Base station positioning method and device, electronic equipment and storage medium
By performing cell grouping and base station positioning prediction on test reports reported by the terminal, the problem of positioning inaccurate caused by base station identification information is solved, and the accuracy of base station positioning is improved.
Patent Information
- Application Number
- CN202411314822.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-20
- Publication Date
- 2025-02-18
Smart Images
Figure CN119485641A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of data processing technology, and in particular to a base station positioning method and device, electronic equipment and storage medium. Background Art
[0002] The positioning of base stations is of great significance for wireless network planning, market insights, and fake base station detection.
[0003] In the related technologies of base station positioning, the positioning of the base station connected to the terminal is usually determined based on the positioning of the terminal, wherein the base station connected to the terminal is determined by the identification information of the base station. In actual applications, the identification information of the base station is reused, and the base station to which the terminal belongs cannot be accurately known, resulting in inaccurate positioning of the base station. Summary of the invention
[0004] The present disclosure provides a method and device for positioning a base station, an electronic device and a storage medium, the main purpose of which is to solve the problem that the base station identification information is reused, the base station to which the terminal belongs cannot be accurately known, and the positioning of the base station is inaccurate.
[0005] According to a first aspect of the present disclosure, a method for positioning a base station is provided, comprising:
[0006] Respectively obtaining test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal;
[0007] According to the location, the physical cell identifier and the frequency point, the test report is grouped into cells to obtain at least one cell group; the cell group includes at least one test report;
[0008] A preset base station location prediction algorithm is called to perform base station location prediction on the at least one cell grouping to obtain the base station location of the at least one cell grouping.
[0009] Optionally, grouping the test report into cells according to the location, the physical cell identifier, and the frequency to obtain at least one cell group includes:
[0010] The test reports having the same physical cell identifier and the same frequency in the test reports are divided into the same data set to be grouped; the data set to be grouped includes at least one test report;
[0011] According to the positions, density clustering is performed on the test reports in the same data set to be grouped to obtain the at least one cell group.
[0012] Optionally, calling a preset base station location prediction algorithm to perform base station location prediction on the at least one cell group to obtain the base station location of the at least one cell group includes:
[0013] Calculating the average value of the positions of all test reports in each cell group to obtain the average value of the positions of each cell group;
[0014] According to the position average value, density clustering is performed on each cell group to obtain at least one base station positioning group; the base station positioning group includes at least one cell group;
[0015] The average value of the positions of all cell groups in each base station positioning group is calculated to obtain the base station positioning.
[0016] Optionally, calculating the average value of the positions of all cell groups in each base station positioning group to obtain the base station positioning includes:
[0017] Traversing each base station positioning group, and determining a cell group in which physical cell identifiers between cell groups in each base station positioning group are continuous as a centroid base station group;
[0018] The average value of the positions of all cell groups in the centroid base station group is calculated to obtain the base station location.
[0019] Optionally, after traversing each base station positioning group and determining a cell group having continuous physical cell identifiers between cell groups in each base station positioning group as a centroid base station group, the method further includes:
[0020] Obtaining the signal receiving power of the centroid base station group;
[0021] If the signal receiving power is greater than or equal to a preset first threshold, determining that the centroid base station group is a macro base station group;
[0022] If the signal receiving power is less than or equal to a preset second threshold, determining that the centroid base station group is an indoor station group; the preset second threshold is less than the preset first threshold;
[0023] If the signal receiving power is greater than the preset second threshold and the signal receiving power is less than the preset first threshold, it is determined that the centroid base station group is a micro base station group.
[0024] Optionally, calculating the average value of the positions of all cell groups in the centroid base station group to obtain the base station location includes:
[0025] Merge the centroid base station groups in the centroid base station grouping that have the same base station grouping type and whose distances between the centroid base station groups are less than a preset third threshold to obtain a target base station group; the base station type is any one of the macro base station grouping, the indoor base station grouping, and the micro base station grouping;
[0026] The average value of the positions of all cells in the target base station group is calculated to obtain the base station location.
[0027] According to a second aspect of the present disclosure, a positioning device for a base station is provided, comprising:
[0028] A first acquisition unit is used to respectively acquire test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal;
[0029] A grouping unit, configured to group the test report into cells according to the location, the physical cell identifier and the frequency point to obtain at least one cell group; the cell group includes at least one test report;
[0030] The prediction unit is used to call a preset base station location prediction algorithm to perform base station location prediction on the at least one cell group to obtain the base station location of the at least one cell group.
[0031] Optionally, the grouping unit includes:
[0032] A division module, configured to divide the test reports having the same physical cell identifier and the same frequency point in the test reports into the same data set to be grouped; the data set to be grouped includes at least one test report;
[0033] The first clustering module is used to perform density clustering on the test reports in the same data set to be grouped according to the positions to obtain the at least one cell grouping.
[0034] Optionally, the prediction unit includes:
[0035] A first calculation module is used to calculate the average value of the positions of all test reports in each cell group to obtain the average value of the positions of each cell group;
[0036] A second clustering module is used to perform density clustering on each cell group according to the position average value to obtain at least one base station positioning group; the base station positioning group includes at least one cell group;
[0037] The second calculation module is used to calculate the average value of the positions of all cell groups in each base station positioning group to obtain the base station positioning.
[0038] Optionally, the second calculation module is further used to:
[0039] Traversing each base station positioning group, and determining a cell group in which physical cell identifiers between cell groups in each base station positioning group are continuous as a centroid base station group;
[0040] The average value of the positions of all cell groups in the centroid base station group is calculated to obtain the base station location.
[0041] Optionally, the device further comprises:
[0042] A second acquisition unit is configured to acquire the signal receiving power of the centroid base station group after traversing each base station positioning group and determining the cell group having continuous physical cell identifiers between the cell groups in each base station positioning group as the centroid base station group;
[0043] A determining unit, configured to determine that the centroid base station group is a macro base station group when the signal reception power is greater than or equal to a preset first threshold;
[0044] The determination unit is further configured to determine that the centroid base station group is an indoor station group when the signal reception power is less than or equal to a preset second threshold; the preset second threshold is less than the preset first threshold;
[0045] The determination unit is further configured to, when the signal reception power is greater than the preset second threshold and the signal reception power is less than the preset first threshold, determine that the centroid base station group is a micro base station group.
[0046] Optionally, the second calculation module is further used to:
[0047] Merge the centroid base station groups in the centroid base station grouping that have the same base station grouping type and whose distances between the centroid base station groups are less than a preset third threshold to obtain a target base station group; the base station type is any one of the macro base station grouping, the indoor base station grouping, and the micro base station grouping;
[0048] The average value of the positions of all cells in the target base station group is calculated to obtain the base station location.
[0049] According to a third aspect of the present disclosure, there is provided an electronic device, including:
[0050] at least one processor; and
[0051] a memory communicatively connected to the at least one processor; wherein,
[0052] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect.
[0053] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute the method described in the first aspect.
[0054] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method as described in the first aspect above.
[0055] The base station positioning method and device, electronic device and storage medium provided by the present disclosure respectively obtain test reports reported by at least two terminals; the test report includes the location, physical cell identifier and frequency of the terminal; the test report is grouped into cells according to the location, physical cell identifier and frequency to obtain at least one cell group; the cell group contains at least one test report; a preset base station positioning prediction algorithm is called to perform base station positioning prediction on the at least one cell group to obtain the base station positioning of the at least one cell group. Compared with the related art, the embodiment of the present disclosure groups the test report into cells according to the location, physical cell identifier and frequency of the terminal in the test report to obtain at least one cell group, performs base station positioning prediction on at least one cell group to obtain the base station positioning of at least one cell group, realizes the positioning of the base station without relying on the base station to which the terminal belongs, avoids the reuse of the identification information of the base station, resulting in inaccurate determination of the base station to which the terminal belongs, and thus improves the accuracy of base station positioning.
[0056] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0057] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.
[0058] Figure 1 A schematic diagram of a flow chart of a base station positioning method provided by an embodiment of the present disclosure;
[0059] Figure 2 A process display diagram of density clustering provided by an embodiment of the present disclosure;
[0060] Figure 3A schematic diagram of a flow chart of a method for grouping cells in a test report provided by an embodiment of the present disclosure;
[0061] Figure 4 A diagram showing the effect of positioning of a base station provided in an embodiment of the present disclosure;
[0062] Figure 5 A schematic diagram of the structure of a positioning device for a base station provided in an embodiment of the present disclosure;
[0063] Figure 6 A schematic diagram of the structure of another base station positioning device provided by an embodiment of the present disclosure;
[0064] Figure 7 A schematic block diagram of an exemplary electronic device provided for an embodiment of the present disclosure. DETAILED DESCRIPTION
[0065] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.
[0066] The following describes a base station positioning method and apparatus, an electronic device, and a storage medium according to an embodiment of the present disclosure with reference to the accompanying drawings.
[0067] Figure 1 A schematic diagram of a flow chart of a base station positioning method provided in an embodiment of the present disclosure.
[0068] like Figure 1 As shown, the method is applied to the server, and the method comprises the following steps:
[0069] Step 101, respectively obtain test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal.
[0070] A terminal is a device that can connect to a base station and communicate with the base station, such as a mobile phone, a tablet computer, an Internet of Things device (IoT devices), etc. However, it should be clear that this statement is not intended to limit the terminal to only mobile phones, tablet computers, and IoT devices, but can also be other devices that can connect to a base station and communicate with the base station.
[0071] A test report is a report sent by a terminal to a base station, which is used to feedback the terminal's measurement results of the signal level, signal quality, interference and other information of the current serving cell and neighboring cells. These measurement results are crucial for the base station to perform network optimization operations such as cell selection, cell reselection, and handover decisions. The test report includes but is not limited to the location of the terminal, the Physical Cell Identifier (PCI) of the serving cell and the PCI of the neighboring cell, the frequency of the serving cell and the frequency of the neighboring cell, the Reference Signal Received Power (RSRP) of the serving cell and the RSRP of the neighboring cell. The test report includes but is not limited to the Measurement Report (MR) and Minimization of Drive-Tests (MDT).
[0072] MDT carries the longitude and latitude of the terminal, while MR does not carry the longitude and latitude of the terminal. Based on the MDT with longitude and latitude or the MR repaired by base station positioning, a fingerprint feature library is established, so that other MRs without longitude and latitude can match their features. Based on the principle of matching the feature library from high to low accuracy of longitude and latitude data, the longitude and latitude are obtained, which is implemented as follows:
[0073] Initial longitude and latitude data acquisition of fingerprint database: For MDT, the longitude and latitude of the terminal location is carried, which has the highest accuracy; for MR whose service cell is a MIMO frequency point, the base station obtains the longitude and latitude of the terminal location through the beam combined with the timing advance (TA), or the angle of arrival (AoA) combined with the time of arrival (ToA) method. For MR whose service cell is a non-MIMO frequency point, the terminal positioning is obtained through the AoA combined with ToA method. Both the beam combined with TA and the AoA combined with ToA methods essentially determine the relative position of the base station and the terminal through the beam directivity of Massive MIMO, or AoA, and then determine the propagation delay between the base station and the terminal through TA or ToA (that is, determine the distance), and further take the intersection of the position and the distance as the terminal positioning. The positioning accuracy of the AoA combined with ToA method is related to the number of measurement frequencies used, and the positioning accuracy of the beam combined with TA method is related to the number of horizontal beams and the accuracy of engineering parameters.
[0074] Fingerprint library establishment and use: For terminals in similar locations, the measured 4 / 5G service cell and neighboring cell PCI, Sync Signal Block Index (SSB_index) and RSRP have similar characteristics. For example, if test report 1 carries longitude and latitude, and test report 2 does not carry longitude and latitude, but the service cell and neighboring cell network indicator values measured in test report 1 and test report 2 are similar, it can be considered that the longitude and latitude of terminal 2 corresponding to test report 2 is similar to that of the terminal corresponding to test report 1.
[0075] Based on the above principle, the RSRP index of the neighboring cell based on the existing longitude and latitude sampling point (obtained by a) is used as the positioning and feature basic data input to learn the longitude and latitude feature vector, thereby establishing a longitude and latitude feature fingerprint library. The learning method can be a known machine learning classification method.
[0076] For test reports without longitude and latitude, perform fingerprint matching from the fingerprint library with the highest positioning accuracy to the lowest, so as to obtain the longitude and latitude backfill values. Matching should be performed from the MDT fingerprint library first, followed by AoA combined with ToA (data source with large bandwidth multi-frequency point measurement), or beam combined with TA's MR fingerprint library. If no match is found, then perform positioning using the sampling point's own AoA combined with ToA (data source without large bandwidth multi-frequency point measurement).
[0077] The location is the specific coordinates or position of the terminal in the geographic space, such as longitude and latitude, or a place name. However, it should be clear that this statement is not intended to limit the location to longitude and latitude or a place name, but may also be other information that can represent the specific coordinates or position of the terminal in the geographic space.
[0078] PCI is an identifier used to identify a cell (the service area of a base station). For a mobile communication network, PCI is a fixed sequence index value with a specific range. It supports a PCI range of 0 to 1023, with a total of 1024 configurable values. Since the actual number of network base stations is much larger than the number of configurable PCIs, network base stations configure multiplexed PCIs based on geographical coverage distinction or broadcast channel interference minimization. That is, one PCI will be reused by multiple cells. Therefore, the base station to which the terminal belongs cannot be determined by PCI alone.
[0079] The frequency is the wireless signal frequency used by the terminal.
[0080] By collecting test reports at different locations, the actual location of base stations in the mobile network can be inferred. This is of great significance for network planning, optimization and positioning services (such as positioning services and location services), and can help optimize network coverage and service quality.
[0081] Step 102: group the test report into cells according to the location, the physical cell identifier and the frequency to obtain at least one cell group; the cell group includes at least one test report.
[0082] According to the obtained test reports, only the test reports of the PCI with the strongest RSRP at each frequency point are retained for each terminal location. The test reports are grouped by frequency point and PCI, and the test reports with the same frequency point and the same PCI are grouped together, thereby constructing the data set to be grouped for the mth PCI of the nth frequency point as Data_n_m.
[0083] The grouped data set Data_n_m is density clustered according to the longitude and latitude of the terminal location, so as to distinguish different belonging cells for the location of the terminal that reuses the PCI. The density clustering method can be a density-based spatial clustering algorithm (DBSCAN) or other density clustering methods. The embodiment of the present disclosure does not limit the density clustering method.
[0084] In order to better understand the process of density clustering, Figure 2 As shown, Figure 2 A density clustering process display diagram provided by the embodiment of the present disclosure, in Figure 2 In the figure, m is the PCI, and k is the grouping sequence number of different test reports of the PCI.
[0085] For each data set Data_n_m to be grouped, the number of test reports in the data set Data_n_m to be grouped is required to be no less than n, where n is an arbitrary value, such as 5. However, it should be clear that this statement is not intended to limit the value of n to 5, but can also be other values. The shortest distance between the longitude and latitude of the location of the terminal in the test report of each data set Data_n_m to be grouped and the test report of other data sets Data_n_m to be grouped should exceed m, where m is an arbitrary value, such as 100 meters. However, it should be clear that this statement is not intended to limit the value of m to 100, but can also be other values. Test reports with the same frequency and PCI are isolated according to their geographical distribution and attributed to a certain cell, and a small number of discrete test reports are discarded, such as extremely far test reports due to cross-region coverage of the cell, to avoid generating redundant cells, polluting other base station positioning basic data, and causing base station positioning deviations in subsequent steps.
[0086] After completing the secondary classification of cell affiliation for each data set Data_n_m to be grouped, the kth cell group generated by clustering the data set of the mth PCI at the nth frequency point can be obtained as Data_n_m_k.
[0087] For ease of understanding, an example is provided as shown in the following table:
[0088] Table 1
[0089]
[0090] The purpose of grouping cells in the test report is to improve the efficiency and accuracy of data analysis, so as to better understand network performance, discover and solve problems, optimize network resource allocation, and support network planning decisions.
[0091] Step 103: Call a preset base station location prediction algorithm to perform base station location prediction on the at least one cell group to obtain the base station location of the at least one cell group.
[0092] The execution process of the preset base station positioning prediction algorithm can be to screen each cell group Data_n_m_k, retain at least T% test reports in the cell group, and obtain the screened cell group. For these T% test reports, calculate the average longitude and latitude, which are marked as lon_n_m_k (longitude) and lat_n_m_k (latitude), respectively, which is the estimated positioning of the cell, and the obtained cell number is cell_n_m_k.
[0093] For ease of understanding, an example is provided as shown in the following table:
[0094]
[0095] In actual networking, the PCI of the same base station is most likely planned continuously, with a small number of them planned discontinuously, and the PCIs between neighboring stations are usually discontinuous. Based on this, the identification of the initial base station centroid grouping of the cell grouping is carried out. For the screened cell grouping, which contains a total of P cells {cell_1_1_1,…cell_N_M_K}, the following processing is performed: define a list of cells with base station numbers to be assigned cell_li st_left, and initialize the setting cell_list_left=cell_list; the number of cells with base station numbers to be assigned P_left, and initialize the setting P_left=P; define the initial base station centroid grouping as BS_list.
[0096] Density aggregation is performed on the P_left cells in cell_list_left, and the method may be DBSCAN or other methods, and the aggregation classification is divided into Q groups; the Q groups are traversed, and for each group, it is determined whether there are cells with continuous PCI: if so, for the cells that are judged to have continuous PCI for the first time, the initial base station centroid number BS_index (BS_0, BS_1...) identifies them, and the newly added base stations are stored in BS_list, and the other cells of the group are placed in P_left. Considering that there is a high probability that the PCI rules of wireless co-station base stations are continuous values, this step takes out the cells with continuous PCI and close distance in each group of each clustering one by one, and identifies them as the same base station. If there are no continuous PCI cells in the group, all cells of the group are placed in P_left. Traverse all groups to obtain P_left and a partial list of initial base station centroids.
[0097] Iterate aggregation until there are no cells with continuous PCIs in all groups. Perform the last density clustering on the remaining discontinuous PCI cells to obtain the remaining initial base station centroid groups. The generated partial initial base station centroids are merged to form BS_list. It is recorded that a total of X initial base station centroids are generated, that is, BS_list = {BS_0,…,BS_X-1}.
[0098] The initial base station centroid group calculation, for the cells belonging to the same initial base station centroid number, calculate the average longitude and latitude to obtain the longitude and latitude positioning of the initial base station centroid, that is, the base station positioning of the cell group. The density clustering can be DBSCAN or other density clustering methods, the Euclidean distance of the sampling point can be set to 50 meters, or other values that meet the network cell distance; the minimum number of sampling points of the category should be set to 1, that is, the base station may only exist in one cell.
[0099] By utilizing multiple test reports in a cell group and combining them with a preset base station location prediction algorithm, information from multiple data sources can be comprehensively considered to more accurately predict the location of the base station. This multi-source data fusion method is more reliable and accurate than relying solely on a single test report for positioning.
[0100] The base station positioning method provided by the present disclosure obtains test reports reported by at least two terminals respectively; the test report includes the location, physical cell identifier and frequency of the terminal; the test report is grouped into cells according to the location, physical cell identifier and frequency to obtain at least one cell group; the cell group contains at least one test report; a preset base station positioning prediction algorithm is called to perform base station positioning prediction on the at least one cell group to obtain the base station positioning of the at least one cell group. Compared with the related art, the embodiment of the present disclosure groups the test report into cells according to the location, physical cell identifier and frequency of the terminal in the test report to obtain at least one cell group, performs base station positioning prediction on at least one cell group to obtain the base station positioning of at least one cell group, realizes the positioning of the base station without relying on the base station to which the terminal belongs, avoids the reuse of the identification information of the base station, resulting in inaccurate determination of the base station to which the terminal belongs, and thus improves the accuracy of base station positioning.
[0101] As a refinement of step 102, when performing the cell grouping of the test report according to the location, the physical cell identifier and the frequency to obtain at least one cell group, it can be implemented in the following manner but not limited to: Figure 3 As shown, Figure 3 A flow chart of a method for grouping cells in a test report provided by an embodiment of the present disclosure includes:
[0102] Step 201: group the test reports with the same physical cell identifier and the same frequency into the same data set to be grouped; the data set to be grouped includes at least one test report.
[0103] To facilitate understanding, an example is provided. Assume that there are four test reports, namely A, B, C, and D, where A and B have the same physical cell identifier and the same frequency, C and D have the same physical cell identifier and the same frequency, and A, B and C, D have different physical cell identifiers and different frequencies. In this case, A and B are divided into one data set to be grouped, and C and D are divided into one data set to be grouped.
[0104] Test reports with the same physical cell ID and frequency are likely to come from the same geographical location or a similar wireless environment. Aggregating these reports together allows for a more accurate analysis of the performance characteristics of that specific cell or frequency, such as signal strength, coverage quality, interference level, etc.
[0105] Step 202: Density clustering is performed on the test reports in the same data set to be grouped according to the positions to obtain the at least one cell grouping.
[0106] By clustering the test reports whose locations are less than a preset location threshold into the same cell group, density clustering of the test reports in the same data set to be grouped is performed according to the location to obtain at least one cell group, wherein the preset location threshold can be any value and is not limited in the embodiments of the present disclosure.
[0107] To facilitate understanding, an example is provided. Suppose there are four test reports, namely A, B, C, and D, and the preset location threshold is 2, where the location of A is (1,2), the location of B is (1,3), the location of C is (6,4), and the location of D is (6,5). The distance between A and B is less than 2, and the distance between C and D is less than 2. In this case, A and B are clustered into the same cell group, and C and D are clustered into the same cell group.
[0108] Although the test report itself already contains location information, directly using this raw location data may not accurately reflect the actual distance relationship between the terminal and the base station. Through density clustering, the location information can be further refined, and the test report can be divided into smaller, more spatially dense areas (i.e., cell groups), thereby improving positioning accuracy. This is especially important for services that require high-precision location information (such as navigation, location-based advertising, etc.).
[0109] As a refinement of step 103, when executing the calling of the preset base station positioning prediction algorithm, performing base station positioning prediction on the at least one cell group, and obtaining the base station positioning of the at least one cell group, it can be implemented in but not limited to the following ways: calculating the average value of the positions of all test reports in each cell group to obtain the position average value of each cell group; density clustering each cell group according to the position average value to obtain at least one base station positioning group; the base station positioning group contains at least one cell group; calculating the average value of the positions of all cell groups in each base station positioning group to obtain the base station positioning.
[0110] For ease of understanding, an example is provided. Assume that there are 4 cell groups, namely a, b, c, and d. Among them, there are three test reports in a, namely E, F, and G, two test reports in b, namely H and I, four test reports in c, namely J, K, L, and M, and two test reports in d, namely N and O. The positions of E, F, G, H, I, J, K, L, M, N, and O are (1,1), (1,2), (1,3), (1,6), (1,8), (6,2), (6,4) respectively. ), (6, 6), (6, 8), (6, 1), (6, 3), then the average position of a is (1, 2), the average position of b is (1, 7), the average position of c is (6, 5), and the average position of d is (6, 2). Since the average position of a and b are similar, a and b are divided into base station positioning group 1. The average position of c and d are similar, so c and d are divided into base station positioning group 2. The base station positioning of base station positioning group 1 is (1, 4.5), and the base station positioning of base station positioning group 2 is (6, 3.5).
[0111] In mobile communication networks, accurate location information is essential for providing services such as navigation, emergency rescue, and location advertising. By further averaging the location averages of all cell groups in the base station positioning group, the base station location can be calculated, which can be used as a reference point for user location estimation. Combined with other positioning technologies (such as GPS, Wi-Fi positioning, etc.), the accuracy and reliability of user location services can be significantly improved.
[0112] As a refinement of the above embodiment, when performing the average value calculation of the positions of all cell groups in each base station positioning group to obtain the base station positioning, it can be implemented in but not limited to the following manner: traversing each base station positioning group, determining the cell group in which there are continuous physical cell identifiers between the cell groups in each base station positioning group as the centroid base station group; calculating the average value of the positions of all cell groups in the centroid base station group to obtain the base station positioning.
[0113] To facilitate understanding, an example is provided. It is assumed that there is only a base station positioning group, and there are 3 cell groups in the base station positioning group, namely e, f, and g. Among them, there are 2 test reports in e, namely P and Q, 2 test reports in f, namely R and S, and 2 test reports in g, namely T and U. The physical cell identifiers of P, Q, R, S, T, and U are 1.1, 1.6, 2.5, 2.6, 3.7, and 3.9 respectively. R and S are determined to be continuous physical cell identifiers, and T, U, T, and U are discontinuous physical cell identifiers. f is determined to be the centroid base station group, and the main average value of f is (3,2). The position average value (3,2) is averaged to obtain the base station positioning (3,2).
[0114] After the cell groups with continuous physical cell identifiers are determined as centroid base station groups, a more accurate and stable positioning point can be obtained by calculating the average value of all cell positions (usually base station positions, because the specific boundaries of the cells may be unknown or difficult to accurately represent) in these cell groups as the centroid. This is because the location information of multiple adjacent base stations or cells can be averaged to smooth out the positioning errors of individual base stations, thereby improving the accuracy of overall positioning.
[0115] In practical applications, in order to better support network planning work such as network insight and network evaluation, it is necessary to determine the type of base station. After traversing each base station positioning group and determining the cell group with continuous physical cell identifiers between the cell groups in each base station positioning group as the centroid base station group, the method for determining the base station type can be implemented in but not limited to the following ways: obtaining the signal receiving power of the centroid base station group; if the signal receiving power is greater than or equal to a preset first threshold, determining the centroid base station group as a macro station group; if the signal receiving power is less than or equal to a preset second threshold, determining the centroid base station group as an indoor station group; the preset second threshold is less than the preset first threshold; if the signal receiving power is greater than the preset second threshold and the signal receiving power is less than the preset first threshold, determining the centroid base station group as a micro station group.
[0116] Exemplarily, a macro station is one of the types of wireless base stations with the widest coverage area, and is usually used to cover a large range of urban and rural areas. The signal coverage range of a macro station can reach several kilometers or even further, providing mobile communication services with wide-area coverage. Macro stations are usually installed on tall buildings, mountain tops or special communication towers, that is, base stations with signal reception power greater than or equal to a preset first threshold; micro stations are types of wireless base stations with smaller coverage than macro stations, and are used to provide local enhanced coverage within the coverage area of macro stations. Micro stations are usually installed on low to medium height structures such as buildings, billboards, and street light poles, and their coverage range is generally between a few hundred meters and one kilometer, that is, base stations with signal reception power greater than a preset second threshold and signal reception power less than a preset first threshold. Indoor stations are wireless base stations specially designed to provide indoor coverage. Due to obstacles and signal attenuation problems inside buildings, indoor stations need to provide sufficiently strong signals to ensure the quality of communication inside buildings. They are usually installed in indoor environments such as large commercial buildings, hospitals, schools, subway stations, etc., that is, base stations whose signal receiving power is less than or equal to a preset second threshold, where the preset first threshold is greater than the preset second threshold, and the preset first threshold and the preset second threshold can be any values, which are not limited in the embodiments of the present disclosure.
[0117] Classifying base stations as macro, indoor or micro helps network operators plan and optimize network resources more effectively. For example, macro base stations usually have wide coverage and are suitable for covering large areas; indoor base stations focus on providing indoor coverage; and micro base stations may be used to fill coverage gaps or provide high-density coverage, and each type requires different resource configuration and optimization strategies.
[0118] As a refinement of the above embodiment, when performing the method of calculating the average position values of all cell groups in the centroid base station group to obtain the base station positioning, it can be implemented in but not limited to the following manner: the centroid base station groups in the centroid base station group with the same base station group type and the distance between the centroid base station groups is less than a preset third threshold are merged to obtain a target base station group; the base station type is any one of the macro station group, the indoor station group and the micro station group; the average position values of all cells in the target base station group are calculated to obtain the base station positioning.
[0119] Based on the base station grouping type (macro station, micro station, indoor station), the centroid base station groups of the same base station grouping type are divided into the same group. For the centroid base station groups in each group, based on density clustering, the centroid base station groups whose distances are less than a preset third threshold are merged and identified as the same target base station group. The preset third threshold can be any value, which is not limited in the embodiments of the present disclosure. The density clustering method can be DBSCAN, or other density clustering methods, which are not limited in the embodiments of the present disclosure.
[0120] In one possible implementation of the embodiment of the present disclosure, in order to better understand the effect of base station positioning, as shown in FIG. Figure 4 As shown, Figure 4 A base station positioning effect display diagram provided in an embodiment of the present disclosure, in Figure 4 In the test, the base station positioning is compared with the actual base station location, and the number of base station positioning and the number of actual base station locations match 100%; the maximum distance deviation between the base station positioning and the actual base station location is only 44 meters.
[0121] In summary, the embodiments of the present disclosure can achieve the following effects:
[0122] The disclosed embodiment groups the test report into cells according to the location of the terminal, the physical cell identifier and the frequency in the test report to obtain at least one cell group, performs base station positioning prediction on the at least one cell group to obtain the base station positioning of the at least one cell group, thereby achieving positioning of the base station without relying on the base station to which the terminal belongs, avoiding reuse of the identification information of the base station, which would lead to inaccurate determination of the base station to which the terminal belongs, and thus improving the accuracy of base station positioning.
[0123] Corresponding to the above-mentioned base station positioning method, the present invention also provides a base station positioning device. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, the details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment, and will not be repeated in the present invention.
[0124] Figure 5 A schematic diagram of a base station positioning device provided in an embodiment of the present disclosure is shown in FIG. Figure 5 As shown, the device is applied to a server and includes:
[0125] The first acquisition unit 31 is used to respectively acquire test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal;
[0126] A grouping unit 32, configured to group the test report into cells according to the location, the physical cell identifier and the frequency, to obtain at least one cell group; the cell group includes at least one test report;
[0127] The prediction unit 33 is used to call a preset base station location prediction algorithm to perform base station location prediction on the at least one cell group to obtain the base station location of the at least one cell group.
[0128] The positioning device of the base station provided by the present disclosure obtains test reports reported by at least two terminals respectively; the test report includes the location, physical cell identifier and frequency of the terminal; the test report is grouped into cells according to the location, physical cell identifier and frequency to obtain at least one cell group; the cell group contains at least one test report; a preset base station positioning prediction algorithm is called to perform base station positioning prediction on the at least one cell group to obtain the base station positioning of the at least one cell group. Compared with the related art, the embodiment of the present disclosure groups the test report into cells according to the location, physical cell identifier and frequency of the terminal in the test report to obtain at least one cell group, performs base station positioning prediction on at least one cell group to obtain the base station positioning of at least one cell group, realizes the positioning of the base station without relying on the base station to which the terminal belongs, avoids the reuse of the identification information of the base station, resulting in inaccurate determination of the base station to which the terminal belongs, and thus improves the accuracy of base station positioning.
[0129] Furthermore, in a possible implementation of this embodiment, Figure 6 As shown, the grouping unit 32 includes:
[0130] A division module 321 is used to divide the test reports with the same physical cell identifier and the same frequency point in the test reports into the same data set to be grouped; the data set to be grouped includes at least one test report;
[0131] The first clustering module 322 is used to perform density clustering on the test reports in the same data set to be grouped according to the positions to obtain the at least one cell grouping.
[0132] Furthermore, in a possible implementation of this embodiment, Figure 6 As shown, the prediction unit 33 includes:
[0133] A first calculation module 331 is used to calculate the average value of the positions of all test reports in each cell group to obtain the average value of the positions of each cell group;
[0134] A second clustering module 332 is used to perform density clustering on each cell group according to the position average value to obtain at least one base station positioning group; the base station positioning group includes at least one cell group;
[0135] The second calculation module 333 is used to calculate the average value of the positions of all cell groups in each base station positioning group to obtain the base station positioning.
[0136] Furthermore, in a possible implementation of this embodiment, Figure 6 As shown, the second calculation module 333 is also used for:
[0137] Traversing each base station positioning group, and determining a cell group in which physical cell identifiers between cell groups in each base station positioning group are continuous as a centroid base station group;
[0138] The average value of the positions of all cell groups in the centroid base station group is calculated to obtain the base station location.
[0139] Furthermore, in a possible implementation of this embodiment, Figure 6 As shown, the device also includes:
[0140] A second acquisition unit 34 is configured to acquire the signal receiving power of the centroid base station group after traversing each base station positioning group and determining the cell group in which the physical cell identifiers between the cell groups in each base station positioning group are continuous as the centroid base station group;
[0141] A determination unit 35, configured to determine that the centroid base station group is a macro base station group when the signal reception power is greater than or equal to a preset first threshold;
[0142] The determination unit 35 is further configured to determine that the centroid base station group is an indoor station group when the signal reception power is less than or equal to a preset second threshold; the preset second threshold is less than the preset first threshold;
[0143] The determination unit 35 is further configured to, when the signal reception power is greater than the preset second threshold value and the signal reception power is less than the preset first threshold value, determine that the centroid base station group is a micro base station group.
[0144] Furthermore, in a possible implementation of this embodiment, Figure 6 As shown, the second calculation module 333 is also used for:
[0145] Merge the centroid base station groups in the centroid base station grouping that have the same base station grouping type and whose distances between the centroid base station groups are less than a preset third threshold to obtain a target base station group; the base station type is any one of the macro base station grouping, the indoor base station grouping, and the micro base station grouping;
[0146] The average value of the positions of all cells in the target base station group is calculated to obtain the base station location.
[0147] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principle is the same, which is no longer limited in the embodiment of the present disclosure.
[0148] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
[0149] Figure 7 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.
[0150] like Figure 7 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 402 or a computer program loaded from a storage unit 408 to a RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O (Input / Output) interface 405 is also connected to the bus 404.
[0151] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.
[0152] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as a positioning method for a base station. For example, in some embodiments, the positioning method for a base station may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the aforementioned base station positioning method in any other appropriate manner (for example, by means of firmware).
[0153] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that may be executed and / or interpreted on a programmable system including at least one programmable processor that may be a special purpose or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.
[0154] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.
[0155] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or a flash memory, an optical fiber, a CD-ROM (Compact Dis sc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
[0156] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).
[0157] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.
[0158] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.
[0159] It should be noted that artificial intelligence is a discipline that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and includes both hardware-level and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include computer vision technology, speech recognition technology, natural language processing technology, as well as machine learning / deep learning, big data processing technology, knowledge graph technology, and other major directions.
[0160] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.
[0161] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.
Claims
1. A method for positioning a base station, characterized in that: include: Respectively obtaining test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal; According to the location, the physical cell identifier and the frequency point, the test report is grouped into cells to obtain at least one cell group; the cell group includes at least one test report; A preset base station location prediction algorithm is called to perform base station location prediction on the at least one cell grouping to obtain the base station location of the at least one cell grouping.
2. The method according to claim 1, characterized in that The performing cell grouping on the test report according to the location, the physical cell identifier and the frequency to obtain at least one cell grouping comprises: The test reports having the same physical cell identifier and the same frequency in the test reports are divided into the same data set to be grouped; the data set to be grouped includes at least one test report; According to the positions, density clustering is performed on the test reports in the same data set to be grouped to obtain the at least one cell group.
3. The method according to claim 1, characterized in that The calling of a preset base station location prediction algorithm to perform base station location prediction on the at least one cell group to obtain the base station location of the at least one cell group includes: Calculating the average value of the positions of all test reports in each cell group to obtain the average value of the positions of each cell group; According to the position average value, density clustering is performed on each cell group to obtain at least one base station positioning group; the base station positioning group includes at least one cell group; The average value of the positions of all cell groups in each base station positioning group is calculated to obtain the base station positioning.
4. The method according to claim 3, characterized in that The calculating the average value of the positions of all cell groups in each base station positioning group to obtain the base station positioning includes: Traversing each base station positioning group, and determining a cell group in which physical cell identifiers between cell groups in each base station positioning group are continuous as a centroid base station group; The average value of the positions of all cell groups in the centroid base station group is calculated to obtain the base station location.
5. The method according to claim 4, characterized in that After traversing each base station positioning group and determining a cell group in which physical cell identifiers between cell groups in each base station positioning group are continuous as a centroid base station group, the method further includes: Obtaining the signal receiving power of the centroid base station group; If the signal receiving power is greater than or equal to a preset first threshold, determining that the centroid base station group is a macro base station group; If the signal receiving power is less than or equal to a preset second threshold, determining that the centroid base station group is an indoor station group; the preset second threshold is less than the preset first threshold; If the signal receiving power is greater than the preset second threshold and the signal receiving power is less than the preset first threshold, it is determined that the centroid base station group is a micro base station group.
6. The method according to claim 5, characterized in that The calculating the average value of the positions of all cell groups in the centroid base station group to obtain the base station positioning comprises: Merge the centroid base station groups in the centroid base station grouping that have the same base station grouping type and whose distances between the centroid base station groups are less than a preset third threshold to obtain a target base station group; the base station type is any one of the macro base station grouping, the indoor base station grouping, and the micro base station grouping; The average value of the positions of all cells in the target base station group is calculated to obtain the base station location.
7. A positioning device for a base station, characterized in that: include: A first acquisition unit is used to respectively acquire test reports reported by at least two terminals; the test reports include the location, physical cell identifier and frequency of the terminal; A grouping unit, configured to group the test report into cells according to the location, the physical cell identifier and the frequency point to obtain at least one cell group; the cell group includes at least one test report; The prediction unit is used to call a preset base station location prediction algorithm to perform base station location prediction on the at least one cell grouping to obtain the base station location of the at least one cell grouping.
8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.
10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 6.