Network optimization method, device and computer readable storage medium

Abstract

Embodiments of the present application provide a network optimization method and device and a computer readable storage medium. The method comprises: acquiring road test data collected at a plurality of sampling points, the road test data comprising reference signal received power and signal to interference plus noise ratio of a main serving cell and a neighboring cell corresponding to the sampling points, and latitude and longitude information; extracting a problem road section from the sampling points according to the road test data, to obtain at least one problem road section; determining a cell to be optimized based on the problem road section, and adjusting a beam parameter of the cell to be optimized according to a preset strategy. In this way, the problem road section is determined according to the road test data of the sampling points, and then the cell that needs to be optimized is determined based on the problem road section, i.e. the interference cell of the problem road section, and then the beam parameter of the interference cell is adjusted according to the preset strategy, so as to reduce the influence of the interference cell on the problem road section, thereby conveniently and quickly optimizing the network coverage quality and effectively reducing the cost.

Description

Technical Field

[0001] This application relates to the field of network technology, and in particular to a network optimization method, apparatus, and computer-readable storage medium. Background Technology

[0002] Currently, communication networks such as 5G are undergoing rapid development, making the accurate evaluation and measurement of network quality a key concern for operators. Road testing is an effective means of verifying 5G network quality and is also a relatively traditional evaluation method for operators. Therefore, improving 5G road coverage quality has become a challenging problem for optimization personnel.

[0003] It is estimated that at present, most methods to improve the quality of 5G road coverage still rely on manual analysis by network optimization personnel and tower workers making adjustments on the towers. The 5G road test optimization process is as follows: specialized road test network optimization personnel analyze the road test quality and provide adjustment plans, and then tower workers go up the towers to adjust the antennas and feeders. However, the above-mentioned methods of network optimization are complex, cumbersome, time-consuming, and costly. Summary of the Invention

[0004] To address the existing technical problems, this application provides a network optimization method, apparatus, and computer-readable storage medium.

[0005] To achieve the above objectives, the technical solution of this application embodiment is implemented as follows:

[0006] In a first aspect, embodiments of this application provide a network optimization method, the method comprising:

[0007] Acquire drive test data collected at multiple sampling points, the drive test data including the reference signal received power and signal-to-interference-plus-noise ratio, latitude and longitude information of the primary serving cell and neighboring cells corresponding to the sampling points;

[0008] Based on the road test data, problem road segments are extracted from the sampling points to obtain at least one problem road segment;

[0009] Based on the problematic road segments, cells to be optimized are identified, and the beam parameters of the cells to be optimized are adjusted according to a preset strategy.

[0010] In one embodiment, after acquiring the road test data collected at multiple sampling points, the method further includes:

[0011] The drive test data is associated with physical cell identification codes based on engineering parameters, and global cell identification codes are associated with the physical cell identification codes of the sampling points.

[0012] In one embodiment, the step of extracting problem road segments from the sampling points based on the drive test data to obtain at least one problem road segment includes:

[0013] The sampling points in which the reference signal received power of the primary serving cell is greater than a preset first threshold and the interference plus noise ratio is less than a preset second threshold are identified as problematic sampling points.

[0014] The problem sampling points are divided into road segments based on the latitude and longitude information, so that the problem sampling points with a distance interval of less than a preset third threshold are assigned to the same candidate problem road segment;

[0015] Candidate problem road segments whose length is greater than a preset fourth threshold are selected as problem road segments.

[0016] In one embodiment, the step of dividing the problem sampling points into road segments based on the latitude and longitude information, so as to assign the problem sampling points with a distance interval less than a preset third threshold to the same candidate problem road segment, includes:

[0017] The problem sampling points are sorted according to the sampling time to obtain the sorted problem sampling points;

[0018] The sorted problem sampling points are traversed, and the distance between the current problem sampling point and the previous problem sampling point is determined based on the latitude and longitude information to see if it is less than or equal to a preset third threshold.

[0019] If the current problem sampling point is less than or equal to the preset third threshold, then the current problem sampling point will be assigned to the candidate problem road segment to which the previous problem sampling point belongs.

[0020] If the value is greater than the preset third threshold, the current problem sampling point will be assigned to a new candidate problem road segment.

[0021] In one embodiment, determining the cell to be optimized based on the problematic road segment includes:

[0022] For each problematic road segment, a target problematic sampling point is determined from the problematic road segment, and the neighboring cell corresponding to the target problematic sampling point is determined as the cell to be optimized for the problematic road segment; wherein, the target problematic sampling point is a sampling point where the difference between the reference signal received power of the neighboring cell and the reference signal received power of the primary serving cell is less than a preset fifth threshold.

[0023] In one embodiment, before adjusting the beam parameters of the cell to be optimized according to a preset strategy, the method further includes:

[0024] For each problematic road segment, the cells to be optimized corresponding to the problematic road segment are sorted in descending order of frequency of occurrence. Based on the sorting results, the beam parameters of the cells to be optimized are adjusted according to a preset strategy.

[0025] In one embodiment, adjusting the beam parameters of the cell to be optimized according to a preset strategy includes:

[0026] For each cell to be optimized, based on the drive test data, at least one first sampling point is obtained with the cell to be optimized as the primary serving cell, and at least one second sampling point and at least one third sampling point are obtained with the cell to be optimized as a neighboring cell and the reference signal received power of the corresponding neighboring cell is greater than a preset sixth threshold; wherein, the primary serving cell corresponding to the second sampling point is different from the primary serving cell corresponding to the third sampling point.

[0027] The first direction angle and first distance between the sampling point with the highest reference signal received power in the first sampling point and the base station, the second direction angle and second distance between the center of the first neighboring area composed of the second sampling points and the base station, and the third direction angle and third distance between the center of the second neighboring area composed of the third sampling points and the base station.

[0028] The beam parameters of the cell to be optimized are adjusted based on the comparison results by comparing the first azimuth angle with the second azimuth angle, the third azimuth angle, and / or the first distance with the second distance and the third distance.

[0029] In one embodiment, the beam parameters include at least one of the following: horizontal beamwidth, vertical beamwidth, azimuth angle, and downtilt angle; adjusting the beam parameters of the cell to be optimized based on the comparison results includes at least one of the following:

[0030] If the first directional angle is greater than the second directional angle and less than the third directional angle, and the difference between the third directional angle and the second directional angle is greater than the current horizontal beamwidth of the cell to be optimized, then the horizontal beamwidth of the cell to be optimized will be reduced.

[0031] If the first directional angle is greater than the third directional angle, the third directional angle is greater than the second directional angle, and the difference between the first directional angle and the third directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first directional angle is greater than the second directional angle, the second directional angle is greater than the third directional angle, and the difference between the first directional angle and the second directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the directional angle of the cell to be optimized will be increased by a preset first angle.

[0032] If the first azimuth angle is less than the third azimuth angle, the third azimuth angle is less than the second azimuth angle, and the difference between the third azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first azimuth angle is less than the second azimuth angle, the second azimuth angle is less than the third azimuth angle, and the difference between the second azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the azimuth angle of the cell to be optimized is reduced by a preset second angle.

[0033] If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, then the downtilt angle of the cell to be optimized is raised by a preset third angle.

[0034] If the minimum of the second distance and the third distance satisfies a second preset relationship with the first distance, then the downtilt angle of the cell to be optimized is reduced by a preset fourth angle.

[0035] If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, and the minimum value of the second distance and the third distance satisfies a second preset relationship with the first distance, then the vertical beamwidth of the cell to be optimized will be reduced.

[0036] Secondly, embodiments of this application provide a network optimization apparatus, including a processor, a memory, and a computer program stored in the memory and executable by the processor. When the computer program is executed by the processor, it implements the network optimization method described in any embodiment of this application.

[0037] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the network optimization method as described in any embodiment of this application.

[0038] The network optimization method, apparatus, and computer-readable storage medium provided in the above embodiments of this application acquire drive test data collected at multiple sampling points. The drive test data includes the reference signal received power and signal-to-interference-plus-noise ratio (SNR) and latitude and longitude information of the primary serving cell and neighboring cells corresponding to the sampling points. Based on the drive test data, problem segments are extracted from the sampling points to obtain at least one problem segment. Based on the problem segment, cells to be optimized are determined, and the beam parameters of the cells to be optimized are adjusted according to a preset strategy. Thus, by identifying problem segments based on the drive test data of the sampling points, and then identifying the cells requiring network optimization (i.e., interfering cells) based on the problem segments, and further adjusting the beam parameters of the interfering cells according to a preset strategy, the impact of interfering cells on problem segments can be reduced. This allows for convenient and quick optimization of network coverage quality and effectively reduces costs.

[0039] The computer-readable storage medium provided in the above embodiments is used to implement the network optimization method provided in the embodiments of this application, and has the same technical effects as the network optimization method. To avoid repetition, it will not be described again here. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a network optimization method in one embodiment of this application;

[0041] Figure 2 This is a schematic diagram of the sampling point distribution in one embodiment of this application. Figure 1 ;

[0042] Figure 3 This is a schematic diagram of the sampling point distribution in one embodiment of this application. Figure 2 ;

[0043] Figure 4 This is a schematic diagram illustrating the specific process of a network optimization method in one embodiment of this application;

[0044] Figure 5 This is a schematic diagram of the network optimization device in one embodiment of this application. Detailed Implementation

[0045] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] In the following description, the phrase "some embodiments" refers to a subset of all possible embodiments. It should be noted that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0048] In the following description, the terms "first, second, and third" are used merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0049] Please see Figure 1This application provides a network optimization method, which can be executed by a network optimization device provided in this application. The network optimization device can be implemented in software and / or hardware. The network optimization method includes, but is not limited to, steps S101, S102, and S103, as detailed below:

[0050] Step S101: Obtain drive test data collected at multiple sampling points. The drive test data includes the reference signal received power and signal-to-interference-plus-noise ratio of the primary serving cell and neighboring cells corresponding to the sampling points, as well as latitude and longitude information.

[0051] Understandably, in mobile communications, after a cell is established, each cell is assigned a reasonable coverage area. However, the actual coverage area of ​​a cell often exceeds its assigned reasonable coverage area, leading to signal interference between cells and overlapping coverage, which affects the network coverage quality in certain areas. Therefore, to test the network quality of one or more areas, testers can use road testing methods and equipment such as frequency sweepers to test different locations and collect road test data at multiple sampling points. It should be noted that the sampling points can be pre-set or randomly determined according to the actual scenario. The road test data may include the reference signal received power and signal-to-interference-plus-noise ratio of the primary serving cell corresponding to the sampling point, the reference signal received power and signal-to-interference-plus-noise ratio of neighboring cells, latitude and longitude information, and cell physical identification code. Among them, the reference signal received power and signal-to-interference-plus-noise ratio of the primary serving cell and neighboring cells corresponding to the sampling point can be used to characterize the signal quality at the location of the sampling point, while the latitude and longitude information corresponding to the sampling point can be used to characterize the specific location of the sampling point.

[0052] Optionally, after acquiring the road test data collected at multiple sampling points, the method further includes:

[0053] The drive test data is associated with physical cell identification codes based on engineering parameters, and global cell identification codes are associated with the physical cell identification codes of the sampling points.

[0054] Understandably, the engineering parameters are information related to drive testing, including but not limited to the site's Global Cell Identifier (GCI), Physical Cell Identifier (PCI), cell name, equipment type, manufacturer, mounting height, azimuth angle, and latitude and longitude. After acquiring drive test data collected from the sampling points, the PCI of the sampling points contained in the drive test data can be correlated with the engineering parameters to obtain the GCI associated with the PCI of the sampling point, which is the GCI corresponding to the sampling point. This allows for rapid determination of the GCI corresponding to a sampling point, further improving the convenience of optimizing network coverage quality.

[0055] Step S102: Extract problem road segments from the sampling points based on the road test data to obtain at least one problem road segment.

[0056] Understandably, multiple adjacent sampling points may all have poor network quality. In this case, these sampling points can be divided into road segments, that is, sampling points with poor network quality and adjacent locations are grouped into the same problem road segment. Optionally, the step of extracting problem road segments from the sampling points based on the drive test data to obtain at least one problem road segment includes: identifying sampling points whose reference signal received power of the primary serving cell is greater than a preset first threshold and whose interference plus noise ratio is less than a preset second threshold as problem sampling points; dividing the problem sampling points into road segments based on the latitude and longitude information, so as to group the problem sampling points whose distance interval is less than a preset third threshold into the same candidate problem road segment; and selecting the candidate problem road segment whose road segment length is greater than a preset fourth threshold as the problem road segment. Optionally, the first threshold and the second threshold can be set according to actual needs. For example, the first threshold can be set to -90dBm, -95dBm, and the second threshold can be set to 6, etc. Since the latitude and longitude information corresponding to a sampling point can characterize its location, based on the latitude and longitude information of different problem sampling points, problem sampling points with a distance interval less than a preset third threshold can be grouped into the same candidate problem road segment. That is, problem sampling points that are close to each other are grouped into the same candidate problem road segment. The third threshold and the fourth threshold can be set according to actual needs. For example, the third threshold can be set to 10 meters, 15 meters, or 20 meters, and the fourth threshold can be set to 30 meters, 40 meters, etc. Understandably, some candidate problem road segments may have shorter segment lengths, indicating that the network quality of these candidate problem road segments may be affected by external environmental factors. Therefore, these candidate problem road segments can be disregarded when determining the cells to be optimized. Since drive test data is usually collected in order of location, the segment length can be the distance interval between the location of the first sampling point and the location of the last sampling point in the segment. The first sampling point can be the sampling point with the earliest sampling time in the segment, and the last sampling point can be the sampling point with the latest sampling time in the segment. Furthermore, the road segment length can also be obtained by calculating the distance interval between adjacent sampling points in the road segment according to the sampling time sequence. It should be noted that the road test data may also include sampling time, sampling number, etc. In this way, by first selecting problem sampling points and then determining problem road segments based on these points, the convenience of optimizing network coverage quality is further improved.

[0057] Optionally, the step of dividing the problem sampling points into road segments based on the latitude and longitude information, so as to group the problem sampling points with a distance interval less than a preset third threshold into the same candidate problem road segment, includes:

[0058] The problem sampling points are sorted according to the sampling time to obtain the sorted problem sampling points;

[0059] The sorted problem sampling points are traversed, and the distance between the current problem sampling point and the previous problem sampling point is determined based on the latitude and longitude information to see if it is less than or equal to a preset third threshold.

[0060] If the current problem sampling point is less than or equal to the preset third threshold, then the current problem sampling point will be assigned to the candidate problem road segment to which the previous problem sampling point belongs.

[0061] If the value is greater than the preset third threshold, the current problem sampling point will be assigned to a new candidate problem road segment.

[0062] Understandably, since the sampling times of each sampling point are different when collecting road test data, but there is a correlation between their locations, sorting the problem sampling points according to their sampling time facilitates the subsequent segmentation of these problem sampling points. Specifically, the sorted problem sampling points are traversed. First, the first problem sampling point, i.e., the one with the earliest sampling time among all problem sampling points, is assigned to the first candidate problem segment. Then, the second problem sampling point is taken as the current problem sampling point. Based on the latitude and longitude information, it is determined whether the distance between the current problem sampling point and the previous problem sampling point, i.e., the first problem sampling point, is less than or equal to a preset third threshold. If so, the current problem sampling point is assigned to the candidate problem segment to which the previous problem sampling point belongs, i.e., the first candidate problem segment. Otherwise, the current problem sampling point is assigned to a new... The candidate problem road segment is the second candidate problem road segment; then, the third problem sampling point is taken as the current problem sampling point. Based on the latitude and longitude information, it is determined whether the distance between the current problem sampling point and the previous problem sampling point, i.e., the second problem sampling point, is less than or equal to a preset third threshold. If so, the current problem sampling point is assigned to the candidate problem road segment to which the previous problem sampling point belongs, i.e., the candidate problem road segment to which the second problem sampling point belongs. Otherwise, the current problem sampling point is assigned to a new candidate problem road segment, i.e., the third candidate problem road segment. This process is repeated until the last problem sampling point is reached. In other words, the first problematic sampling point is assigned to the first candidate problem road segment. If the distance between the second and first problematic sampling points is less than or equal to a preset third threshold, the second problematic sampling point is assigned to the first candidate problem road segment. Similarly, if the distance between the third and second problematic sampling points is less than or equal to the preset third threshold, the third problematic sampling point is also assigned to the first candidate problem road segment. If the distance between the second and first problematic sampling points is greater than the preset third threshold, the second problematic sampling point is assigned to the second candidate problem road segment. This process continues until all problematic sampling points have been traversed. This allows for accurate and rapid identification of problematic road segments with poor network coverage, further improving the convenience of optimizing network coverage quality.

[0063] Step S103: Determine the cell to be optimized based on the problem road segment, and adjust the beam parameters of the cell to be optimized according to a preset strategy.

[0064] Here, the cell to be optimized can be considered as an interfering cell in the problematic road segment. Optionally, determining the cell to be optimized based on the problematic road segment includes: for each problematic road segment, determining a target problematic sampling point from the problematic road segment, and determining the neighboring cells corresponding to the target problematic sampling point as the cells to be optimized for the problematic road segment; wherein, the target problematic sampling point is a sampling point where the difference between the reference signal received power of the neighboring cell and the reference signal received power of the primary serving cell is less than a preset fifth threshold. It can be understood that if the difference between the reference signal received power of the neighboring cell corresponding to a sampling point and the reference signal received power of the primary serving cell is small, it indicates that the neighboring cell has a large interference with the sampling point. The fifth threshold can be set according to actual needs, for example, the fifth threshold can be set to 3dBm, 5dBm, 9dBm, etc. For each problematic road segment, there may be multiple target problematic sampling points determined from the problematic road segment, and correspondingly, there may be multiple cells to be optimized for the problematic road segment. Thus, determining the cell to be optimized based on the reference signal received power of the primary serving cell and neighboring cells corresponding to the sampling points in the problematic road segment is simple and convenient.

[0065] Optionally, before adjusting the beam parameters of the cell to be optimized according to a preset strategy, the method further includes:

[0066] For each problematic road segment, the cells to be optimized corresponding to the problematic road segment are sorted in descending order of frequency of occurrence. Based on the sorting results, the beam parameters of the cells to be optimized are adjusted according to a preset strategy.

[0067] Understandably, for each problematic road segment, there may be multiple target problem sampling points identified within that segment. The neighboring cells corresponding to different target problem sampling points may be the same or different. In this case, after determining the neighboring cells corresponding to each target problem sampling point as the cells to be optimized for that problematic road segment, the number of times the neighboring cells corresponding to different target problem sampling points are the same cell can be counted. That is, the occurrence count of the cells to be optimized for that problematic road segment can be counted. Then, the cells to be optimized for that problematic road segment are sorted in descending order of occurrence count. Finally, based on the sorting results, the beam parameters of the cells to be optimized are adjusted according to a preset strategy. For example, suppose a problematic road segment contains target problem sampling points a, b, c, d, e, and f, and the neighboring cells corresponding to sampling points a and b are cell A, the neighboring cell corresponding to sampling point c is cell B, and the neighboring cells corresponding to sampling points d, e, and f are cell C. In this case, the cells to be optimized for that problematic road segment can be arranged in the order C, A, and B. It should be noted that the more frequently a problematic road segment appears, the greater the overlapping coverage interference of that cell on the problematic road segment, meaning that the beam parameters of that cell need to be adjusted as soon as possible. Furthermore, for each problematic road segment, the cells to be optimized corresponding to that segment are sorted in descending order of frequency of occurrence, or they can be sorted based on the frequency exceeding a preset proportion. For example, for a problematic road segment, if the frequency of several cells to be optimized exceeds a preset proportion for all cells to be optimized corresponding to that problematic road segment, these cells can be sorted in descending order of frequency of occurrence. This sorting based on the frequency of occurrence allows for the priority or rapid adjustment of cells that have a significant impact on network coverage quality, further improving the convenience of optimizing network coverage quality.

[0068] Optionally, adjusting the beam parameters of the cell to be optimized according to a preset strategy includes:

[0069] For each cell to be optimized, based on the drive test data, at least one first sampling point is obtained with the cell to be optimized as the primary serving cell, and at least one second sampling point and at least one third sampling point are obtained with the cell to be optimized as a neighboring cell and the reference signal received power is greater than a preset sixth threshold; wherein, the primary serving cell corresponding to the second sampling point is different from the primary serving cell corresponding to the third sampling point.

[0070] The first direction angle and first distance between the sampling point with the highest reference signal received power in the first sampling point and the base station, the second direction angle and second distance between the center of the first neighboring area composed of the second sampling points and the base station, and the third direction angle and third distance between the center of the second neighboring area composed of the third sampling points and the base station.

[0071] The beam parameters of the cell to be optimized are adjusted based on the comparison results by comparing the first azimuth angle with the second azimuth angle, the third azimuth angle, and / or the first distance with the second distance and the third distance.

[0072] Understandably, since the drive test data for each sampling point includes information such as the primary serving cell, neighboring cells, the reference signal received power and signal-to-interference-plus-noise ratio of the primary serving cell, the reference signal received power of the neighboring cells, and latitude and longitude information, at least one first sampling point with the cell to be optimized as the primary serving cell, at least one second sampling point with the cell to be optimized as a neighboring cell and the reference signal received power of the corresponding neighboring cell being greater than a preset sixth threshold, and at least one third sampling point can be obtained based on the drive test data. The primary serving cell corresponding to the second sampling point is different from the primary serving cell corresponding to the third sampling point. It should be noted that due to the influence of adjacent or overlapping cell coverage, other sampling points around a sampling point with one cell as the primary serving cell may be located in different cells. For example, assuming the cell to be optimized is cell A, the primary serving cell of the first sampling point a is cell A, the neighboring cell of the second sampling point b is cell B, and the neighboring cell of the third sampling point c is cell C, then cell B and cell C may be located to the left or right or in front or behind cell A, respectively. The sixth threshold can be set according to actual needs. For example, the sixth threshold can be set to -100dBm, -105dBm, etc.

[0073] It should be noted that the first neighboring cell area can be considered as composed of multiple second sampling points identical to the primary serving cell, and the width of the first neighboring cell area can be considered as the distance interval between the two second sampling points located farthest apart within the first neighboring cell area. The second neighboring cell area can be considered as composed of multiple third sampling points identical to the primary serving cell, and the width of the second neighboring cell area can be considered as the distance interval between the two third sampling points located farthest apart within the second neighboring cell area. The seventh threshold can be set according to actual needs, for example, the seventh threshold can be set to 30 meters, 40 meters, etc. The acquisition of the azimuth angle and the distance can refer to existing technologies and will not be elaborated here. Thus, by adjusting the beam parameters of the cell to be optimized according to the coverage of the cell to be optimized, accurate optimization of the network quality of problem road segments can be achieved, further improving the accuracy and convenience of optimizing network coverage quality.

[0074] Optionally, the beam parameters include at least one of the following: horizontal beamwidth, vertical beamwidth, azimuth angle, and downtilt angle; adjusting the beam parameters of the cell to be optimized based on the comparison results includes at least one of the following:

[0075] If the first directional angle is greater than the second directional angle and less than the third directional angle, and the difference between the third directional angle and the second directional angle is greater than the current horizontal beamwidth of the cell to be optimized, then the horizontal beamwidth of the cell to be optimized will be reduced.

[0076] If the first directional angle is greater than the third directional angle, the third directional angle is greater than the second directional angle, and the difference between the first directional angle and the third directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first directional angle is greater than the second directional angle, the second directional angle is greater than the third directional angle, and the difference between the first directional angle and the second directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the directional angle of the cell to be optimized will be increased by a preset first angle.

[0077] If the first azimuth angle is less than the third azimuth angle, the third azimuth angle is less than the second azimuth angle, and the difference between the third azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first azimuth angle is less than the second azimuth angle, the second azimuth angle is less than the third azimuth angle, and the difference between the second azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the azimuth angle of the cell to be optimized is reduced by a preset second angle.

[0078] If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, then the downtilt angle of the cell to be optimized is raised by a preset third angle.

[0079] If the minimum of the second distance and the third distance satisfies a second preset relationship with the first distance, then the downtilt angle of the cell to be optimized is reduced by a preset fourth angle.

[0080] If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, and the minimum value of the second distance and the third distance satisfies a second preset relationship with the first distance, then the vertical beamwidth of the cell to be optimized will be reduced.

[0081] Here, shrinking the horizontal beamwidth of the cell to be optimized can be done by shrinking it by a preset angle value, such as 2 degrees, 5 degrees, etc. The preset first angle, the preset second angle, the preset third angle, and the preset fourth angle can be set according to actual needs; for example, the preset first angle can be set to 10 degrees, the preset second angle can be set to 10 degrees, the preset third angle can be set to 3 degrees, and the preset fourth angle can be set to 3 degrees, etc. Shrinking the vertical beamwidth of the cell to be optimized can also be done by shrinking it by a preset angle value, such as 2 degrees, 5 degrees, etc. The current horizontal beamwidth of the cell to be optimized can be obtained in advance and included in the engineering parameters.

[0082] See Figure 2 The diagram shows the distribution of sampling points. Solid black dots represent the first sampling point, with the cell to be optimized as the primary serving cell. Blank dots on the left represent the second sampling point, with the cell to be optimized as a neighboring cell. Blank dots on the right represent the third sampling point, with the cell to be optimized as a neighboring cell. Indicates the first direction angle. Indicates the second direction angle. Indicates the third-direction angle, Indicates the first distance. Indicates the second distance. Indicates the third distance.

[0083] Wherein, the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, which can be expressed as follows:

[0084]

[0085] in, It means to hang high.

[0086] The minimum of the second distance and the third distance satisfies a second preset relationship with the first distance, which can be expressed as follows:

[0087] .

[0088] In this way, by simply adjusting the beam parameters of the cell to be optimized, i.e. the interfering cell on the problem road segment, the impact of the interfering cell on the problem road segment can be quickly reduced.

[0089] In summary, the network optimization method provided in this application determines problem road segments based on drive test data from sampling points, then determines the cells that need network optimization based on the problem road segments, i.e., the interfering cells that affect the problem road segments, and then adjusts the beam parameters of the interfering cells according to a preset strategy to reduce the impact of the interfering cells on the problem road segments. This method can conveniently and quickly optimize network coverage quality and effectively reduce costs.

[0090] To provide a more systematic understanding of the network optimization method provided in the embodiments of this application, specific examples are given below.

[0091] The main implementation principle of the network optimization method provided in this embodiment is as follows:

[0092] (I) Preparation of Road Test Data

[0093] Drive test data is converted into a drive test sampling point table and presented on a map based on latitude and longitude and an electronic map. If there is significant data loss, the nearest frequency scan data within a certain time period is searched, and neighboring cell information is supplemented accordingly. Simultaneously, the drive test data is associated with Physical Cell Identification (PCI) based on engineering parameters, and the PCI of the drive test data is associated with the Cell Global Identifier (CGI), generating a line graph.

[0094] Here, existing routine road test software such as Dingli can be used to export road test data, which can then be converted into a road test sampling point table. The electronic map can include Mapinfo, ArCGis, etc. The engineering parameters can include information such as the site's CGI, PCI, cell name, equipment type, manufacturer, mounting height, direction angle, and latitude and longitude. Specifically, PCI association is performed on the road test data based on the engineering parameters, and the PCI of the road test data is associated with the CGI. This can be achieved by selecting the nearest PCI site around the sampling point as the target cell based on the PCI of the sampling point in the road test data; the CGI of this target cell is then the target CGI.

[0095] (II) Generation of Problem Road Sections

[0096] Problem points are defined as drive test points or sampling points where the Reference Signal Received Power (RSRP) of the primary serving cell is higher than -90dBm and the Signal to Interference plus Noise Ratio (SINR) is less than 6. Problem sampling points (hereinafter referred to as problem points) are selected from the drive test data and then sorted according to the order of sampling time.

[0097] The first problem point belongs to the first problem segment, and the process continues from the second problem point. If the distance between the current problem point and the previous problem point is less than 15 meters, the current problem point is merged into the problem segment to which the previous problem point belongs; otherwise, a new problem segment is generated, and the current problem point is assigned to the new problem segment.

[0098] For each problem road segment obtained, the distance between the beginning and end of each problem road segment is calculated by combining time and latitude and longitude information to obtain the length of the problem road segment, and problem road segments with a length of less than 30 meters are eliminated.

[0099] (III) Optimization of road sections with poor SINR values

[0100] For all problematic road sections in (II), the following procedures shall be performed in sequence:

[0101] (1) For each row of problem points in the problem road segment, calculate the difference in RSRP between the neighboring cell and the primary serving cell. If the difference is less than 5 dBm, add the neighboring cell to the candidate cell list. After calculating the problem sampling points in the entire problem road segment, count the number of times the candidate cells appear in the candidate cell list. If the number of times a candidate cell appears exceeds 20%, sort these candidate cells. Each row of problem points is a sampling point. The sorting is based on the severity of interference to the problem road segment from all neighboring cells, with more severe interference ranking higher.

[0102] (2) Select the first cell as the cell to be optimized, find the road segment where the cell is the main serving cell from the entire drive test data, and find the sampling point where the cell is a neighboring cell and the corresponding RSRP is greater than -100dBm.

[0103] (3) Based on the location relationship between the cell as the primary cell and neighboring cells, and in conjunction with the location of the base station, the direction of cell adjustment is determined. The specific determination principles are as follows:

[0104] a) Please refer to again Figure 2 Connect the strongest RSRP sampling point in the main service segment to the base station (site), and take its direction angle as... The distance is Areas that are adjacent to each other and extend for more than 30 meters are numbered. The center of each adjacent area is connected to the base station, and the azimuth angles are calculated. , ..., the distance is , , ...

[0105] b) If and , If the horizontal beamwidth is [not specified], then the horizontal beamwidth of the cell will be reduced. See [reference needed]. Figure 3 (a) When the azimuth angle of the cell where the primary serving sampling point is located is between the azimuth angles of the two neighboring cell regions where the neighboring cell sampling point is located, the horizontal beamwidth of the cell is reduced. Here, the primary serving sampling point refers to the sampling point with the cell as the primary serving cell, while the neighboring cell sampling point refers to the sampling point with the cell as a neighboring cell.

[0106] if and or and Then the azimuth angle of the cell will be increased by 10 degrees. (See also...) Figure 3 (b) When the cell where the main service sampling point is located is to the right of the neighboring cell area where the neighboring sampling point is located, the directional angle of the cell will be turned to the left.

[0107] if and or and Then the azimuth angle of the cell will be reduced by 10 degrees. (See also...) Figure 3 (c) When the cell where the main service sampling point is located is to the left of the neighboring cell area where the neighboring sampling point is located, the directional angle of the cell is turned to the right.

[0108] If the formula is satisfied

[0109] The slope angle of the residential area will then be increased by 3 degrees. (See also...) Figure 3 (d) The first distance corresponding to the main service sampling point of the cell and the second and third distances corresponding to the neighboring cell areas satisfy the formula. At that time, the downward tilt angle of the community was raised.

[0110] If the formula is satisfied

[0111]

[0112] This will reduce the downslope angle of the residential area by 3 degrees. (See also...) Figure 3 (e) The first distance corresponding to the main service sampling point of the cell and the second and third distances corresponding to the neighboring cell areas satisfy the formula At that time, the downward tilt angle of the lowered community is reduced.

[0113] If the formula and If all conditions are met, the vertical beamwidth of the cell will be reduced.

[0114] (4) Following the above method, adjust the second cell in the candidate cell list until all cells in the candidate cell list for the problem road segment are completed.

[0115] (5) After all problem road sections have been calculated, the solution is output and the road test optimization is completed.

[0116] Based on the main implementation principles of the above network optimization methods, please refer to... Figure 4 The following is a schematic diagram of a network optimization method provided in an embodiment of this application, including the following steps:

[0117] Step S201: Based on the PCI in the drive test data, associate the primary serving cell of the drive test sampling point according to the principle of closest distance;

[0118] Step S202: Filter out problematic road sections from the road test data that have an RSRP value greater than -95dBm, a SINR value less than 6, and a length exceeding 50 meters;

[0119] Step S203: Perform the following iterative operation according to the problem road segment number;

[0120] Step S204: Locate two interfering cells in the problem segment whose average RSRP value differs from that of the primary serving cell by less than 3 dBm;

[0121] Step S205: For each interfering cell, determine whether the main serving segment of the interfering cell has been found. If yes, proceed to step S206; otherwise, proceed to step S208.

[0122] Step S206: Determine the coverage direction of the interfering cell by combining other signals from the interfering cell;

[0123] Step S207: Strengthen the main service segment of the interfering cell and weaken other interfering segments;

[0124] Step S208: Combine other signals from the interfering cell to weaken the overall cell signal;

[0125] Step S209: The interference cell adjustment for the problematic road segment is completed. Return to step S203.

[0126] In summary, the network optimization method provided in this application analyzes tabular data generated by routine drive test tools, analyzes common coverage issues such as continuous SINR value differences, identifies and analyzes the coverage of adjustable 5G NR sites in the surrounding area, and automatically resolves local coverage problem points by adjusting weight parameters using expert experience algorithms. That is, using drive test data as the primary data source and leveraging expert experience algorithms, it analyzes the actual coverage of problem road segments and improves the SINR value index in the drive test data by changing the coverage range of major neighboring cells and reducing their impact on the main coverage cell. This method is highly efficient and accurate. In other words, it analyzes interfering cells in problem road segments and automatically improves their coverage by reducing their impact on those segments.

[0127] Based on the same inventive concept as the foregoing embodiments, this invention provides a network optimization device, such as... Figure 5 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 5 The processor 310 shown in the diagram does not indicate that there is only one processor 310, but only indicates the positional relationship of the processor 310 relative to other devices. In practical applications, there can be one or more processors 310; similarly, Figure 5 The memory 311 shown in the diagram has the same meaning, that is, it is only used to indicate the positional relationship of memory 311 relative to other devices. In practical applications, there can be one or more memories 311. When the processor 310 runs the computer program, the network optimization method applied to the above-described device is implemented.

[0128] The device may also include at least one network interface 312. The various components of the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to implement communication between these components. In addition to a data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general designated all buses as Bus System 313.

[0129] The memory 311 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 311 described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0130] The memory 311 in this embodiment of the invention is used to store various types of data to support the operation of the device. Examples of such data include: any computer programs used to operate on the device, such as operating systems and applications; contact data; phonebook data; messages; pictures; videos, etc. The operating system includes various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications, such as media players, browsers, etc., used to implement various application services. Here, the program implementing the method of this embodiment of the invention can be included in the application.

[0131] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described network optimization method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A network optimization method, characterized in that, The method includes: Acquire drive test data collected at multiple sampling points, the drive test data including the reference signal received power and signal-to-interference-plus-noise ratio, latitude and longitude information of the primary serving cell and neighboring cells corresponding to the sampling points; Based on the road test data, problem road segments are extracted from the sampling points to obtain at least one problem road segment; Based on the problematic road segments, cells to be optimized are identified, and the beam parameters of the cells to be optimized are adjusted according to a preset strategy. Specifically, for each problematic road segment, a target problematic sampling point is determined from the problematic road segment, and the neighboring cells corresponding to the target problematic sampling point are determined as the cells to be optimized for the problematic road segment. The target problematic sampling point is a sampling point where the difference between the reference signal received power of the neighboring cell and the reference signal received power of the primary serving cell is less than a preset fifth threshold. For each cell to be optimized, based on the drive test data, at least one first sampling point with the cell to be optimized as the primary serving cell, at least one second sampling point with the cell to be optimized as a neighboring cell and the reference signal received power of the corresponding neighboring cell being greater than a preset sixth threshold, and at least one... The third sampling point; wherein the primary serving cell corresponding to the second sampling point is different from the primary serving cell corresponding to the third sampling point; the first directional angle and first distance between the sampling point with the highest reference signal received power in the first sampling points and the base station, the second directional angle and second distance between the center of the first neighboring cell region composed of the second sampling points and the base station with a width greater than a preset seventh threshold, and the third directional angle and third distance between the center of the second neighboring cell region composed of the third sampling points and the base station with a width greater than a preset seventh threshold; the first directional angle is compared with the second directional angle, the third directional angle, and / or the first distance is compared with the second distance, the third distance, and the beam parameters of the cell to be optimized are adjusted according to the comparison results; The beam parameters include at least one of the following: horizontal beamwidth, vertical beamwidth, azimuth angle, and downtilt angle; adjusting the beam parameters of the cell to be optimized based on the comparison results includes at least one of the following: If the first directional angle is greater than the second directional angle and less than the third directional angle, and the difference between the third directional angle and the second directional angle is greater than the current horizontal beamwidth of the cell to be optimized, then the horizontal beamwidth of the cell to be optimized will be reduced. If the first directional angle is greater than the third directional angle, the third directional angle is greater than the second directional angle, and the difference between the first directional angle and the third directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first directional angle is greater than the second directional angle, the second directional angle is greater than the third directional angle, and the difference between the first directional angle and the second directional angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the directional angle of the cell to be optimized will be increased by a preset first angle. If the first azimuth angle is less than the third azimuth angle, the third azimuth angle is less than the second azimuth angle, and the difference between the third azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, or if the first azimuth angle is less than the second azimuth angle, the second azimuth angle is less than the third azimuth angle, and the difference between the second azimuth angle and the first azimuth angle is greater than half of the current horizontal beamwidth of the cell to be optimized, then the azimuth angle of the cell to be optimized is reduced by a preset second angle. If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, then the downtilt angle of the cell to be optimized is raised by a preset third angle; wherein, the first preset relationship is that the maximum value of the second distance and the third distance satisfies the following formula with respect to the first distance: in, Indicates hanging high; Indicates the first distance; Indicates the second distance; Indicates the third distance; If the minimum of the second distance and the third distance satisfies a second preset relationship with the first distance, then the downtilt angle of the cell to be optimized is reduced by a preset fourth angle; wherein, the second preset relationship is that the minimum of the second distance and the third distance satisfies the following formula with respect to the first distance: If the maximum value of the second distance and the third distance satisfies a first preset relationship with the first distance, and the minimum value of the second distance and the third distance satisfies a second preset relationship with the first distance, then the vertical beamwidth of the cell to be optimized will be reduced.

2. The method as described in claim 1, characterized in that, After acquiring the road test data collected at multiple sampling points, the process also includes: The drive test data is associated with physical cell identification codes based on engineering parameters, and global cell identification codes are associated with the physical cell identification codes of the sampling points.

3. The method as described in claim 1, characterized in that, The step of extracting problem road segments from the sampling points based on the road test data to obtain at least one problem road segment includes: The sampling points in which the reference signal received power of the primary serving cell is greater than a preset first threshold and the signal-to-interference-plus-noise ratio is less than a preset second threshold are identified as problematic sampling points. The problem sampling points are divided into road segments based on the latitude and longitude information, so that the problem sampling points with a distance interval of less than a preset third threshold are assigned to the same candidate problem road segment; Candidate problem road segments whose length is greater than a preset fourth threshold are selected as problem road segments.

4. The method as described in claim 3, characterized in that, The step of dividing the problem sampling points into road segments based on the latitude and longitude information, so as to group the problem sampling points with a distance interval less than a preset third threshold into the same candidate problem road segment, includes: The problem sampling points are sorted according to the sampling time to obtain the sorted problem sampling points; The sorted problem sampling points are traversed, and the distance between the current problem sampling point and the previous problem sampling point is determined based on the latitude and longitude information to see if it is less than or equal to a preset third threshold. If the current problem sampling point is less than or equal to the preset third threshold, then the current problem sampling point is assigned to the candidate problem road segment to which the previous problem sampling point belongs; If the value is greater than the preset third threshold, the current problem sampling point will be assigned to a new candidate problem road segment.

5. The method as described in claim 1, characterized in that, Before adjusting the beam parameters of the cell to be optimized according to the preset strategy, the method further includes: For each problematic road segment, the cells to be optimized corresponding to the problematic road segment are sorted in descending order of frequency of occurrence. Based on the sorting results, the beam parameters of the cells to be optimized are adjusted according to a preset strategy.

6. A network optimization device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the network optimization method as described in any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the network optimization method as described in any one of claims 1 to 5.

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