Positioning method and device of inter-coverage cell, storage medium and computer device

Abstract

This invention provides a method, apparatus, storage medium, and computer device for locating cells with out-of-coverage coverage. The method includes: constructing a data matrix based on acquired sampling point identifiers, primary serving cell identifiers, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information; generating a TA distribution curve based on the data matrix; calculating the TA distribution curve using a differential vector method to generate an out-of-coverage distance interval; selecting sampling points within the out-of-coverage distance interval based on the data matrix; calculating the sampling points using a DBSCAN clustering algorithm to generate an out-of-coverage area; and determining the out-of-coverage cell based on acquired macro base station information and the out-of-coverage area. The technical solution provided by this invention can determine out-of-coverage cells based on the TA distribution curve, improving the accuracy of locating out-of-coverage cells.

Description

[Technical Field]

[0001] This invention relates to the field of wireless communication technology, and in particular to a positioning method, apparatus, storage medium, and computer device for cross-area coverage cells. [Background Technology]

[0002] For 4G and 5G networks, some base stations may have excessively high antenna height or low elevation angle, causing the coverage distance of the cell to extend beyond the coverage area of ​​other base stations. In these areas, the signal strength received by mobile phones is better, which can cause interference with other cells and affect the user experience.

[0003] In related technologies, over-coverage issues are difficult to detect. Currently, the investigation of over-coverage issues generally relies on on-site testing, but on-site testing is not accurate. The main method for locating over-coverage cells is to comprehensively determine whether a cell is an over-coverage cell based on the cell's average TA and the distance between stations. This method filters out a large number of over-coverage cells, resulting in a large error in locating the over-coverage cells, making the located over-coverage cells very inaccurate. [Summary of the Invention]

[0004] In view of this, embodiments of the present invention provide a method, apparatus, storage medium, and computer device for locating out-of-area coverage cells, so as to improve the accuracy of locating out-of-area coverage cells.

[0005] On one hand, embodiments of the present invention provide a positioning method for cross-cell coverage, including:

[0006] A data matrix is ​​constructed based on the obtained sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information.

[0007] Generate the TA distribution curve based on the data matrix;

[0008] The TA distribution curve is calculated using the differential vector calculation method to generate the cross-area coverage distance interval;

[0009] Based on the data matrix, sampling points for the cross-coverage distance interval are selected;

[0010] The sampling points are calculated using the DBSCAN clustering algorithm to generate the cross-area coverage area;

[0011] Based on the obtained macro base station information and the over-coverage area, the over-coverage cell is determined.

[0012] Optionally, the step of calculating the TA distribution curve using the differential vector method to generate the cross-coverage distance interval includes:

[0013] The peak position is generated by calculating the TA distribution curve using the difference vector calculation method.

[0014] Generating secondary peak positions based on the aforementioned peak positions;

[0015] Based on the secondary peak position, a cross-area coverage distance range is generated.

[0016] Optionally, the step of calculating the TA distribution curve using the difference vector method to generate the peak position includes:

[0017] Based on the number of sampling points obtained in the i-th TA interval, generate the first difference vector;

[0018] Generate a second difference vector based on the first difference vector;

[0019] Generate a third difference vector based on the second difference vector;

[0020] Determine whether the third difference vector is a set value;

[0021] If the third difference vector is determined to be a set value, then the peak value corresponding to the number of sampling points in the (i+1)th TA interval is determined as the peak value.

[0022] Optionally, generating the secondary peak position based on the peak position includes:

[0023] The peak position is calculated using the differential vector method to generate the proportion of TA sampling points;

[0024] Determine whether the proportion of the TA sampling points is the second largest and whether it is greater than a set multiple of the proportion of the largest TA sampling points;

[0025] If it is determined that the proportion of the TA sampling point is the second largest and whether it is greater than a set multiple of the proportion of the largest TA sampling point, then the peak position where the proportion of the TA sampling point is the second largest and greater than the set multiple of the proportion of the largest TA sampling point is determined as the secondary peak position.

[0026] Optionally, generating the first difference vector based on the number of sampling points in the i-th TA interval includes:

[0027] Through the formula Diff v(i) = V(i+1) - V(i), i∈1,2,3…N-1. The number of sampling points in the i-th TA interval is calculated to generate the first difference vector, where i represents the i-th TA interval, V(i) represents the number of sampling points in the i-th TA interval, and Diff... v (i) is the first difference vector.

[0028] Optionally, generating the second difference vector based on the first difference vector includes:

[0029] Using the formula Trend = sign(Diff) v ), If Trend(i) = 0, Trend(i+1) ≥ 0, Trend(i) = 1; if Trend(i) = 0, Trend(i+1) < 0, Trend(i) = -1, calculate the first difference vector to generate the second difference vector, where Diff v (i) is the first difference vector, and Trend is the second difference vector.

[0030] Optionally, generating the third difference vector based on the second difference vector includes:

[0031] The second difference vector is calculated using the formula R = diff(Trend) = Trend(i+1) - Trend(i) to generate the third difference vector, where Trend is the second difference vector and R is the third difference vector.

[0032] On the other hand, embodiments of the present invention provide a positioning device for cross-cell coverage, comprising:

[0033] The construction module is used to construct a data matrix based on the acquired sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information;

[0034] The first generation module is used to generate a TA distribution curve based on the data matrix;

[0035] The second generation module is used to calculate the TA distribution curve using the differential vector calculation method to generate the cross-area coverage distance interval.

[0036] The selection module is used to select sampling points in the cross-coverage distance interval based on the data matrix;

[0037] The third generation module is used to calculate the sampling points using the DBSCAN clustering algorithm to generate the cross-area coverage area;

[0038] The determination module is used to determine the over-coverage cell based on the acquired macro base station information and the over-coverage area.

[0039] On the other hand, embodiments of the present invention provide a storage medium, including: the storage medium includes a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute the above-mentioned location method for cross-coverage cells.

[0040] On the other hand, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that the program instructions, when loaded and executed by the processor, implement the steps of the above-described method for locating cells with cross-area coverage.

[0041] The technical solution for locating over-coverage cells provided in this invention involves constructing a data matrix based on acquired sampling point identifiers, primary serving cell identifiers, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information. A TA distribution curve is generated based on the data matrix. The TA distribution curve is then calculated using a differential vector method to generate an over-coverage distance interval. Sampling points within the over-coverage distance interval are selected based on the data matrix. The sampling points are then calculated using the DBSCAN clustering algorithm to generate an over-coverage area. Finally, the over-coverage cell is determined based on the acquired macro base station information and the over-coverage area. This technical solution improves the accuracy of locating over-coverage cells by enabling the determination of over-coverage cells based on the TA distribution curve. [Attached Image Description]

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating a positioning method for a cross-coverage cell provided in an embodiment of the present invention;

[0044] Figure 2 This is a schematic diagram of the TA distribution curve;

[0045] Figure 3 for Figure 1 The process involves calculating the TA distribution curve using the differential vector method to generate a flowchart of the cross-area coverage distance interval.

[0046] Figure 4 for Figure 3The TA distribution curve is calculated using the difference vector method, and a flowchart of the peak position is generated.

[0047] Figure 5 This is a schematic diagram of the structure of a positioning device for cross-cell coverage provided in an embodiment of the present invention;

[0048] Figure 6 for Figure 5 A schematic diagram of the structure of the second generation module;

[0049] Figure 7 This is a schematic diagram of a computer device provided in an embodiment of the present invention.

Detailed Implementation Methods

[0050] To better understand the technical solution of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0051] It should be understood that the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0052] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0054] This invention provides a method for locating cells with cross-area coverage. Figure 1 A flowchart of a positioning method for cross-cell coverage provided in an embodiment of the present invention is shown below. Figure 1 As shown, the method includes:

[0055] Step 102: Construct a data matrix based on the obtained sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information.

[0056] In this embodiment of the invention, each step is performed by a computer device. For example, the computer device includes a computer or a tablet computer.

[0057] In this embodiment of the invention, before step 102, the method further includes: obtaining the sampling point identifier, the primary serving cell identifier, the primary serving cell timing advance (TA) information, and the primary serving cell antenna angle of arrival (AOA) information from the user equipment. For example, the user equipment includes a mobile phone or a tablet computer.

[0058] In this step, a data matrix is ​​constructed based on the acquired sampling point identifier, primary serving cell identifier, primary serving cell TA information, and primary serving cell AOA information. For example, the data matrix is...

[0059]

[0060] Among them, SamplingPointID n ScellID is the sampling point identifier, and ScellID is the primary serving cell identifier. MR.LteScTadv n Main service community TA information, MR.LteScAOA n Provides AOA information for the primary service community.

[0061] In this embodiment of the invention, the primary serving cell TA information MR.LteScTadv n It can be used to determine the distance between user equipment and base station. Therefore, the TA distribution curve of cells with cross-area coverage will show that there are many users with coverage near the cell, fewer users with coverage at intermediate distances, and many users with coverage at extremely far distances, which is second only to the number of users near the base station.

[0062] Step 104: Generate the TA distribution curve based on the data matrix.

[0063] Specifically, based on the data matrix, a TA distribution curve is generated according to the primary serving cell identifier ScellID.

[0064] Figure 2 A schematic diagram of the TA distribution curve, such as... Figure 2 As shown, Figure 2 The horizontal axis represents time in milliseconds, and the vertical axis represents the number of sampling points. The values ​​that conform to the following... Figure 2 Cells exhibiting the characteristics shown are identified as suspected cells with out-of-area coverage.

[0065] Step 106: Calculate the TA distribution curve using the differential vector calculation method to generate the cross-area coverage distance interval.

[0066] In this embodiment of the invention, Figure 3 for Figure 1 The method uses the difference vector method to calculate the TA distribution curve and generate a flowchart of the cross-coverage distance interval, such as... Figure 3As shown, step 106 includes:

[0067] Step 1062: Calculate the TA distribution curve using the difference vector method to generate the peak position.

[0068] In this embodiment of the invention, the peak position satisfies that the first derivative of the TA distribution curve is 0 and the second derivative is negative. Since the TA distribution is a discrete distribution, the peak position of the TA distribution curve can be determined using the difference vector calculation method.

[0069] In this embodiment of the invention, Figure 4 for Figure 3 The method of difference vector calculation is used to calculate the TA distribution curve and generate a flowchart of the peak position, such as... Figure 4 As shown, step 1062 includes:

[0070] Step A1: Generate the first difference vector based on the number of sampling points in the i-th TA interval.

[0071] Specifically, through the formula Diff v (i) = V(i+1) - V(i), i∈1,2,3…N-1. This calculates the number of sampling points in the i-th TA interval, generating the first difference vector, where i represents the i-th TA interval, V(i) represents the number of sampling points in the i-th TA interval, and Diff... v (i) is the first difference vector.

[0072] Step A2: Generate the second difference vector based on the first difference vector.

[0073] Specifically, through the formula Trend = sign(Diff) v ), If Trend(i) = 0, Trend(i+1) ≥ 0, Trend(i) = 1; if Trend(i) = 0, Trend(i+1) < 0, Trend(i) = -1, calculate the first difference vector to generate the second difference vector, where Diff... v (i) is the first difference vector, and Trend is the second difference vector.

[0074] In this step, the sign function operation is performed on the first difference vector, that is, the Diff function is traversed. v If Diff v If (i) > 0, then take 1; if Diff v (i) < 0, then take -1, if Diff v If (i) = 0, then the value is 0.

[0075] Step A3: Generate the third difference vector based on the second difference vector.

[0076] Specifically, the second difference vector is calculated using the formula R = diff(Trend) = Trend(i+1) - Trend(i) to generate the third difference vector, where Trend is the second difference vector and R is the third difference vector.

[0077] Step A4: Determine if the third difference vector is the set value. If yes, proceed to step A5; otherwise, the process ends.

[0078] In this embodiment of the invention, the set value can be set to -2.

[0079] In this embodiment of the invention, if the third difference vector is determined to be a set value, it indicates that the peak position of the TA distribution curve has been found; if the third difference vector is determined not to be a set value, it indicates that the peak position of the TA distribution curve has not been found.

[0080] Step A5: Determine the peak value corresponding to the number of sampling points in the (i+1)th TA interval as the peak value.

[0081] In this step, the peak value of the peak position corresponding to the number of sampling points in the (i+1)th TA interval is V(i+1).

[0082] Step 1064: Generate the secondary peak position based on the peak position.

[0083] In this embodiment of the invention, step 1064 includes:

[0084] Step B1: Calculate the peak position using the differential vector method to generate the proportion of TA sampling points.

[0085] Step B2: Determine whether the proportion of TA sampling points is the second largest and whether it is greater than the set multiple of the proportion of the largest TA sampling points. If yes, proceed to step B3; otherwise, the process ends.

[0086] In this step, if it is determined that the proportion of TA sampling points is the second largest and greater than the set multiple of the proportion of the largest TA sampling points, then the peak position of the TA sampling point proportion that is the second largest and greater than the set multiple of the proportion of the largest TA sampling points is the secondary peak position; if it is determined that the proportion of TA sampling points is not the second largest or less than or equal to the set multiple of the proportion of the largest TA sampling points, then the secondary peak position has not been found.

[0087] Step B3: Determine the peak position of the second largest TA sampling point with a proportion that is greater than the proportion of the largest TA sampling point by a set multiple.

[0088] In this step, the proportion of TA sampling points is calculated for the peak positions obtained by the differential vector method. The peak with the largest proportion of TA sampling points is the main peak, which is the area with normal coverage that is close to the base station. The peak with the second largest proportion of TA sampling points, which also meets the condition that the proportion of sampling points is greater than a set multiple of the proportion of sampling points of the main peak, is determined as the secondary peak position, which is also the peak point of the over-coverage area.

[0089] Step 1066: Generate the over-coverage distance interval based on the secondary peak position.

[0090] In this step, based on the TA interval i of the secondary peak position, difference calculations are performed from i to the left and right, i.e., V(i)-V(i+1) and V(i)-V(i-1) are calculated. If V(i)-V(i+1)>0, V(i+1)-V(i+2) is calculated until V(i+M)-V(i+M+1)≤0. If V(i)-V(i-1)>0, V(i-1)-V(i-2) is calculated until V(iN)-V(iN-1)≤0. Then, the distance interval of the over-coverage area is determined to be [iN, i+M]. Here, N and M are set parameters.

[0091] Step 108: Select sampling points for the cross-coverage distance interval based on the data matrix.

[0092] For example, based on the data matrix, sampling points within the cross-coverage distance interval [iN, i+M] are selected.

[0093] Step 110: Calculate the sampling points using the DBSCAN clustering algorithm to generate the cross-area coverage area.

[0094] In this embodiment of the invention, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is a representative density-based clustering algorithm. Unlike partitioning and hierarchical clustering methods, it defines a cluster as the largest set of density-connected points, enabling it to divide regions with sufficiently high density into clusters and discover clusters of arbitrary shapes in noisy spatial databases.

[0095] In this step, set the scanning radius to eps and the minimum number of included points to minPts. Start with an unvisited sampling point, find all points within the range of eps. If the number of all nearby points ≥ minPts, the current point is marked as a core point and a new cluster is assigned, and the point is marked as visited. Recursively, use the same method to process all unvisited points within this cluster. If their neighboring points have not been assigned a cluster, then assign the newly created cluster label to them. If they are core points, visit their neighboring points in turn until there are no more core samples within the eps of the cluster. If the number of all nearby points < minPts, the point is temporarily marked as a noise point. Select a new unvisited point and repeat the above process until N clusters as set are clustered, which is the out-of-coverage area.

[0096] Step 112: Determine the out-of-coverage cell according to the obtained macro station information and the out-of-coverage area.

[0097] In this step, obtain the macro station information from the server. Centered on the out-of-coverage area, check whether there is a reasonably covered macro station within a radius of R. The connection distribution between the clustering cluster and the cell is within the range of the azimuth angle of the cell ±δ degrees, that is, the coverage direction of the macro station includes the out-of-coverage area obtained in step 110. If there is such a macro station, confirm that this cell is an out-of-coverage problem cell.

[0098] In the technical solution provided by the embodiment of the present invention, a data matrix is constructed according to the obtained sampling point identifier, primary serving cell identifier, primary serving cell time advance TA information, and primary serving cell antenna angle of arrival AOA information; according to the data matrix, a TA distribution curve is generated; through the differential vector calculation method, the TA distribution curve is calculated to generate an out-of-coverage distance interval; according to the data matrix, sampling points in the out-of-coverage distance interval are selected; through the clustering algorithm DBSCAN, the sampling points are calculated to generate an out-of-coverage area; according to the obtained macro station information and the out-of-coverage area, the out-of-coverage cell is determined. In the technical solution provided by the embodiment of the present invention, the out-of-coverage cell can be determined based on the TA distribution curve, which improves the accuracy of locating the out-of-coverage cell.

[0099] The technical solution provided in this invention can present a cell TA distribution curve based on the sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information reported by the user equipment. If the TA distribution curve shows characteristics such as many users near the cell, fewer users at intermediate distances, and many users at extremely far locations, second only to the number of users near the base station, then the cell is considered a suspected over-coverage cell. Then, combined with the sampling points reported by the user equipment, the over-coverage area is calculated. Finally, the macro base station information in the server is correlated to determine whether there is a reasonable covering base station in the over-coverage area. If so, the cell is determined to be an over-coverage cell. The data reported by the user is true and objective, and the determination accuracy is high.

[0100] This invention provides a positioning device for cross-area coverage cells. Figure 5 This is a schematic diagram of the structure of a positioning device for cross-cell coverage provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the device includes: a construction module 11, a first generation module 12, a second generation module 13, a selection module 14, a third generation module 15, and a determination module 16.

[0101] The construction module 11 is used to construct a data matrix based on the acquired sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information.

[0102] The first generation module 12 is used to generate a TA distribution curve based on the data matrix.

[0103] The second generation module 13 is used to calculate the TA distribution curve using the differential vector calculation method to generate the cross-area coverage distance interval.

[0104] The selection module 14 is used to select sampling points in the cross-coverage distance interval based on the data matrix.

[0105] The third generation module 15 is used to calculate the sampling points using the clustering algorithm DBSCAN to generate the cross-area coverage area.

[0106] The determination module 16 is used to determine the over-coverage cell based on the acquired macro base station information and the over-coverage area.

[0107] In this embodiment of the invention, Figure 6 for Figure 5 A schematic diagram of the structure of the second generation module 13 is shown below. Figure 6 As shown, the second generation module 13 includes: a first generation submodule 131, a second generation submodule 132, and a third generation submodule 133.

[0108] The first generation submodule 131 is used to calculate the TA distribution curve using the difference vector calculation method to generate the peak position.

[0109] The second generation submodule 132 is used to generate a secondary peak position based on the peak position.

[0110] The third generation submodule 133 is used to generate a cross-area coverage distance interval based on the secondary peak position.

[0111] In this embodiment of the invention, the first generation submodule 131 is specifically used to generate a first difference vector based on the number of sampling points in the i-th TA interval; generate a second difference vector based on the first difference vector; generate a third difference vector based on the second difference vector; determine whether the third difference vector is a set value; if it is determined that the third difference vector is a set value, then the peak position corresponding to the number of sampling points in the (i+1)-th TA interval is determined as the peak position.

[0112] In this embodiment of the invention, the third generation submodule 132 is specifically used to calculate the secondary peak position using the differential vector calculation method to generate the proportion of TA sampling points; determine whether the proportion of TA sampling points is the second largest and whether it is greater than a set multiple of the largest TA sampling point proportion; if it is determined that the proportion of TA sampling points is the second largest and whether it is greater than a set multiple of the largest TA sampling point proportion, then the peak position where the proportion of TA sampling points is the second largest and greater than a set multiple of the largest TA sampling point proportion is determined as the secondary peak position.

[0113] In this embodiment of the invention, the first generation submodule 131 is specifically used to generate the formula Diff. v (i) = V(i+1) - V(i), i∈1,2,3…N-1. The number of sampling points in the i-th TA interval is calculated to generate the first difference vector, where i represents the i-th TA interval, V(i) represents the number of sampling points in the i-th TA interval, and Diff... v (i) is the first difference vector.

[0114] In this embodiment of the invention, the first generation submodule 131 is specifically used to generate the formula Trend = sign(Diff) v ), If Trend(i) = 0, Trend(i+1) ≥ 0, Trend(i) = 1; if Trend(i) = 0, Trend(i+1) < 0, Trend(i) = -1, calculate the first difference vector to generate the second difference vector, where Diff v (i) is the first difference vector, and Trend is the second difference vector.

[0115] In this embodiment of the invention, the first generation submodule 131 is specifically used to calculate the second difference vector using the formula R = diff(Trend) = Trend(i+1) - Trend(i) to generate the third difference vector, where Trend is the second difference vector and R is the third difference vector.

[0116] In the technical solution provided by this invention, a data matrix is ​​constructed based on the acquired sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information. A TA distribution curve is generated based on the data matrix. The TA distribution curve is calculated using the differential vector method to generate over-coverage distance intervals. Sampling points within the over-coverage distance intervals are selected based on the data matrix. The sampling points are calculated using the DBSCAN clustering algorithm to generate over-coverage areas. The over-coverage cell is determined based on the acquired macro base station information and the over-coverage area. This technical solution can determine over-coverage cells based on the TA distribution curve, improving the accuracy of locating over-coverage cells.

[0117] The positioning device for cross-cell coverage provided in this embodiment can be used to achieve the above. Figure 1 , Figure 3 and Figure 4 The method for locating cells that cross coverage zones is described in detail in the above-described embodiment of the method for locating cells that cross coverage zones, and will not be repeated here.

[0118] This invention provides a storage medium that includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the steps of the above-described method for locating cells with overlapping coverage. For a detailed description, please refer to the embodiments of the above-described method for locating cells with overlapping coverage.

[0119] This invention provides a computer device, including a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, they implement the steps of the above-described method for locating cells with overlapping coverage. For a detailed description, please refer to the above-described method for locating cells with overlapping coverage.

[0120] Figure 7 This is a schematic diagram of a computer device provided in an embodiment of the present invention. Figure 7As shown, the computer device 20 in this embodiment includes a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program 23, it implements the positioning method for cross-cell coverage as described in the embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when the processor 21 executes the computer program, it implements the functions of each model / unit in the positioning device for cross-cell coverage as described in the embodiment. To avoid repetition, these details are not elaborated here.

[0121] Computer device 20 includes, but is not limited to, processor 21 and memory 22. Those skilled in the art will understand that... Figure 7 This is merely an example of computer device 20 and does not constitute a limitation on computer device 20. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0122] The processor 21 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0123] The memory 22 can be an internal storage unit of the computer device 20, such as a hard disk or RAM of the computer device 20. The memory 22 can also be an external storage device of the computer device 20, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device 20. Furthermore, the memory 22 can include both internal and external storage units of the computer device 20. The memory 22 is used to store computer programs and other programs and data required by the computer device. The memory 22 can also be used to temporarily store data that has been output or will be output.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0125] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units through some interfaces, and may be electrical, mechanical, or other forms.

[0126] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0128] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of the present invention. 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.

[0129] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A positioning method for cross-area coverage cells, characterized in that, include: A data matrix is ​​constructed based on the obtained sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information. Generate the TA distribution curve based on the data matrix; The TA distribution curve is calculated using the differential vector calculation method to generate the cross-area coverage distance interval; Based on the data matrix, sampling points for the cross-coverage distance interval are selected; The sampling points are calculated using the DBSCAN clustering algorithm to generate the cross-area coverage area; Based on the obtained macro base station information and the over-coverage area, the over-coverage cell is determined.

2. The method according to claim 1, characterized in that, The step of calculating the TA distribution curve using the differential vector method to generate the cross-coverage distance interval includes: The peak position is generated by calculating the TA distribution curve using the difference vector calculation method. Generating secondary peak positions based on the aforementioned peak positions; Based on the secondary peak position, a cross-area coverage distance range is generated.

3. The method according to claim 2, characterized in that, The step of calculating the TA distribution curve and generating the peak position using the difference vector calculation method includes: Based on the number of sampling points obtained in the i-th TA interval, generate the first difference vector; Generate a second difference vector based on the first difference vector; Generate a third difference vector based on the second difference vector; Determine whether the third difference vector is a set value; If the third difference vector is determined to be a set value, then the peak value corresponding to the number of sampling points in the (i+1)th TA interval is determined as the peak value.

4. The method according to claim 2, characterized in that, The step of generating a secondary peak position based on the peak position includes: The peak position is calculated using the differential vector method to generate the proportion of TA sampling points; Determine whether the proportion of the TA sampling points is the second largest and whether it is greater than a set multiple of the proportion of the largest TA sampling points; If it is determined that the proportion of the TA sampling point is the second largest and whether it is greater than a set multiple of the proportion of the largest TA sampling point, then the peak position where the proportion of the TA sampling point is the second largest and greater than the set multiple of the proportion of the largest TA sampling point is determined as the secondary peak position.

5. The method according to claim 3, characterized in that, The step of generating the first difference vector based on the number of sampling points in the i-th TA interval includes: Through the formula Diff v (i) = V(i+1) - V(i), i∈1,2,3…N-1. The number of sampling points in the i-th TA interval is calculated to generate the first difference vector, where i represents the i-th TA interval, V(i) represents the number of sampling points in the i-th TA interval, and Diff... v (i) is the first difference vector.

6. The method according to claim 3, characterized in that, The step of generating the second difference vector based on the first difference vector includes: The second difference vector is generated by calculating the first difference vector using the following formula: Trend=sign(Diff v ); If Trend(i) = 0 and Trend(i+1) ≥ 0, then Trend(i) = 1; If Trend(i) = 0 and Trend(i+1) < 0, then Trend(i) = -1; Among them, Diff v (i) is the first difference vector, and Trend is the second difference vector.

7. The method according to claim 3, characterized in that, The step of generating the third difference vector based on the second difference vector includes: The second difference vector is calculated using the formula R = diff(Trend) = Trend(i+1) - Trend(i) to generate the third difference vector, where Trend is the second difference vector and R is the third difference vector.

8. A positioning device for cross-area coverage, characterized in that, include: The construction module is used to construct a data matrix based on the acquired sampling point identifier, primary serving cell identifier, primary serving cell time advance (TA) information, and primary serving cell antenna angle of arrival (AOA) information; The first generation module is used to generate a TA distribution curve based on the data matrix; The second generation module is used to calculate the TA distribution curve using the differential vector calculation method to generate the cross-area coverage distance interval. The selection module is used to select sampling points in the cross-coverage distance interval based on the data matrix; The third generation module is used to calculate the sampling points using the DBSCAN clustering algorithm to generate the cross-area coverage area; The determination module is used to determine the over-coverage cell based on the acquired macro base station information and the over-coverage area.

9. A storage medium, characterized in that, include: The storage medium includes a stored program, wherein, when the program is executed, it controls the device where the storage medium is located to perform the location method for cross-coverage cells as described in any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, the memory for storing information including program instructions, and the processor for controlling the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by the processor, they implement the steps of the location method for cross-coverage cells as described in any one of claims 1 to 7.

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