Antenna azimuth angle adjustment method based on 5G user distribution and related equipment

Abstract

The embodiment of the invention discloses an antenna azimuth angle adjustment method based on 5G user distribution and related equipment, and aims to solve the problem of network coverage caused by mismatching of an antenna azimuth angle and a user main distribution area. The method comprises the following steps: acquiring measurement report data of a target cell, wherein the measurement report data at least comprises horizontal arrival angle information of a 5G user sampling point; determining the actual distribution angle of the 5G user sampling points according to the horizontal angle of arrival information and the antenna azimuth angle of the target cell; performing ascending sorting on the 5G user sampling points according to the actual distribution angle, and determining a suggested adjustment azimuth angle of an antenna azimuth angle by adopting a median algorithm based on a sorting result; and adjusting the antenna azimuth according to the suggested azimuth.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to an antenna azimuth adjustment method and related equipment based on 5G user distribution. Background Technology

[0002] In 5G network deployment, the antenna azimuth / electronic azimuth of the base station antenna in the serving cell determines the pointing direction of its main beam, directly affecting the coverage area of ​​the serving cell and the quality of service for users. The antenna azimuth / electronic azimuth refers to the angle between the direction of the antenna's main lobe and true north.

[0003] In practical applications, due to factors such as planning deviations, parameter input errors, or environmental changes, there may be a mismatch between the antenna azimuth angle and the user's main distribution area due to unreasonable antenna azimuth angle settings. In other words, the antenna main lobe direction may fail to cover the user's main distribution area, ultimately resulting in poor network coverage and affecting user experience.

[0004] Therefore, how to solve the network coverage problem caused by the mismatch between the antenna azimuth / electronic azimuth and the main distribution area of ​​users is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides an antenna azimuth adjustment method based on 5G user distribution to solve the network coverage problem caused by the mismatch between the antenna azimuth and the main distribution area of ​​users in the prior art.

[0006] This application also provides an antenna azimuth adjustment device based on 5G user distribution, an electronic device, a computer-readable storage medium, and a computer program product.

[0007] The embodiments of this application adopt the following technical solutions: In a first aspect, embodiments of this application provide an antenna azimuth adjustment method based on 5G user distribution, including: Obtain measurement report data for the target cell. The measurement report data shall include at least the horizontal angle of arrival information of the 5G user sampling points. The actual distribution angle of 5G user sampling points is determined based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell; The 5G user sampling points are sorted in ascending order according to the actual distribution angle, and the median algorithm is used to determine the suggested adjustment of the antenna azimuth angle based on the sorting results. Adjust the antenna azimuth angle as recommended.

[0008] Secondly, embodiments of this application provide an antenna azimuth adjustment device based on 5G user distribution, including an acquisition module, a determination module, a processing module, and an adjustment module, wherein: The acquisition module is used to acquire measurement report data of the target cell. The measurement report data includes at least the horizontal angle of arrival information of the 5G user sampling points. The determination module is used to determine the actual distribution angle of 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell; The processing module is used to sort the 5G user sampling points in ascending order according to the actual distribution angle, and to determine the suggested adjustment azimuth angle of the antenna azimuth angle based on the sorting result using the median algorithm. The adjustment module is used to adjust the antenna azimuth angle according to the recommendations.

[0009] Thirdly, embodiments of this application provide an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the antenna azimuth adjustment method based on 5G user distribution as described above.

[0010] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the antenna azimuth adjustment method based on 5G user distribution as described above.

[0011] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the antenna azimuth adjustment method based on 5G user distribution as described above.

[0012] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The method provided in this application obtains the horizontal angle of arrival information of 5G user sampling points and determines their actual distribution angle. Then, the median algorithm is used to calculate the suggested adjustment value of the antenna azimuth angle. This enables the adjustment of the antenna azimuth angle to be accurately aligned with the angle center of the actual user distribution, so that the antenna main lobe direction can more accurately cover the main distribution area of ​​users. This effectively solves the network coverage problem caused by the mismatch between the antenna azimuth angle and the user distribution, and improves user perception and network service quality. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1A schematic diagram illustrating the implementation process of an antenna azimuth adjustment method based on 5G user distribution provided in this application embodiment; Figure 2 This is a schematic diagram illustrating the implementation process of a method for correcting the azimuth angle of an antenna, provided in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the implementation process of a method for determining a suggested azimuth angle adjustment for an antenna, provided in an embodiment of this application. Figure 4 A schematic diagram illustrating the adjustment of antenna azimuth angle provided in an embodiment of this application; Figure 5 This is a schematic diagram illustrating the implementation process of a method for determining whether an antenna azimuth angle is abnormal, provided in an embodiment of this application. Figure 6 This is a schematic diagram illustrating the implementation process of a method for determining antenna anomalies provided in an embodiment of this application; Figure 7 This application provides a schematic diagram illustrating the implementation process of a method for constructing a difference relationship table. Figure 8 A schematic diagram illustrating the consistency of RSRP difference between 4G and 5G co-located cells under the same propagation path, provided for an embodiment of this application; Figure 9 This application provides a schematic diagram of the specific structure of an antenna azimuth adjustment device based on 5G user distribution. Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0016] To address the network coverage issues caused by the mismatch between antenna azimuth angle and the actual geographical distribution of users in existing technologies, this application provides an antenna azimuth angle adjustment method based on 5G user distribution.

[0017] The execution subject of this method can be various types of computing devices, or it can be an application or app installed on the computing device. The computing device can be a user terminal such as a mobile phone, tablet computer, or smart wearable device, or it can be a server.

[0018] For ease of description, this application uses a server as the execution subject of the method in its embodiments to illustrate the method. Those skilled in the art will understand that this embodiment uses a server as an example to describe the method, which is merely an illustrative example and does not limit the scope of protection of the corresponding claims.

[0019] Specifically, the implementation flow of the method provided in this application embodiment is as follows: Figure 1 As shown, steps 102 to 108 are included: Step 102: Obtain the measurement report data of the target cell. The measurement report data shall include at least the horizontal angle of arrival information of the 5G user sampling points.

[0020] In this embodiment of the application, the target cell refers to the 5G serving cell for which antenna azimuth angle analysis and adjustment are to be performed. It can be uniquely determined by one or more parameters such as the cell's global identifier, such as CGI / NR Cell Identity, Physical-layer Cell Identity (PCI), and center frequency / carrier frequency identifier, such as NR ARFCN / frequency number.

[0021] 5G user sampling points refer to signal measurement data points with geographical locations that are triggered and reported by the network side when a 5G user terminal accesses the 5G network.

[0022] Measurement report data can be obtained from the raw measurement report output (MRO) data collected by the network management / operation and maintenance system. Specifically, 5G MRO data and 4G MRO data and corresponding signaling data covering the target cell can be extracted from the network management side, and then the MRO fields are parsed to obtain the measurement report data of the target cell.

[0023] In this embodiment of the application, the measurement report data includes at least horizontal angle of arrival information. This horizontal angle of arrival information is the Horizontal Angle of Arrival (HAOA) field in the Measurement Report, which is a coded horizontal angle of arrival value, not a directly usable angle, used to characterize the relative position of the signal's direction of arrival.

[0024] In some embodiments, the measurement report data may also include one or more of the following information: (1) User identification information of 5G user sampling points, such as the AMFUENGAP application identifier Amfuengapid used to distinguish different users; (2) Timestamps corresponding to 5G user sampling; Each sampling point corresponds to at least one timestamp and an identifier used to distinguish 5G user terminals / sessions, such as the AMFUENGAP application identifier Amfuengapid, which allows multiple signal measurement data points generated by the same user terminal within a continuous time period to be grouped into the same user's sampling set.

[0025] (3) Target cell measurement information may include, but is not limited to, the Reference Signal Receiving Power (RSRP), Reference Signal Received Quality (RSRQ), center frequency, and Physical Cell Identifier (PCI) of the primary serving cell.

[0026] (4) Neighbor cell measurement information may include the reference signal received power (RSRP) of one or more neighboring cells of the target cell, such as MR.NRncssrsrp1 (i.e., RSRP of neighboring cell 1), MR.NRncssrsrp2 (i.e., RSRP of neighboring cell 2), MR.NRncssrsrp3 (i.e. RSRP of neighboring cell 3), physical cell identifier (PCI) and center frequency, etc.

[0027] (5) Angle of arrival information, which may include the antenna horizontal angle of arrival HAOA calculated from the horizontal angle of arrival information MR.hAOA. The antenna horizontal angle of arrival HAOA refers to the horizontal angle in the direction of the main lobe of the antenna.

[0028] Taking the measurement report data listed above as an example, in some embodiments, the neighbor cell relationship and the A3 event handover pair in the event-triggered measurement report (MRE) can be combined to merge multiple signal measurement data points generated by the same user terminal within a continuous time period into the same user's sampling set. Specifically, all sampling points of the same Amfuengapid can be merged to obtain the sampling point data of the user corresponding to that Amfuengapid. At this time, the MRO data packet may include TimeStamp, Amfuengapid, MR.NRScEarfcn (5G serving cell carrier number), MR.NRScPci (5G serving cell physical cell identifier), MR.NRncssrsrp (5G neighbor cell reference signal received power), MR.NRncssrsrp1 (neighbor cell 1 RSRP), MR.NRncssrsrp2 (neighbor cell 2 RSRP), MR.NRncssrsrp3 (neighbor cell 3 RSRP), MR.ltencrsrp (LTE neighbor cell reference signal received power), and HAOA information.

[0029] Furthermore, considering that some key parameters (such as RSRP, RSRQ, SINR, etc.) in the MRO data extracted directly from the network management system are usually recorded in the form of index values ​​within predefined intervals, such as MR.NRScRSRP, MR.NRScRSRQ, etc., these index values ​​cannot be directly used for quantitative calculation. Therefore, in some optional implementations, during the data processing and analysis phase, these index values ​​can be parsed and converted to restore them to physically meaningful values. Taking the parsing of NR RSRP as an example, the RSRP index values ​​of the target cell and neighboring cells, such as MR.NRScRSRP, MR.NRNcRSRP, can be uniformly parsed into specific values ​​in dBm according to the conversion formula RSRP = MR.NRScRSRP - 156, so as to facilitate subsequent coverage assessment and optimization decisions.

[0030] Step 104: Determine the actual distribution angle of 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell.

[0031] Among them, the actual distribution angle of users, also known as the actual distribution angle of user sampling points HAOA, is used to characterize the actual distribution direction of user sampling points in geographic space relative to due north.

[0032] In this embodiment, the horizontal angle of arrival (AHA) of the antenna can be calculated based on the AHA information and the angular resolution. Then, the AHA is added to the azimuth angle to obtain the actual distribution angle. The angular resolution refers to the measurement resolution or quantization step size of the AHA. For example, with an angular resolution of 0.5, the AHA reported by each user sampling point is discretized in 0.5° steps.

[0033] In this embodiment of the application, the antenna azimuth angle, also known as the electronic azimuth angle, refers to the angle between the direction of the antenna main lobe and the due north direction.

[0034] Specifically, the horizontal angle of arrival (HAOA) can be calculated first using the horizontal angle of arrival (MR.hAOA) and angular resolution according to the formula: HAOA = MR.hAOA × angular resolution. Then, a transformation from the relative coordinate system to the absolute geographic coordinate system is performed. The calculated HAOA is added to the antenna azimuth angle, which represents the absolute geographic direction of the antenna main lobe, to obtain the actual distribution angle of the 5G user sampling points, i.e.: The actual distribution angle of 5G user sampling points = antenna horizontal angle of arrival HAOA + antenna azimuth angle.

[0035] Through this transformation, the directional information of each user sampling point is unified into an absolute coordinate system with geographic north as the 0-degree reference, thus directly reflecting the actual distribution direction of users in geographic space.

[0036] In one optional implementation, considering that the antenna azimuth angle recording information of the target cell may not be accurate due to factors such as incorrect parameter input, directly determining the actual distribution angle of 5G user sampling points based on this erroneous antenna azimuth angle recording information may lead to unreliable actual distribution angles, thereby affecting the accuracy and reliability of subsequent antenna azimuth angle optimization decisions. To avoid this problem and ensure the accuracy and reliability of subsequent antenna azimuth angle optimization decisions, the antenna azimuth angle can be corrected before performing step 104.

[0037] Optionally, considering that 4 / 5G networks are typically built using a co-location approach, with a co-location ratio of over 80%, co-location data can be used to perform cell azimuth calibration calculations.

[0038] In some embodiments, such as Figure 2 As shown, correcting the antenna azimuth angle may include the following steps 202 to 208: Step 202: Determine the 4G user sampling point data of the 4G cell co-located with the target cell. The 4G user sampling point data includes at least the geographical location information and reference signal received power (RSRP) of the 4G user sampling point.

[0039] After identifying the 4G cell co-located with the target cell, information such as the 4G user sampling points, the geographical location of the 4G user sampling points, and the reference signal received power of the 4G cell can be obtained.

[0040] Step 204: Map the geographic location information of 4G user sampling points and 5G user sampling points to a unified geographic grid system, and associate and merge the 4G user sampling points and 5G user sampling points with the same grid number after mapping.

[0041] In some embodiments, in order to spatially align 4G user sampling points with 5G user sampling points, a unified geographic raster system can be used to rasterize (GIS) the 4G user sampling points and 5G user sampling points.

[0042] In some embodiments, before mapping the geographic location information of 4G user sampling points and 5G user sampling points to a unified geographic grid system, it is necessary to first determine the geographic location information of the 5G user sampling points. Specifically, considering that 5G and 4G networks often adopt a co-location deployment method, and that 5G terminals continuously measure and report the signal information of surrounding 4G cells while connecting to the 5G network, and that the coverage area of ​​these 4G cells and their correspondence with geographic locations are known and accurate, the geographic location information of 5G user sampling points can be obtained by extracting the 4G inter-frequency measurement information carried in the 5G measurement report data, such as the Physical Cell Identifier (PCI) and Reference Received Power (RSRP) of 4G cells, and matching it with a high-precision 4G wireless network fingerprint database.

[0043] After obtaining the geographic location information of 5G user sampling points, the following steps can be used to map the geographic location information of 4G user sampling points and 5G user sampling points to a unified geographic grid system, and then associate and merge the 4G user sampling points and 5G user sampling points with the same grid number after mapping: First, the geographical location information of 5G user sampling points is rasterized.

[0044] (1) Establish a unified geographic grid system in advance, for example, divide the target area with a preset spatial resolution (such as 50m×50m or 100m×100m) to form multiple geographic grids; each geographic grid corresponds to a unique grid number / spatial identifier.

[0045] (2) Based on the geographical location information of the 5G user sampling points, such as longitude and latitude values, each 5G user sampling point is mapped to the corresponding raster cell. After the mapping is completed, each 5G user sampling point is assigned the raster number / spatial identifier of its geographical raster, thereby organizing the scattered sampling point data into a structured dataset in units of raster cells.

[0046] In some embodiments, considering the systematic differences in propagation loss, diffraction capability, and coverage range among different operating frequency bands, directly mixing sampling points from different frequency bands for rasterization would lead to distortion of the reference signal received power statistics within the same geographic grid, thereby affecting the continuity and comparability of signal distribution between grids. Therefore, when rasterizing 5G user sampling points, it is necessary to first classify the 5G user sampling points according to their operating frequency bands, and then perform geographic raster mapping separately for the 5G user sampling points within each frequency band. For example, 5G user sampling points in the 900 MHz (or 800 MHz) band are rasterized separately, and 5G user sampling points in the 2.6 GHz (or 3.5 GHz, which has similar propagation characteristics) band are rasterized separately. This ensures that the 5G user sampling points within each geographic grid are under the same or similar propagation conditions after rasterization, thereby improving the accuracy and stability of subsequent antenna azimuth correction based on the 4G / 5G grid RSRP difference.

[0047] Secondly, the geographical location information of 4G user sampling points is rasterized.

[0048] When rasterizing the geographic location information of 4G user sampling points, the same rasterization method as that used for 5G user sampling points can be adopted, including using the same preset spatial resolution. For details, please refer to the relevant content on the rasterization method of 5G user sampling points, which will not be repeated here.

[0049] It should be noted that when rasterizing 4G user sampling points, it is also necessary to first classify 5G user sampling points according to their operating frequency bands, and then perform geographic raster mapping on the 5G user sampling points in each frequency band.

[0050] Finally, the data obtained from 4G rasterization processing is merged with the data obtained from 5G rasterization processing. This involves associating and merging 4G and 5G raster data with the same raster number to form joint 4G / 5G raster sampling data under the same geographical raster. In some embodiments, to reduce systematic bias introduced by frequency band differences, 4G and 5G frequency bands with similar frequency bands or similar propagation characteristics can be selected during the merging stage.

[0051] Step 206: For each first geographic grid in the unified geographic grid system, calculate the difference between the RSRP of the 4G user sampling point and the RSRP of the 5G user sampling point in the first geographic grid to obtain the RSRP difference corresponding to the first geographic grid.

[0052] The first geographic grid refers to a geographic grid that includes sampling point data from both 4G and 5G users.

[0053] In some embodiments, for multiple user sampling points within the same geographic raster, a robust statistical method can be used to generate the representative RSRP value of the geographic raster. Specifically, the maximum and minimum RSRP of the user sampling points within the geographic raster can be removed first, and then the average or median of the remaining RSRPs can be taken to obtain the reference signal received power value corresponding to the geographic raster.

[0054] Specifically, in this embodiment of the application, when calculating the RSRP of a 4G user sampling point within a first geographic grid, the maximum and minimum RSRPs of the 4G user sampling points within the geographic grid can be removed first, and then the average or median of the remaining RSRPs can be taken to obtain the reference signal received power of the 4G user sampling points within the geographic grid. .

[0055] Similarly, when calculating the RSRP of 5G user sampling points within a certain first geographic grid, the maximum and minimum RSRPs of the 5G user sampling points within that geographic grid can be removed first. Then, the mean or median of the remaining RSRPs can be taken to obtain the reference signal received power of the 5G user sampling points within that geographic grid. .

[0056] Subsequently, based on the reference signal received power of 5G user sampling points within the same first geographic grid... and the reference signal received power of 4G user sampling points Obtain the 4 / 5G RSRP difference in this grid. .

[0057] For example, suppose that in a unified geographic raster system, a first geographic raster G1 contains 6 4G user sampling points and 5 5G user sampling points, and its corresponding RSRP values ​​are shown in Table 1 and Table 2 below: Table 1

[0058] Then calculate the reference signal received power of the 4G user sampling points within geographic grid G1. When doing so, you can first remove the largest RSRP ( 85) and minimum RSRP ( 105 dBm), the remaining RSRP includes { 88, 90, 92, 97}, therefore, it can be calculated that (Taking the average as an example) is:

[0059] Secondly, the RSRP values ​​corresponding to the five 5G user sampling points within geographic grid G1 are shown in Table 2 below: Table 2

[0060] Then calculate the reference signal received power of the 5G user sampling points within geographic grid G1. When doing so, you can first remove the largest RSRP ( 78) and minimum RSRP ( 95dBm), the remaining RSRP includes { 80, 82, 85}, after sorting, we can get { 85, 82, 80}, therefore, it can be determined (Taking the median as an example) 82dBm.

[0061] Finally, based on the representative values ​​of the reference received power of 5G and 4G within the same geographical grid G1, the RSRP difference for that grid is calculated: Substitute the values: .

[0062] In this embodiment, within the unified geographic grid G1: the representative value of the reference signal received power of the 4G user sampling points is... 91.75 dBm; the representative value of the reference signal received power at the 5G user sampling point is... 82dBm; the corresponding 4G / 5G RSRP difference is 9.75dB; this difference can be used to characterize the signal coverage gain of 5G relative to 4G within the same geographic grid.

[0063] By analogy, the differences between all geographic rasters in the unified geographic raster system can be obtained.

[0064] The difference between the 4G reference signal received power and the 5G reference signal received power within each geographic grid was calculated. After calculation, the difference data can be further processed using GIS. Specifically, the data corresponding to each geographic raster can be processed... It is associated with its spatial location information (such as latitude and longitude or planar coordinates) to generate The spatial distribution characteristics within the target area, i.e., obtaining The distributed grid set. This spatial distribution characteristic information can intuitively reflect the strength variation trend of 5G signal relative to 4G signal in different directions.

[0065] To further analyze the spatial characteristics of this difference and eliminate interference from propagation environmental factors, the Cost231-Hata propagation model can be introduced to theoretically estimate the path loss within the same geographic grid. According to this model, the path loss... (Unit: dB) can be calculated using the following formula:

[0066] in, Indicates the carrier frequency; The effective height of the transmitting antenna (unit: m); The effective height of the receiving antenna; It is an environmental correction factor related to the height of the receiving antenna; This refers to the distance between the transmitter and receiver. This is a cell type correction factor (such as macro cell, micro cell, etc.). For terrain correction factors (such as plains, hills, urban areas, etc.); This is for multipath fading margin or other systematic compensation terms; It represents a logarithm with base 10.

[0067] Because 4G and 5G cells are deployed at the same location, their transmission positions are the same, and within the same geographical grid, the effective height of the transmitting antenna is... Distance between the transmitter and receiver Community type correction coefficient Terrain correction factor The parameters are basically the same. Therefore, if the antenna azimuth angles of the two are perfectly aligned, then theoretically... It should tend to be constant. Conversely, if it is actually observed... Significant changes in azimuth indicate a deviation between the main lobe direction of the 5G antenna and that of the 4G antenna. Therefore, by combining the aforementioned Cost231-Hata propagation model to verify the rationality of the difference distribution, signal anomalies caused by inaccurate antenna azimuth angles can be effectively identified, providing a reliable basis for subsequent antenna azimuth angle correction.

[0068] Step 208: Correct the antenna azimuth angle based on the RSRP difference corresponding to all first geographic grids.

[0069] In this embodiment of the application, based on the RSRP difference values ​​corresponding to all first geographic grids, a second geographic grid with an RSRP difference value greater than a preset difference threshold is identified from all first geographic grids; the direction vector between the target cell and the grid center of the second geographic grid is determined; the median value of the direction vector is selected as the calibration value of the antenna azimuth angle using a median algorithm; and the antenna azimuth angle is corrected based on the calibration value of the antenna azimuth angle.

[0070] For example, assuming the target cell's coordinates are (116.38°E, 39.92°N) and the preset difference threshold is 6.2 dB, the relevant information for all first geographic grids is shown in Table 3 below: Table 3

[0071] Then, from all geographic grates, the second geographic grates with RSRP differences greater than a preset difference threshold can be identified as G1, G2, G3, and G4. Next, for each of these four grates, the direction vectors pointing from the target cell (116.38°E, 39.92°N) to the center of the second geographic grates (G1, G2, G3, G4) are calculated as follows: 132°, 135°, 138°, 140°. These direction vectors are then sorted in ascending order: 132°, 135°, 138°, 140°. Finally, the median value (the median between the second direction vector 135° and the third direction vector 138°) is taken as 136.5° as the calibration value for the antenna azimuth, and the antenna azimuth is corrected based on this calibration value.

[0072] Step 106: Sort the 5G user sampling points in ascending order according to the actual distribution angle, and use the median algorithm to determine the suggested adjustment azimuth angle of the antenna based on the sorting results.

[0073] In the embodiments of this application, such as Figure 3 As shown, the suggested adjustment azimuth angle for the antenna can be determined by following steps 302 to 306: Step 302: Sort the 5G user sampling points in ascending order according to their corresponding actual distribution angles to obtain the sorting results of the 5G user sampling points.

[0074] In step 302, the 5G user sampling points can be sorted in ascending order according to their corresponding actual distribution angles, from smallest to largest, to obtain a sorting result containing all 5G user sampling points. This sorting operation allows for an ordered representation of the spatial distribution direction of 5G user sampling points within the target cell's coverage area, providing a basis for subsequently determining the main distribution direction of users.

[0075] Step 304: Determine the actual distribution angle of the target corresponding to the 5G user sampling point located at the median of the sorting results using the median algorithm.

[0076] Based on the sorting results obtained in step 302, the median algorithm is used to determine the target actual distribution angle. Specifically, when the number of 5G user sampling points in the sorting results is odd, the actual distribution angle corresponding to the 5G user sampling point located at the median of the sorting results is selected as the target actual distribution angle; when the number of 5G user sampling points in the sorting results is even, the average of the actual distribution angles corresponding to the two middle 5G user sampling points can be selected as the target actual distribution angle.

[0077] By using the median algorithm to determine the actual distribution angle of the target, the impact of a small number of abnormal user sampling points or measurement errors on the azimuth adjustment results can be effectively reduced, so that the determined actual distribution angle of the target can more realistically reflect the main distribution direction of 5G users.

[0078] Step 306: Determine the actual distribution angle of the target as the suggested adjusted azimuth angle of the antenna azimuth.

[0079] It is recommended to adjust the azimuth angle to guide the adjustment of the antenna azimuth angle or electronic azimuth angle of the target cell, so that the direction of the antenna main lobe is consistent with the main distribution area of ​​the user.

[0080] For example, assuming the target cell acquires 7 5G user sampling points within a certain time window, the actual distribution angle information of each sampling point and its corresponding location is shown in Table 4 below: Table 4

[0081] First, the 5G user sampling points can be sorted in ascending order according to their actual distribution angles, resulting in the following sorting: Q1, Q6, Q... 14 Q3, Q 26 Q4, Q2, Q7, Q8, Q9, Q 10 Q 11 Q 12 Q 13 Q 15 Q 16 Q 17 Q 18 Q 19 Q 20 Q 21 Q 22 Q 23 Q 24 Q 25 Q5, Q 27 .

[0082] Secondly, since the number of sampling points is 3+3+19+2=27 (an odd number), the actual distribution angle of the sampling point located at the median in the sorting result (i.e., the sampling point at the 14th position in the sorting result) can be selected as the target actual distribution angle, that is, the sampling point Q. 13 The actual distribution angle of 10° is taken as the actual distribution angle of the target.

[0083] Finally, the actual distribution angle of the target, 10°, can be determined as the suggested adjustment azimuth angle for the antenna azimuth.

[0084] Step 108: Adjust the antenna azimuth angle according to the suggestion.

[0085] In this embodiment of the application, the suggested adjustment azimuth angle can be used as the adjustment target to adjust the antenna azimuth angle so that the adjusted antenna azimuth angle is the same as the suggested adjustment azimuth angle.

[0086] For example, following the example in step 106 above, assuming it is recommended to adjust the azimuth angle to 10°, then the antenna azimuth angle can be adjusted to 10°.

[0087] like Figure 4 The diagram shown is a schematic representation of adjusting the azimuth angle of an antenna according to an embodiment of this application. Figure 4 As can be seen from this, assuming the antenna azimuth angle is... Figure 4 The original azimuth angle of the cell shown can be adjusted to [the desired azimuth angle] after receiving a suggestion to adjust the azimuth angle. Figure 4 The suggested adjustment of the azimuth position is shown, where This is a suggestion to adjust the azimuth angle, that is, the adjusted antenna azimuth angle.

[0088] Considering that in practical applications, cell coverage quality and user experience are affected by a variety of factors, not all coverage anomalies stem from unreasonable antenna azimuth settings. In some scenarios, weak 5G signal coverage or poor user experience may be caused by non-azimuth factors such as building obstruction in the main lobe direction, abnormal antenna front-to-back ratio, antenna hardware performance degradation, feeder aging, or abnormal installation parameters. Adjusting the antenna azimuth directly without distinguishing the causes of these anomalies may not only fail to improve coverage but could also cause the antenna main lobe to deviate from the effective user area, resulting in wasted wireless resources or even introducing new coverage degradation. Therefore, before adjusting the antenna azimuth, it is necessary to further determine whether the coverage anomaly of the target cell is one that can be improved through azimuth optimization. For example, if there is a significant deviation between the actual user distribution direction and the current antenna azimuth and the main lobe propagation path is not continuously obstructed, or if it falls under the category of antenna anomalies or obstruction scenarios requiring on-site investigation and hardware processing, then appropriate optimization or handling strategies can be adopted for different causes. This ensures that the generated azimuth adjustment recommendations are feasible in practice and improves the accuracy and reliability of automated network optimization and operation and maintenance decisions.

[0089] In one optional implementation, when determining whether a coverage anomaly in a target cell belongs to the type that can be improved through azimuth angle optimization, it can first be determined whether the antenna azimuth angle is abnormal; if the antenna azimuth angle is determined to be abnormal, it can then be further determined whether the anomaly belongs to the type that can be improved by adjusting the antenna azimuth angle. Specifically, as follows... Figure 5 As shown, the following steps can be used to determine if the antenna azimuth angle is abnormal: Step 502: Based on the 5G user sampling points and the antenna azimuth angle, determine the number of first sampling points of 5G user sampling points in the direction of the antenna main lobe and the number of second sampling points of 5G user sampling points in the opposite direction.

[0090] The main lobe direction refers to the beam direction in the antenna radiation pattern that has the strongest energy and mainly covers the user area. The back direction refers to all other directions besides the main lobe direction.

[0091] In some embodiments, step 502 above can be implemented through the following steps: (1) Rasterize the 5G user sampling points to obtain multiple geographic grids corresponding to the 5G user sampling points.

[0092] In this embodiment, all 5G user sampling points can be rasterized according to geographical location, that is, the geographical space is divided into multiple geographical grates, such as geohash grates. For each sampling point, a unique identifier of its geographical grates is calculated, such as the grates number geohashd, and the number of 5G user sampling points contained in each geohashd is counted.

[0093] Optionally, the distance from the center point of each geographic raster to the target cell can be further calculated to form a raster statistics table. Specifically, for each geographic raster, the latitude and longitude of its center point can be taken. Then, the distance from the center point to the target cell location is calculated, and a raster statistics table is established for each target cell. As shown in Table 5 below, this raster statistics table can at least include: geohashid, target cell CGI (or cell identifier), the number of sampling points of the 5G user sampling points, geohashid longitude, geohashid latitude, and the distance from the target cell to geohashid.

[0094] Table 5

[0095] In Table 5, “XXX” represents an example value for geohashid; “XXXXXXXX” represents an example value for the cell identifier of the target cell CGI; “XXXXXXX” represents an example value for geohashid longitude; “XXXXXXX” represents an example value for geohashid latitude; and “500” represents an example value for distance.

[0096] (2) For each geographic grid, determine the direction vector between the grid center point of the geographic grid and the target cell, and determine the direction angle of the direction vector relative to the due north direction.

[0097] In this embodiment of the application, the direction vector (or grid vector) between the center point of the geographic grid and the target cell can be obtained based on the latitude and longitude coordinates of each geographic grid, with the latitude and longitude position of the target cell as the origin. Then, calculate the direction vector. Angle with due north and will Round the vector to the nearest integer, and use the result as the direction angle of the direction vector relative to true north.

[0098] (3) Determine the antenna main lobe angle range based on the direction vector relative to the due north direction and the antenna azimuth angle, and count the number of 5G user sampling points within the antenna main lobe angle range as the first sampling point number.

[0099] In this embodiment, the antenna azimuth angle can first be used as a reference, and the direction vector relative to the due north direction can be used as the direction angle. Subtracting the antenna azimuth angle yields the angle between the antenna azimuth angle and the direction in which the angle is located. , that is .

[0100] Secondly, considering that the half-power beamwidth of existing antennas in the horizontal plane is usually distributed within a certain angular range, and that the antenna azimuth angle or electronic azimuth angle and other engineering parameters may have certain deviations during actual deployment, maintenance, and recording, and to ensure that the main coverage direction near the 65-degree half-power angle of the antenna can be fully included in the statistical analysis and has the necessary engineering tolerance, therefore, it is advisable to... Determine the antenna main lobe angle range, and... The number of all 5G user sampling points within the range is used as the first sampling point number.

[0101] The horizontal half-power beamwidth (HPBW) of a current network antenna refers to the angle between two directions when the main lobe power density drops by 3dB (halfway down) from its maximum value on the antenna's horizontal radiation pattern. It determines the antenna's beamwidth in the horizontal plane. Specifically, a narrower angle results in stronger directivity and better anti-interference capability, but the signal at the sector boundary is more likely to weaken; a wider angle provides better coverage at the sector boundary, but is more prone to beam distortion and cross-sector interference. Thus, when performing front-to-back distribution analysis on 5G user sampling points, it can cover the main service area corresponding to the theoretical main lobe width while also accommodating azimuth parameter errors and fluctuations in signal edge regions. This allows for a more accurate reflection of the user distribution along the actual main lobe direction, avoiding misjudgments caused by small angular deviations, and improving the accuracy, stability, and engineering applicability of antenna azimuth anomaly judgment results.

[0102] (4) The number of sampling points of other 5G user sampling points outside the antenna main lobe angle range is determined as the number of second sampling points.

[0103] Step 504: Calculate the ratio of the number of sampling points before and after the first sampling point to the number of the second sampling point.

[0104] The front-to-back sampling ratio refers to the ratio of the number of sampling points in the direction of the antenna's main lobe to the number of sampling points in the opposite direction. By analyzing the distribution of the front-to-back sampling ratio, we can assess whether the antenna azimuth angle is abnormal and whether the antenna performance has degraded.

[0105] In this embodiment of the application, the ratio of sampling points before and after sampling is also the ratio of the number of first sampling points to the number of second sampling points. .

[0106] Step 506: If the ratio of the sampling points before and after is less than the preset azimuth angle anomaly judgment threshold, then the antenna azimuth angle is determined to be abnormal.

[0107] In this embodiment, the preset azimuth anomaly judgment threshold can be set to 2.33. If the ratio of the sampling points before and after the sampling point is less than 2.33, the antenna azimuth is determined to be abnormal. Conversely, if the ratio of the sampling points before and after the sampling point is greater than or equal to 2.33, the antenna azimuth is considered normal.

[0108] In some embodiments, when an abnormal antenna azimuth angle is determined, it is further determined whether the abnormality can be avoided by adjusting the antenna azimuth angle. For example... Figure 6 As shown, the specific steps include the following: Step 602: Determine the 5G user sampling points whose reference signal received power is within a preset range in the main lobe direction and the back direction of the antenna, and use them as the main lobe analysis set and the back direction analysis set, respectively.

[0109] In this embodiment of the application, all sampling points with a maximum RSRP value of ±3dB between the first number of sampling points and the second number of sampling points obtained in step 502 above can be used as the main lobe set to be analyzed and the back lobe set to be analyzed, respectively.

[0110] Step 604: Determine the average first reference signal received power and the first time advance of the 5G user sampling points in the main lobe analysis set.

[0111] In this embodiment of the application, the RSRP of the 5G user sampling points in the main lobe analysis set can be averaged to obtain the average received power of the first reference signal. And find the first lead time for the relevant 5G user sampling points.

[0112] Step 606: Determine the second time advance of 5G user sampling points in the back-to-analysis set, and determine the target 5G user sampling points in the back-to-analysis set with the same second time advance as the first time advance based on the second time advance.

[0113] In this embodiment of the application, a second time advance of 5G user sampling points in the set to be analyzed can be determined, and combined with the first time advance in step 604, target 5G user sampling points with the same second time advance as the first time advance can be determined from the set to be analyzed.

[0114] Step 608: Calculate the average received power of the second reference signal at the target 5G user sampling points.

[0115] After obtaining the target 5G user sampling points, the RSRP of the target 5G user sampling points can be averaged to obtain the average received power of the second reference signal. .

[0116] Step 610: If the difference between the average received power of the first reference signal and the average received power of the second reference signal is less than the preset antenna performance attenuation judgment threshold, then the antenna of the target cell is determined to be abnormal.

[0117] In this embodiment, the preset antenna performance attenuation judgment threshold can be set to 20dB. Based on this, if the difference between the average received power of the first reference signal and the average received power of the second reference signal, that is... If the value is less than 20dB, it indicates antenna performance degradation, which is identified as an antenna anomaly, and a list of cells with antenna anomalies is obtained. Conversely, if... A reading of 20dB or higher indicates that the antenna is functioning normally. However, if the antenna is flagged as faulty, further on-site inspection is required.

[0118] In some embodiments, for cells with antenna malfunctions, further analysis can be conducted to determine if there are any obstructions. Specifically, if the cell with antenna malfunctions also exhibits an abnormal antenna azimuth angle, the ratio of the number of sampling points before and after sampling can be calculated. If the ratio is less than or equal to 1.2, it indicates that the cell may have large-scale building obstructions.

[0119] In some embodiments, when it is determined that a large-scale building block is suspected to be obstructing a cell, it can be further determined whether the obstruction actually exists. Specifically: (1) Based on the direction of the main lobe of the cell, the spatial area within ±30° is counted; (2) In this spatial area, the outdoor sampling points within 100 to 200 meters from the base station are rasterized, and the number of sampling points in each geographic grid is counted; (3) If there are 8 or more consecutive grids in this range, and the number of outdoor sampling points in each grid is less than 20, it is determined that there is a significant building block obstructing the front of the cell; (4) Otherwise, it is considered that the abnormal front-to-back ratio of the sampling point is not caused by the obstruction, but may be due to other factors such as antenna performance attenuation or installation problems. On-site testing is required, and it is recommended to replace the antenna equipment.

[0120] In some embodiments, for cells with obstructions, the following steps can be taken: First, analyze the buildings in the main direction of the cell using map operations and determine the antenna azimuth of the cell. Second, conduct an on-site survey to determine the building obstructions and optimize the antenna azimuth based on the initially determined azimuth.

[0121] Furthermore, in order to quantitatively characterize the coverage gain differences in different directions before adjusting the antenna azimuth angle and accurately predict the 5G RSRP changes that may be brought about by the azimuth angle adjustment, in one optional implementation, before adjusting the antenna azimuth angle, the angle of arrival (AOA) of the 4G cell and the horizontal angle of arrival (HAOA) of the 5G cell can be aligned under the same time lead (TA) condition based on the sampling points of the 4G cell co-located with the target 5G cell, and the difference between the 5G and 4G reference signal received power (RSRP) at each azimuth angle can be counted to construct a direction-dependent RSRP difference model (or difference relationship table). In this way, the stable RSRP deviation mainly caused by the difference in antenna directional gain can be extracted on the basis of effectively eliminating the influence of common factors such as propagation distance and path loss. Thus, the high-precision and high-stability spatial distribution data of the 4G network can be used as a benchmark to perform directional calibration of the 5G signal strength, providing a reliable and quantifiable basis for the effect evaluation of the subsequent azimuth angle adjustment scheme and the prediction of the RSRP distribution after optimization, significantly improving the accuracy, robustness and engineering practicality of automated optimization.

[0122] Specifically, such as Figure 7 As shown, a direction-dependent RSRP difference model (or difference relationship table) can be constructed through the following specific implementation steps: Step 702: Obtain the TA and RSRP of 5G user sampling points based on the measurement report data.

[0123] The measurement report data includes the timing advance (TA) and reference signal received power (RSRP) of the 5G user sampling points.

[0124] In this embodiment of the application, multiple 5G user sampling points can be obtained by parsing the 5G measurement report data (e.g., 5G MRO / MR data) corresponding to the target cell. Each 5G user sampling point includes at least: (1) Time Advance (TA), which is used to characterize the propagation delay between the terminal and the base station, and thus reflects the quantitative indicator of the propagation distance; under the same standard / configuration, the same TA usually means that the sampling point and the base station are in a similar range. (2) Reference Signal Received Power (RSRP), which is used to characterize the signal strength at the sampling point.

[0125] Optionally, if the RSRP in the measurement report is a discrete coded value, the coded value can be further converted into an actual power value for subsequent difference information calculation.

[0126] Step 704: Divide the 5G user sampling points into multiple TA groups according to the TA, where the TAs of 5G user sampling points located in the same TA group are the same.

[0127] After obtaining the TA (Target Acquisition Parameter) for each 5G user sampling point, the 5G user sampling points can be grouped according to the TA to obtain multiple TA groups. Specifically: 5G user sampling points with the same TA value are assigned to the same TA group; different TA values ​​correspond to different TA groups.

[0128] Step 706: Within each TA group, classify the 5G user sampling points into the corresponding angle value or angle interval according to the actual distribution angle of the 5G user sampling points, and calculate the 5GRSRP representative value corresponding to the 5G user sampling points in each angle value or angle interval.

[0129] In this embodiment, for each TA group, the 5G user sampling points can be further classified and aggregated statistically based on their actual distribution angles. Specifically, the actual distribution angles can be mapped to discrete angle values ​​or angle intervals, for example, rounded down to the nearest 1°, or divided into intervals of several degrees, to obtain multiple angle categories. Then, the 5G user sampling points within the same TA group are assigned to their corresponding angle categories. Finally, for each angle category (i.e., each angle value or angle interval), the RSRP of the 5G user sampling points assigned to that angle category is aggregated to obtain the representative 5G RSRP value corresponding to that angle category. The representative RSRP value can be the mean or median, etc.

[0130] Through the above processing, we can obtain the statistical results of 5G signal strength at different directional angles under the same TA.

[0131] Step 708: Obtain the RSRP representative value of the 4G sampling point of the co-located 4G cell with the target cell in the corresponding TA group and the corresponding angle value or angle interval.

[0132] In this embodiment, the reason for introducing the representative RSRP value of a 4G cell co-located with the target 5G cell under the same TA grouping and azimuth angle when constructing the direction-dependent RSRP difference model is mainly because: under co-location conditions, 4G and 5G sampling points at the same TA and azimuth angle have highly consistent propagation paths and environments, and their path loss and external environmental influences are statistically similar; by aligning the measurement results of 4G and 5G under the same TA and azimuth angle conditions, the influence of propagation distance and path differences can be largely eliminated, so that the obtained difference information mainly reflects the directional gain difference of the antenna at different azimuth angles. Meanwhile, 4G networks have a long deployment time and abundant measurement samples, and their RSRP statistical results have high stability and reliability. Using them as a reference benchmark helps to directionally calibrate the 5G signal strength, reduce the impact of 5G measurement fluctuations on difference modeling, and ensure that the constructed direction-dependent RSRP difference model can form a stable azimuth angle and difference mapping relationship. This provides a reliable quantitative basis for the prediction and evaluation of the subsequent antenna azimuth angle adjustment effect, thereby improving the accuracy and practicality of azimuth angle optimization decisions.

[0133] In some embodiments, when obtaining the representative RSRP value of 4G sampling points in the corresponding TA group and corresponding angle value or angle interval of a 4G cell co-located with the target cell, the TA and RSRP of the 4G sampling points can be obtained from the 4G measurement data first, and the angle of arrival information (e.g., AOA) of the 4G sampling points can be obtained. Then, the 4G sampling points are grouped by TA; within each TA group, the 4G sampling points are discretized and classified into the same angle value or angle interval as 5G according to the direction angle corresponding to AOA; the RSRP of the 4G sampling points in each angle category is aggregated to obtain the corresponding representative RSRP value of the 4G sampling points.

[0134] Step 710: For each TA group, each angle value or angle interval, calculate the difference information between the 5G RSRP representative value and the 4G sampling point RSRP representative value to generate a difference relationship table for different angles under the same TA.

[0135] After obtaining the RSRP representative values ​​of 5G and 4G under the same TA group and the same angle category, the difference information between the 5G RSRP representative value and the 4G RSRP representative value can be calculated for each TA group, each angle value or angle interval, and a difference relationship table corresponding to different angles under the same TA can be constructed accordingly. Table 6 below is an exemplary difference relationship table provided by an embodiment of this application.

[0136] Table 6

[0137] In this embodiment, if the azimuth angles are the same, the difference between the 5G RSRP representative value and the 4G RSRP representative value should be consistent. When the azimuth angles are different, the difference between the 5G RSRP representative value and the 4G RSRP representative value will be different due to the different gains of the antenna in different directions, but the difference should remain consistent within the same direction. Figure 8 As shown, although the azimuth angles of 4G and 5G cells are different, because 4G and 5G cells are co-located, the sampling points at the same location follow the same path. The only difference is the antenna gain at different angles, leading to variations in RSRP. However, the difference in sampling points along the same path remains consistent. That is, when the 5G HAOA and 4G AOA are the same, the corresponding TA should also be the same, thus maintaining a consistent difference.

[0138] In one implementation, before adjusting the antenna azimuth angle according to the proposed adjustment, the RSRP distribution after the antenna azimuth angle adjustment can be predicted. Specifically, the angular offset of the proposed adjustment azimuth angle relative to the antenna azimuth angle can be determined; an angular mapping can be performed on the difference relationship table based on the angular offset to determine the difference compensation amount corresponding to the angular offset; and the RSRP of the 5G user sampling points can be compensated according to the difference compensation amount to obtain the predicted RSRP after the antenna azimuth angle adjustment.

[0139] In practical applications, the angles of all 5G cell HAOAs and their corresponding 4G AOAs determined in step 710 can be combined. And list the differences between different HAOA The differences are shown in Table 7 below: Table 7

[0140] In Table 7, XXXXXN (N∈[1,15]) represents the RSRP difference when HAOA = N°.

[0141] The table above can be used to determine the corresponding relationships between different HAOA. Difference.

[0142] Secondly, the recommended azimuth adjustment angle relative to the antenna azimuth angle can be determined. Based on this angle offset, an angle mapping is performed on the difference relationship table to determine the corresponding difference compensation amount. For example, assuming the recommended azimuth adjustment angle relative to the antenna azimuth angle is an increase of 6 degrees clockwise, the corresponding value in Table 7 above can be read. The difference in HAOA of all sampling points is obtained, and the 6-degree time difference value corresponding to the HAOA of each 5G user sampling point is read. That is, the RSRP of the 5G user sampling points can be compensated according to the difference compensation amount, based on the 4G user sampling points, to obtain the predicted RSRP distribution of the 5G user sampling points after adjusting the antenna azimuth angle, as follows:

[0143] in, This represents the predicted RSRP of 5G user sampling points after adjusting the antenna azimuth angle. This represents the difference in HAOA between 4G user sampling points; Indicates reading from the table ; This represents the 6-degree time difference value corresponding to the HAOA of each 5G user sampling point.

[0144] The method provided in this application obtains the horizontal angle of arrival information of 5G user sampling points and determines their actual distribution angle. Then, the median algorithm is used to calculate the suggested adjustment value of the antenna azimuth angle. This enables the adjustment of the antenna azimuth angle to be accurately aligned with the angle center of the actual user distribution, so that the antenna main lobe direction can more accurately cover the main distribution area of ​​users. This effectively solves the network coverage problem caused by the mismatch between the antenna azimuth angle and the user distribution, and improves user perception and network service quality.

[0145] To address the network coverage issues caused by the mismatch between antenna azimuth and the main user distribution area in existing technologies, this application provides an antenna azimuth adjustment device based on 5G user distribution. A schematic diagram of the specific structure of this device is shown below. Figure 9 As shown, it includes an acquisition module 91, a determination module 92, a processing module 93, and an adjustment module 94. The functions of each module are as follows: The acquisition module 91 is used to acquire the measurement report data of the target cell. The measurement report data includes at least the horizontal angle of arrival information of the 5G user sampling points. The determination module 92 is used to determine the actual distribution angle of 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell; The processing module 93 is used to sort the 5G user sampling points in ascending order according to the actual distribution angle, and to determine the suggested adjustment azimuth angle of the antenna azimuth angle based on the sorting result using the median algorithm. Adjustment module 94 is used to adjust the antenna azimuth angle according to the recommendations.

[0146] Optionally, module 92 is used for: Calculate the antenna's horizontal angle of arrival based on the horizontal angle of arrival information and its angular resolution. Add the antenna's horizontal angle of arrival to its azimuth angle to obtain the actual distribution angle.

[0147] Optionally, processing module 93 is used for: The 5G user sampling points are sorted in ascending order according to their corresponding actual distribution angles to obtain the sorting results of the 5G user sampling points. The actual distribution angle of the target corresponding to the 5G user sampling point located at the median of the sorting results is determined by the median algorithm. The recommended adjustment of the azimuth angle is to determine the actual distribution angle of the target as the antenna azimuth angle.

[0148] Optionally, the device further includes a correction module; the correction module includes: The determining unit is used to determine the 4G user sampling point data of the 4G cell co-located with the target cell. The 4G user sampling point data includes at least the geographical location information and the reference signal received power RSRP of the 4G user sampling point. The mapping unit is used to map the geographic location information of 4G user sampling points and 5G user sampling points to a unified geographic grid system, and to associate and merge 4G user sampling points and 5G user sampling points with the same grid number after mapping. The calculation unit is used to calculate the difference between the RSRP of 4G user sampling points and the RSRP of 5G user sampling points in each first geographic grid in the unified geographic grid system, so as to obtain the RSRP difference corresponding to the first geographic grid. The correction unit is used to correct the antenna azimuth angle based on the RSRP difference corresponding to all first geographic grids; The first geographic grid refers to a geographic grid that includes sampling point data from both 4G and 5G users.

[0149] Optional, a correction unit is used for: Based on the RSRP difference values ​​corresponding to all first geographic rasters, identify the second geographic rasters whose RSRP difference values ​​are greater than a preset difference threshold from all first geographic rasters; Determine the direction vector between the target cell and the center of the second geographic grid; The median value of the direction vector is selected as the calibration value of the antenna azimuth angle using the median algorithm; Correct the antenna azimuth angle based on the calibration value of the antenna azimuth angle.

[0150] Optionally, the device also includes an azimuth anomaly detection module, comprising: The sampling point number determination unit is used to determine the first sampling point number of 5G user sampling points in the main lobe direction and the second sampling point number of 5G user sampling points in the opposite direction based on the 5G user sampling points and the antenna azimuth angle. The sampling point ratio calculation unit is used to calculate the sampling point ratio between the first sampling point number and the second sampling point number. The judgment unit is used to determine that the antenna azimuth angle is abnormal if the ratio of the sampling point before and after is less than the preset azimuth angle abnormality judgment threshold.

[0151] Optionally, the sampling point number determination unit includes: The 5G user sampling points are rasterized to obtain multiple geographic rasters corresponding to the 5G user sampling points. For each geographic raster, determine the direction vector between the raster center point and the target cell, and determine the direction angle of the direction vector relative to true north. The antenna main lobe angle range is determined based on the direction vector relative to the north direction and the antenna azimuth angle. The number of sampling points of 5G user sampling points within the antenna main lobe angle range is counted as the first sampling point number. The number of sampling points for other 5G user sampling points outside the antenna main lobe angle range is determined as the second number of sampling points.

[0152] Optionally, the device is also used for: 5G user sampling points whose reference signal received power is within a preset range in the main lobe direction and the back direction of the antenna are determined and used as the main lobe analysis set and the back analysis set, respectively. Determine the mean received power of the first reference signal and the first time advance of the 5G user sampling points in the main lobe analysis set; Determine the second time advance of 5G user sampling points in the back-to-analysis set, and determine the target 5G user sampling points in the back-to-analysis set with the same second time advance as the first time advance based on the second time advance. Calculate the average received power of the second reference signal at the target 5G user sampling points; If the difference between the average received power of the first reference signal and the average received power of the second reference signal is less than the preset antenna performance attenuation judgment threshold, then the antenna of the target cell is determined to be abnormal.

[0153] Optionally, the measurement report data includes the timing advance (TA) and reference signal received power (RSRP) of the 5G user sampling points. The device is also used for: Obtain the TA and RSRP of 5G user sampling points based on measurement report data; Based on the TA, 5G user sampling points are divided into multiple TA groups, and 5G user sampling points located in the same TA group have the same TA. Within each TA group, the 5G user sampling points are classified into the corresponding angle value or angle interval according to the actual distribution angle of the 5G user sampling points, and the 5G RSRP representative value corresponding to the 5G user sampling points in each angle value or angle interval is calculated. Obtain the RSRP representative value of the 4G sampling point of the co-located 4G cell with the target cell in the corresponding TA group and the corresponding angle value or angle range; For each TA group, each angle value, or each angle range, calculate the difference between the representative 5G RSRP value and the representative 4G sampling point RSRP value to generate a difference relationship table for different angles under the same TA.

[0154] Optionally, the device is also used for: Determine the recommended adjustment of the azimuth angle relative to the antenna azimuth angle; Angle mapping is performed on the difference relationship table based on the angle offset to determine the difference compensation amount corresponding to the angle offset. The RSRP of 5G user sampling points is compensated according to the difference compensation amount to obtain the predicted RSRP after adjusting the antenna azimuth angle.

[0155] The device provided in this application obtains the horizontal angle of arrival information of 5G user sampling points and determines their actual distribution angle. Then, it uses the median algorithm to calculate the suggested adjustment value of the antenna azimuth angle. This enables the adjustment of the antenna azimuth angle to be accurately aligned with the angle center of the actual user distribution, so that the antenna main lobe direction can more accurately cover the main distribution area of ​​users. This effectively solves the network coverage problem caused by the mismatch between the antenna azimuth angle and the user distribution, and improves user perception and network service quality.

[0156] Figure 10 To illustrate the hardware structure of an electronic device according to various embodiments of this application, the electronic device may include a processor 1001 and a memory 1002 storing computer program instructions. Specifically, the processor 1001 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of this application.

[0157] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to an electronic device. In a particular embodiment, memory 1002 may be a non-volatile solid-state memory.

[0158] In one embodiment, the memory 1002 may be a read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0159] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the antenna azimuth adjustment methods based on 5G user distribution in the above embodiments.

[0160] In one example, the electronic device may also include a communication interface 1003 and a bus 1010. For example, Figure 10 As shown, the processor 1001, memory 1002, and communication interface 1003 are connected through bus 1010 and complete communication with each other.

[0161] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0162] Bus 1010 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1010 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0163] Furthermore, in conjunction with the antenna azimuth adjustment method based on 5G user distribution in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any one of the antenna azimuth adjustment methods based on 5G user distribution in the above embodiments.

[0164] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0165] The above description is merely a specific implementation example of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0166] Secondly, those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0168] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0169] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0170] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0171] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0172] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0173] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0174] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for adjusting the azimuth angle of an antenna based on 5G user distribution, characterized in that, include: Obtain measurement report data for the target cell, wherein the measurement report data includes at least the horizontal angle of arrival information of the 5G user sampling points; The actual distribution angle of the 5G user sampling points is determined based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell; The 5G user sampling points are sorted in ascending order according to the actual distribution angle, and the suggested adjustment azimuth angle of the antenna is determined by the median algorithm based on the sorting results. Adjust the azimuth angle of the antenna according to the recommendations.

2. The method as described in claim 1, characterized in that, Determining the actual distribution angle of the 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell includes: Calculate the antenna's horizontal angle of arrival based on the horizontal angle of arrival information and the angular resolution of the horizontal angle of arrival information; The actual distribution angle is obtained by adding the horizontal angle of arrival of the antenna to the azimuth angle of the antenna.

3. The method as described in claim 1, characterized in that, The step of sorting the 5G user sampling points in ascending order according to the actual distribution angle, and determining the suggested adjustment azimuth angle of the antenna azimuth angle using the median algorithm based on the sorting results, includes: The 5G user sampling points are sorted in ascending order according to their corresponding actual distribution angles to obtain the sorting result of the 5G user sampling points. The median algorithm is used to determine the actual distribution angle of the target corresponding to the 5G user sampling point located at the median of the sorting results. The suggested adjustment azimuth angle is determined by setting the actual distribution angle of the target as the azimuth angle of the antenna.

4. The method as described in claim 1, characterized in that, Before determining the actual distribution angle of the 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell, the method further includes: Determine the 4G user sampling point data of the 4G cell co-located with the target cell, wherein the 4G user sampling point data includes at least the geographical location information and reference signal received power RSRP of the 4G user sampling point; The geographic location information of the 4G user sampling points and the geographic location information of the 5G user sampling points are mapped to a unified geographic grid system, and the 4G user sampling points and 5G user sampling points with the same grid number after mapping are associated and merged. For each first geographic grid in the unified geographic grid system, the difference between the RSRP of the 4G user sampling point and the RSRP of the 5G user sampling point in the first geographic grid is calculated to obtain the RSRP difference corresponding to the first geographic grid. The antenna azimuth angle is corrected based on the RSRP difference values ​​corresponding to all the first geographic grids; The first geographic raster refers to a geographic raster that simultaneously includes the sampling point data of the 4G user sampling points and the sampling point data of the 5G user sampling points.

5. The method as described in claim 4, characterized in that, The step of correcting the antenna azimuth angle based on the RSRP difference corresponding to all the first geographic grids includes: Based on the RSRP difference values ​​corresponding to all the first geographic graticles, identify the second geographic graticle whose RSRP difference value is greater than a preset difference threshold from all the first geographic graticles; Determine the direction vector between the target cell and the grid center of the second geographic grid; The median value of the direction vector is selected as the calibration value of the antenna azimuth angle using the median algorithm; The antenna azimuth angle is corrected according to the calibration value of the antenna azimuth angle.

6. The method as described in claim 1, characterized in that, Before adjusting the antenna azimuth angle according to the suggestion, the method further includes: determining whether the antenna azimuth angle is abnormal; The determination of whether the antenna azimuth angle is abnormal includes: Based on the 5G user sampling points and the antenna azimuth angle, determine the number of first sampling points of 5G user sampling points in the direction of the antenna main lobe and the number of second sampling points of 5G user sampling points in the opposite direction. Calculate the ratio of the number of sampling points before and after the first sampling point to the number of sampling points before and after the second sampling point; If the ratio of the sampling points before and after is less than the preset azimuth anomaly judgment threshold, then the antenna azimuth is determined to be abnormal.

7. The method as described in claim 6, characterized in that, The step of determining the first number of 5G user sampling points in the main lobe direction and the second number of 5G user sampling points in the opposite direction based on the 5G user sampling points and the antenna azimuth angle includes: The 5G user sampling points are rasterized to obtain multiple geographic grids corresponding to the 5G user sampling points. For each of the geographic grids, the direction vector between the grid center point of the geographic grid and the target cell is determined, and the azimuth angle of the direction vector relative to true north is determined. The antenna main lobe angle range is determined based on the directional angle of the direction vector relative to the due north direction and the antenna azimuth angle, and the number of sampling points of 5G user sampling points within the antenna main lobe angle range is counted as the first sampling point number. The number of sampling points for other 5G user sampling points outside the main lobe angle range of the antenna is determined as the second number of sampling points.

8. The method as described in claim 6 or 7, characterized in that, If the antenna azimuth angle is determined to be abnormal, the method further includes: 5G user sampling points whose reference signal received power is within a preset range in the main lobe direction and the back direction of the antenna are determined and used as the main lobe analysis set and the back analysis set, respectively. Determine the average first reference signal received power and the first time advance of the 5G user sampling points within the main lobe analysis set; Determine the second time advance of the 5G user sampling points in the back-to-analysis set, and determine the target 5G user sampling points in the back-to-analysis set whose second time advance is the same as the first time advance based on the second time advance. Calculate the average received power of the second reference signal at the target 5G user sampling points; If the difference between the average received power of the first reference signal and the average received power of the second reference signal is less than a preset antenna performance attenuation judgment threshold, then the antenna of the target cell is determined to be abnormal.

9. The method as described in claim 1, characterized in that, The measurement report data includes the timing advance (TA) and reference signal received power (RSRP) of the 5G user sampling points, and the method further includes: Based on the measurement report data, obtain the TA and RSRP of the 5G user sampling points; The 5G user sampling points are divided into multiple TA groups according to the TA, wherein the TAs of 5G user sampling points located in the same TA group are the same. Within each TA group, the 5G user sampling points are classified into corresponding angle values ​​or angle intervals according to the actual distribution angle of the 5G user sampling points, and the 5G RSRP representative value corresponding to each 5G user sampling point within the angle value or angle interval is calculated. Obtain the RSRP representative value of the 4G sampling points of the 4G cell co-located with the target cell in the corresponding TA group and the corresponding angle value or angle range; For each TA group, each angle value, or each angle interval, the difference information between the 5G RSRP representative value and the 4G sampling point RSRP representative value is calculated to generate a difference relationship table for different angles under the same TA.

10. The method as described in claim 9, characterized in that, The method further includes: Determine the angular offset of the suggested adjustment azimuth angle relative to the antenna azimuth angle; Based on the angle offset, the difference relationship table is mapped to an angle to determine the difference compensation amount corresponding to the angle offset; The RSRP of the 5G user sampling points is compensated according to the difference compensation amount to obtain the predicted RSRP after adjusting the antenna azimuth angle.

11. An antenna azimuth adjustment device based on 5G user distribution, characterized in that, It includes an acquisition module, a determination module, a processing module, and an adjustment module, among which: The acquisition module is used to acquire measurement report data of the target cell, wherein the measurement report data includes at least the horizontal angle of arrival information of the 5G user sampling points; The determination module is used to determine the actual distribution angle of the 5G user sampling points based on the horizontal angle of arrival information and the antenna azimuth angle of the target cell; The processing module is used to sort the 5G user sampling points in ascending order according to the actual distribution angle, and to determine the suggested adjustment azimuth angle of the antenna azimuth angle based on the sorting result using the median algorithm. The adjustment module is used to adjust the azimuth angle of the antenna according to the suggestion.

12. An electronic device, characterized in that, include: The memory, the processor, and the computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the antenna azimuth adjustment method based on 5G user distribution as described in any one of claims 1 to 10.

13. 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 steps of the antenna azimuth adjustment method based on 5G user distribution as described in any one of claims 1 to 10.

14. A computer program product, characterized in that, The method includes a computer program that, when executed by a processor, implements the antenna azimuth adjustment method based on 5G user distribution as described in any one of claims 1 to 10.

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