Method of determining base station cell location and determining apparatus

Abstract

The present disclosure provides a method and a device for determining the location of a base station cell, and relates to the field of communication. The first N neighbor cells with high occurrence frequency in the user measurement report of a to-be-tested base station cell are counted. If the locations of the first N neighbor cells with a preset proportion are not within a preset range of the to-be-tested base station cell, it is determined that the location of the to-be-tested base station cell is abnormal. The base station cell account is associated with the first N neighbor cells by combining the frequency points and PCI of the first N neighbor cells in the user measurement report, and all possible locations of the first N neighbor cells are obtained. The density clustering is performed on all possible locations of the first N neighbor cells, and multiple location combinations of the first N neighbor cells are obtained. The optimal location combination is determined from the multiple location combinations of the first N neighbor cells. The location of the to-be-tested base station cell is determined according to the locations of the first N neighbor cells in the optimal location combination. Thus, a scheme for determining the location of a base station cell without using the location information of a user measurement report is realized.

Description

Technical Field

[0001] This disclosure relates to the field of communications, and in particular to a method and apparatus for determining the location of a base station cell. Background Technology

[0002] In mobile networks, base station cell location information serves as a crucial engineering parameter, playing a vital role in network construction, optimization, and user location tracking. Base station cell location information is typically maintained manually, and errors can occur due to data entry mistakes, lack of timely updates, or inadequate management.

[0003] In 4G networks, user equipment (UE) sends a Measurement Report (MR) containing location information to the base station cell. This location information is obtained through the Assisted Global Positioning System (AGPS). Then, the distance between the base station cell and the MR location is calculated using Time Advance (TA), known as the TA distance. Finally, a circle is drawn with the MR location as the center and the TA distance as the radius; the intersection of these circles represents the location of the base station cell.

[0004] However, 5G network MR does not yet support AGPS, therefore, 5G network MR does not carry location information, and thus, 5G network cannot use the methods of 4G network to determine the location of base station cells. Summary of the Invention

[0005] This disclosure provides a solution for determining the location of a base station cell without relying on location information from user measurement reports. It is applicable to scenarios where the location of a base station cell is determined in a communication network where the user measurement report does not carry location information, such as scenarios where the location of a base station cell is determined in a 5G network.

[0006] This disclosure provides a method for determining the location of a base station cell, including:

[0007] Analyze the N neighboring cells that appear most frequently in the user measurement reports of the base station to be tested;

[0008] If the locations of the top N neighboring cells with the preset percentage are not within the preset range of the cell under test, the location of the cell under test is determined to be abnormal.

[0009] By associating the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger, all possible locations of the top N neighbor cells can be obtained.

[0010] Density clustering is performed on all possible locations of the first N neighboring regions to obtain various combinations of the locations of the first N neighboring regions;

[0011] Determine the optimal location combination from multiple combinations of the first N neighboring cells;

[0012] The location of the cell to be tested is determined based on the locations of the first N neighboring cells in the optimal location combination.

[0013] In some embodiments, the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test are counted, including:

[0014] Based on coverage strength, select user measurement reports that are adjacent to the base station cell under test from the user measurement reports of the base station cell under test.

[0015] Based on the frequency point and PCI combined neighbor cell identifier, the top N neighbor cells that appear most frequently in the user measurement reports of the selected base station cells to be tested are statistically analyzed.

[0016] In some embodiments, determining that the location of the base station cell under test is abnormal includes:

[0017] The statistics are based on the location of the base station cell in the database, and include the total number of cells within the distance between base station cells and the number of the top N neighboring cells within that distance.

[0018] If the proportion of the number of the top N neighboring cells within the cell spacing range of the base station to the total number of cells within the cell spacing range of the base station is less than a preset proportion, the location of the base station cell under test is determined to be abnormal.

[0019] In some embodiments, counting the number of the top N neighboring cells within the cell spacing range centered on the location in the ledger of the base station cell under test includes:

[0020] Based on the base station cell ledger lookup, with the ledger location of the base station cell under test as the center, within the non-multiplexed range of the frequency point and PCI combined neighbor cell identifier, the neighbor cell whose frequency point and PCI combined neighbor cell identifier is the same as the frequency point and PCI combined neighbor cell identifier in the user measurement report of the base station cell under test, and whose nearest neighbor cell is the ledger location of the base station cell under test, and its location information.

[0021] From the found neighboring cells, count the number of the top N neighboring cells within the cell spacing range centered on the location of the cell to be tested.

[0022] In some embodiments, based on the base station cell ledger search, the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report are located at all positions within a preset search range, and are used as all possible locations of the top N neighbor cells.

[0023] In some embodiments, density clustering is performed on all possible locations of the first N neighboring regions to obtain multiple combinations of the locations of the first N neighboring regions. This includes: using a preset clustering algorithm to perform density clustering on all possible locations of the first N neighboring regions to obtain several clusters, each cluster corresponding to a combination of the locations of the first N neighboring regions.

[0024] In some embodiments, determining the optimal location combination from a plurality of location combinations of the top N neighboring cells includes:

[0025] For each of the first N neighbor cell location combinations, based on the base station cell ledger search, with the centroid of the location combination as the center, the neighbor cell whose frequency and PCI combination neighbor cell identifiers are the same as those in the user measurement report of the base station cell under test, and whose nearest neighbor cell is to the centroid location, and whose location information is the same, is obtained in order to obtain the number of neighbor cells corresponding to each location combination.

[0026] For each combination of the locations of the first N neighboring cells, count the number of the first N neighboring cells within the cell spacing range centered on the centroid of the location combination;

[0027] The optimal location combination is determined based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination.

[0028] In some embodiments, determining the optimal location combination based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the inter-cell spacing centered on the centroid of the location combination includes:

[0029] Sort the position combinations first according to the number of neighboring cells corresponding to each position combination in descending order;

[0030] The location combinations are sorted in a second order based on the number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination.

[0031] The optimal position combination is the one where the sum of the first and second sorted indices is minimized.

[0032] In some embodiments, determining the location of the base station cell to be tested based on the locations of the top N neighboring cells in the optimal location combination includes: taking a weighted average of the locations of the top N neighboring cells in the optimal location combination according to the number of times the top N neighboring cells appear in the user measurement reports of the base station cell to be tested, and using the weighted average result as the location of the base station cell to be tested.

[0033] In some embodiments, the base station cell to be tested includes a 5G base station cell.

[0034] In some embodiments, the non-multiplexed range of the frequency point and PCI combined neighbor cell identifier is configurable.

[0035] In some embodiments, the cell spacing range of the base stations is configurable.

[0036] Some embodiments of this disclosure provide an apparatus for determining the location of a base station cell, comprising: a memory; and a processor coupled to the memory, the processor being configured to perform a method for determining the location of a base station cell based on instructions stored in the memory.

[0037] This disclosure provides an apparatus for determining the location of a base station cell, comprising:

[0038] The anomaly detection module is configured to count the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test; if the locations of the top N neighboring cells with a preset percentage are not within the preset range of the base station cell under test, the location of the base station cell under test is determined to be abnormal.

[0039] The clustering module is configured to associate the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger to obtain all possible locations of the top N neighbor cells; perform density clustering on all possible locations of the top N neighbor cells to obtain multiple location combinations of the top N neighbor cells; and determine the optimal location combination from the multiple location combinations of the top N neighbor cells.

[0040] The location determination module is configured to determine the location of the cell to be tested based on the locations of the first N neighboring cells in the optimal location combination.

[0041] Some embodiments of this disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of a method for determining the location of a base station cell. Attached Figure Description

[0042] The accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. This disclosure can be more clearly understood from the following detailed description with reference to the accompanying drawings.

[0043] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0044] Figure 1 A flowchart illustrating a method for determining the location of a base station cell according to some embodiments of this disclosure is shown.

[0045] Figure 2 A flowchart illustrating a method for determining the location of a base station cell according to some embodiments of this disclosure is shown.

[0046] Figure 3 A schematic diagram of the structure of an apparatus for determining the location of a base station cell according to some embodiments of the present disclosure is shown.

[0047] Figure 4 A schematic diagram of the structure of an apparatus for determining the location of a base station cell according to some embodiments of the present disclosure is shown. Detailed Implementation

[0048] The technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0049] Unless otherwise stated, the terms "first," "second," etc., used in this disclosure are used to distinguish different objects and are not used to indicate size or sequence.

[0050] Figure 1 A flowchart illustrating a method for determining the location of a base station cell according to some embodiments of this disclosure is shown.

[0051] like Figure 1 As shown, the method for determining the location of a base station cell in this embodiment includes steps 110-160.

[0052] In step 110, the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test are counted.

[0053] The base station cell under test is, for example, a 5G base station cell. The user measurement report, or simply measurement report, may not carry location information. The user equipment reports the measurement reports of neighboring cells to the primary serving cell. The "N" in the first N neighboring cells is configurable; for example, N=10, but is not limited to the example given.

[0054] The top N neighboring cells that appear most frequently in user measurement reports of the base station cell under test include:

[0055] First, based on coverage strength, such as RSRP (Reference Signal Receiving Power), user measurement reports that are adjacent to the cell under test are selected from the user measurement reports of the cell under test.

[0056] Since location points with excellent coverage quality in a base station cell are generally located near the base station cell, MRs adjacent to the base station cell can be selected based on the coverage strength RSRP. For example, from the MRs of the base station cell under test, MRs with an RSRP greater than a strength threshold (e.g., -105dBm) can be selected as MRs adjacent to the base station cell under test. The strength threshold can be set or adjusted according to the actual situation.

[0057] Then, based on the frequency point and PCI combined neighbor cell identifier, the top N neighbor cells that appear most frequently in the user measurement reports of the selected base station cells to be tested are statistically analyzed.

[0058] Since neighboring cells in MR are identified using frequency point and physical cell ID (PCI), the neighboring cells in the MR of the base station cell under test are counted by combining the frequency point and PCI neighboring cell ID.

[0059] It should be noted that frequency points and PCIs can be reused in communication systems. For example, in a 5G network, there are only 1008 PCIs in total, and according to specifications, they generally should not overlap within a 6-kilometer range. The non-reused range (referred to as the non-reused range) of the combined frequency point and PCI neighbor cell identifier can be set to, for example, a 6-kilometer range.

[0060] In step 120, if the locations of the top N neighboring cells with the preset percentage are not within the preset range of the base station cell under test, the location of the base station cell under test is determined to be abnormal.

[0061] Determining an abnormal location for a base station cell under test involves: calculating the total number of cells within the distance between base station cells, centered on the cell's registered location, and the number of its top N neighboring cells within that distance. If the ratio of the number of its top N neighboring cells within that distance to the total number of cells within that distance is less than a preset percentage, the base station cell under test is determined to have an abnormal location. Here, "abnormal location" refers to an inaccurate location of the base station cell under test.

[0062] For example, if the preset percentage is set to 70%, and the total number of cells within the distance between base station cells is greater than 10, and the number of the top N neighboring cells within the distance between base station cells is less than 7, then the proportion of the number of the top N neighboring cells within the distance between base station cells to the total number of cells within the distance between base station cells is less than 70%, then the location of the base station cell under test is considered abnormal; otherwise, the location of the base station cell under test is considered normal.

[0063] The process of counting the top N neighboring cells within the cell spacing range centered on the registered location of the base station cell under test includes: based on the base station cell register lookup, identifying the neighboring cells whose frequency and PCI combined neighboring cell identifiers are identical to those in the user measurement report of the base station cell under test, and whose closest neighboring cells are located within the non-multiplexed range of the frequency and PCI combined neighboring cell identifiers of the base station cell under test, and whose location information is the same; and from the found neighboring cells, counting the top N neighboring cells within the cell spacing range centered on the registered location of the base station cell under test.

[0064] The non-reuse range of the frequency point and PCI combined neighbor cell identifier is configurable; for example, the non-reuse range can be configured to a range of 6 kilometers. The base station cell spacing range is configurable; for example, the base station cell spacing range can be configured to a range of 1.5 kilometers.

[0065] In step 130, by associating the frequency points and PCI combined neighbor cell identifiers of the first N neighbor cells in the user measurement report with the base station cell ledger, all possible locations of the first N neighbor cells are obtained.

[0066] By associating the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger, all possible locations of the top N neighbor cells are obtained. This includes all locations within a preset search range based on the base station cell ledger search, where the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells fall, and these locations are considered as all possible locations of the top N neighbor cells. The preset search range is, for example, a city or prefecture. Locations are, for example, latitude and longitude coordinates.

[0067] In step 140, density clustering is performed on all possible locations of the first N neighboring regions to obtain various combinations of the locations of the first N neighboring regions.

[0068] Density clustering is performed on all possible locations of the first N neighboring regions to obtain multiple combinations of the locations of the first N neighboring regions. This includes: using a preset clustering algorithm, density clustering is performed on all possible locations of the first N neighboring regions to obtain several clusters, and each cluster corresponds to a combination of the locations of the first N neighboring regions.

[0069] Clustering algorithms, such as the DBSCAN algorithm, are used, with the radius set to the distance between base station cells (e.g., 1.5 km) and the minimum number of points in a cluster set to 7. Clustering algorithms can eliminate cases of incorrect neighbor cell locations and filter out abnormal noise points, further improving the accuracy of predicting base station cell locations based on neighbor cell locations.

[0070] In step 150, the optimal location combination is determined from the multiple location combinations of the top N neighboring cells, that is, the actual neighboring cell location combination of the base station cell to be tested.

[0071] Determining the optimal location combination from multiple combinations of the top N neighboring cells includes:

[0072] First, for each of the first N neighbor cell location combinations, based on the base station cell ledger search, with the centroid of the location combination as the center, the neighbor cell whose frequency and PCI combination neighbor cell identifiers are the same as those in the user measurement report of the base station cell under test, and whose nearest neighbor cell is to the centroid location, and their location information, in order to obtain the number of neighbor cells corresponding to each location combination;

[0073] Then, for each combination of the locations of the first N neighboring cells, the number of the first N neighboring cells within the cell spacing range centered on the centroid of the location combination is counted.

[0074] Finally, the optimal location combination is determined based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination.

[0075] For example, the location combinations are sorted in the first order according to the number of neighboring cells corresponding to each location combination in descending order; the location combinations are sorted in the second order according to the number of the top N neighboring cells within the distance between base station cells centered on the centroid of the location combination; the location combination with the smallest sum of the first and second sorting numbers is determined as the optimal location combination.

[0076] Therefore, the optimal location combination is determined by having the largest number of neighboring cells and the largest number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination.

[0077] In step 160, the location of the cell to be tested is determined based on the locations of the first N neighboring cells in the optimal location combination.

[0078] For example, the locations of the top N neighboring cells in the optimal location combination are weighted and averaged according to the number of times the top N neighboring cells appear in the user measurement reports of the base station cell to be tested, and the weighted average result is used as the location of the base station cell to be tested.

[0079] For example, the locations of the top N neighboring cells in the optimal location combination are averaged, and the average result is used as the location of the cell to be tested.

[0080] The former weighted average method determines the location of base station cells more accurately than the latter average method.

[0081] The above embodiments implement a scheme that can determine the location of a base station cell without using the location information in the user measurement report. It can be applied to scenarios where the user measurement report does not carry location information to determine the location of a base station cell, such as scenarios where a 5G network determines the location of a base station cell.

[0082] Figure 2 A flowchart illustrating a method for determining the location of a base station cell according to some embodiments of this disclosure is shown.

[0083] like Figure 2 As shown, the method for determining the location of a base station cell in this embodiment includes steps 210-2100.

[0084] 210. Use the base station cell MR and base station cell ledger as input data. The base station cell MR may not contain location information. The base station cell ledger records the location, frequency, PCI, and other engineering parameters (referred to as engineering parameters) of the base station cells.

[0085] 220, Obtain the MR of the cell to be tested.

[0086] In one scenario, all the MRs of the cell to be tested in the input data are used as the MR of the cell to be tested.

[0087] In another scenario, from all the MRs of the cell to be tested in the input data, the MRs adjacent to the cell to be tested are selected based on the coverage strength RSRP and used as the MRs of the cell to be tested.

[0088] For example, from all MRs of the cell under test, MRs with RSRP greater than a strength threshold (e.g., -105dBm) are selected as MRs adjacent to the cell under test. The strength threshold can be set or adjusted according to the actual situation.

[0089] 230. Since neighboring cells in MR are identified using frequency point and physical cell identifier (PCI), the neighboring cells in the MR of the base station cell under test are counted according to the combination of frequency point and PCI neighboring cell identifier.

[0090] 240. The location of the base station cell under test is obtained by associating the base station cell ledger with the base station cell ledger. Centered on the location of the base station cell under test, the neighboring cells within a 6-kilometer radius that have the same frequency and PCI combination as the user measurement report of the base station cell under test and are closest to the location of the base station cell under test are obtained. The location of the neighboring cells associated with the current location of the base station cell under test is also obtained.

[0091] 250. Count the top N neighboring cells (e.g., N = 10) in descending order of frequency of occurrence in the MR (Mean Interpretation) data of the cell under test. Count the number of cells in the cell register within a 1.5 km radius of the cell under test's location, and the number of the top N neighboring cells within this radius. Assuming a preset percentage for determining location anomalies is 70%, if the number of cells near the cell under test in the cell register is greater than 10, and the top 10 neighboring cells are less than 7, then the cell under test is considered to have a location anomaly, and the following steps are continued to obtain the actual location of the cell under test. Otherwise, the cell under test is considered to have a normal location, and the process repeats for the next cell. The specific values ​​of the above parameters can be set dynamically based on the inter-cell spacing of existing 5G base stations using big data statistics.

[0092] 260. If the location of the cell under test is found to be abnormal, it means that the neighboring cells associated with the frequency and PCI are also incorrect. It is necessary to search for all possible corresponding cell locations based on the frequency and PCI in the MR. For example, based on the frequency and PCI of the top 10 neighboring cells of the cell under test, associate them with the 5G base station cell ledger parameter table, search within a certain range (usually a certain city), and output the ledger parameter locations (longitude and latitude) corresponding to all neighboring cell identifiers with frequency and PCI combinations.

[0093] 270. Due to the reuse of PCI, a large number of neighboring cells are found based on PCI searches, requiring clustering to determine the density of these neighboring cells. Density clustering is used to output the top 10 neighboring cell clusters, with each cluster corresponding to one of the top 10 neighboring cell location combinations. For example, the DBSCAN algorithm is used for clustering, with parameters set to a radius of 1.5 kilometers and a minimum number of points of 7. The specific parameter values ​​are dynamically adjusted based on the inter-cell spacing of the existing 5G base stations using big data statistics. Density clustering can eliminate cases where neighboring cell locations are also incorrect, filtering out abnormal noise points and further improving the accuracy of predicting base station cell locations based on neighboring cell locations.

[0094] 280. Based on all the above neighbor cell location combinations, re-associate the base station cell ledger parameters with the centroid of the cluster of the neighbor cell location combination as the center. Search for the location information corresponding to the MR intermediate frequency point and PCI of the base station cell under test within a 6-kilometer range of the cluster centroid, and output the list of neighbor cells searched for each neighbor cell location combination cluster.

[0095] 290. Based on the neighbor cell list obtained from the re-search, and combined with the MR data of the base station cell under test, the number of associated neighbor cells and the number of TOPN neighbor cells near the base station cell under test are statistically analyzed. The optimal neighbor cell location combination is output as the combination with the highest number of associated neighbor cells and the highest number of TOPN neighbor cells near the base station cell under test. For example, by calculating the neighbor cell number ranking from largest to smallest for each cluster's associated data parameters, and the ranking from largest to smallest for the number of TOP10 neighbor cells within 1.5 kilometers of the cluster center point, the cluster-corresponding neighbor cell location combination with the smallest sum of the two rankings is output as the actual neighbor cell location combination for the base station cell under test.

[0096] 2100. Based on the actual location neighbor cell set obtained from the search, the locations of the top 10 neighbor cells are weighted and averaged according to the frequency of their occurrence in the MR data. The calculation result is used as the location (longitude and latitude) of the base station cell to be tested.

[0097] The above embodiments implement a scheme that can determine the location of a base station cell without using the location information in the user measurement report. It can be applied to scenarios where the user measurement report does not carry location information to determine the location of a base station cell, such as scenarios where a 5G network determines the location of a base station cell.

[0098] If the top 10 neighboring cells with a large amount of MR data in the main cell are widely distributed, and there are many neighboring sites with non-MR data nearby, then most of the top 10 neighboring cells will access the furthest point instead of the nearest site, indicating that this does not conform to the network coverage plan of accessing the nearest point most of the time. If most of the top 10 neighboring cells are concentrated, and there are also many neighboring sites with MR data near the main cell, it indicates that the actual location is relatively close.

[0099] Figure 3 A schematic diagram of the structure of an apparatus for determining the location of a base station cell according to some embodiments of the present disclosure is shown.

[0100] like Figure 3 As shown, the apparatus 300 for determining the location of a base station cell in this embodiment includes modules 310-340, wherein module 310 is optional.

[0101] The data processing module 310 is configured to collect data such as base station cell MR and base station cell ledger, and can perform cleaning and other functions. This data can be used as input data.

[0102] The anomaly identification module 320 is configured to count the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test; if the locations of the top N neighboring cells with a preset percentage are not within the preset range of the base station cell under test, the location of the base station cell under test is determined to be abnormal.

[0103] Clustering module 330 is configured to associate the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger to obtain all possible locations of the top N neighbor cells; perform density clustering on all possible locations of the top N neighbor cells to obtain multiple location combinations of the top N neighbor cells; and determine the optimal location combination from the multiple location combinations of the top N neighbor cells.

[0104] The location determination module 340 is configured to determine the location of the base station cell to be tested based on the locations of the first N neighboring cells in the optimal location combination.

[0105] The anomaly identification module 320 is configured to filter user measurement reports that are adjacent to the base station cell under test from the user measurement reports of the base station cell under test based on coverage strength; and to count the top N neighboring cells that appear most frequently in the user measurement reports of the filtered base station cell under test based on the combination of frequency point and PCI neighbor cell identifier.

[0106] The anomaly identification module 320 is configured to count the total number of cells within the cell spacing range of the base station cell centered on the cell location of the base station cell under test, and the number of the top N neighboring cells within the cell spacing range. If the proportion of the number of the top N neighboring cells within the cell spacing range to the total number of cells within the cell spacing range is less than a preset proportion, the location of the base station cell under test is determined to be abnormal.

[0107] The clustering module 330 is configured to search all positions of the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells within a preset search range based on the base station cell ledger search, and to use these positions as all possible locations of the top N neighbor cells.

[0108] Clustering module 330 is configured to use a preset clustering algorithm to perform density clustering on all possible locations of the first N neighboring regions to obtain several clusters, each cluster corresponding to a combination of the locations of the first N neighboring regions.

[0109] The clustering module 330 is configured to, for each location combination of the top N neighboring cells, search the base station cell ledger and, with the centroid of the location combination as the center, find the neighboring cells whose frequency and PCI combination neighboring cell identifiers are the same as those in the user measurement report of the base station cell under test, and whose nearest neighboring cell is to the centroid, and their location information, so as to obtain the number of neighboring cells corresponding to each location combination; for each location combination of the top N neighboring cells, count the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination; and determine the optimal location combination based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination.

[0110] The location determination module 340 is configured to perform a weighted average of the locations of the top N neighboring cells in the optimal location combination based on the number of times the top N neighboring cells appear in the user measurement reports of the base station cell to be tested, and use the weighted average result as the location of the base station cell to be tested.

[0111] Figure 4 A schematic diagram of the structure of an apparatus for determining the location of a base station cell according to some embodiments of the present disclosure is shown.

[0112] like Figure 4 As shown, the apparatus 400 for determining the location of a base station cell in this embodiment includes: a memory 410 and a processor 420 coupled to the memory 410. The processor 420 is configured to execute the method for determining the location of a base station cell in any of the foregoing embodiments based on instructions stored in the memory 410.

[0113] For example, the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test are counted; if the locations of the top N neighboring cells with a preset percentage are not within the preset range of the base station cell under test, the location of the base station cell under test is determined to be abnormal; by associating the frequency points and PCI combination neighboring cell identifiers of the top N neighboring cells in the user measurement reports with the base station cell ledger, all possible locations of the top N neighboring cells are obtained; density clustering is performed on all possible locations of the top N neighboring cells to obtain multiple combinations of the locations of the top N neighboring cells; the optimal location combination is determined from the multiple combinations of the locations of the top N neighboring cells; and the location of the base station cell under test is determined based on the locations of the top N neighboring cells in the optimal location combination.

[0114] The memory 410 may include, for example, system memory, fixed non-volatile storage media, etc. The system memory stores, for example, the operating system, application programs, boot loader, and other programs.

[0115] The device 400 may also include an input / output interface 430, a network interface 440, and a storage interface 450. These interfaces 430, 440, and 450, as well as the memory 410 and processor 420, can be connected, for example, via a bus 460. The input / output interface 430 provides a connection interface for input / output devices such as a monitor, mouse, keyboard, and touchscreen. The network interface 440 provides a connection interface for various networked devices. The storage interface 450 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0116] Some embodiments of this disclosure provide a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of a method for determining the location of a base station cell.

[0117] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more non-transitory computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer program code.

[0118] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] The above description is only a preferred embodiment of this disclosure and is not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A method for determining the location of a base station cell, characterized in that, include: Analyze the N neighboring cells that appear most frequently in the user measurement reports of the base station to be tested; If the locations of the top N neighboring cells with the preset percentage are not within the preset range of the cell under test, the location of the cell under test is determined to be abnormal. By associating the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger, all possible locations of the top N neighbor cells can be obtained. Density clustering is performed on all possible locations of the first N neighboring regions to obtain various combinations of the locations of the first N neighboring regions; The optimal location combination is determined from multiple combinations of the top N neighboring cells. This includes: for each combination of the top N neighboring cells, based on a search of the base station cell ledger, using the centroid of the location combination as the center, identifying the neighboring cells whose frequency and PCI combination identifiers are the same as those in the user measurement report of the base station cell under test, and whose nearest neighboring cell is to the centroid, and their location information, in order to obtain the number of neighboring cells corresponding to each location combination; for each combination of the top N neighboring cells, counting the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination; and determining the optimal location combination based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination. The location of the cell to be tested is determined based on the locations of the first N neighboring cells in the optimal location combination.

2. The method according to claim 1, characterized in that, The top N neighboring cells that appear most frequently in user measurement reports of the base station cell under test include: Based on coverage strength, select user measurement reports that are adjacent to the base station cell under test from the user measurement reports of the base station cell under test. Based on the frequency point and PCI combined neighbor cell identifier, the top N neighbor cells that appear most frequently in the user measurement reports of the selected base station cells to be tested are statistically analyzed.

3. The method according to claim 1, characterized in that, The location anomalies of the base station cell under test include: The statistics are based on the location of the base station cell in the database, and include the total number of cells within the distance between base station cells and the number of the top N neighboring cells within that distance. If the proportion of the number of the top N neighboring cells within the cell spacing range of the base station to the total number of cells within the cell spacing range of the base station is less than a preset proportion, the location of the base station cell under test is determined to be abnormal.

4. The method according to claim 3, characterized in that, The statistics include the number of the top N neighboring cells within the cell spacing range, centered on the location of the base station cell under test. Based on the base station cell ledger lookup, with the ledger location of the base station cell under test as the center, within the non-multiplexed range of the frequency point and PCI combined neighbor cell identifier, the neighbor cell whose frequency point and PCI combined neighbor cell identifier is the same as the frequency point and PCI combined neighbor cell identifier in the user measurement report of the base station cell under test, and whose nearest neighbor cell is the ledger location of the base station cell under test, and its location information. From the found neighboring cells, count the number of the top N neighboring cells within the cell spacing range centered on the location of the cell to be tested.

5. The method according to claim 1, characterized in that, By associating the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger, all possible locations of the top N neighbor cells can be obtained, including: Based on the base station cell ledger search, the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report are located in all positions within the preset search range, and are used as all possible locations of the top N neighbor cells.

6. The method according to claim 1, characterized in that, Density clustering is performed on all possible locations of the first N neighboring regions, resulting in various combinations of the locations of the first N neighboring regions, including: Using a pre-defined clustering algorithm, density clustering is performed on all possible locations of the first N neighboring regions to obtain several clusters. Each cluster corresponds to a combination of the locations of the first N neighboring regions.

7. The method according to claim 1, characterized in that, The optimal location combination is determined based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination. Sort the position combinations first according to the number of neighboring cells corresponding to each position combination in descending order; The location combinations are sorted in a second order based on the number of the top N neighboring cells within the cell spacing range centered on the centroid of the location combination. The optimal position combination is the one where the sum of the first and second sorted indices is minimized.

8. The method according to claim 1, characterized in that, The location of the cell to be tested is determined based on the locations of the first N neighboring cells in the optimal location combination, including: The locations of the top N neighboring cells in the optimal location combination are weighted and averaged according to the frequency of their appearance in the user measurement reports of the base station cell under test. The weighted average result is then used as the location of the base station cell under test.

9. The method according to claim 4, characterized in that, The base station cells to be tested include 5G base station cells. The non-reuse range of the frequency point and PCI combined neighbor cell identifier is configurable. The cell spacing range of base stations is configurable.

10. An apparatus for determining the location of a base station cell, characterized in that, include: Memory; as well as A processor coupled to the memory, the processor being configured to perform the method of any one of claims 1-9 based on instructions stored in the memory.

11. An apparatus for determining the location of a base station cell, characterized in that, include: The anomaly identification module is configured to count the top N neighboring cells that appear most frequently in the user measurement reports of the base station cell under test; If the locations of the top N neighboring cells with the preset percentage are not within the preset range of the cell under test, the location of the cell under test is determined to be abnormal. The clustering module is configured to associate the frequency points and PCI combined neighbor cell identifiers of the top N neighbor cells in the user measurement report with the base station cell ledger to obtain all possible locations of the top N neighbor cells; and to perform density clustering on all possible locations of the top N neighbor cells to obtain multiple combinations of the locations of the top N neighbor cells. The optimal location combination is determined from multiple combinations of the top N neighboring cells. This includes: for each combination of the top N neighboring cells, based on a search of the base station cell ledger, using the centroid of the location combination as the center, identifying the neighboring cells whose frequency and PCI combination identifiers are the same as those in the user measurement report of the base station cell under test, and whose nearest neighboring cell is to the centroid, and their location information, in order to obtain the number of neighboring cells corresponding to each location combination; for each combination of the top N neighboring cells, counting the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination; and determining the optimal location combination based on the number of neighboring cells corresponding to each location combination and the number of the top N neighboring cells within the base station cell spacing range centered on the centroid of the location combination. The location determination module is configured to determine the location of the base station cell to be tested based on the locations of the first N neighboring cells in the optimal location combination.

12. A non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the method according to any one of claims 1-9.

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