Space division multiplexing stream number optimization method, device, equipment and computer storage medium

Abstract

The present application discloses a method, apparatus, device, and computer storage medium for optimizing the number of spatial division multiplexing flows. The method includes: obtaining a three-dimensional electronic map and P-group configuration data of a preset area, the preset area including Q cells, the configuration data being used to configure the cells, the Q cells being associated with K grids, and each cell being associated with at least one grid; performing multipath channel simulation in the three-dimensional electronic map based on the P-group configuration data to obtain a RANK simulation result, the RANK simulation result including K first RANK values ​​corresponding to the K grids; clustering the K grids based on the K first RANK values ​​to obtain a target area, the target area including at least one grid; configuring the cells corresponding to the target area to optimize the RANK of the target area. The embodiment of the present application can efficiently determine a target area for RANK optimization, and by configuring the cells corresponding to the target area, effective optimization of the overall RANK of the area can be achieved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, apparatus, device, and computer storage medium for optimizing the number of spatial division multiplexing flows. Background Art

[0002] In 5G communication systems, terminals perform signal estimation based on radio reference signals to determine the number of spatially multiplexed streams (5G RANK, hereinafter referred to as RANK). RANK refers to the maximum number of streams with the lowest downlink channel coherence for a terminal, and is often a key factor influencing the 5G service experience.

[0003] In the related art, the RANK of a terminal is usually obtained based on drive testing to further optimize the RANK. However, due to the complex actual wireless environment, it is difficult for the related art to effectively optimize the overall RANK in the area. Summary of the Invention

[0004] The embodiments of the present application provide a method, apparatus, device and computer storage medium for optimizing the number of spatial division multiplexing flows to solve the problem that related technologies are difficult to achieve effective optimization of the overall RANK within a region.

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing the number of spatial division multiplexing flows, the method comprising:

[0006] Obtaining a three-dimensional electronic map of a preset area and P sets of configuration data, where the preset area includes Q cells, the configuration data is used to configure the cells, the Q cells are associated with K grids, and each cell is associated with at least one grid, where P and K are both integers greater than 1, and Q is a positive integer;

[0007] Based on the P group of configuration data, a multipath channel simulation is performed in the three-dimensional electronic map to obtain a RANK simulation result, where the RANK simulation result includes K first RANK values ​​corresponding to the K grids;

[0008] Clustering K grids based on K first RANK values ​​to obtain a target area, where the target area includes at least one grid;

[0009] Configure the cells corresponding to the target area to optimize the RANK of the target area.

[0010] In a second aspect, an embodiment of the present application provides a device for optimizing the number of spatial division multiplexing flows, the device comprising:

[0011] A first acquisition module is configured to acquire a three-dimensional electronic map of a preset area and P sets of configuration data, where the preset area includes Q cells, the configuration data is used to configure the cells, the Q cells are associated with K grids, and each cell is associated with at least one grid, where P and K are both integers greater than 1, and Q is a positive integer;

[0012] A simulation module is used to perform multipath channel simulation in a three-dimensional electronic map based on the P group configuration data to obtain a RANK simulation result of the number of spatial division multiplexing streams, wherein the RANK simulation result includes K first RANK values ​​corresponding to the K grids;

[0013] A clustering module, configured to cluster K grids based on the K first RANK values ​​to obtain a target area, where the target area includes at least one grid;

[0014] The configuration optimization module is used to configure the cells corresponding to the target area to optimize the RANK of the target area.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions;

[0016] When the processor executes the computer program instructions, the method for optimizing the number of spatial division multiplexing streams as shown in the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method for optimizing the number of spatial division multiplexing flows as shown in the first aspect is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, characterized in that when the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the spatial division multiplexing flow number optimization method as shown in the first aspect.

[0019] The method for optimizing the number of spatial division multiplexing flows provided in an embodiment of the present application obtains a three-dimensional electronic map of a preset area and P sets of configuration data. The preset area includes Q cells. The configuration data is used to configure the cells. The Q cells are associated with K grids, and each cell is associated with at least one grid. Based on the P sets of configuration data, multipath channel simulation is performed in the three-dimensional electronic map to obtain a RANK simulation result for the number of spatial division multiplexing flows. The RANK simulation result includes K first RANK values ​​corresponding to the K grids. The K grids are clustered based on the K first RANK values ​​to obtain a target area. The target area includes at least one grid. The cells corresponding to the target area are configured to optimize the RANK of the target area. Based on the application of the three-dimensional electronic map, multipath channel simulation, and clustering algorithm, the embodiment of the present application can relatively easily and accurately determine a target area for RANK optimization. By configuring the cells corresponding to the target area, the overall RANK of the area can be effectively optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1 Schematic diagram of the flow of the method for optimizing the number of spatial division multiplexing flows provided in an embodiment of the present application;

[0022] Figure 2 It is the schematic diagram of propagation model simulation and correction;

[0023] Figure 3 It is a flow chart of a method for optimizing the number of space division multiplexing flows in a specific application example;

[0024] Figure 4 is another flow chart of a method for optimizing the number of space division multiplexing streams in a specific application example;

[0025] Figure 5 This is a schematic diagram of the structure of the space division multiplexing flow number optimization device provided in an embodiment of the present application;

[0026] Figure 6 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0029] In order to solve the problems of the prior art, the embodiments of the present application provide a method, apparatus, device and computer storage medium for optimizing the number of spatial division multiplexing flows. The following first introduces the method for optimizing the number of spatial division multiplexing flows provided by the embodiments of the present application.

[0030] Figure 1 FIG. 1 shows a flow chart of a method for optimizing the number of spatial division multiplexing flows provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0031] Step 101: Obtain a three-dimensional electronic map of a predetermined area and P sets of configuration data. The predetermined area includes Q cells. The configuration data is used to configure the cells. The Q cells are associated with K grids, and each cell is associated with at least one grid. P and K are both integers greater than 1, and Q is a positive integer.

[0032] Step 102: Based on the P group configuration data, perform multipath channel simulation in the three-dimensional electronic map to obtain a spatial division multiplexing stream number (hereinafter referred to as RANK) simulation result, where the RANK simulation result includes K first RANK values ​​corresponding to the K grids;

[0033] Step 103: clustering the K grids based on the K first RANK values ​​to obtain a target area, where the target area includes at least one grid;

[0034] Step 104: configure the cells corresponding to the target area to optimize the RANK of the target area.

[0035] The spatial division multiplexing flow number optimization method provided in the embodiment of the present application can be applied to electronic devices, which can be mobile electronic devices, such as smart mobile terminals or portable computers, or fixed electronic devices, such as servers or industrial computers.

[0036] In step 101 , the range of the preset area can be set according to actual needs, and the preset area can have a corresponding three-dimensional electronic map.

[0037] By way of example, a three-dimensional electronic map may include characteristic information of typical buildings in a preset area, such as altitude, building width, and height.

[0038] In practical applications, the three-dimensional electronic map can also be updated in combination with some road test data. For example, the billboards or traffic facilities on both sides of the street can be scanned by lidar, and the scanning results can be mapped to the three-dimensional electronic map to achieve the update of the three-dimensional electronic map.

[0039] The preset area may include Q cells. Generally speaking, the preset area may be divided into a grid. For example, in some examples, the space corresponding to the preset area may be divided into multiple grids of preset sizes, such as 5m×5m×5m grids. Of course, in actual applications, the grid size may be adjusted as needed.

[0040] Each grid may be associated with at least one cell. For example, the association may be expressed as a terminal located in a grid being able to communicate with a base station of the associated cell.

[0041] In this embodiment, the Q cells mentioned above may be associated with K grids, and each cell may be associated with at least one grid. In actual applications, a grid may be associated with one cell or multiple cells.

[0042] The configuration data can be used to configure the cell. For example, the configuration data can be used to configure the operating frequency band or maximum transmit power of the cell base station, or the tilt angle or antenna weight of the cell base station antenna.

[0043] Of course, in some application scenarios, the configuration data may also include other cell working parameters, such as the latitude and longitude of the cell base station, antenna height, etc. These cell working parameters can also be used to configure the cell in order to optimize the site selection of the cell base station and other contents.

[0044] In step 102, the electronic device may perform multipath channel simulation in a three-dimensional electronic map based on the P group configuration data.

[0045] For example, based on configuration data, parameters such as cell latitude and longitude, azimuth, antenna height, downtilt angle, and antenna weight can be configured on a 3D electronic map to build a 3D ray propagation model. Multipath channel simulation is achieved through 3D ray tracing technology, combined with building elevation, width, height, and orientation information from the 3D electronic map.

[0046] It is easy to understand that for each set of configuration data, corresponding simulation results can be obtained through multipath channel simulation. The simulation results may include the above-mentioned RANK simulation results. For example, the RANK simulation results may include the RANK values ​​of each grid under each set of configuration data. In some possible implementations, the simulation results may also include simulation values ​​of parameters such as Reference Signal Received Power (RSRP). Of course, in this embodiment, the simulation values ​​of parameters other than the RANK value in the simulation results may not be specifically limited.

[0047] In this embodiment, the RANK simulation result may include K first RANK values ​​corresponding to K grids.

[0048] In some examples, under one set of configuration data in the P sets of configuration data, the mean of the RANK values ​​of the K grids can be maximized, and the RANK value of each grid when the mean is maximized can be used as the first RANK value corresponding to each grid.

[0049] In other examples, the first RANK value can be determined on a cell-by-cell basis. For example, for a cell, under one configuration data in the P group configuration data, the average RANK value of all grids associated with the cell can be maximized. The RANK value of each grid associated with the cell when the average value is maximized can be determined as the first RANK value of each grid associated with the cell. For other cells, the first RANK value of each grid associated with the cell can be determined in a similar manner.

[0050] Of course, the K first RANK values ​​corresponding to the K grids can also be obtained from the above RANK simulation results in other ways. In general, the first RANK value can reflect the optimal or near-optimal RANK value that the grid can obtain under the cell configuration corresponding to the P configuration parameters.

[0051] The purpose of RANK optimization, to a certain extent, can be considered to enable each grid to obtain the optimal or near-optimal RANK value through the configuration of the cell.

[0052] In step 103 , the electronic device may cluster K grids based on the K first RANK values ​​to obtain a target area.

[0053] For example, electronic devices can use density-based clustering algorithms, such as Density-Based Spatial Clustering of Applications with Noise (DBSCAN) or mean-shift clustering. Of course, in practical applications, other clustering algorithms can also be used to cluster the K grids.

[0054] In practical applications, electronic devices can be clustered directly based on the K first RANK values. The resulting target area can be a continuous area with a relatively high or relatively low RANK. For target areas with relatively low RANK, the RANK of the target area can be optimized by optimizing the configuration of the cells corresponding to the target area, such as improving hardware configuration or adjusting the location of the cell base stations.

[0055] Alternatively, the electronic device may calculate the difference between the K first RANK values ​​and the actual RANK values ​​of the K grids, cluster them according to the difference, and obtain a target area with optimization potential. Subsequently, the RANK of the target area may be optimized by configuring and optimizing the cells corresponding to the target area.

[0056] As shown above, the target area can be a relatively continuous area obtained based on the clustering algorithm. Compared with RANK optimization for scattered grids, RANK optimization for continuous target areas is more in line with the RANK optimization requirements in actual scenarios.

[0057] In step 104, the cells corresponding to the target area may be configured to optimize the RANK of the target area.

[0058] In some examples, all or part of the configuration data in the above-mentioned P group configuration data, or newly selected configuration data, can be combined to perform multipath channel simulation on the above-mentioned three-dimensional electronic map to obtain configuration data that makes the RANK values ​​of all grids in the target area reach the optimal value, and based on the configuration data, the cell corresponding to the target area is configured to achieve RANK optimization of the target area.

[0059] Of course, in actual applications, electronic devices can also configure cells corresponding to the target area in combination with drive test data. For example, the terminal can be used to measure the RANK value at the actual grid position, and the cell configuration can be optimized based on the measured RANK value, thereby achieving RANK optimization of the target area.

[0060] The method for optimizing the number of spatial division multiplexing flows provided in an embodiment of the present application obtains a three-dimensional electronic map of a preset area and P sets of configuration data. The preset area includes Q cells. The configuration data is used to configure the cells. The Q cells are associated with K grids, and each cell is associated with at least one grid. Based on the P sets of configuration data, multipath channel simulation is performed in the three-dimensional electronic map to obtain a RANK simulation result for the number of spatial division multiplexing flows. The RANK simulation result includes K first RANK values ​​corresponding to the K grids. The K grids are clustered based on the K first RANK values ​​to obtain a target area. The target area includes at least one grid. The cells corresponding to the target area are configured to optimize the RANK of the target area. Based on the application of the three-dimensional electronic map, multipath channel simulation, and clustering algorithm, the embodiment of the present application can relatively easily and accurately determine a target area for RANK optimization. By configuring the cells corresponding to the target area, the overall RANK of the area can be effectively optimized.

[0061] In some implementations, the three-dimensional electronic map and configuration data obtained in step 101 may include the following contents.

[0062] The three-dimensional electronic map can include typical building feature information in a preset area, such as altitude, building width and height.

[0063] Each set of configuration data may include at least one of New Radio (NR) cell working parameters, base station configuration data, and antenna files.

[0064] Among them, NR cell working parameters may include base station latitude and longitude, antenna height, azimuth, downtilt angle, site type, coverage scenario, etc.

[0065] Base station configuration data may include key information such as 5G millimeter frequency band, maximum transmit power, switching / reselection parameter configuration, and neighboring cell configuration.

[0066] Taking the 64TR antenna as an example, the antenna file can include 17 sets of typical broadcast beam weights for 5G millimeter antennas, 12 types of adjustable electrical downtilt angles (-2° to +9°) and 95 types of adjustable electronic azimuth angles (-47° to +47°), for a total of 5091 antenna weight combinations.

[0067] As shown above, the electronic device can also be applied to drive test data. In this embodiment, the drive test data can be NR drive test data, which may include sampling point information such as the latitude and longitude of the test terminal, synchronization signal and broadcast physical channel block-RSRP (Synchronization Signal and Physical Broadcast CHannel block-RSRP, SSB-RSRP), signal to interference plus noise ratio (Signal to Interference plus Noise Ratio, SINR), RANK measured value, transmission mode (Transmission Mode, TM), modulation and coding strategy (Modulation and Coding Scheme, MCS) and so on.

[0068] Optionally, the above step 101, performing multipath channel simulation in a three-dimensional electronic map based on the P group configuration data to obtain a RANK simulation result, includes:

[0069] According to the 3D electronic map and configuration data, a propagation model based on 3D ray tracing is established;

[0070] Based on the propagation model and the preset Multiple Input Multiple Output (MIMO) channel model, a multipath channel simulation is performed to obtain a RANK simulation result.

[0071] With some examples, in this embodiment, the three-dimensional electronic map can be considered as a model of a preset area obtained through three-dimensional geographic modeling.

[0072] Based on three-dimensional geographic modeling, combined with the content of each set of configuration data, such as the latitude and longitude of the NR cell, azimuth, antenna height, downtilt angle, and antenna weight, a propagation model based on three-dimensional ray tracing (hereinafter referred to as the 3D ray tracing propagation model) can be established.

[0073] As shown above, a 3D electronic map can include information about typical building features in a preset area, such as building elevation, orientation angle, etc. The 3D ray tracing propagation model combines this information to simulate the multipath effect of a cell using 3D ray tracing technology.

[0074] Specifically, in this embodiment, the multipath effect of the cell can be realized by using the MIMO channel model. Based on the propagation model and the MIMO channel model, a multipath channel simulation can be implemented to obtain a RANK simulation result.

[0075] In some embodiments, multipath channel simulation of the propagation model and the MIMO channel model can obtain the multipath parameters required by the MIMO channel model and calculate the MIMO channel matrix. The RANK value of each grid can be obtained by evaluating the correlation of the MIMO channels in the MIMO channel matrix.

[0076] For example, electronic devices can perform singular value decomposition (SVD) on the MIMO channel matrix to obtain the eigenvalues ​​of the MIMO channel matrix. The size of the eigenvalues ​​of the MIMO channel matrix can reflect the degree of channel correlation. Based on the correlation, the RANK value between each grid and any cell base station can be determined.

[0077] Based on the above examples, electronic equipment can perform three-dimensional geographic modeling based on information such as building altitude and orientation angle in the three-dimensional electronic map, and establish a 3D ray tracing propagation model based on the latitude and longitude, azimuth, antenna height, downtilt angle and antenna weight of the NR cell to simulate the multipath effect of the 5G cell, which can effectively improve the accuracy of the simulation results.

[0078] Optionally, before performing multipath channel simulation based on the propagation model and the preset MIMO channel model to obtain a RANK simulation result, the method further includes:

[0079] Obtaining measured values ​​of a target parameter in a plurality of grids in the K grids under at least one set of preset configuration data, where the target parameter includes at least one of a RANK value and an RSRP;

[0080] Acquire simulated values ​​of target parameters in a plurality of grids of the K grids obtained based on multipath channel simulation under at least one set of preset configuration data;

[0081] Model parameters of the propagation model are modified based on measured values ​​and simulated values ​​of the target parameter in a plurality of the K grids.

[0082] The preset configuration data may be one or more groups of configuration data in the above-mentioned P group configuration data, or data other than the P group configuration data, for example, actual configuration data of a cell in a preset area, etc., which is not specifically limited here.

[0083] The target parameter may be at least one of a RANK value and an RSRP.

[0084] Under the preset configuration data, the drive test terminal may collect measured values ​​of target parameters such as RANK or RSRP in multiple grids among the K grids.

[0085] Corresponding to the measured values, under the above-mentioned preset configuration data, simulated values ​​of target parameters in multiple grids can also be obtained through multipath channel simulation.

[0086] It's easy to understand that the closer the measured and simulated values ​​are, the better the multipath channel simulation results. Therefore, based on the measured and simulated values ​​of the target parameter in multiple of the K grids, the model used in the multipath channel simulation can be modified so that the modified model can output more accurate simulation values.

[0087] The multiple grids involved in the measured value collection can correspond one-to-one to the multiple grids involved in the simulation value. By correcting the model parameters of the propagation model, the measured value and the simulation value of the target parameter in the same grid can be equal to or similar.

[0088] Alternatively, the multiple grids involved in the acquisition of measured values ​​may or may not correspond one-to-one to the multiple grids involved in the simulation values. By modifying the model parameters of the propagation model, the statistical parameters of the measured values ​​of the target parameter in the multiple grids are made equal to or similar to the statistical parameters of the simulated values. The statistical parameters may be mean square error (MSE), average value, or other parameters, which are not specifically limited here.

[0089] This embodiment corrects the model parameters in the above-mentioned propagation model based on the measured values ​​and simulation values ​​of the target parameters, so that the relevant simulation values ​​obtained based on the corrected propagation model can be closer to the actual situation, thereby improving the simulation accuracy of the multipath channel simulation for the preset area or target area.

[0090] like Figure 2 As shown, the following describes a process of correcting the model parameters of the propagation model based on measured values ​​and simulation values ​​in conjunction with a specific implementation.

[0091] Upon receiving the preset configuration data, the propagation model can output the multipath parameters required by the MIMO channel model and the RSRP simulation values ​​for multiple grids based on the preset configuration data. The multipath parameters are input into the MIMO channel model to generate RANK simulation results, including the RANK simulation values ​​for multiple grids.

[0092] Similarly, in the preset configuration data, based on the drive test method, the RSRP measured values ​​and the RANK measured values ​​of multiple grids in the preset area can be obtained.

[0093] The electronic device can respectively obtain the MSEs for the RSRP simulation values ​​of the multiple grids and the RSRP measured values ​​of the multiple grids, and can modify the model parameters of the propagation model based on the two MSEs.

[0094] Similarly, the electronic device can also obtain MSEs for the RANK simulation values ​​of multiple grids and the RANK measured values ​​of multiple grids respectively, and based on the two MSEs, the model parameters of the propagation model can be corrected to achieve model parameter calibration.

[0095] Based on the above implementation methods, it can be seen that the embodiments of the present application can combine three-dimensional ray tracing technology and MIMO channel model modeling to complete RANK precise simulation, use ray tracing technology to simulate the multipath environment, and combine the measured RSRP / RANK data to correct the model parameters used in the simulation, which can more accurately reflect the actual wireless environment, improve the simulation accuracy of the multipath effect of the 5G cell, and have richer and more comprehensive evaluation methods than the existing technology that relies on road test data.

[0096] Optionally, based on the P group configuration data, multipath channel simulation is performed in the three-dimensional electronic map to obtain a RANK simulation result of the number of spatial division multiplexing flows, including:

[0097] Based on multipath channel simulation, K RANK simulation values ​​corresponding to K grids are obtained under each set of configuration data;

[0098] Obtain P RANK means corresponding to the first cell under P groups of configuration data, where the first cell is any cell among the Q cells, and a RANK mean is the average of the RANK simulation values ​​corresponding to all grids associated with the first cell under one set of configuration data;

[0099] In a case where the first cell corresponds to the maximum value among P RANK averages, the RANK simulation value corresponding to each grid associated with the first cell is determined as the first RANK value.

[0100] The present embodiment is described below with reference to a specific application example.

[0101] In this application example, multipath channel simulation can be implemented in a simulation model including the above-mentioned propagation model and MIMO channel model, and each set of configuration data can correspond to a set of wireless radio frequency signal + three-dimensional coverage beam (hereinafter referred to as RF+Pattern) adjustment schemes.

[0102] For example, as shown above, a set of configuration data may include base station configuration data and antenna files, and by configuring these contents, RF+Pattern settings can be implemented. Accordingly, a set of configuration data may correspond to a set of RF+Pattern adjustment solutions.

[0103] The embodiment of the present application uses multiple sets of configuration data when performing multipath channel simulation, which is equivalent to a process of simulating the RF+Pattern adjustment scheme for adjusting antennas (such as massive MIMO antennas, etc.) in the simulation model.

[0104] This application example can simulate the multipath propagation effect of Q cells in a preset area under each set of RF+Pattern adjustment schemes. Since the preset area can be pre-divided into several three-dimensional grids (such as the 5m×5m×5m grid mentioned above), when simulating the multipath propagation effect, the RANK simulation value of each grid can be obtained, which is recorded as R ij , where R ij It can be specifically interpreted as the RANK simulation value of the j-th grid in the i-th cell, where i is a positive integer less than or equal to Q, and j is a positive integer.

[0105] Take the RANK mean of all grids (i.e., all grids associated with the first cell) within the coverage of the i-th cell (corresponding to the first cell mentioned above) as the RANK mean corresponding to the i-th cell under this group of RF+Pattern adjustment schemes, and record it as Then the following relationship exists:

[0106]

[0107] Wherein, n is the total number of grids within the coverage of the ith cell, or the number of all grids associated with the ith cell.

[0108] Based on the above formula, we can see that the RANK mean of the i-th cell is the average value of the RANK simulation values ​​corresponding to all grids associated with the i-th cell (i.e., the first cell) under a set of configuration data. Under the P-group RF+Pattern adjustment scheme, P corresponding

[0109] In this application example, P The maximum value among P The maximum value in may correspond to a corresponding RF+Pattern adjustment scheme, and the RANK simulation value of each grid covered by the i-th cell obtained under the RF+Pattern adjustment scheme may be used as the first RANK value mentioned above.

[0110] Based on the above application examples, it can be seen that this embodiment can adjust the configuration data to enable each cell to produce a higher RANK environment as a whole. On this basis, the first RANK value of each grid is determined, so that when the preset area has a better RANK environment as a whole, further RANK optimization solutions for local areas can be sought to improve the RANK optimization effect of the preset area.

[0111] In some embodiments, when a grid is simultaneously covered by multiple cells, based on the method for determining the first RANK value described above, the grid may obtain multiple initial first RANK values ​​corresponding to the multiple cells. In some embodiments, the largest first RANK value among these multiple initial first RANK values ​​may be used as the final first RANK value for the grid.

[0112] Since the first RANK value can be used to reflect the RANK value that the corresponding grid can theoretically reach through optimization, determining the final first RANK of the grid associated with multiple cells as the largest initial first RANK helps to better determine the grid for RANK optimization.

[0113] Optionally, after obtaining P RANK mean values ​​corresponding to the first cell under P groups of configuration data, the method further includes:

[0114] Obtain a target configuration data set associated with the first cell, the target configuration data set including M groups of configuration data, wherein under the M groups of configuration data, the M RANK means corresponding to the first cell are the largest M RANK means among P RANK means, where M is a positive integer less than or equal to P;

[0115] Step 104, configuring the cell corresponding to the target area, specifically includes:

[0116] The cells corresponding to the target area are configured based on the target configuration data set associated with the cells corresponding to the target area.

[0117] As shown above, the RANK mean corresponding to the i-th cell under any set of RF+Pattern adjustment schemes (corresponding to the configuration data) can be recorded as In order to reflect The relationship between the configuration data and the Recorded as p is an integer less than or equal to P.

[0118] Will Sort by size from large to small, and record the RANK mean corresponding to the i-th cell ranked at position x as Where x is an integer less than or equal to P. The target configuration data set associated with the i-th cell is denoted as S i , then we can have:

[0119]

[0120] That is, the target configuration data set S associated with the i-th cell i M RANK means may be included, and these M RANK means may be the largest M RANK means among all P RANK means.

[0121] It is easy to understand that the first cell mentioned above can be any cell, and for other cells, the associated target configuration data set can also be determined in a similar manner. i The i value in can be any positive integer less than or equal to Q. For Q cells, Q associated target configuration data sets can be determined.

[0122] On the basis of obtaining Q target configuration data sets associated with Q cells, in step 104, when the cell corresponding to the target area is determined, each target configuration data set associated with the cell corresponding to the target area can be determined, and configuration data can be selected from these target configuration data sets for configuring the cell.

[0123] The configuration data in the target configuration data set can be considered as configuration data that can enable at least one cell to obtain a better RANK environment. Therefore, in step 104, the configuration data in these target configuration data sets are selected to configure the cell. On the one hand, it helps to ensure that the target area after the cell configuration can still have a relatively good overall RANK environment. On the other hand, it can also reduce the number of configuration data that needs to be traversed in order to find the configuration data corresponding to the better RANK environment, thereby improving the RANK optimization efficiency.

[0124] Optionally, configuring the cell corresponding to the target area based on the target configuration data set associated with the cell corresponding to the target area includes:

[0125] Obtaining L groups of configuration data, where the L group of configuration data is all or part of the configuration data in the P group of configuration data, where L is an integer greater than 1 and less than or equal to P;

[0126] Based on the L sets of configuration data, multipath channel simulation is performed in the three-dimensional electronic map to obtain L simulation results corresponding to the L sets of configuration data, each simulation result including a first simulation parameter of each grid included in the target area, the first simulation parameter including at least one of a RANK simulation value, RSRP, SINR, and an overlap coverage result;

[0127] Performing weighted processing on the first simulation parameter in each simulation result to obtain L first weighted processing results corresponding to the L groups of configuration data;

[0128] The cell corresponding to the target area is configured based on the configuration data corresponding to the optimal first weighted processing result.

[0129] In combination with some examples, the L group of configuration data may be the configuration data in the target configuration data set associated with the cell corresponding to the target area. Of course, in some feasible implementations, the L group of configuration data may also be all the P group of configuration data.

[0130] In the above embodiment, the method of performing multipath channel simulation in a three-dimensional electronic map based on configuration data is described in detail, which will not be repeated here.

[0131] Through multipath channel simulation, L simulation results corresponding to L groups of configuration data can be obtained. Each simulation result includes first simulation parameters of each grid included in the target area. These first simulation parameters may include at least one of RANK simulation value, RSRP, SINR, and overlap coverage result.

[0132] In conjunction with a specific application example, the first simulation parameter may include sub-parameters such as RANK simulation value, RSRP, SINR, and overlap coverage result. These sub-parameters can be recorded as X1, X2, X3, and X4, respectively. To reflect the relationship between the first simulation parameter and the grid, the sub-parameters corresponding to the j-th grid can be recorded as as well as

[0133] For each parameter in the first simulation parameter, a corresponding weight can be assigned and recorded as W RANK 、W RSRP 、W SINR and W overlap , weighted processing is performed on the first simulation parameter in each simulation result, and the corresponding first weighted processing result is recorded as V, then:

[0134]

[0135] Where n′ is the total number of grids included in the target area.

[0136] Each set of configuration data can correspond to a first weighted processing result V, which is a quantized value. Therefore, the electronic device can determine the first weighted processing result V with the highest value and the corresponding configuration data, and can further use the configuration data to configure the cell corresponding to the target area, thereby achieving RANK optimization of the target area.

[0137] Optionally, after configuring the cell corresponding to the target area based on the configuration data corresponding to the optimal weighted processing result, the method further includes:

[0138] Obtain a preset type grid, where the preset type grid is a grid associated with multiple cells;

[0139] respectively acquiring second simulation parameters between the preset type grid and each second cell, where the second cell is a cell associated with the preset type grid, and the second simulation parameters include at least one of a RANK simulation value, RSRP, SINR, and a communication distance;

[0140] Performing weighted processing on the second simulation parameters respectively to obtain a plurality of second weighted processing results corresponding to all the second cells respectively;

[0141] The second cell corresponding to the optimal second weighted processing result is configured as the primary serving cell of the preset type grid.

[0142] In the previous embodiment, it can be considered that the configuration is performed for the cell, thereby facilitating the creation of a high RANK network environment. In this embodiment, it can be considered that the grid is allowed to occupy a high RANK by selecting the primary serving cell, thereby achieving intelligent RANK optimization.

[0143] In conjunction with a specific application example, the second simulation parameter may include sub-parameters such as RANK simulation value, RSRP, SINR, and communication distance. To facilitate distinction from the first simulation parameter above, each sub-parameter in the second simulation parameter can be recorded as Rank, RSRP, SINR, and Distance. At the same time, corresponding weights can be assigned to these sub-parameters, respectively, recorded as k, r, s, and d. The second weighted processing result can be recorded as Z, then:

[0144] Z=Rank×k+Rsrp×r+Sinr×s+Distance×d

[0145] Any preset type grid can be associated with multiple cells (i.e., the second cells mentioned above). For each second cell, the corresponding second weighted processing result Z can be obtained, and the second cell corresponding to the optimal second weighted processing result Z can be configured as the main service cell of the preset type grid.

[0146] As shown in the table below, the score of the second cell associated with a preset type grid (corresponding to the second weighted processing result) and the determination of the cell type are described below in conjunction with a specific application example.

[0147]

[0148] Among them, the Cell Name in the table can correspond to the number of each second cell. As for the calculation process of each weight and the main service score Z, they can be set as needed and will not be described in detail here.

[0149] In some implementations, the optimal primary service cell distribution result is determined based on the primary service score Z, and a parameter adjustment plan including switching, reselection, and maximum transmit power is output based on the cell base station configuration data, so as to enable the terminal to successfully occupy a high RANK cell, thereby improving user perception.

[0150] In some implementations, the weights of the RANK simulation value (corresponding to Rank), SINR (corresponding to Sinr), and Rsrp in the second simulation parameter may be determined according to RSRP (corresponding to Rsrp) in the second simulation parameter.

[0151] Specifically, when Rsrp is less than the preset threshold, the weight of Rsrp, the weight of Rank, and the weight of Sinr decrease in turn; when Rsrp is greater than the preset threshold, the weight of Rank, the weight of Sinr, and the weight of Rsrp decrease in turn.

[0152] For example, based on Rsrp, two weight factor combinations are categorized: 1) When Rsrp is less than -90dBm, Rsrp has a higher weight, followed by Rank, and Sinr has a lower weight; 2) When Rsrp is greater than or equal to -90dBm, Rank has a higher weight, followed by Sinr, and Rsrp has a lower weight. The Distance weight does not change with Rsrp.

[0153] In some implementations, in order to achieve a better 5G network experience in the target area, after the first round of RANK optimization scheme is implemented, data such as the three-dimensional electronic map may be updated to conduct a second round of iterative optimization.

[0154] Optionally, clustering the K grids based on the K first RANK values ​​includes:

[0155] Get the K existing network RANK values ​​corresponding to the K grids. The existing network RANK value corresponding to any grid is the RANK value of any grid in the existing network.

[0156] According to the K first RANK values ​​and the K existing network RANK values, K RANK difference values ​​corresponding to the K grids are obtained;

[0157] Based on the K RANK differences, density-based DBSCAN is performed on the K grids.

[0158] When obtaining K first RANK values ​​of K grids, the electronic device can obtain existing network RANK data, which can be the RANK value of each grid in the existing network environment, that is, the existing network RANK data can include K existing network RANK values ​​corresponding to K grids.

[0159] In some implementations, the RANK value of the existing network can be obtained through drive testing, or by performing multipath channel simulation based on configuration data corresponding to the existing network. The specific method for obtaining the RANK value of the existing network is not limited here.

[0160] According to the K first RANK values ​​and the K existing network RANK values, K RANK difference values ​​corresponding to the K grids are obtained.

[0161] With some examples, each RANK difference can correspond to a specific numerical value. The DBSCAN clustering algorithm can perform clustering based on the RANK differences corresponding to each grid to obtain a clustering result. The clustering result can reflect the distribution of the RANK differences at each level.

[0162] The above-mentioned levels can be associated according to the setting of the RANK difference threshold. In combination with a specific application example, the RANK difference corresponding to a grid can be considered as the RANK improvement potential value of the grid. The network optimization target sets the RANK potential value threshold (corresponding to the RANK difference threshold). It is assumed that the RANK improvement potential values ​​of 0.6, 0.3, and 0.1 are used to divide the grid into three categories: high, medium, and low potential. The criterion for judging the high potential for RANK improvement is that the grid RANK improvement potential value is greater than or equal to 0.6. According to the distribution of RANK improvement potential values ​​of different grids, the DBSCAN clustering algorithm is used to form clusters of grids with high potential values ​​for RANK improvement, thereby identifying high-RANK potential areas. The high-RANK potential area can correspond to the above-mentioned target area.

[0163] In some embodiments, the electronic device can use the AlphaShape algorithm to extract high-potential RANK regions, determine the three-dimensional boundary shape AlphaShape(x, y, z) of the region to be optimized, and obtain the three-dimensional spatial distribution characteristics of the high-potential regions. The electronic device can then identify cells surrounding the high-potential regions by correlating the RANK simulation results. These cells can correspond to the target regions described above.

[0164] Of course, in practical applications, when performing density-based DBSCAN on K grids, the threshold and other data used can be set as needed.

[0165] Based on the results of DBSCAN clustering, this embodiment can identify target areas with high optimization potential, which in turn facilitates the subsequent targeted configuration of cells associated with the target area and achieves RANK optimization of the target area. Compared with the solution of independently identifying cells with poor RANK environments and optimizing them, this embodiment can significantly improve the overall RANK environment of the target area and more easily identify potential communication risks.

[0166] like Figure 3 and Figure 4 As shown, in some specific application examples, the method for optimizing the number of spatial division multiplexing flows provided in the embodiment of the present application may include steps 301 to 304.

[0167] Step 301: Data input and correction.

[0168] Using 3D electronic maps, NR cell engineering parameters, road test data, base station configuration data, antenna files, etc. as input, the system completes data parsing, association and storage. At the same time, the 3D electronic map is corrected through map editing and the building model is reconstructed using point cloud data to obtain a high-precision 3D electronic map.

[0169] Step 302: Obtain multipath simulation and RANK simulation results.

[0170] Multipath simulation is performed using 3D ray tracing. The 3D ray tracing propagation model is calibrated using field-measured data. The calibrated propagation model is used to simulate and output the multipath parameters required for the MIMO model. The MIMO channel matrix is ​​generated, and channel correlation is evaluated using SVD. This results in a high-precision, gridded RANK simulation. Based on these RANK simulation results, the optimal RANK solution set (corresponding to the target configuration dataset described above) is obtained.

[0171] Step 303: potential value evaluation.

[0172] Based on the RANK simulation results, the RANK improvement potential value of each grid is calculated, and the high-potential areas are identified using the DBSCAN clustering algorithm. The surrounding 5G cells are locked by correlating the RANK simulation results.

[0173] Step 304: Output the optimization solution.

[0174] Based on the RANK simulation results of the cell, the RSRP, SINR, RANK (rank of the communication matrix), and overlapping coverage simulation results of the primary cell and neighboring cells are integrated, and an RF+Pattern adjustment scheme is output through a voting algorithm (corresponding to the process of determining the configuration data based on the first weighted processing result in the above embodiment) to enable the network to generate a high RANK; by modeling the primary service cell, a parameter adjustment scheme including switching, reselection, and transmission power is output to enable the terminal to occupy a high RANK cell.

[0175] Based on the above application examples, it can be seen that the embodiments of this application provide a method for optimizing the number of spatial division multiplexing flows, a RANK precision simulation strategy based on three-dimensional electronic maps, 3D ray tracing technology and MIMO channel modeling, a RANK improvement potential value evaluation method based on the DBSCAN clustering algorithm, and a RANK optimization solution based on voting + main service cell modeling output. The three key algorithms ultimately realize intelligent optimization of 5G RANK flows and effectively improve the 5G network speed.

[0176] Through 3D ray tracing technology and MIMO channel modeling, accurate RANK simulation is completed. Ray tracing technology is used to simulate the multipath environment, and simulation parameters are corrected in combination with measured RSRP / RANK data. This can more accurately reflect the wireless environment of the existing network and improve the simulation accuracy of the multipath effect of 5G cells. Compared with existing technologies that rely on drive test data, it provides richer and more comprehensive evaluation methods.

[0177] When determining the optimal RANK solution for high-potential areas, the RANK values ​​of each group of RF+Pattern adjustment solutions for the cells corresponding to the high-potential areas are arranged in descending order, and the adjustment solutions corresponding to the top M simulated optimal RANK values ​​are taken as the optimal RANK solution set S that can be achieved by 5G cells in the current multipath environment. With the goal of optimizing the overall performance in the area, a voting algorithm that combines the number of flows, coverage, quality, and overlapping coverage is used to traverse the optimal RANK solution set for each cell, and finally output the optimized solution for the entire area, which greatly reduces the number of solutions that need to be traversed and eliminates the possibility of RANK improvement and other performance degradation.

[0178] On the premise that the overall RANK simulation results of the cell are optimal, the DBSCAN clustering algorithm is used to identify areas with obvious high-potential grid clusters, thereby associating them with surrounding 5G cells and collaboratively improving the terminal RANK flow in high-potential areas. This method breaks the existing technology's idea of ​​"identifying poor cells and optimizing poor cells", resulting in a greater overall improvement and easier identification of potential risks.

[0179] The multi-dimensional voting optimization algorithm and main service cell modeling algorithm based on the information of the main service cell and neighboring cells and channel quality enable the network to first generate a high RANK value environment and then enable the terminal to occupy high RANK. That is, based on the RF+Pattern optimization solution, the network generates high RANK. By adjusting the parameters such as switching, reselection, and transmission power, the terminal can effectively occupy high RANK, and realize the automatic output of the RANK intelligent optimization solution. The two optimization solutions of "generating high RANK" and "occupying high RANK" are separated. The main service cell modeling is based on the main service cell plus the adjacent cells. The main service cell is first clarified and the solution of occupying the main service cell is output later. The logic is clear and the overall RANK is steadily promoted.

[0180] like Figure 5 As shown, the embodiment of the present application also provides a space division multiplexing flow number optimization device, the device comprising:

[0181] A first acquisition module 501 is configured to acquire a three-dimensional electronic map of a predetermined area and P sets of configuration data, where the predetermined area includes Q cells, the configuration data is used to configure the cells, the Q cells are associated with K grids, and each cell is associated with at least one grid, where P and K are both integers greater than 1, and Q is a positive integer;

[0182] A simulation module 502 is configured to perform multipath channel simulation in a three-dimensional electronic map based on the P group configuration data to obtain a RANK simulation result, where the RANK simulation result includes K first RANK values ​​corresponding to the K grids;

[0183] A clustering module 503 is configured to cluster K grids based on the K first RANK values ​​to obtain a target area, where the target area includes at least one grid;

[0184] The configuration optimization module 504 is used to configure cells corresponding to the target area to optimize the RANK of the target area.

[0185] Optionally, the simulation module 502 includes:

[0186] A first acquiring unit is configured to acquire, based on multipath channel simulation, K RANK simulation values ​​corresponding to the K grids under each set of configuration data;

[0187] A second obtaining unit is configured to obtain P RANK mean values ​​corresponding to a first cell under P groups of configuration data, where the first cell is any cell among the Q cells, and a RANK mean value is an average value of RANK simulation values ​​corresponding to all grids associated with the first cell under one group of configuration data;

[0188] The first determining unit is configured to determine, when the first cell corresponds to a maximum value among P RANK average values, a RANK simulation value corresponding to each grid associated with the first cell as a first RANK value.

[0189] Optionally, the above device may further include:

[0190] A second acquisition module is configured to acquire a target configuration data set associated with the first cell, the target configuration data set including M groups of configuration data, wherein under the M groups of configuration data, the M RANK means corresponding to the first cell are the largest M RANK means among P RANK means, where M is a positive integer less than or equal to P;

[0191] The configuration optimization module 504 may be specifically used to:

[0192] The cells corresponding to the target area are configured based on the target configuration data set associated with the cells corresponding to the target area.

[0193] Optionally, the configuration optimization module 504 includes:

[0194] a third acquiring unit, configured to acquire L groups of configuration data, where the L groups of configuration data are all or part of the configuration data in the P groups of configuration data, and L is an integer greater than 1 and less than or equal to P;

[0195] a fourth acquisition unit, configured to perform multipath channel simulation in the three-dimensional electronic map based on the L sets of configuration data, and obtain L simulation results corresponding to the L sets of configuration data, each simulation result including a first simulation parameter of each grid included in the target area, the first simulation parameter including at least one of a RANK simulation value, RSRP, SINR, and an overlap coverage result;

[0196] a first weighted processing unit, configured to perform weighted processing on the first simulation parameter in each simulation result to obtain L first weighted processing results corresponding to the L groups of configuration data;

[0197] The first configuration unit is configured to configure a cell corresponding to the target area based on configuration data corresponding to the optimal first weighted processing result.

[0198] Optionally, the above device may further include:

[0199] A fifth acquiring unit is configured to acquire a preset type grid, where the preset type grid is a grid associated with multiple cells;

[0200] a sixth acquiring unit, configured to respectively acquire a second simulation parameter between the preset type grid and each second cell, where the second cell is a cell associated with the preset type grid, and the second simulation parameter includes at least one of a RANK simulation value, RSRP, SINR, and a communication distance;

[0201] a second weighted processing unit, configured to perform weighted processing on the second simulation parameters respectively to obtain a plurality of second weighted processing results corresponding to all the second cells respectively;

[0202] The second configuration unit is configured to configure the second cell corresponding to the optimal second weighted processing result as a primary serving cell of a preset type grid.

[0203] Optionally, the clustering module 503 includes:

[0204] A seventh obtaining unit is configured to obtain K existing network RANK values ​​corresponding to the K grids, wherein the existing network RANK value corresponding to any grid is the RANK value of any grid in the existing network;

[0205] an eighth obtaining unit, configured to obtain K RANK difference values ​​corresponding to the K grids according to the K first RANK values ​​and the K existing network RANK values;

[0206] The clustering unit is used to perform DBSCAN on K grids based on K RANK differences.

[0207] Optionally, the simulation module 502 includes:

[0208] An establishing unit for establishing a propagation model based on three-dimensional ray tracing according to the three-dimensional electronic map and configuration data;

[0209] The simulation unit is used to perform multipath channel simulation based on the propagation model and the preset multiple-input multiple-output MIMO channel model to obtain a RANK simulation result.

[0210] Optionally, the above device may further include:

[0211] a ninth obtaining unit, configured to obtain measured values ​​of a target parameter in a plurality of grids among the K grids under at least one set of preset configuration data, the target parameter comprising at least one of a RANK value and an RSRP;

[0212] a tenth obtaining unit, configured to obtain simulation values ​​of target parameters in a plurality of grids among the K grids, obtained based on multipath channel simulation under at least one set of preset configuration data;

[0213] The correction unit is used to correct the model parameters of the propagation model based on the measured values ​​and the simulated values ​​of the target parameters in multiple grids of the K grids.

[0214] Optionally, the configuration data includes at least one of the following: cell location, cell azimuth, antenna height, antenna tilt, and antenna weight.

[0215] It should be noted that the spatial division multiplexing flow number optimization device is a device corresponding to the above-mentioned spatial division multiplexing flow number optimization method. All implementation methods in the above-mentioned method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0216] Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0217] The electronic device may include a processor 601 and a memory 602 storing computer program instructions.

[0218] Specifically, the processor 601 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0219] The memory 602 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 602 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 602 may include removable or non-removable (or fixed) media. Where appropriate, the memory 602 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 602 is a non-volatile solid-state memory.

[0220] In certain embodiments, the memory 602 may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical, or other physical / tangible memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0221] The processor 601 reads and executes computer program instructions stored in the memory 602 to implement any one of the methods for optimizing the number of spatial division multiplexing flows in the above embodiments.

[0222] In one example, the electronic device may further include a communication interface 603 and a bus 610. Figure 6As shown, the processor 601, the memory 602, and the communication interface 603 are connected via a bus 610 and communicate with each other.

[0223] The communication interface 603 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0224] Bus 610 includes hardware, software or both, and the components of online data flow metering equipment are coupled to each other. For example, and not limitation, bus can include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 610 can include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0225] In addition, in conjunction with the spatial division multiplexing flow number optimization method in the above embodiments, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the spatial division multiplexing flow number optimization methods in the above embodiments is implemented.

[0226] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0227] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0228] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0229] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0230] The above is only a specific implementation method of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited to this. Any technician familiar with this technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the scope of protection of this application.

Claims

1. A method for optimizing the number of space division multiplexing streams, characterized in that: include: Obtaining a three-dimensional electronic map of a preset area and P sets of configuration data, wherein the preset area includes Q cells, the configuration data is used to configure the cells, the Q cells are associated with K grids, and each cell is associated with at least one grid, P and K are both integers greater than 1, and Q is a positive integer; Based on the P group configuration data, multipath channel simulation is performed in the three-dimensional electronic map to obtain a RANK simulation result of the number of spatial division multiplexing flows, wherein the RANK simulation result includes K first RANK values ​​corresponding to the K grids; Clustering the K grids based on the K first RANK values ​​to obtain a target area, where the target area includes at least one of the grids, wherein the clustering method includes a density-based noise application spatial clustering method or mean shift clustering, and the target area is a continuous area with a relatively high RANK or a continuous area with a relatively low RANK; configuring cells corresponding to the target area to optimize the RANK of the target area; The configuring of the cell corresponding to the target area includes: Obtaining L groups of configuration data, where the L groups of configuration data are all or part of the configuration data in the P groups of configuration data, where L is an integer greater than 1 and less than or equal to P; Based on the L sets of configuration data, performing multipath channel simulation in the three-dimensional electronic map to obtain L simulation results corresponding to the L sets of configuration data, each of the simulation results including a first simulation parameter of each grid included in the target area, the first simulation parameter including at least one of a RANK simulation value, a reference signal received power (RSRP), a signal to interference plus noise ratio (SINR), and an overlap coverage result; performing weighted processing on the first simulation parameter in each of the simulation results to obtain L first weighted processing results corresponding to the L groups of configuration data; The cell corresponding to the target area is configured based on the configuration data corresponding to the optimal first weighted processing result.

2. The method according to claim 1, characterized in that The performing of multipath channel simulation in the three-dimensional electronic map based on the P group configuration data to obtain a spatial division multiplexing stream number RANK simulation result includes: Based on multipath channel simulation, respectively obtaining K RANK simulation values ​​corresponding to the K grids under each set of the configuration data; Obtaining P RANK mean values ​​corresponding to a first cell under the P set of configuration data, where the first cell is any one of the Q cells, and one RANK mean value is an average of RANK simulation values ​​corresponding to all grids associated with the first cell under a set of configuration data; In a case where the first cell corresponds to the maximum value among the P RANK averages, the RANK simulation value corresponding to each grid associated with the first cell is determined as the first RANK value.

3. The method according to claim 2, characterized in that After obtaining P RANK means corresponding to the first cell under the P group configuration data, the method further includes: Obtain a target configuration data set associated with the first cell, the target configuration data set including M groups of configuration data, wherein under the M groups of configuration data, the M RANK means corresponding to the first cell are the largest M RANK means among the P RANK means, where M is a positive integer less than or equal to P; The configuring of the cell corresponding to the target area specifically includes: The cells corresponding to the target area are configured based on the target configuration data set associated with the cells corresponding to the target area.

4. The method according to claim 1, wherein After configuring the cell corresponding to the target area based on the configuration data corresponding to the optimal first weighted processing result, the method further includes: Acquire a preset type grid, where the preset type grid is a grid associated with a plurality of the cells; Respectively acquiring second simulation parameters between the preset type grid and each second cell, where the second cell is a cell associated with the preset type grid, the second simulation parameter including at least one of a RANK simulation value, RSRP, SINR, and a communication distance; performing weighted processing on each of the second simulation parameters to obtain a plurality of second weighted processing results corresponding to all of the second cells; The second cell corresponding to the optimal second weighted processing result is configured as the primary serving cell of the preset type grid.

5. The method according to claim 1, wherein Clustering the K grids based on the K first RANK values ​​includes: Obtain K existing network RANK values ​​corresponding to the K grids, the existing network RANK value corresponding to any of the grids being the RANK value of any of the grids in the existing network; Obtaining K RANK difference values ​​corresponding to the K grids according to the K first RANK values ​​and the K existing network RANK values; Density-based noise spatial clustering DBSCAN is performed on the K grids according to the K RANK differences.

6. The method according to claim 1, characterized in that The performing of multipath channel simulation in the three-dimensional electronic map based on the P group configuration data to obtain a spatial division multiplexing stream number RANK simulation result includes: establishing a propagation model based on three-dimensional ray tracing according to the three-dimensional electronic map and the configuration data; A multipath channel simulation is performed based on the propagation model and a preset multiple-input multiple-output (MIMO) channel model to obtain the RANK simulation result.

7. The method according to claim 6, characterized in that Before performing multipath channel simulation based on the propagation model and a preset multiple-input multiple-output (MIMO) channel model to obtain the RANK simulation result, the method further includes: Obtaining measured values ​​of a target parameter in a plurality of grids in the K grids under at least one set of preset configuration data, the target parameter comprising at least one of a RANK value and an RSRP; Acquire simulation values ​​of the target parameter in a plurality of grids of the K grids, obtained based on multipath channel simulation under at least one set of preset configuration data; Based on the measured values ​​and the simulated values ​​of the target parameter in a plurality of the K grids, the model parameters of the propagation model are modified.

8. The method according to claim 6, characterized in that The configuration data includes at least one of the following: cell location, cell azimuth, antenna height, antenna tilt, and antenna weight.

9. A space division multiplexing stream number optimization device, characterized in that: The device comprises: A first acquisition module is configured to acquire a three-dimensional electronic map of a preset area and P sets of configuration data, wherein the preset area includes Q cells, the configuration data is used to configure the cells, the Q cells are associated with K grids, and each cell is associated with at least one grid, P and K are both integers greater than 1, and Q is a positive integer; a simulation module, configured to perform multipath channel simulation in the three-dimensional electronic map based on the P group configuration data, and obtain a RANK simulation result of the number of spatial division multiplexing flows, wherein the RANK simulation result includes K first RANK values ​​corresponding to the K grids; a clustering module, configured to cluster the K grids based on the K first RANK values ​​to obtain a target area, where the target area includes at least one of the grids, wherein a clustering method includes a density-based noise application spatial clustering method or mean shift clustering, and the target area is a continuous area with a relatively high RANK or a continuous area with a relatively low RANK; A configuration optimization module, configured to configure cells corresponding to the target area to optimize the RANK of the target area; Wherein, the configuration optimization module includes: a third acquiring unit, configured to acquire L groups of configuration data, where the L groups of configuration data are all or part of the configuration data in the P groups of configuration data, and L is an integer greater than 1 and less than or equal to P; a fourth acquisition unit, configured to perform multipath channel simulation in the three-dimensional electronic map based on the L sets of configuration data, and obtain L simulation results corresponding to the L sets of configuration data, each of the simulation results including a first simulation parameter of each grid included in the target area, the first simulation parameter including at least one of a RANK simulation value, a reference signal received power (RSRP), a signal to interference plus noise ratio (SINR), and an overlap coverage result; a first weighted processing unit, configured to perform weighted processing on the first simulation parameter in each of the simulation results to obtain L first weighted processing results corresponding to the L groups of configuration data; The first configuration unit is configured to configure a cell corresponding to the target area based on configuration data corresponding to the optimal first weighted processing result.

10. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for optimizing the number of spatial division multiplexing flows as described in any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for optimizing the number of spatial division multiplexing streams according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for optimizing the number of spatial division multiplexing streams as described in any one of claims 1 to 8.

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