic device comprising a processor and a memory having a computer program stored therein, characterized in that, The processor implements the uplink interference identification method in any one of claims 1 to 6 when executing the computer program.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the uplink interference identification method in any one of claims 1 to 6.

11. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the uplink interference identification method in any one of claims 1 to 6.

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