Remote cell identification methods, devices, electronic equipment and computer program products
By acquiring the location information of the minimized drive test MDT sampling points, calculating the local density and cluster center distance, and automatically identifying remote cells, the problem of low efficiency in manual identification in existing technologies is solved, and efficient network planning and optimization are achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA MOBILE GRP GUANGDONG CO LTD
- Filing Date
- 2022-04-11
- Publication Date
- 2026-05-26
AI Technical Summary
In existing technologies, the identification of remote cells and the determination of latitude and longitude information rely on manual on-site surveys, resulting in low efficiency and high costs. This makes it impossible to achieve batch automated data collection and correction, which affects network planning and optimization.
By acquiring the location information of the minimized MDT sampling points on the road test, calculating the local density and the distance to the cluster centers, and using the cluster core location information, the remote cells can be automatically identified, reducing the workload of manual verification.
It enables automated identification of remote cells, reduces labor costs, and improves the efficiency of network planning and optimization.
Smart Images
Figure CN116956085B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wireless communication service technology, specifically to a remote cell identification method, device, electronic device, and computer program product. Background Technology
[0002] Remote Radio Units (RUUs) are a crucial means of extending coverage, addressing weak signal strength at coverage edges and enhancing site depth coverage. Furthermore, due to the flexible installation and low construction cost of RUU-based remote cells, they are now widely used. Therefore, identifying whether a current cell is a remote cell and determining its latitude and longitude information is a vital component of refined chemical parameter management. Inaccurate remote cell identification and latitude / longitude information cause significant difficulties for network planning and optimization, necessitating urgent correction. However, current methods for identifying remote cells and determining their latitude and longitude information typically rely on manual on-site surveys, which are labor-intensive, inefficient, and lack the capability for automated batch collection and correction.
[0003] In the prior art, by measuring the reference signal test values of user equipment in each radio frequency remote unit within a multi-radio remote unit cell, the base station selects the radio frequency remote unit with the highest test value as the working radio frequency remote unit of the user equipment, thereby reducing the waste of channel resources in the resource allocation of multiple radio frequency remote units sharing the cell.
[0004] The aforementioned prior art has the following disadvantages:
[0005] The inability to identify whether the current cell is a remote cell can easily lead to errors in network planning and optimization. Summary of the Invention
[0006] This application provides a remote cell identification method to solve the technical problem of automated identification of remote cells.
[0007] In a first aspect, embodiments of this application provide a method for identifying remote cells, including:
[0008] Obtain the location information of the minimum drive test MDT sampling points for the cell to be identified;
[0009] The local density corresponding to each MDT sampling point in the cell to be identified is determined based on the MDT sampling point location information.
[0010] The cluster center distance for each MDT sampling point is determined based on the local density corresponding to each MDT sampling point.
[0011] The cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0012] Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell.
[0013] In one embodiment, identifying whether each cell to be identified is a remote cell is based on the clustering core location information of all cells to be identified under the same site, including:
[0014] Based on the cluster core location information of all cells to be identified under the same site, the first cluster core distance between the current cell to be identified and the other cells to be identified under the same site is determined, resulting in N first cluster core distances;
[0015] The average distance between the N first cluster cores is obtained by averaging the distances of the first cores.
[0016] Based on the cluster core location information of all cells to be identified under the same site, the second cluster core distance between any two cells to be identified is determined, resulting in M second cluster core distances.
[0017] The average distance between the M second cluster cores is obtained by averaging the distances of the M second cluster cores.
[0018] The average distance of the first core is compared with the judgment distance setting value. If the average distance of the first core is greater than the judgment distance setting value, the current cell to be identified is determined to be a remote cell. The judgment distance setting value is n times the average distance of the second core.
[0019] In one embodiment, the cluster center distance corresponding to each MDT sampling point is determined based on the local density corresponding to each MDT sampling point, including:
[0020] The distance to the cluster center corresponding to the sampling point with the highest density is determined based on the local density corresponding to each MDT sampling point; the sampling point with the highest density is the MDT sampling point with the highest local density among all MDT sampling points.
[0021] The cluster center distances for each remaining sampling point are determined based on the local density corresponding to each MDT sampling point. The remaining sampling points are the MDT sampling points remaining after removing the sampling point with the highest density from all MDT sampling points.
[0022] In one embodiment, determining the cluster center distance corresponding to the sampling point with the highest density based on the local density corresponding to each MDT sampling point includes:
[0023] The sampling point with the maximum density is determined based on the local density corresponding to each MDT sampling point.
[0024] The farthest sampling point is determined based on the sampling point with the highest density. The farthest sampling point is the MDT sampling point that is farthest from the sampling point with the highest density among all MDT sampling points.
[0025] The distance between the sampling point with the highest density and the most distant sampling point is determined as the cluster center distance corresponding to the sampling point with the highest density.
[0026] In one embodiment, determining the cluster center distance for each remaining sampling point based on the local density corresponding to each MDT sampling point includes:
[0027] Determine the straight-line distance between the current remaining sampling point and each MDT sampling point with a local density greater than the current remaining sampling point to obtain a set of distance quantities;
[0028] The minimum value of the distance in the distance set is determined as the distance to the cluster center corresponding to the current remaining sampling point.
[0029] In one embodiment, the cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point, including:
[0030] The cluster center distances corresponding to each MDT sampling point are normalized to obtain the normalized distances corresponding to each MDT sampling point.
[0031] The normalized distance and local density corresponding to each MDT sampling point are multiplied to obtain the clustering core parameters corresponding to each MDT sampling point.
[0032] Remove the cluster core parameters corresponding to the sampling points with the highest density, and determine the maximum value of the cluster core parameters among the cluster core parameters corresponding to each remaining sampling point;
[0033] The MDT sampling points corresponding to the maximum values of the cluster core parameters are determined as cluster cores, and the location information corresponding to the cluster cores is determined as cluster core location information.
[0034] In one embodiment, after identifying whether each cell to be identified is a remote cell based on the clustering core location information of all cells to be identified under the same site, the process includes:
[0035] The cluster core location information of the remote cell is determined as the antenna location information of the remote cell.
[0036] In one embodiment, determining the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information includes:
[0037] The distance between sampling points is compared with the preset radius of the density-to-determine region. The density-to-determine region is a circular area with the density-to-determine MDT sampling point as the center and the preset radius as the radius. The distance between sampling points is the distance between any MDT sampling point and the density-to-determine MDT sampling point.
[0038] If the distance between sampling points is less than the preset radius, the count is incremented by one; if the distance between sampling points is greater than or equal to the preset radius, the count is incremented by zero, until the distance between sampling points corresponding to each MDT sampling point is compared, and the total count is obtained.
[0039] The total count is determined as the local density corresponding to the MDT sampling point with undetermined density.
[0040] In one embodiment, obtaining the location information of the minimized drive test MDT sampling points for the cell to be identified includes:
[0041] Construct an initial MDT data information table. The information types of the initial MDT data information table include field name information, field type information, and Chinese field name information.
[0042] Perform MDT data entry operation according to information type to obtain target MDT data information table;
[0043] Obtain the MDT sampling point location information of the cell to be identified based on the target MDT data information table.
[0044] Secondly, embodiments of this application provide a remote cell identification device, comprising:
[0045] The sampling point location information acquisition module is used to acquire the location information of the minimum drive test MDT sampling points of the cell to be identified;
[0046] The local density determination module is used to determine the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information.
[0047] The cluster center distance determination module is used to determine the cluster center distance of each MDT sampling point based on the local density corresponding to each MDT sampling point.
[0048] The cluster core location information determination module is used to determine the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0049] The identification module is used to identify whether each cell to be identified is a remote cell based on the cluster core location information of all cells to be identified under the same site.
[0050] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the steps of the remote cell identification method described in the first aspect.
[0051] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the remote cell identification method described in the first aspect.
[0052] The remote cell identification method, apparatus, electronic device, and computer program product provided in this application obtain the location information of the minimized drive test MDT sampling points of the cell to be identified. Based on the location information of the MDT sampling points, the local density corresponding to each MDT sampling point in the cell to be identified is determined. Then, based on the local density corresponding to each MDT sampling point, the cluster center distance corresponding to each MDT sampling point is determined. Thus, based on the local density and the cluster center distance corresponding to each MDT sampling point, the cluster core location information of the cell to be identified is determined. Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell, realizing automatic identification of remote cells, reducing the workload of manual on-site verification, reducing labor costs, and efficiently supporting network planning and network optimization. Attached Figure Description
[0053] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0054] Figure 1 This is one of the flowcharts illustrating the remote cell identification method provided in the embodiments of this application;
[0055] Figure 2 This is the second flowchart illustrating the remote cell identification method provided in this application embodiment;
[0056] Figure 3 This is the third flowchart illustrating the remote cell identification method provided in this application embodiment;
[0057] Figure 4 This is a schematic diagram of the structure of the remote cell identification device provided in the embodiments of this application;
[0058] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0060] Figure 1 This is one of the flowcharts illustrating the remote cell identification method provided in this application. (Refer to...) Figure 1 This application provides a remote cell identification method, which may include:
[0061] Step 101: Obtain the location information of the minimum drive test MDT sampling points of the cell to be identified.
[0062] Specifically, the cell to be identified refers to a cell under the same base station that is waiting to be identified as a remote cell. There can be multiple cells under the same base station, depending on the actual application. The cell referred to is a cellular cell, which is the area covered by one or part of a base station in a cellular mobile communication system, within which a mobile station can reliably communicate with the base station via a wireless channel. Furthermore, remote refers to radio frequency remote transmission, a technology that converts baseband signals into optical signals for transmission and amplifies them at a remote end. Therefore, in this embodiment, a remote cell refers to a cell that requires radio frequency remote transmission technology for signal enhancement.
[0063] Minimization of Drive-Test (MDT) is a technology used in communication systems to automatically collect and analyze user terminal measurement reports containing location information, minimizing the workload of manual drive testing. MDT sampling point location information refers to the location information of the sampling points selected during automated data collection by the communication system; this location information includes, but is not limited to, latitude and longitude information.
[0064] Step 102: Determine the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information.
[0065] In this embodiment of the application, local density is defined as the number of MDT sampling points within a preset range centered on any MDT sampling point. It can be understood that if the number of MDT sampling points within the preset range is larger, then the local density corresponding to the MDT sampling point at the center of the preset range is considered to be larger.
[0066] Step 103: Determine the cluster center distance for each MDT sampling point based on the local density corresponding to each MDT sampling point.
[0067] In this embodiment, besides the MDT sampling point with the highest local density, the cluster center distance is used to reflect the minimum distance between the current MDT sampling point and all sampling points with a local density greater than the current MDT sampling point, while the cluster center distance of the MDT sampling point with the highest local density is defined as the maximum distance between all MDT sampling points and it. It can be understood that MDT sampling points that are far from the sample cluster (i.e., the MDT sampling points with high local density) and have low local density themselves are considered to be at the edge of the sample cluster.
[0068] Step 104: Determine the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0069] Understandably, MDT sampling points with small cluster center distances and high local density, while having high local density, are too close to the MDT sampling point with the highest local density, thus they cannot become cluster cores and are merely normal sampling points. Conversely, MDT sampling points with large cluster center distances and low local density are sampling points located at the edge of cluster clusters and can be considered noise, therefore they cannot be selected as cluster cores. MDT sampling points with both large cluster center distances and high local density, being far from the MDT sampling point with the highest local density, are not affected by it and can therefore become cluster cores. For example, consider a first economic cluster consisting of a first city, a second city, and others. While the second city is also a modern city, its proximity to the first city means that the more modern and resource-rich first city should be the cluster core, while the second city is merely a normal point. Similarly, the third city, also a modern and resource-rich city, is far from the first city, thus it can serve as the cluster core, joining with the fourth city and others to form the second economic cluster.
[0070] It is understood that the examples above are merely illustrative and are not intended to be the only possible solutions for a better understanding of the technical solutions.
[0071] After determining the cluster core, the location information of the sampling points corresponding to the cluster core can be obtained from the MDT sampling point location information, thereby determining the location information of the cluster core.
[0072] Step 105: Identify whether each cell to be identified is a remote cell based on the cluster core location information of all cells to be identified under the same site.
[0073] Understandably, based on the cluster core location information, the distribution of all cells to be identified under the same site can be clearly known. If a cell to be identified is significantly far away from other cells to be identified, exceeding a certain distance threshold, then the cell to be identified is considered a remote cell.
[0074] The following beneficial effects can be seen from the above embodiments:
[0075] By acquiring the location information of the minimized drive test MDT sampling points of the cell to be identified, the local density corresponding to each MDT sampling point in the cell to be identified is determined based on the location information of the MDT sampling points. Then, the cluster center distance corresponding to each MDT sampling point is determined based on the local density of each MDT sampling point. Thus, the cluster core location information of the cell to be identified is determined based on the local density and the cluster center distance of each MDT sampling point. Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell, realizing automatic identification of remote cells, reducing the workload of manual on-site verification, reducing labor costs, and efficiently supporting network planning and network optimization.
[0076] To facilitate understanding, an embodiment of the remote cell identification method is provided below. In practical applications, the first core average distance and the second core average distance are determined based on the cluster core location information of all cells to be identified under the same site. The identification judgment of the cells to be identified is made based on the first core average distance and the second core average distance, and then the cluster core location information corresponding to the cells to be identified that are determined to be remote cells is used as the antenna location information of the remote cells.
[0077] Figure 2 This is the second flowchart illustrating the remote cell identification method provided in this application. (Refer to...) Figure 2 This application provides a remote cell identification method, which may include:
[0078] Step 201: Determine the first core mean distance based on the cluster core location information of all cells to be identified under the same site.
[0079] Specifically, based on the cluster core location information of all cells to be identified under the same site, the first cluster core distance between the current cell to be identified and the other cells to be identified under the same site is determined, resulting in N first cluster core distances, where N is a positive integer. For example, assuming there are 5 cells to be identified under the same site, and the current cell to be identified is cell number 2, then the first cluster core distance between cell number 2 and the other cells to be identified needs to be determined. In this case, the other cells to be identified refer to cells number 1, 3, 4, and 5. Since the cluster core location information of all cells to be identified contains the latitude and longitude information corresponding to all cells to be identified, the first cluster core distance can be calculated using the latitude and longitude information, ultimately resulting in 5 first cluster core distances.
[0080] It is understood that the above description of the specific values in the process of determining the first cluster core distance is only illustrative and is intended to better understand the technical solution. In practical applications, the actual values in the process of determining the first cluster core distance need to be determined according to the actual application situation, and no unique limitation is made here.
[0081] The average distance of the N first cluster cores is obtained by averaging the distances of the first cores. It can be understood that the average distance of the first cores can be regarded as the average distance between the cluster core of the current cell to be identified and the cluster cores of the other cells to be identified.
[0082] Step 202: Determine the second core mean distance based on the cluster core location information of all cells to be identified under the same site.
[0083] Specifically, based on the cluster core location information of all cells to be identified under the same site, the second cluster core distance between any two cells to be identified is determined, resulting in M second cluster core distances, where M is a positive integer. For example, assuming there are 3 cells to be identified under the same site, any two cells to be identified can be cell 1 and cell 2, cell 1 and cell 3, or cell 2 and cell 3. Since the cluster core location information of all cells to be identified contains the latitude and longitude information corresponding to all cells, the second cluster core distance between the above cells can be determined using the latitude and longitude information, ultimately resulting in 3 second cluster core distances.
[0084] It is understood that the above description of the specific values in the process of determining the second cluster core distance is only illustrative and is intended to better understand the technical solution. In practical applications, the actual values in the process of determining the second cluster core distance need to be determined according to the actual application situation, and no unique limitation is made here.
[0085] The average distance between the M second cluster cores is obtained by averaging the distances between them. It can be understood that the average distance between the two pairs of lines connecting the cluster cores of all the cells to be identified at the same site can be regarded as the average distance between them.
[0086] It is understandable that there is no strict time limit between steps 201 and 202; they can be executed simultaneously or sequentially, depending on the actual application. No single limit is set here.
[0087] Step 203: Compare the average distance of the first core with the set value of the judgment distance, and determine whether the cell to be identified is a remote cell based on the comparison result.
[0088] Specifically, the average distance of the first core is compared with the set judgment distance. If the average distance of the first core is greater than the set judgment distance, the current cell to be identified is determined to be a remote cell. In this embodiment, the set judgment distance is defined as n times the average distance of the second core, where n is a positive integer. For example, n can be 2. Therefore, when the average distance of the first core is greater than twice the average distance of the second core, the current cell to be identified is considered to be too far from other cells to be identified under the same site, located at the edge of the site's signal coverage area, requiring the intervention of radio frequency remote technology to enhance the signal. Thus, the current cell to be identified is determined to be a remote cell. It is understood that in practical applications, the value of n is diverse and needs to be determined according to the actual application situation; no single value is specified here.
[0089] Repeat steps 201 to 203 until all cells to be identified have been identified as remote cells.
[0090] Step 204: Determine the location information of the remote cell antenna.
[0091] Determining the cluster core location information of a remote cell as the antenna location information of the remote cell can be understood as follows: after determining that the current cell to be identified is a remote cell, the cluster core location information corresponding to the current cell to be identified is used as the antenna location information of the remote cell. The antenna location information of the remote cell refers to the location information of the antenna equipment installed in the remote cell, such as the latitude and longitude of the antenna equipment.
[0092] The following beneficial effects can be seen from the above embodiments:
[0093] The first core average distance is determined based on the cluster core location information of all cells to be identified under the same site, and the second core average distance is determined based on the cluster core location information of all cells to be identified under the same site. The first core average distance is compared with the judgment distance setting value, and the comparison result determines whether the current cell to be identified is a remote cell. The cluster core location information of the remote cell is determined as the antenna location information of the remote cell. This method efficiently identifies remote cells and their antenna location information, thereby efficiently supporting network planning and network optimization work and saving a lot of labor costs.
[0094] To facilitate understanding, an embodiment of the remote cell identification method is provided below. In practical applications, the local density corresponding to each MDT sampling point in the cell to be identified is determined based on the MDT sampling point location information. The cluster center distance corresponding to each MDT sampling point is determined based on the local density. Finally, the cluster core location information is determined based on the local density and the cluster center distance.
[0095] Figure 3 This is the third flowchart illustrating the remote cell identification method provided in this application. (Refer to...) Figure 3 This application provides a remote cell identification method, which may include:
[0096] Step 301: Obtain the location information of the minimum drive test MDT sampling points of the cell to be identified.
[0097] In this embodiment of the application, an initial MDT data information table can be constructed. The information types of the initial MDT data information table include field name information, field type information, and field Chinese name information. For example, the field name information may include, but is not limited to, intcellid, flong, and flat. The field type information may include, but is not limited to, bigint and double. The field Chinese name information can correspond to the meaning of the field name information. For example, cell ID corresponds to intcellid, longitude corresponds to flong, and latitude corresponds to flat, etc. The setting method needs to be determined according to the actual application situation and is not uniquely limited here.
[0098] Data is entered into the database based on the information type, that is, data is entered into the database according to the field format corresponding to the information type, thereby obtaining the target MDT data information table. The location information of the MDT sampling points of the cell to be identified is obtained from the target MDT data information table. For example, the cells under the same site can be determined based on the intenbid and intcellid information, and the location information of all MDT sampling points of all cells to be identified can be determined based on the flong and flat information, without being unique, which improves the convenience of querying and improves the efficiency of obtaining the location information of the MDT sampling points of the cell to be identified.
[0099] Step 302: Determine the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information.
[0100] In this embodiment, the density-to-be-determined region is defined as a circular region centered on the density-to-be-determined MDT sampling point and with a preset radius. The density-to-be-determined MDT sampling point refers to an MDT sampling point whose local density is awaiting determination. Specifically, the sampling point spacing is determined based on the MDT sampling point location information. The sampling point spacing is the distance between any MDT sampling point and the density-to-be-determined MDT sampling point. The sampling point spacing is compared with the preset radius of the density-to-be-determined region. If the sampling point spacing is less than the preset radius, it indicates that the MDT sampling point is within the density-to-be-determined region, and the count is incremented by one. If the sampling point spacing is greater than or equal to the preset radius, it indicates that the MDT sampling point is outside the density-to-be-determined region, and the count is incremented by zero. This process continues until the sampling point spacing for each MDT sampling point has been compared, resulting in a total count. This total count is determined as the local density corresponding to the density-to-be-determined MDT sampling point, and can be expressed by the following formula:
[0101]
[0102]
[0103] Where, d ij d represents the distance between sampling points. c Let be the preset radius, i be the MDT sampling point with undetermined density, j be any MDT sampling point, and p be the distance from the target area. i This represents the local density corresponding to the MDT sampling point with undetermined density.
[0104] For example, the preset radius can be set to 4.6 meters. In practical applications, there are various ways to set the preset radius, which need to be set according to the actual application situation. There is no single limitation here.
[0105] Step 303: Determine the cluster center distance corresponding to the sampling point with the highest density based on the local density corresponding to each MDT sampling point.
[0106] In this embodiment, the sampling point with the highest density is the MDT sampling point with the highest local density among all MDT sampling points. Specifically, the local densities corresponding to each MDT sampling point can be sorted from largest to smallest, so that the sampling point with the highest density can be determined based on the local density corresponding to each MDT sampling point.
[0107] Since the location information of all MDT sampling points is known, the farthest sampling point can be determined based on the sampling point with the highest density. The farthest sampling point is the MDT sampling point that is farthest from the sampling point with the highest density among all MDT sampling points. Therefore, the distance between the sampling point with the highest density and the farthest sampling point is determined as the cluster center distance corresponding to the sampling point with the highest density.
[0108] Step 304: Determine the cluster center distance for each remaining sampling point based on the local density corresponding to each MDT sampling point.
[0109] In this embodiment, the remaining sampling points are the MDT sampling points remaining after removing the sampling point with the highest density from all MDT sampling points. The determination of the cluster center distance corresponding to each remaining sampling point can be specifically achieved through the following steps:
[0110] The straight-line distances between the current remaining sampling point and each MDT sampling point with a local density greater than the current remaining sampling point are determined to obtain a set of distance quantities. For example, assuming sampling point i is the sampling point with the highest density, the relationship between the local densities of sampling points j, k, and q and the local density of sampling point i is Pi > Pj > Pk > Pq, where Pi is the local density of sampling point i, Pj is the local density of sampling point j, Pk is the local density of sampling point k, and Pq is the local density of sampling point q. Sampling points j, k, and q are the remaining sampling points. If the current remaining sampling point is sampling point q, then the set of distance quantities for sampling point q is {dqk, dqj, dqi}, where dqk is the straight-line distance between sampling point q and sampling point k, dqj is the straight-line distance between sampling point q and sampling point j, and dqi is the straight-line distance between sampling point q and sampling point i.
[0111] It is understood that the above description of determining the set of distance quantities is only exemplary. In practical applications, the set of distance quantities needs to be determined according to the actual application situation, and there is no unique limitation here.
[0112] The minimum value of the distance in the distance set is determined as the cluster center distance corresponding to the current remaining sampling point. For example, assuming the distance set of sampling point q is {dqk,dqj,dqi}, then the cluster center distance corresponding to sampling point q is Min(dqk,dqj,dqi), which is to take the minimum value among dqk, dqj, and dqi.
[0113] Repeat the above steps until the cluster center distances for all remaining sampling points are determined.
[0114] Step 305: Determine the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0115] The cluster center distances corresponding to each MDT sampling point are normalized to obtain the normalized distances corresponding to each MDT sampling point. The normalization process transforms the feature values of the samples to the same dimension, mapping the data to the interval [0,1] or [-1,1], which is determined only by the extreme values of the variables and is essentially a linear transformation.
[0116] In this embodiment, the normalized distance and local density corresponding to each MDT sampling point can be multiplied to obtain the clustering core parameters corresponding to each MDT sampling point. The clustering core parameters corresponding to the sampling point with the highest density are removed. The maximum value of the clustering core parameters is determined among the clustering core parameters corresponding to each remaining sampling point. The MDT sampling point corresponding to the maximum value of the clustering core parameters is determined as the clustering core. Alternatively, the normalized distance and local density corresponding to each MDT sampling point can be combined and projected onto a two-dimensional coordinate system. The coordinates of each MDT sampling point are determined in the two-dimensional coordinate system, where the horizontal coordinate is the local density and the vertical coordinate is the normalized distance. Then, the coordinate points corresponding to the sampling points with the highest density are removed from the two-dimensional coordinate system. Among the remaining coordinate points, perpendicular lines are drawn from the remaining coordinate points to the horizontal and vertical coordinates to obtain a rectangle with the same number of remaining coordinate points. The MDT sampling point corresponding to the coordinate point of the rectangle with the largest area is selected as the clustering core.
[0117] Furthermore, since the location information of all MDT sampling points is known, the location information corresponding to the cluster core is determined as the cluster core location information.
[0118] The following beneficial effects can be seen from the above embodiments:
[0119] By acquiring the location information of the minimized drive test MDT sampling points of the cell to be identified, the local density corresponding to each MDT sampling point in the cell to be identified is determined based on the location information of the MDT sampling points. Then, the cluster center distance corresponding to the sampling point with the highest density is determined based on the local density of each MDT sampling point. The cluster center distance corresponding to each remaining sampling point is determined based on the local density of each MDT sampling point, thereby determining the cluster center distance corresponding to all MDT sampling points. Finally, the cluster core location information of the cell to be identified is determined based on the local density and the cluster center distance of each MDT sampling point. This improves the accuracy and processing efficiency of the cluster core location information, provides efficient support for the identification of distant cells, and thus efficiently supports network planning and network optimization, saving a lot of labor costs.
[0120] The remote cell identification device provided in the embodiments of this application is described below. The remote cell identification device described below can be referred to in correspondence with the remote cell identification method described above.
[0121] Figure 4 This is a schematic diagram of the remote cell identification device provided in an embodiment of this application. (Refer to...) Figure 4 This application provides a remote cell identification device, which may include:
[0122] The sampling point location information acquisition module is used to acquire the location information of the minimum drive test MDT sampling points of the cell to be identified;
[0123] The local density determination module is used to determine the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information.
[0124] The cluster center distance determination module is used to determine the cluster center distance of each MDT sampling point based on the local density corresponding to each MDT sampling point.
[0125] The cluster core location information determination module is used to determine the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0126] The identification module is used to identify whether each cell to be identified is a remote cell based on the cluster core location information of all cells to be identified under the same site.
[0127] The remote cell identification device provided in this application obtains the location information of the minimized drive test MDT sampling points of the cell to be identified, determines the local density corresponding to each MDT sampling point in the cell to be identified based on the location information of the MDT sampling points, and then determines the cluster center distance corresponding to each MDT sampling point based on the local density of each MDT sampling point. Thus, the cluster core location information of the cell to be identified is determined based on the local density and the cluster center distance of each MDT sampling point. Based on the cluster core location information of all cells to be identified under the same site, the device identifies whether each cell to be identified is a remote cell, thereby achieving automatic identification of remote cells, reducing the workload of manual on-site verification, reducing labor costs, and efficiently supporting network planning and network optimization.
[0128] In one embodiment, identifying whether each cell to be identified is a remote cell is based on the clustering core location information of all cells to be identified under the same site, including:
[0129] Based on the cluster core location information of all cells to be identified under the same site, the first cluster core distance between the current cell to be identified and the other cells to be identified under the same site is determined, resulting in N first cluster core distances;
[0130] The average distance between the N first cluster cores is obtained by averaging the distances of the first cores.
[0131] Based on the cluster core location information of all cells to be identified under the same site, the second cluster core distance between any two cells to be identified is determined, resulting in M second cluster core distances.
[0132] The average distance between the M second cluster cores is obtained by averaging the distances of the M second cluster cores.
[0133] The average distance of the first core is compared with the judgment distance setting value. If the average distance of the first core is greater than the judgment distance setting value, the current cell to be identified is determined to be a remote cell. The judgment distance setting value is n times the average distance of the second core.
[0134] In one embodiment, the cluster center distance corresponding to each MDT sampling point is determined based on the local density corresponding to each MDT sampling point, including:
[0135] The distance to the cluster center corresponding to the sampling point with the highest density is determined based on the local density corresponding to each MDT sampling point; the sampling point with the highest density is the MDT sampling point with the highest local density among all MDT sampling points.
[0136] The cluster center distances for each remaining sampling point are determined based on the local density corresponding to each MDT sampling point. The remaining sampling points are the MDT sampling points remaining after removing the sampling point with the highest density from all MDT sampling points.
[0137] In one embodiment, determining the cluster center distance corresponding to the sampling point with the highest density based on the local density corresponding to each MDT sampling point includes:
[0138] The sampling point with the maximum density is determined based on the local density corresponding to each MDT sampling point.
[0139] The farthest sampling point is determined based on the sampling point with the highest density. The farthest sampling point is the MDT sampling point that is farthest from the sampling point with the highest density among all MDT sampling points.
[0140] The distance between the sampling point with the highest density and the most distant sampling point is determined as the cluster center distance corresponding to the sampling point with the highest density.
[0141] In one embodiment, determining the cluster center distance for each remaining sampling point based on the local density corresponding to each MDT sampling point includes:
[0142] Determine the straight-line distance between the current remaining sampling point and each MDT sampling point with a local density greater than the current remaining sampling point to obtain a set of distance quantities;
[0143] The minimum value of the distance in the distance set is determined as the distance to the cluster center corresponding to the current remaining sampling point.
[0144] In one embodiment, the cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point, including:
[0145] The cluster center distances corresponding to each MDT sampling point are normalized to obtain the normalized distances corresponding to each MDT sampling point.
[0146] The normalized distance and local density corresponding to each MDT sampling point are multiplied to obtain the clustering core parameters corresponding to each MDT sampling point.
[0147] Remove the cluster core parameters corresponding to the sampling points with the highest density, and determine the maximum value of the cluster core parameters among the cluster core parameters corresponding to each remaining sampling point;
[0148] The MDT sampling points corresponding to the maximum values of the cluster core parameters are determined as cluster cores, and the location information corresponding to the cluster cores is determined as cluster core location information.
[0149] In one embodiment, after identifying whether each cell to be identified is a remote cell based on the clustering core location information of all cells to be identified under the same site, the process includes:
[0150] The cluster core location information of the remote cell is determined as the antenna location information of the remote cell.
[0151] In one embodiment, determining the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information includes:
[0152] The distance between sampling points is compared with the preset radius of the density-to-determine region. The density-to-determine region is a circular area with the density-to-determine MDT sampling point as the center and the preset radius as the radius. The distance between sampling points is the distance between any MDT sampling point and the density-to-determine MDT sampling point.
[0153] If the distance between sampling points is less than the preset radius, the count is incremented by one; if the distance between sampling points is greater than or equal to the preset radius, the count is incremented by zero, until the distance between sampling points corresponding to each MDT sampling point is compared, and the total count is obtained.
[0154] The total count is determined as the local density corresponding to the MDT sampling point with undetermined density.
[0155] In one embodiment, obtaining the location information of the minimized drive test MDT sampling points for the cell to be identified includes:
[0156] Construct an initial MDT data information table. The information types of the initial MDT data information table include field name information, field type information, and Chinese field name information.
[0157] Perform data entry operations based on information type to obtain the target MDT data information table;
[0158] Obtain the MDT sampling point location information of the cell to be identified based on the target MDT data information table.
[0159] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call a computer program in the memory 530 to execute steps of the remote cell identification method, such as:
[0160] Obtain the location information of the minimum drive test MDT sampling points for the cell to be identified;
[0161] The local density corresponding to each MDT sampling point in the cell to be identified is determined based on the MDT sampling point location information.
[0162] The cluster center distance for each MDT sampling point is determined based on the local density corresponding to each MDT sampling point.
[0163] The cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0164] Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell.
[0165] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0166] On the other hand, this application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the steps of the remote cell identification method provided in the above embodiments, such as including:
[0167] Obtain the location information of the minimum drive test MDT sampling points for the cell to be identified;
[0168] The local density corresponding to each MDT sampling point in the cell to be identified is determined based on the MDT sampling point location information.
[0169] The cluster center distance for each MDT sampling point is determined based on the local density corresponding to each MDT sampling point.
[0170] The cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0171] Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell.
[0172] On the other hand, embodiments of this application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the methods provided in the above embodiments, such as including:
[0173] Obtain the location information of the minimum drive test MDT sampling points for the cell to be identified;
[0174] The local density corresponding to each MDT sampling point in the cell to be identified is determined based on the MDT sampling point location information.
[0175] The cluster center distance for each MDT sampling point is determined based on the local density corresponding to each MDT sampling point.
[0176] The cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point.
[0177] Based on the cluster core location information of all cells to be identified under the same site, each cell to be identified is identified as a remote cell.
[0178] The processor-readable storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).
[0179] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.
[0180] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.
[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for identifying remote cells, characterized in that, include: Obtain the location information of the minimum drive test MDT sampling points for the cell to be identified; Based on the location information of the MDT sampling points, the local density corresponding to each MDT sampling point in the cell to be identified is determined respectively. The cluster center distances for each MDT sampling point are determined based on the local density of each MDT sampling point, including: determining the cluster center distance for the sampling point with the highest density based on the local density of each MDT sampling point; the sampling point with the highest density is the MDT sampling point with the highest local density among all MDT sampling points; and determining the cluster center distances for each remaining sampling point based on the local density of each MDT sampling point, the remaining sampling points being the MDT sampling points remaining after removing the sampling point with the highest density from all MDT sampling points. The cluster core location information of the cell to be identified is determined based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point. Based on the cluster core location information of all cells to be identified under the same site, identify whether each cell to be identified is a remote cell; The step of determining the cluster center distance corresponding to the maximum density sampling point based on the local density corresponding to each MDT sampling point includes: determining the maximum density sampling point based on the local density corresponding to each MDT sampling point; determining the farthest sampling point based on the maximum density sampling point, wherein the farthest sampling point is the MDT sampling point farthest from the maximum density sampling point among all MDT sampling points; and determining the distance between the maximum density sampling point and the farthest sampling point as the cluster center distance corresponding to the maximum density sampling point. The step of determining the cluster center distances corresponding to each remaining sampling point based on the local density corresponding to each MDT sampling point includes: determining the straight-line distances between the current remaining sampling point and each MDT sampling point whose local density is greater than the current remaining sampling point, thereby obtaining a set of distance values; and determining the minimum value of the distance values in the set of distance values as the cluster center distances corresponding to the current remaining sampling point.
2. The remote cell identification method according to claim 1, characterized in that, The step of identifying whether each cell to be identified is a remote cell based on the clustering core location information of all cells to be identified under the same site includes: Based on the cluster core location information of all cells to be identified under the same site, the first cluster core distance between the current cell to be identified and the other cells to be identified under the same site is determined, resulting in N first cluster core distances; The average distance between the N first cluster cores is obtained by averaging the distances between them. Based on the cluster core location information of all cells to be identified under the same site, the second cluster core distance between any two cells to be identified is determined, resulting in M second cluster core distances. The average distance between the M second cluster cores is obtained by averaging the distances between them. The first core average distance is compared with the determination distance setting value. If the first core average distance is greater than the determination distance setting value, the current cell to be identified is determined to be the remote cell. The determination distance setting value is n times the second core average distance.
3. The remote cell identification method according to claim 1, characterized in that, The step of determining the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point includes: The cluster center distances corresponding to each MDT sampling point are normalized to obtain the normalized distances corresponding to each MDT sampling point. The normalized distance and local density corresponding to each MDT sampling point are multiplied to obtain the clustering core parameters corresponding to each MDT sampling point. Remove the clustering core parameters corresponding to the sampling point with the highest density, and determine the maximum value of the clustering core parameters among the clustering core parameters corresponding to each remaining sampling point; The MDT sampling point corresponding to the maximum value of the cluster core parameter is determined as the cluster core, and the location information corresponding to the cluster core is determined as the cluster core location information.
4. The remote cell identification method according to claim 1, characterized in that, After identifying whether each cell to be identified is a remote cell based on the clustering core location information of all cells to be identified under the same site, the process includes: The cluster core location information of the remote cell is determined as the antenna location information of the remote cell.
5. The remote cell identification method according to claim 1, characterized in that, The step of determining the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information includes: The distance between sampling points is compared with the preset radius of the density-to-determine region, which is a circular area with the density-to-determine MDT sampling point as the center and the preset radius as the radius. The distance between sampling points is the distance between any MDT sampling point and the density-to-determine MDT sampling point. If the distance between the sampling points is less than the preset radius, the count is incremented by one; if the distance between the sampling points is greater than or equal to the preset radius, the count is incremented by zero, until the distance between the sampling points corresponding to each MDT sampling point is compared, and the total count is obtained. The total count is determined as the local density corresponding to the MDT sampling point with undetermined density.
6. The remote cell identification method according to claim 1, characterized in that, The step of obtaining the location information of the minimized drive test MDT sampling points for the cell to be identified includes: Construct an initial MDT data information table, wherein the information types of the initial MDT data information table include field name information, field type information, and Chinese field name information; Perform MDT data entry operation according to the information type to obtain the target MDT data information table; Obtain the MDT sampling point location information of the cell to be identified based on the target MDT data information table.
7. A remote cell identification device, characterized in that, include: The sampling point location information acquisition module is used to acquire the location information of the minimum drive test MDT sampling points of the cell to be identified; The local density determination module is used to determine the local density corresponding to each MDT sampling point in the cell to be identified based on the MDT sampling point location information. The cluster center distance determination module is used to determine the cluster center distance corresponding to each MDT sampling point based on the local density corresponding to each MDT sampling point, including: determining the cluster center distance corresponding to the sampling point with the highest density based on the local density corresponding to each MDT sampling point; the sampling point with the highest density is the MDT sampling point with the highest local density among all MDT sampling points; and determining the cluster center distance corresponding to each remaining sampling point based on the local density corresponding to each MDT sampling point, the remaining sampling points are the MDT sampling points remaining after removing the sampling point with the highest density from all MDT sampling points; The cluster core location information determination module is used to determine the cluster core location information of the cell to be identified based on the local density corresponding to each MDT sampling point and the cluster center distance corresponding to each MDT sampling point. The identification module is used to identify whether each cell to be identified is a remote cell based on the cluster core location information of all cells to be identified under the same site. The step of determining the cluster center distance corresponding to the maximum density sampling point based on the local density corresponding to each MDT sampling point includes: determining the maximum density sampling point based on the local density corresponding to each MDT sampling point; determining the farthest sampling point based on the maximum density sampling point, wherein the farthest sampling point is the MDT sampling point farthest from the maximum density sampling point among all MDT sampling points; and determining the distance between the maximum density sampling point and the farthest sampling point as the cluster center distance corresponding to the maximum density sampling point. The step of determining the cluster center distances corresponding to each remaining sampling point based on the local density corresponding to each MDT sampling point includes: determining the straight-line distances between the current remaining sampling point and each MDT sampling point whose local density is greater than the current remaining sampling point, thereby obtaining a set of distance values; and determining the minimum value of the distance values in the set of distance values as the cluster center distances corresponding to the current remaining sampling point.
8. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the remote cell identification method according to any one of claims 1 to 6.
9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the remote cell identification method according to any one of claims 1 to 6.