Ssb beam optimization method and apparatus

Abstract

The application discloses a SSB beam optimization method and device. The method comprises the following steps: periodically acquiring measurement reports of all users in the coverage range of a target base station; determining a weighted graph corresponding to SSB beams of the target base station based on the measurement reports; determining the number of subgraphs and the segmentation interface of the weighted graph, and segmenting the weighted graph to obtain a plurality of subgraphs; for each subgraph, determining the center of the coverage range of the SSB beam corresponding to the current subgraph based on the closeness centrality of each vertex in the current subgraph, and determining the radius of the coverage range by taking the first coverage rate of the coverage range containing the current subgraph being not lower than a preset coverage rate threshold as a constraint condition and taking the second coverage rate of the coverage range containing other subgraphs being the lowest as an optimization target. The application solves the technical problem that the related art relies on artificial experience to optimize SSB beams, resulting in low optimization efficiency and poor effectiveness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile communication, in particular to a SSB beam optimization method and device. BACKGROUND

[0002] Massive MIMO (Multiple Input Multiple Output), that is, massive antenna technology, as a core technology for improving network coverage, user experience and system capacity in the fifth generation mobile communication, has been widely applied in mobile communication networks. As one of the important works of mobile network optimization, the SSB (Synchronized Signal Block) beam optimization of Massive MIMO is currently usually manually set different SSB beam quantity, direction and other working parameters according to the topography environment in the current antenna coverage direction, in order to optimize the signal coverage of the mobile network. However, since this work needs to be based on road test data and field investigation in most scenarios, and the coverage scheme is selected by the technical personnel subjectively, therefore, this optimization method leads to difficulty in guaranteeing the optimization efficiency and effectiveness.

[0003] At present, no effective solution has been proposed for the above problems. SUMMARY

[0004] Embodiments of the present application provide a SSB beam optimization method and device, to at least solve the technical problem that the related art relies on manual experience to optimize the SSB beam, resulting in low optimization efficiency and poor effectiveness.

[0005] According to an aspect of the embodiments of the present application, a SSB beam optimization method is provided, comprising: periodically obtaining measurement reports of all users in a coverage range of a target base station, wherein the measurement reports at least include SSB sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect the incident angle information of terminal signal transmission of the users to the target base station; determining a weighted graph corresponding to SSB beams of the target base station based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of the users as graph vertices, takes switching pairs of the users from a first graph vertex to a second graph vertex within a preset time interval as edges, and the weight value of the graph vertex is a first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is a second count of switching from the first graph vertex to the second graph vertex; determining the number of partition sub-graphs and the partition interface of the weighted graph, and partitioning the weighted graph according to the number of partition sub-graphs and the partition interface to obtain a plurality of sub-graphs, wherein each sub-graph corresponds to an SSB beam; for each sub-graph, determining the center of the coverage range of the SSB beam corresponding to the current sub-graph based on the closeness centrality of each sub-graph vertex in the current sub-graph, and determining the radius of the coverage range corresponding to the SSB beam corresponding to the current sub-graph under the constraint condition that the first coverage rate of the coverage range containing the current sub-graph is not less than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

[0006] Optionally, the SSB sub-beam direction parameters include: horizontal arrival angle and vertical arrival angle, wherein the determining the weighted graph corresponding to the SSB beam direction of the target base station based on the measurement reports comprises: taking the SSB sub-beam direction parameters of the users as graph vertices, counting the first count of the SSB sub-beam direction parameter corresponding to each graph vertex, and taking the first count as the weight value of the graph vertex; taking the switching pairs of the users from a first graph vertex to a second graph vertex within a preset time interval as edges, and determining whether the first SSB sub-beam direction parameter corresponding to the first graph vertex and the second SSB sub-beam direction parameter corresponding to the second graph vertex are the same; in the case that the first SSB sub-beam direction parameter and the second SSB sub-beam direction parameter are not the same, counting the second count of switching from the first graph vertex to the second graph vertex, and taking the second count as the weight value of the corresponding edge; establishing a two-dimensional coordinate system with the antenna center direction as the coordinate origin, with the horizontal arrival angle as the horizontal axis, and with the vertical arrival angle as the vertical axis, and adding the graph vertices, edges, weight values of the graph vertices, and weight values of the edges into the two-dimensional coordinate system to obtain the weighted graph corresponding to the SSB beams of the target base station.

[0007] Optionally, after obtaining the weighted graph corresponding to the SSB beams of the target base station, the method further comprises: calculating the degree centrality of each graph vertex in the weighted graph, and removing the graph vertices with a degree centrality of 0 in the weighted graph.

[0008] Optionally, the determining the number of partition subgraphs and the partition interface of the weighted graph comprises: determining the number of partition subgraphs of the weighted graph based on preset environmental factors, wherein the environmental factors comprise at least one of the following: uplink / downlink ratio of air interface frame structure, remote radio unit (RRU) capability / active antenna unit (AAU) capability; and determining the partition interface of the weighted graph by using an improved community discovery algorithm, wherein the improved community discovery algorithm is a merging processing of nodes less than a preset weight threshold in an initial agglomeration tree constructed based on a modularity-based community discovery algorithm.

[0009] Optionally, the determining the partition interface of the weighted graph by using the improved community discovery algorithm comprises: first step: performing community division and community aggregation on the weighted graph by using a modularity-based community discovery algorithm, constructing a corresponding initial agglomeration tree, and determining weight values of nodes in the initial agglomeration tree, wherein each node represents a subgraph, and the weight value of the node is determined by weight values of two graph vertices contained by the node, and the weight value of a root node of the agglomeration tree is a total weight value of the weighted graph; second step: setting a weight threshold of each node, wherein the weight threshold is less than the total weight value of the weighted graph; third step: repeatedly performing the following splitting step until the number of weight values of nodes in the agglomeration tree that are not less than the weight threshold is the same as the number of partition subgraphs or the number of weight values of nodes in the agglomeration tree that are not less than the weight threshold is zero: traversing each parent node in the agglomeration tree from top to bottom from the root node of the agglomeration tree to determine a target parent node with the maximum weight value; judging a size relationship between the weight value of each target child node under the target parent node and the weight threshold; in a case where the weight value of the target child node is not less than the weight threshold, dividing the target child node into a first node set, wherein the first node set at least comprises the root node corresponding to the weighted graph; in a case where the weight value of the target child node is less than the weight threshold, dividing the target child node into a stack, wherein each node in the stack is sequentially stacked according to a position of the node in the initial agglomeration tree, and the lower the position of the node in the initial agglomeration tree, the closer the node is to the top of the stack; fourth step: traversing a top node of the stack to obtain a second node set by aggregating all nodes under a subtree with a parent node of the current top node as the root node in the agglomeration tree; fifth step: for each intersection node in an intersection of the first node set and the second node set, calculating a distance value of the intersection node and a top node of the stack, and merging the intersection node and the target top node if the distance value is less than a preset distance threshold; sixth step: repeatedly performing the fourth step and the fifth step until all nodes in the stack are merged into the first node set; seventh step: determining the partition interface of the weighted graph based on the updated first node set obtained in the sixth step.

[0010] Optionally, the center of the coverage range of the SSB beam corresponding to the current subgraph is determined based on the closeness centrality of each subgraph vertex in the current subgraph, and the radius of the coverage range is determined with a constraint condition that a first coverage rate of the coverage range containing the current subgraph is not lower than a preset coverage rate threshold and an optimization target that a second coverage rate of the coverage range containing other subgraphs is the lowest, including: calculating the closeness centrality of each subgraph vertex in the current subgraph by using the closeness centrality algorithm, and determining a target subgraph vertex with a closeness centrality greater than a preset threshold; taking the SSB sub-beam direction parameter of the target subgraph vertex as the center of the coverage range of the SSB beam corresponding to the current subgraph, and taking the half-power angle parameter of the SSB beam corresponding to the current subgraph as the radius, to determine the coverage range of the SSB beam corresponding to the current subgraph, wherein the half-power angle parameter includes a horizontal half-power angle and a vertical half-power angle; determining a constraint condition that a ratio of a sum of weight values of each subgraph vertex belonging to the current subgraph in the coverage range to a sum of weight values of all subgraph vertices of the current subgraph is not less than the coverage rate threshold, and determining an optimization target that a ratio of a sum of weight values of other subgraph vertices of other subgraphs in the coverage range to a sum of weight values of all nodes in the coverage range is the smallest; and calculating the half-power angle parameter of the SSB beam corresponding to the current subgraph by using a linear programming algorithm based on the constraint condition and the optimization target.

[0011] Optionally, after obtaining the plurality of subgraphs, the method further includes: evaluating the performance of the SSB beam corresponding to each subgraph by using a preset evaluation index, wherein the evaluation index includes at least one of the following: modularity, balance, and total edge weight between subgraphs.

[0012] According to another aspect of the embodiments of the present application, the SSB beam optimization device is also provided, comprising: an acquisition module, configured to acquire measurement reports of all users in a coverage range of a target base station, wherein the measurement reports at least include SSB sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect incident angle information of terminal signal transmission of the users to the target base station; a determination module, configured to determine a weighted graph corresponding to SSB beams of the target base station based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of the users as graph vertices, takes switching pairs of the users from a first graph vertex to a second graph vertex in a preset time interval as edges, and a weight value of the graph vertex is a first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and a weight value of the edge is a second count of switching from the first graph vertex to the second graph vertex; a division module, configured to determine a number of divided sub-graphs and a division interface of the weighted graph, and divide the weighted graph according to the number of divided sub-graphs and the division interface to obtain a plurality of sub-graphs, wherein each sub-graph corresponds to one SSB beam; and the determination module is further configured to, for each sub-graph, determine a center of a coverage range of the SSB beam corresponding to the current sub-graph based on a closeness centrality of each sub-graph vertex in the current sub-graph, and determine a radius of the coverage range by taking a first coverage rate of the coverage range containing the current sub-graph being not less than a preset coverage rate threshold as a constraint condition and taking a second coverage rate of the coverage range containing other sub-graphs being the lowest as an optimization target.

[0013] According to another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, comprising a stored computer program, wherein a device where the non-volatile storage medium is located executes the SSB beam optimization method described above by running the computer program.

[0014] According to another aspect of the embodiments of the present application, a computer program product is also provided, comprising a stored computer program, wherein the computer program is executed by a processor to implement the SSB beam optimization method described above.

[0015] In the embodiments of the present application, the Massive MIMO SSB beam service is abstracted into a weighted graph according to the measurement report of the mobile network service, and when the weighted graph is divided by applying the graph computing technology, the balance degree on the agglomeration / splitting tree is added on the basis of the traditional community discovery algorithm based on modularity, the combination of the community splitting based on modularity and the community selection according to the balance is realized, so that the Massive MIMO SSB beams are efficiently divided, the efficiency of the beam division is greatly improved, and the technical problem that the SSB beams are optimized by relying on artificial experience in the related art, resulting in low optimization efficiency and poor effectiveness is solved. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0017] Figure 1 is a hardware structure block diagram of an optional computer terminal for implementing an SSB beam optimization method according to an embodiment of the application;

[0018] Figure 2 is a flowchart of an optional SSB beam optimization method according to an embodiment of the application;

[0019] Figure 3 is a schematic diagram of an optional two-dimensional coordinate system according to an embodiment of the application;

[0020] Figure 4 is a counting schematic diagram of an optional type 1 and type 2 according to an embodiment of the application;

[0021] Figure 5 is a schematic diagram of an optional weighted graph G according to an embodiment of the application;

[0022] Figure 6 is a schematic diagram of an initial agglomerate tree corresponding to the weighted graph G according to an embodiment of the application;

[0023] Figure 7 is a schematic diagram of an execution result of an optional step S4 according to an embodiment of the application;

[0024] Figure 8 is a comparison schematic diagram of a split interface of an agglomerate tree corresponding to the weighted graph G according to an embodiment of the application;

[0025] Figure 9 is a division schematic diagram of an optional weighted graph G according to an embodiment of the application;

[0026] Figure 10 is a coverage range schematic diagram of each subgraph obtained by dividing the weighted graph G according to an embodiment of the application;

[0027] Figure 11 is a structural schematic diagram of an optional SSB beam optimization apparatus according to an embodiment of the application;

[0028] Figure 12 is a structural schematic diagram of an optional electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0029] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0031] In addition, the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties. For example, an interface is provided between the system and the relevant user or institution. Before obtaining the relevant information, the interface needs to send a request to the aforementioned user or institution, and after receiving the consent information fed back by the aforementioned user or institution, the relevant information is obtained.

[0032] In order to better understand the embodiments of the present application, the technical terms involved in the embodiments of the present application are explained as follows:

[0033] Massive MIMO (Massive Multiple Input Multiple Output) is an extension of MIMO technology. The channel number using Massive MIMO technology can reach 32 or 64, the number of antenna array can reach 192, 512 or even higher, and the gain greatly exceeds that of traditional devices. At the same time, Massive MIMO increases the coverage of vertical dimension space on the basis of horizontal dimension space, and the signal radiation shape is a flexible three-dimensional electromagnetic beam, so Massive MIMO can exploit spatial dimension resources, so that multiple users in the coverage range of the base station can communicate with the base station at the same time on the same time-frequency resource using the spatial degrees of freedom provided by the large-scale antenna, and the multiplexing capability of the frequency spectrum resource between multiple users is improved.

[0034] SSB (Synchronized Signal Block) refers to a signal block that is synchronized in time, frequency and phase, which contains some necessary control information for device access, system information, RRC (Radio Resource Control) connection, etc. The important role of SSB beam in 5G NR network is timing and frequency synchronization, as well as location and mobility management. In 5G NR network, SSB beam is also used as a reference signal for access and measurement.

[0035] Measurement Report (MR) is an important data basis for mobile network optimization. The measurement results reported by the physical layer can be used to trigger events such as cell selection / reselection and handover in the radio resource control sublayer of the system, and can also be used for system operation and maintenance to observe the running state of the system.

[0036] AOA (Angle of Arrival) is a key data for intelligent antenna measurement, which is used to represent the angle of arrival of the terminal signal to the base station. Currently, the angle of arrival can be obtained by antenna array technology, because for signals arriving at the antenna array from different angles, there will be a time difference in the signals received by each antenna, and this time difference corresponds to the angle of arrival of the signal, which is the basic idea of the AOA algorithm.

[0037] Graph is an abstract data structure used to represent the relationship between objects, which is described using vertices and edges. The vertex represents the object, and the edge represents the relationship between the objects. Generally, the data that can be abstracted as a graph is graph data.

[0038] Graph Processing is a process of modeling data in the form of a graph to obtain results that are difficult to obtain with a flat perspective, that is, using a graph as a data model to express and solve problems.

[0039] Weighted graph: it is based on the definition of a graph, and a certain weight is added to each edge to form a weighted graph, which can be represented as G (V, E), where V is the vertex set of graph G, and E is the edge set of G. Then (u, v) represents the edge connecting vertex u and vertex v, w (v) = x represents the weight of vertex v is x, and w (u, v) = y represents the weight of the edge connecting vertex u and vertex v is y.

[0040] Community Detection is a process of finding the community structure of a given network graph. The community structure is a phenomenon of a set of nodes in the network graph, which is a common feature in the network. Generally, when the node sets of each community do not intersect with each other, it is called a non-overlapping (Disjoint) community; otherwise, it is called an overlapping (Overlapping) community. The community (Community) is a subgraph corresponding to a sub-set of nodes with relatively close connections.

[0041] The community detection algorithm is a community detection algorithm based on graph theory, which was proposed by Guido Caldarelli and Nicola Pellow of Italy in 1999. The basic idea of the algorithm is to divide the nodes in the network into different communities through an iterative process, so that the nodes in the same community are connected closely, and the nodes in different communities are connected sparsely.

[0042] The Louvain algorithm, also known as the Fast unfolding algorithm, is a community detection algorithm based on modularity. The basic idea is that the nodes in the network try to traverse all the neighbor community labels, and select the community label that maximizes the modularity increment. After maximizing the modularity, each community is regarded as a new node, and the process is repeated until the modularity no longer increases.

[0043] Embodiment 1

[0044] According to the embodiments of the present application, a method embodiment of an SSB beam optimization method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown herein.

[0045] The method embodiment provided by the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the SSB beam optimization method is shown. As shown in FIG. 1, the computer terminal includes a processor 101, a memory 102, a bus 103, a communication interface 104, and a display 105. Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0046] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0047] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the SSB beam optimization method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the SSB beam optimization method of the aforementioned application. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0048] The transmission device 106 is configured to receive or send data via a network. The network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network interface controller (NIC) that can connect to other network devices through a base station to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module that is configured to communicate with the Internet wirelessly.

[0049] The display can be a liquid crystal display (LCD) that is touch screen type, for example, which can enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0050] In the above operating environment, Figure 2 is a flowchart of an optional SSB beam optimization method according to an embodiment of the present application, as shown in the figure, the method includes steps S202-S208, wherein: Figure 2

[0051] In step S202, measurement reports of all users in a coverage range of a target base station are periodically obtained.

[0052] In the technical solution provided in step S202, the SSB beam optimization system can periodically obtain the measurement reports of all users in the coverage range of the target base station according to a preset time interval, for example, obtaining the measurement reports in turn every 60 seconds. The measurement report of each user obtained each time includes at least an SSB sub-beam direction parameter of the user, and the SSB sub-beam direction parameter is used to reflect incident angle information of a terminal signal of the user transmitted to the target base station. Therefore, the SSB sub-beam direction parameter can also be referred to as an angle of arrival (AOA), and the AOA includes horizontal AOA (HAOA) and vertical AOA (VAOA) two data, which are used to realize two-dimensional positioning of the terminal signal of the user.

[0053] In step S204, a weighted graph corresponding to SSB beams of the target base station is determined based on the measurement reports.

[0054] In the technical solution provided in step S204, the SSB beam optimization system abstracts the beam direction of the Massive MIMO SSB beam of the target base station into a weighted graph using the obtained measurement reports of all users, where the weighted graph can be represented by a letter G; the graph vertex of the weighted graph G can be represented by a letter V, which refers to the SSB sub-beam direction parameter of each user, that is, V i = {HAOA​i ,VAOA i}; the edge of the weighted graph G can be represented by the letter E, which refers to the switching of each single user from the first SSB sub-beam direction parameter corresponding to the first graph vertex to the second SSB sub-beam direction parameter corresponding to the second graph vertex within a preset time interval; the weight value of the graph vertex V can be represented by the letter x, which refers to the first count of the {HAOA, LAOA} combination; the weight value of the edge E can be represented by the letter y, which refers to the second count of the switching pair composed of the first SSB sub-beam direction parameter corresponding to the first graph vertex and the second SSB sub-beam direction parameter corresponding to the second graph vertex.

[0055] In step S206, the number of partition sub-graphs and the partition interface of the weighted graph are determined, and the weighted graph is partitioned according to the number of partition sub-graphs and the partition interface to obtain a plurality of sub-graphs.

[0056] In the technical solution provided in step S206, the SSB beam optimization system determines the number of partition sub-graphs and the partition interface of the weighted graph G, and partitions the weighted graph according to the determined number of partition sub-graphs and the partition interface to obtain a plurality of sub-graphs, so that each sub-graph includes one or more users with the same SSB beam direction, that is, each sub-graph corresponds to an SSB beam.

[0057] In step S208, for each sub-graph, the center of the coverage range of the SSB beam corresponding to the current sub-graph is determined based on the closeness centrality of each sub-graph vertex in the current sub-graph, and the radius of the coverage range of the SSB beam corresponding to the current sub-graph is determined with the constraint condition that the first coverage rate of the coverage range of the SSB beam corresponding to the current sub-graph containing the current sub-graph is not less than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

[0058] In the technical solution provided in step S208, the SSB beam optimization system sequentially traverses each sub-graph divided, and determines the center of the coverage range of the SSB beam corresponding to the current sub-graph from the plurality of sub-graph vertices in the current sub-graph according to the closeness centrality of each sub-graph vertex in the current sub-graph, and determines the radius of the coverage range of the SSB beam corresponding to the current sub-graph with the constraint condition that the first coverage rate of the coverage range of the SSB beam corresponding to the current sub-graph containing the current sub-graph is not less than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

[0059] The above method of the embodiment will be further introduced below.

[0060] As an optional implementation, in the technical solution provided in step S204, the method can include:

[0061] Step S2041, taking the SSB sub-beam direction parameters of each user as the graph vertex, counting the first count of the SSB sub-beam direction parameters corresponding to each graph vertex, and taking the first count as the weight value of the graph vertex;

[0062] Step S2042, taking the switching pair of each user from the first graph vertex to the second graph vertex within a preset time interval as the edge, and judging whether the first SSB sub-beam direction parameter corresponding to the first graph vertex and the second SSB sub-beam direction parameter corresponding to the second graph vertex are the same;

[0063] Step S2043, in the case that the first SSB sub-beam direction parameter and the second SSB sub-beam direction parameter are not the same, counting the second count of the first graph vertex switching to the second graph vertex, and taking the second count as the weight value of the corresponding edge;

[0064] Step S2044, taking the antenna center direction as the coordinate origin, taking the horizontal angle of arrival as the horizontal axis, and taking the vertical angle of arrival as the vertical axis to establish a two-dimensional coordinate system, and adding the graph vertex, the edge, the weight value of the graph vertex, and the weight value of the edge into the two-dimensional coordinate system to obtain the weighted graph corresponding to the SSB beam of the target base station.

[0065] Specifically, the generation process of the weighted graph provided by the above steps S2041-S2044 can be understood as:

[0066] First step: taking the antenna center direction as the coordinate origin, taking the HAOA field as the horizontal coordinate axis, and taking the VAOA field as the vertical coordinate axis to construct a two-dimensional coordinate system, as shown in Figure 3 Each coordinate point in the two-dimensional coordinate system can represent different SSB sub-beam direction parameters. In addition, since the numerical range of HAOA and VAOA is ±90°, and the precision is 1°, the above two-dimensional coordinate system can include 181*181 points, a total of 32761 points.

[0067] Second step: periodically obtain the MR records of each user in the coverage of the target base station (the MR records at least contain HAOA field and VAOA field), and sort them in the order of acquisition time, and take the MR records of the two adjacent times in the preset time interval (such as 60 seconds) as the MR record pair (namely SSB sub-beam direction parameter), wherein: in the case that the HAOA field and the VAOA field in the above two adjacent MR records are the same, mark the MR record pair as type 1 (namely the same beam direction twice, and the SSB sub-beam direction of the user does not switch); in the case that the HAOA field and the VAOA field in the above two adjacent MR records are different, mark the MR record pair as type 2 (namely different beam direction twice, and the SSB sub-beam direction of the user switches), and combine the HAOA field and the VAOA field in the first MR record before switching as the first SSB sub-beam direction parameter, and combine the HAOA field and the VAOA field in the second MR record after switching as the second SSB sub-beam direction parameter, then the first SSB sub-beam direction parameter and the second SSB sub-beam direction parameter can be combined as a set of switching relationship pairs.

[0068] Third step: take the different combinations of the HAOA field and the VAOA field as the graph vertex V; at the same time, take the switching relationship pairs (the combination of the HAOA field and the VAOA field before switching and the combination of the HAOA field and the VAOA field after switching) corresponding to each type 2 as the edge E, and the switching direction from the combination of the HAOA field and the VAOA field before switching to the combination of the HAOA field and the VAOA field after switching as the direction of the edge E.

[0069] Fourth step: count the number of different type 1 to obtain the corresponding count 1 (namely the first count), and take the count 1 (namely the first count) as the weight value x of the corresponding graph vertex V, wherein the number of graph vertices V is 32761 (namely 181*181); at the same time, count the number of switching relationship pairs corresponding to different type 2 to obtain the corresponding count 2 (namely the second count), and take the count 2 (namely the second count) as the weight value y of the corresponding edge E (namely the switching relationship pair from the combination of the HAOA field and the VAOA field before switching to the combination of the HAOA field and the VAOA field after switching). As shown in Figure 4

[0070] Fifth step: add all the graph vertices V and their weight values x, edges E and their weight values y to the two-dimensional coordinate system constructed in the first step to obtain the weighted graph G, as shown in Figure 5

[0071] ​​Further, since the above-constructed weighted graph G can include isolated nodes which do not contribute to network performance, in the network optimization process, these isolated nodes can be deleted, which not only simplifies the network topology result, but also effectively reduces noise and interference in the network, so that beamforming can be more accurately directed to the direction with actual communication needs.

[0072] Optionally, the SSB beam optimization system can delete the graph vertices with a degree centrality of 0 in the weighted graph G, thereby realizing the optimization of the weighted graph G. Wherein, the degree centrality is the most direct measurement index to describe the node centrality in network analysis. The larger the node degree of a node, the higher the degree centrality of the node, and the more important the node in the network. Therefore, the expression of the above-mentioned degree centrality is as follows:

[0073]

[0074] Wherein, k represents the number of existing edges connected to node i, and N-1 represents the number of edges connected to node i and other graph vertices.

[0075] In order to ensure the performance of the wireless network, the SSB beam optimization system can follow the following two principles when dividing the beams of the Massive MIMO SSB of the target base station:

[0076] First, the SSB beam points to the busy area, and the traffic difference between the SSB beams cannot be too large. This requires the weighted graph G to be divided into multiple subgraphs with approximately equal weights, wherein the wireless network is abstracted as a weighted graph, so the weight values of each subgraph are approximately equal;

[0077] Second, the SSB beam boundary is far away from the busy area, which requires the edge weight between each subgraph to be the smallest.

[0078] As an optional implementation, in order to meet the above two conditions when dividing the weighted graph, the SSB beam optimization system can determine the number of divided subgraphs and the division interface of the weighted graph by the following method:

[0079] Step S2061, determine the number of divided subgraphs of the weighted graph based on the preset environmental factors.

[0080] Specifically, in the actual problem of beam division of Massive MIMO SSB, the number of divided subgraphs k can be determined according to actual engineering conditions, considering factors including but not limited to: air interface frame structure uplink / downlink ratio, RRU(Remote Radio Unit) / AAU(Antenna Unit) capability, wireless network environment, etc. It should be noted that the value range of the number of divided subgraphs k can be generally 1≤k≤8.

[0081] For example, the correspondence between different air interface frame structures and the number k of partition sub-graphs is shown in Table 1.

[0082] Figure 1

[0083] Frame period Frame structure Range of values for K 2.5ms single period DDDSU 1≤k≤7 2.5ms double period DDDSUDDSUU 1≤k≤7 2ms single period DDSU 1≤k≤5 5ms single period DDDDDDDSUU 1≤k≤8

[0084] wherein D (Data) represents a data field; S (Synchronization) represents a synchronization field, used for aligning frames at a receiving end; and U (User) represents a user field, used for transmitting user-specific information.

[0085] In step S2062, the partition interface of the weighted graph is determined by using the improved community discovery algorithm.

[0086] Since the termination condition of the traditional community discovery algorithm is the best result of modularity or the preset number of communities (nodes) to terminate the algorithm, the balance requirement is not considered, and thus the problem of unbalanced community weight values inevitably occurs. The Massive MIMO SSB beam division has a requirement for community weight balance (different beam service balance), that is, a community with an excessively large weight value cannot be generated, and thus the community with a large weight value needs to be further split. Therefore, the present embodiment supplements the existing community discovery algorithm, that is, the search process on the split / aggregation tree is used to find the partition interface that optimizes the balance, and thus the improved community discovery algorithm can also be called the Balanced-Louvain-v2 algorithm. The main idea of the improved community discovery algorithm is to merge the nodes in the initial aggregation tree constructed based on the modularity-based community discovery algorithm and less than the preset weight threshold.

[0087] Optionally, the step S2062 can also be implemented by the following steps:

[0088] First step: the community division and community aggregation of the weighted graph are performed by using the modularity-based community discovery algorithm, an initial aggregation tree is constructed, and the weight values of the nodes in the initial aggregation tree are determined, wherein each node represents a sub-graph, the weight value of the node is determined by the weight values of the two graph vertices contained by the node, and the weight value of the root node of the aggregation tree is the total weight value of the weighted graph;

[0089] Second step: set the weight threshold of each node, wherein the weight threshold is less than the total weight value of the weighted graph.

[0090] Step 3: Repeat the following splitting step until the number of nodes in the condensed tree whose weight values are not less than the weight threshold is equal to the number of partition subgraphs or the number of nodes in the condensed tree whose weight values are not less than the weight threshold is zero: traverse each parent node in the condensed tree from top to bottom, determine a target parent node with the maximum weight value; determine the size relationship between the weight value of each target child node under the target parent node and the weight threshold;

[0091] In the case that the weight value of the target child node is not less than the weight threshold, the target child node is divided into the first node set, wherein the first node set at least includes the root node corresponding to the weighted graph;

[0092] In the case that the weight value of the target child node is less than the weight threshold, the target child node is divided into the stack, wherein each node in the stack is sequentially stacked according to the position of the node in the initial condensed tree, and the lower the position of the node in the initial condensed tree, the closer the node is to the top of the stack;

[0093] Step 4: Traverse the top node of the stack, and aggregate all nodes under the subtree with the parent node of the current top node as the root node in the condensed tree to obtain a second node set;

[0094] Step 5: For each intersection node in the intersection of the first node set and the second node set, calculate the distance value between the intersection node and the top node in the stack, and merge the intersection node with the target top node if the distance value is less than a preset distance threshold;

[0095] Step 6: Repeat the fourth step and the fifth step until all nodes in the stack are merged into the first node set;

[0096] Step 7: Determine the partition interface of the weighted graph according to the updated first node set obtained in the sixth step.

[0097] The improved community discovery algorithm for determining the partition interface of the weighted graph G will be described below through specific examples and related drawings, including:

[0098] Step S1, applying the Louvain algorithm, performing community division and community aggregation on the weighted graph G to construct an initial condensed tree.

[0099] Specifically, the above step S1 can be further refined as follows:

[0100] Step S11, regarding each node in the weighted graph G as a separate community (or subgraph), and initializing the modularity of each node.

[0101] Step S12, constructing an adjacency matrix of the weighted graph G according to the connections between nodes and the weight values of edges.

[0102] Step S13, for each graph vertex i in the weighted graph, sequentially try to assign the graph vertex i to the community where each of its neighbor nodes is located, and calculate the module gain AQ after assignment by the following formula:

[0103]

[0104] wherein the above k i ,in represents the sum of edge weights between the nodes in the community where the neighbor node is located and the graph vertex i.

[0105] Step S14, according to the calculated module gain AQ, determine to merge the graph vertex i into the community where the neighbor node with the largest AQ is located.

[0106] Step S15, take the nodes in the same community as a new node, and update the adjacency matrix or edge list of the graph according to the new node to reflect the new community structure.

[0107] Step S16, recalculate the internal edge weight (i.e. the sum of the weight values of the edges contained in the new node) and the external edge weight (i.e. the sum of the weight values of the edges connected to the new node) of the new node, and update the module of the entire weighted graph G according to the new node.

[0108] Step S17, repeat the above steps S13-S16 until the module Q cannot be further increased by merging nodes or a certain predetermined stopping condition is reached.

[0109] Step S18, construct and output the initial agglomerative tree according to the nodes and order of merging in each merging process.

[0110] Figure 6 is a schematic diagram of an initial agglomerative tree corresponding to a weighted graph G according to an embodiment of the present application, as Figure 6 shown. Each node in the initial agglomerative tree represents a subgraph (i.e. a community), and the edges between the nodes represent the history of node merging. The leaf nodes of the agglomerative tree correspond to the original graph vertices in the weighted graph G, and the internal nodes represent larger community nodes formed by merging. In addition, since the hierarchical structure of the agglomerative tree reflects the order of node merging, the nodes at the lower levels of the agglomerative tree represent smaller communities, and the nodes at the higher levels of the agglomerative tree represent larger communities. The weight value of each node in the agglomerative tree is equal to the sum of the internal edge weight of the node and the weight values of the two graph vertices contained in the node, Figure 6 wherein each tree node represents a different weight value through different color depths, and the larger the weight value, the darker the corresponding color.

[0111] Step S2: Initialize the number of subgraphs to be divided, k0 = 3, and initialize the weight value of each independent subgraph to a minimum of w0 (i.e., the weight threshold). Here, w0 is less than the weight value of the weighted graph G, and the weight value of the weighted graph G is equal to the sum of the weight values ​​of all vertices and edges of the graph. At the same time, set up two data structures, including: a first node set S: which is the set of all nodes (i.e., subgraphs) with a weight value not less than w0. Initialize the first node set S to contain only one element, i.e., the weighted graph G itself, i.e., S = *G + ; a stack M: which is the set of all nodes (i.e., subgraphs) with a weight value less than w0. Initialize the stack M = φ (φ represents an empty set).

[0112] Step S3: Traverse the agglomerative tree from top to bottom starting from the root node. For the j-th iteration, if each parent node G... j In the initial split tree, it has been split into two child nodes G. j1 and G j2 Then G can be determined according to the following rules. j1 and G j2 Assign it to the first node set S or the stack M:

[0113] (I) If G j ∈M j-1 or That is, G j The stack M belonging to the previous iteration or the current subgraph G j If the set S and stack M do not belong to the previous iteration process, it indicates that the current subgraph G... j If a node has already been split or its parent node has a small weight and does not need to be split, then the split can be skipped. j The splitting process, i.e., the first node set S and the stack M remain unchanged;

[0114] (II) If G j ∈S j-1 And G j1 The weight value is less than w0, and G j2 If the weight value of G is less than w0, it means that G... j1 and G j2 Parent node G j If the weight value of G is small, there is no need to split; in this case, we can skip splitting G. j The splitting process, i.e., the first node set S and the stack M remain unchanged;

[0115] (III) If G j ∈S j-1 And G j1 The weight value of G is not less than w0. j2 If the weight value is less than w0, then G will be... j1 Partition to the first node set S, and then to G. j2partitioning G to the stack M, at this time the updated first node set S j = {S j-1 -G j}∪{G j1} and the updated M j = {M j-1}∪{G j2};

[0116] (IV) If G j ∈S j-1 , and the weight value of G j1 is less than w0, and the weight value of G j2 is not less than w0, then partitioning G j1 to the stack M, and partitioning G j2 to the first node set S, at this time the updated first node set S j = {S j-1 -G j}∪{G j2} and the updated M j = {M j-1}∪{G j1};

[0117] (V) If G j ∈S j-1 , and the weight value of G j1 is not less than w0, and the weight value of G j2 is not less than w0, then partitioning G j1 , G j2 to the first node set S, at this time the updated first node set S j = {S j-1 -G j}∪{G j1 , G j2};

[0118] Step S4: repeating the above step S3 until the number of elements (i.e. the number of subgraphs) k in the first node set S is equal to 3, or k = 0 is terminated; wherein if k = 0, it means that the weight value of all nodes in the first node set S is less than w0, at this time the algorithm ends and returns "no k nodes satisfying the minimum independent subgraph weight w0 (meaning that w0 is set too high, or k0 is set too large)"; if k = 3, then the stack M includes 0 or a number of nodes with weight values less than w0, and the first node set S contains 3 nodes with weights not less than w0, as shown in Figure 7 .

[0119] Step S5: judging whether the stack M is empty or not, if yes, the algorithm ends, and the result returns the first node set S, otherwise, each node in the stack M is stacked in turn according to the position of each node in the condensed tree and in the order from bottom to top, that is, the lower the position of the subgraph in the condensed tree, the closer the node to the top of the stack.

[0120] Step S6: therefore, for each top node m in the stack M (that is, selecting a lowermost subgraph element m from M in the order from bottom to top on the condensed tree), all nodes under the sub-tree with the parent node of the top node m as the root node on the initial condensed tree are traversed to obtain a second node set P.

[0121] Step S7: for each intersection node n in the intersection N of the first node set S and the second node set P, the distance average value l mn of each subgraph vertex corresponding to each top node m is calculated, and the target top node m with the minimum distance average value l mn is merged into the intersection node n.

[0122] Step S8: repeating the above steps S5-S7 until all nodes (that is, subgraphs) in the stack M are merged into the first node set S.

[0123] Step S9: determining the partition interface of the weighted graph G according to the first node set S updated in the above step S8, as shown in Figure 8 It can be seen that the partition interface determined by the improved community discovery algorithm adopted in the present application considers the balance of the community weight value, so that the weight values of the divided subgraphs are roughly equivalent.

[0124] It should be noted that the present application does not make specific limitations on the execution order of the above steps S2061 and S2062, which can be executed in the order of step S2061 first and then step S2062, or in the order of step S2062 first and then step S2061, or simultaneously.

[0125] Further, using the partition interface and the number of partition subgraphs of the weighted graph G determined by the above steps S2061 and S2062, the weighted graph G shown in Figure 6 can be divided to obtain the division result shown in Figure 9 .

[0126] As an optional implementation, in the technical solution provided in the above step S208, each subgraph divided by the weighted graph G is traversed, and the following steps are executed in a loop to determine the radius of the coverage range of the SSB beam corresponding to each subgraph:

[0127] Step S2081, the closeness centrality of each subgraph vertex in the current subgraph is calculated by using the closeness centrality algorithm, and a target subgraph vertex with a closeness centrality greater than a preset threshold is determined.

[0128] The expression of the closeness centrality is as follows:

[0129]

[0130] Wherein, C(q) represents the closeness centrality value of vertex q, d(p, q) represents the distance between vertices p and q, and the distance between the subgraph vertices p and q is the reciprocal of the weight value of the edge between the vertices p and q.

[0131] Step S2082, the SSB beam direction parameter of the target subgraph vertex is taken as the center of the coverage range of the SSB beam corresponding to the current subgraph, and the half-power angle parameter of the SSB beam corresponding to the current subgraph is taken as the radius to determine the coverage range of the SSB beam corresponding to the current subgraph, wherein the half-power angle parameter includes: horizontal half-power angle, vertical half-power angle.

[0132] Step S2083, the ratio of the sum of the weight values of each subgraph vertex belonging to the current subgraph in the coverage range to the sum of the weight values of all subgraph vertices of the current subgraph is not less than the coverage rate threshold as a constraint condition, and the minimum ratio of the sum of the weight values of the other subgraph vertices of the other subgraph in the coverage range to the sum of the weight values of all nodes in the coverage range is taken as an optimization target.

[0133] Step S2084, based on the constraint condition and the optimization target, a linear programming algorithm is used to calculate the half-power angle parameter corresponding to the SSB beam corresponding to the current subgraph.

[0134] Each subgraph S i obtained by dividing the weighted graph G i The radius of the coverage range of the SSB beam i corresponding to each subgraph S

[0135] First, the closeness centrality values of the subgraph vertices of the current subgraph S i are calculated by using the closeness centrality algorithm, and the coordinates (HAOA i , VAOA i ) corresponding to the target subgraph vertex with the maximum closeness centrality (i.e. greater than a preset threshold) are selected as the horizontal direction angle and the vertical direction angle of the SSB beam i corresponding to the current subgraph S i .

[0136] Then, the coverage rate threshold Cob j, a horizontal half-power angle RH, a vertical half-power angle RV, wherein the ranges of RH, RV are constrained by the configurable parameters of the smart antenna system, such as 1≤RH≤90, 1≤RV≤65, a coverage threshold Cob j which can be set according to actual application scenarios, such as Cob j = 94%.

[0137] Finally, within the ranges of RH∈,RH min , RH max , RV∈,RV min , RV max , search for an ellipse Q i (ie, coverage range) with 1 as the step size, which is centered at (HAOA i , VAOA i ), and the horizontal radius and the vertical radius are (RH i , RV i ) respectively, and the ratio of the sum of the subgraph vertex weights (ie, count1+count2) of the subgraphs in the ellipse Q i to the sum of the weights of all subgraph vertices of the current subgraph S i is not less than the preset coverage threshold Cob j , then the sum of the subgraph vertex weights of the non-current subgraph S i (ie, other subgraphs) in the ellipse Q i is not less than the minimum (RH i , RV i ) of the sum of the vertex weights in the ellipse Q i . Therefore, the above linear programming problem can be represented by the following formula:

[0138]

[0139] Therefore, RH i calculated by the above formula is taken as the horizontal half-power angle of the SSB beam i corresponding to the current subgraph S i , and RV i is taken as the vertical half-power angle of the SSB beam i corresponding to the current subgraph S i . Repeat the above steps until the half-power angle parameters of the three subgraphs shown in FIG. 6 are determined, and the coverage range of the SSB beam is obtained as shown in FIG. 7. Figure 9 Figure 10 In addition, the SSB beam optimization system can also use a preset evaluation index to evaluate the performance of the SSB beam corresponding to each subgraph, so as to evaluate the division quality of the SSB beam of the target base station. Wherein, the above evaluation index includes at least one of the following: modularity, balance, and sum of edge weights between subgraphs.

[0140] In addition, the SSB beam optimization system can also use a preset evaluation index to evaluate the performance of the SSB beam corresponding to each subgraph, so as to evaluate the division quality of the SSB beam of the target base station. Wherein, the above evaluation index includes at least one of the following: modularity, balance, and sum of edge weights between subgraphs. ​

[0141] Specifically, modularity is a crucial metric in community detection algorithms, used to measure the quality of community (i.e., subgraph) partitioning. It reflects the tightness of connections between nodes within a community and the sparseness of connections between communities. Generally, modularity values ​​range from -1 to 1, with higher values ​​indicating better community partitioning. Therefore, the expression for modularity can be written as:

[0142]

[0143] Among them, A ij k represents the edge weight between node i and node j. i ,k j Let i and j represent the degrees of nodes i and j (i.e., the sum of the weights of all edges connected to them), respectively; m represents the total number of edges; and c represents the total number of edges. i ,c j δ(c) represents the community label of node i and node j. i ,c j δ(c) is an indicator function; if node i and node j belong to the same community, then δ(c) i ,c j =1, otherwise 0.

[0144] Generally, signal beam allocation in wireless communication systems requires that the traffic volume between SSB beams within the same community be as large as possible (i.e., large weight value within the community), while the amount of SSB beam switching between different communities be as small as possible (i.e., small weight value at the community boundary). Therefore, the quality of SSB beam allocation in Massive MIMO can be evaluated based on the modularity.

[0145] The aforementioned balance is a metric used to measure the evenness of community (i.e., subgraph) partitioning. In community detection, balance B provides a better understanding of whether the traffic distribution within each community is uniform, preventing some communities from having excessively high traffic while others have insufficient traffic. The definition of balance B is typically related to the sum of the vertices' weights within a community, where the weight sum refers to the total traffic of all vertices (or nodes) within each community. Therefore, the expression for balance can be written as:

[0146]

[0147] Among them, W i The sum of the vertex weights and edge weights of the i-th community can be expressed as: W i =∑ i, j w ij δ(c i ,c j )+∑i k i , w ij denotes the weight of the edge from vertex i to vertex j, denotes the mean of W i ; n denotes the total number of communities.

[0148] Generally, by minimizing the balance B, i.e., reducing the standard deviation, it can be ensured that the total amount of traffic in each community will not differ too much when the community division is performed, thereby avoiding uneven resource allocation and improving the overall efficiency and fairness of the system.

[0149] The sum of inter-subgraph edge weights W o denotes the sum of the weight values of all edges connecting different communities (i.e., subgraphs). Here, the "edge weight" can be understood as the weight of the edge connecting two, and the weight can represent the traffic volume, data transmission volume, signal strength, or other related indicators. In a wireless communication system, the weight can represent the traffic volume when switching from one SSB beam to another SSB beam. Therefore, the sum of inter-subgraph edge weights W o can be understood as the switching traffic volume between different SSB beams. Therefore, the expression of the sum of inter-subgraph edge weights can be written as:

[0150]

[0151] Generally, if W o is large, it means that the connection between different communities is close and the traffic volume is large; if W o is small, it means that the connection between communities is sparse and the traffic volume is small. Therefore, by minimizing W o , a higher quality community division can be obtained. This is because a smaller W o means that the connection within the community is closer, and the connection between communities is more sparse, which helps to improve the efficiency and stability of the network.

[0152] In the above technical solution, the Massive MIMO SSB beam traffic of the base station is abstracted into a weighted graph according to the actual measurement report of the mobile network traffic; at the same time, when the weighted graph is divided by applying the graph computing technology, the balance degree on the agglomeration / splitting tree is increased on the basis of the existing hierarchical community discovery algorithm, the combination of community splitting based on modularity and community selection based on balance is realized, thereby efficiently dividing the Massive MIMO SSB beam and greatly improving the efficiency of beam division; in addition, the linear programming algorithm is used to match the best working parameters for the divided SSB beam, and the actual application landing of the theoretical algorithm is realized in a closed loop.

[0153] Embodiment 2

[0154] Based on the embodiment 1 of the present application, an embodiment of an SSB beam optimization device is further provided, which executes the SSB beam optimization method of the above-mentioned embodiments when running. Wherein, Figure 11 is a structural schematic diagram of an optional SSB beam optimization device according to the embodiment of the present application, as Figure 11 shown, the SSB beam optimization device at least includes an acquisition module 111, a first determination module 112, a division module 113 and a second determination module 114, wherein:

[0155] The acquisition module 111 is configured to acquire the measurement report of all users in the coverage range of the target base station, wherein the measurement report at least includes the SSB sub-beam direction parameter, and the SSB sub-beam direction parameter is used to reflect the incident angle information of the terminal signal transmitted by the user to the target base station;

[0156] The first determination module 112 is configured to determine the weighted graph corresponding to the SSB beam of the target base station based on the measurement report, wherein the weighted graph takes the SSB sub-beam direction parameter of each user as the graph vertex, takes the switching pair of each user from the first graph vertex to the second graph vertex within the preset time interval as the edge, and the weight value of the graph vertex is the first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is the second count of switching from the first graph vertex to the second graph vertex;

[0157] The division module 113 is configured to determine the number of divided sub-graphs and the division interface of the weighted graph, and divide the weighted graph according to the number of divided sub-graphs and the division interface to obtain a plurality of sub-graphs, wherein each sub-graph corresponds to an SSB beam;

[0158] The second determination module 114 is configured to, for each sub-graph, determine the center of the coverage range of the SSB beam corresponding to the current sub-graph based on the closeness centrality of each sub-graph vertex in the current sub-graph, and determine the radius of the coverage range with the constraint condition that the first coverage rate of the coverage range containing the current sub-graph is not lower than the preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

[0159] It should be noted that each module in the above-mentioned SSB beam optimization device can be a program module (for example, a program instruction set for realizing a certain specific function), or a hardware module, and for the latter, it can be in the following form, but not limited to: the form of each module is a processor, or the functions of each module are realized by a processor.

[0160] Embodiment 3

[0161] According to the embodiment of the present application, a non-volatile storage medium is also provided, which stores a program. When the program is executed, the device in which the non-volatile storage medium is located performs the SSB beam optimization method in embodiment 1.

[0162] Optionally, the device in which the non-volatile storage medium is located performs the following steps by executing the program:

[0163] In step S202, measurement reports of all users within the coverage of the target base station are periodically obtained, wherein the measurement report at least includes an SSB sub-beam direction parameter, and the SSB sub-beam direction parameter is used to reflect the incident angle information of the terminal signal transmitted by the user to the target base station.

[0164] In step S204, a weighted graph corresponding to the SSB beams of the target base station is determined based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of each user as the graph vertices, takes the switching pairs of each user from a first graph vertex to a second graph vertex within a preset time interval as the edges, and the weight value of the graph vertex is the first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is the second count of switching from the first graph vertex to the second graph vertex.

[0165] In step S206, the number of partition sub-graphs and the partition interface of the weighted graph are determined, and the weighted graph is partitioned according to the number of partition sub-graphs and the partition interface, to obtain a plurality of sub-graphs, wherein each sub-graph corresponds to an SSB beam.

[0166] In step S208, for each sub-graph, the center of the coverage range of the SSB beam corresponding to the current sub-graph is determined based on the closeness centrality of each sub-graph vertex in the current sub-graph, and the radius of the coverage range is determined under the constraint condition that the first coverage rate of the coverage range containing the current sub-graph is not lower than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

[0167] According to the embodiment of the present application, a computer program product is also provided, which includes a stored computer program. When the computer program is executed by a processor, the SSB beam optimization method in embodiment 1 is implemented.

[0168] Optionally, the computer program implements the following steps:

[0169] In step S202, measurement reports of all users within the coverage of the target base station are periodically obtained, wherein the measurement report at least includes an SSB sub-beam direction parameter, and the SSB sub-beam direction parameter is used to reflect the incident angle information of the terminal signal transmitted by the user to the target base station.

[0170] In step S204, a weighted graph corresponding to the SSB beams of the target base station is determined based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of each user as graph vertices, takes the switching pairs of each user from a first graph vertex to a second graph vertex within a preset time interval as edges, and the weight value of the graph vertex is the first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is the second count of switching from the first graph vertex to the second graph vertex.

[0171] In step S206, the number of segmented subgraphs and the segmentation interface of the weighted graph are determined, and the weighted graph is segmented according to the number of segmented subgraphs and the segmentation interface to obtain a plurality of subgraphs, wherein each subgraph corresponds to an SSB beam.

[0172] In step S208, for each subgraph, the center of the coverage range of the SSB beam corresponding to the current subgraph is determined based on the closeness centrality of each subgraph vertex within the current subgraph, and the radius of the coverage range is determined under the constraint condition that the first coverage rate of the coverage range containing the current subgraph is not lower than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other subgraphs is the lowest.

[0173] According to the embodiments of the present application, a processor is also provided, which is used to run a program, wherein the program performs the SSB beam optimization method in embodiment 1 when running.

[0174] Optionally, the program performs the following steps when running:

[0175] In step S202, the measurement reports of all users within the coverage range of the target base station are periodically obtained, wherein the measurement reports at least include SSB sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect the incident angle information of the terminal signal transmitted by the user to the target base station.

[0176] In step S204, a weighted graph corresponding to the SSB beams of the target base station is determined based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of each user as graph vertices, takes the switching pairs of each user from a first graph vertex to a second graph vertex within a preset time interval as edges, and the weight value of the graph vertex is the first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is the second count of switching from the first graph vertex to the second graph vertex.

[0177] In step S206, the number of segmented subgraphs and the segmentation interface of the weighted graph are determined, and the weighted graph is segmented according to the number of segmented subgraphs and the segmentation interface to obtain a plurality of subgraphs, wherein each subgraph corresponds to an SSB beam.

[0178] In step S208, for each subgraph, a center of a coverage range of the SSB beam corresponding to the current subgraph is determined based on the closeness centrality of each subgraph vertex within the current subgraph, and a radius of the coverage range is determined with a constraint that a first coverage rate of the coverage range containing the current subgraph is not lower than a preset coverage rate threshold, and an optimization target that a second coverage rate of the coverage range containing other subgraphs is the lowest.

[0179] According to the embodiments of the present application, an electronic device is also provided, wherein, Figure 12 is a structural schematic diagram of an optional electronic device according to the embodiments of the present application, as Figure 12 shown, the electronic device includes one or more processors; a memory for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement a program for running, wherein the program is configured to execute the SSB beam optimization method in the above-mentioned embodiment 1 when running.

[0180] Optionally, the processor is configured to implement the following steps by a computer program:

[0181] In step S202, measurement reports of all users within a coverage range of a target base station are periodically acquired, wherein the measurement reports at least include SSB sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect incident angle information of terminal signal transmission of the users to the target base station;

[0182] In step S204, a weighted graph corresponding to the SSB beam of the target base station is determined based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of each user as graph vertices, takes switching pairs of each user from a first graph vertex to a second graph vertex within a preset time interval as edges, and a weight value of the graph vertex is a first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and a weight value of the edge is a second count of switching from the first graph vertex to the second graph vertex;

[0183] In step S206, the number of segmented subgraphs and the segmentation interface of the weighted graph are determined, and the weighted graph is segmented according to the number of segmented subgraphs and the segmentation interface to obtain a plurality of subgraphs, wherein each subgraph corresponds to an SSB beam;

[0184] In step S208, for each subgraph, a center of a coverage range of the SSB beam corresponding to the current subgraph is determined based on the closeness centrality of each subgraph vertex within the current subgraph, and a radius of the coverage range is determined with a constraint that a first coverage rate of the coverage range containing the current subgraph is not lower than a preset coverage rate threshold, and an optimization target that a second coverage rate of the coverage range containing other subgraphs is the lowest.

[0185] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0186] In the above-described embodiments of the present application, the description of each embodiment focuses on different aspects, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0187] In the several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the embodiment described above is only a schematic and illustrative, for example, the division of the unit can be a logical function division, and in actual implementation, another division mode can be adopted, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, unit or module, which can be electrical or other forms.

[0188] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0189] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0190] If the integrated unit is realized in the form of software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the related art or the whole or part of the technical solutions can be embodied in the form of software product, and the computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The foregoing storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and various program codes that can be stored in the medium.

[0191] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principle of the present application, some improvements and refinements can be made, which should be regarded as the protection scope of the present application.

Claims

1. A method for optimizing SSB beams, characterized in that, The method comprises: periodically acquiring measurement reports of all users in the coverage range of a target base station, wherein the measurement reports at least include synchronization signal block (SSB) sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect the incident angle information of terminal signal transmission of the users to the target base station; determining a weighted graph corresponding to the SSB beams of the target base station based on the measurement reports, wherein the weighted graph takes the SSB sub-beam direction parameters of each user as a graph vertex, takes the switching pairs of each user from a first graph vertex to a second graph vertex within a preset time interval as an edge, and the weight value of the graph vertex is a first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and the weight value of the edge is a second count of switching from the first graph vertex to the second graph vertex; determining the number of partition sub-graphs and the partition interface of the weighted graph, and partitioning the weighted graph according to the number of partition sub-graphs and the partition interface to obtain a plurality of sub-graphs, wherein each sub-graph corresponds to an SSB beam; for each sub-graph, determining the center of the coverage range of the SSB beam corresponding to the current sub-graph based on the close centrality of each sub-graph vertex in the current sub-graph, and determining the radius of the coverage range with the constraint condition that the first coverage rate of the coverage range containing the current sub-graph is not less than a preset coverage rate threshold, and the optimization target that the second coverage rate of the coverage range containing other sub-graphs is the lowest.

2. The method of claim 1, wherein, The SSB sub-beam direction parameters include: horizontal angle of arrival (AoA) and vertical AoA, wherein determining the weighted graph corresponding to the SSB beam direction of the target base station based on the measurement reports comprises: taking the SSB sub-beam direction parameters of each user as a graph vertex, counting the first count of the SSB sub-beam direction parameter corresponding to each graph vertex, and taking the first count as the weight value of the graph vertex; taking the switching pairs of each user from the first graph vertex to the second graph vertex within a preset time interval as an edge, and determining whether the first SSB sub-beam direction parameter corresponding to the first graph vertex and the second SSB sub-beam direction parameter corresponding to the second graph vertex are the same; in the case that the first SSB sub-beam direction parameter and the second SSB sub-beam direction parameter are not the same, counting the second count of switching from the first graph vertex to the second graph vertex, and taking the second count as the weight value of the corresponding edge; establishing a two-dimensional coordinate system with the antenna center direction as the coordinate origin, the horizontal AoA as the horizontal axis, and the vertical AoA as the vertical axis, and adding the graph vertex, the edge, the weight value of the graph vertex, and the weight value of the edge into the two-dimensional coordinate system to obtain the weighted graph corresponding to the SSB beams of the target base station.

3. The method of claim 2, wherein, After obtaining the weighted graph corresponding to the SSB beams of the target base station, the method further comprises: calculating the degree centrality of each graph vertex in the weighted graph, and removing the graph vertex with a degree centrality of 0 in the weighted graph.

4. The method of claim 1, wherein, determining the number of partition sub-graphs and the partition interface of the weighted graph comprises: determine a number of split subgraphs of the weighted graph based on preset environmental factors, wherein the environmental factors comprise at least one of the following: uplink / downlink ratio of air interface frame structure, remote radio unit (RRU) capability / active antenna unit (AAU) capability; determine a split interface of the weighted graph by using an improved community discovery algorithm, wherein the improved community discovery algorithm is a merging process of nodes with a weight value less than a preset weight threshold in an initial agglomeration tree constructed based on a modularity-based community discovery algorithm.

5. The method of claim 4, wherein, determine a split interface of the weighted graph by using an improved community discovery algorithm, comprising: a first step: performing community division and community aggregation on the weighted graph by using a modularity-based community discovery algorithm, constructing a corresponding initial agglomeration tree, and determining a weight value of each node in the initial agglomeration tree, wherein each node represents a subgraph, and the weight value of the node is determined by weight values of two graph vertices included in the node, and a weight value of a root node of the agglomeration tree is a total weight value of the weighted graph; a second step: setting a weight threshold of each node, wherein the weight threshold is less than the total weight value of the weighted graph; a third step: repeatedly performing a splitting step until a number of nodes with a weight value not less than the weight threshold in the agglomeration tree is the same as the number of split subgraphs or the number of nodes with a weight value not less than the weight threshold in the agglomeration tree is zero: traversing each parent node in the agglomeration tree from top to bottom from the root node of the agglomeration tree, determining a target parent node with a maximum weight value, judging a size relationship between a weight value of each target child node under the target parent node and the weight threshold, dividing the target child node into a first node set in a case where the weight value of the target child node is not less than the weight threshold, wherein the first node set at least includes a root node corresponding to the weighted graph, and dividing the target child node into a stack in a case where the weight value of the target child node is less than the weight threshold, wherein each node in the stack is sequentially stacked according to a position of the node in the initial agglomeration tree, and the lower the position of the node in the initial agglomeration tree, the closer the node is to a top of the stack; a fourth step: traversing a top node of the stack, and obtaining a second node set by aggregating all nodes under a subtree with a parent node of a current top node as a root node in the agglomeration tree; a fifth step: for each intersection node in an intersection of the first node set and the second node set, calculating a distance value between the intersection node and a top node in the stack, and merging the intersection node and the target top node in a case where the distance value is less than a preset distance threshold; a sixth step: repeating the fourth step and the fifth step until all nodes in the stack are merged into the first node set; a seventh step: determining the split interface of the weighted graph based on the updated first node set obtained in the sixth step.

6. The method of claim 1, wherein, determine a center of a coverage range of an SSB beam corresponding to the current subgraph based on the closeness centrality of each subgraph vertex in the current subgraph, and determine a radius of the coverage range with a constraint that a first coverage rate of the coverage range containing the current subgraph is not less than a preset coverage rate threshold and an optimization target that a second coverage rate of the coverage range containing other subgraphs is the lowest, comprising: calculating the closeness centrality of each subgraph vertex in the current subgraph by using a closeness centrality algorithm, and determining a target subgraph vertex with the closeness centrality greater than a preset threshold; determining the center of the coverage range of the SSB beam corresponding to the current subgraph as the SSB sub-beam direction parameter of the target subgraph vertex, and determining the radius of the coverage range of the SSB beam corresponding to the current subgraph as the half-power angle parameter of the SSB beam, wherein the half-power angle parameter includes a horizontal half-power angle and a vertical half-power angle; determining that a ratio of a sum of weight values of each subgraph vertex belonging to the current subgraph in the coverage range to a sum of weight values of all subgraph vertices of the current subgraph is not less than the constraint that the coverage rate threshold, and determining that a ratio of a sum of weight values of other subgraph vertices of other subgraphs in the coverage range to a sum of weight values of all nodes in the coverage range is the optimization target that the minimum; based on the constraint and the optimization target, calculating the half-power angle parameter corresponding to the SSB beam corresponding to the current subgraph by using a linear programming algorithm.

7. The method of claim 1, wherein, After obtaining a plurality of subgraphs, the method further comprises: evaluating the performance of the SSB beam corresponding to each subgraph by using a preset evaluation index, wherein the evaluation index includes at least one of the following: modularity, balance, and total edge weight between subgraphs.

8. An SSB beam optimization apparatus, comprising: comprising: an acquisition module, configured to acquire measurement reports of all users in a coverage range of a target base station, wherein the measurement reports at least include SSB sub-beam direction parameters, and the SSB sub-beam direction parameters are used to reflect incident angle information of terminal signal transmission of the users to the target base station; a determination module, configured to determine a weighted graph corresponding to SSB beams of the target base station based on the measurement reports, wherein the weighted graph takes SSB sub-beam direction parameters of each user as a graph vertex, takes a handover pair from a first graph vertex to a second graph vertex within a preset time interval of each user as an edge, and a weight value of the graph vertex is a first count of the SSB sub-beam direction parameter corresponding to the graph vertex, and a weight value of the edge is a second count of handover from the first graph vertex to the second graph vertex; a division module, configured to determine a number of divided subgraphs and a division interface of the weighted graph, and divide the weighted graph according to the number of divided subgraphs and the division interface to obtain a plurality of subgraphs, wherein each subgraph corresponds to an SSB beam; A determining module is configured to, for each of the subgraphs, determine a center of a coverage range of an SSB beam corresponding to a current subgraph based on the closeness centrality of each subgraph vertex within the current subgraph, and determine a radius of the coverage range with a constraint that a first coverage rate of the current subgraph contained in the coverage range is not lower than a preset coverage rate threshold and an optimization target that a second coverage rate of other subgraphs contained in the coverage range is the lowest.

9. A non-volatile storage medium, comprising: The non-volatile storage medium stores a computer program, and a device in which the non-volatile storage medium is located executes the SSB beam optimization method in any one of claims 1 to 7 by running the computer program.

10. A computer program product, characterised in that, The computer program is executed by a processor to implement the SSB beam optimization method in any one of claims 1 to 7. The computer program is executed by a processor to implement the SSB beam optimization method in any one of claims 1 to 7.

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