Antenna weight adaptive adjustment method and device, equipment, storage medium and product

Abstract

The invention discloses an antenna weight adaptive adjustment method and device, equipment, a storage medium and a product, and the method comprises the steps: taking a horizontal arrival angle, a vertical arrival angle and a time advance as coordinate axes, and building a sector ring body grid model; mapping sampling point data in the network measurement data into the sector ring body grid model, and determining the total user distribution number in each sector ring body grid; the sampling point data comprises a horizontal arrival angle, a vertical arrival angle and a time advance of a corresponding sampling point; performing clustering analysis on the total user distribution number in each sector ring body grid, and determining a hot spot area with dense user distribution; according to the hot spot area, adjusting an antenna weight through an antenna weight library; according to the method, through the established sector ring body grid model, the sampling points are firstly converged to the grid, and then the user distribution is analyzed, so that the calculation efficiency can be effectively improved, the calculation power configuration requirement is reduced, the rapid identification of the hot spot is realized, and the user perception of the hot spot region is improved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to an antenna weight adaptive adjustment method, apparatus, device, storage medium, and product. Background Technology

[0002] Massive MIMO (Multi-Input Multiple-Output) antenna technology is a key technology for improving system capacity and spectrum utilization in 5G networks, helping to enhance user access and dwell capabilities. 5G Massive MIMO antennas introduce four remotely adjustable weighting elements: horizontal beamwidth, vertical beamwidth, azimuth, and tilt. Changes in antenna weighting affect the azimuth and tilt of the beam, thereby altering the cell's coverage radius and shape.

[0003] Currently, common methods for 5G MIMO antenna weight planning and optimization include two main approaches. One is static pre-setting, which involves matching weights to different scenarios based on 5G MIMO antenna beam combinations and parameter characteristics, such as wide coverage, building coverage, and hotspot coverage. This uses conventional network parameter optimization models to fix empirical values ​​for several scenario-specific weight combinations. Another common approach is to slice the weight optimization scheme into hourly segments and use prediction algorithms such as convolutional neural networks to achieve time-sharing weight matching between network coverage and user location, enabling hourly-granular weight adjustments based on user distribution predictions. However, existing 5G MIMO antenna weight planning and optimization methods require location-related technologies to determine user location. For massive amounts of user and their movement location information, this approach suffers from low computational efficiency and high computing power requirements, making it difficult to quickly identify hotspots and impacting user experience in hotspot areas. Summary of the Invention

[0004] To address the problems existing in the prior art, embodiments of the present invention provide an antenna weight adaptive adjustment method, apparatus, device, storage medium, and product, which can effectively improve computing efficiency, reduce computing power configuration requirements, achieve rapid hotspot identification, and enhance user perception in hotspot areas.

[0005] In a first aspect, embodiments of the present invention provide an adaptive antenna weight adjustment method, comprising: A fan-shaped grid model is established using the horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes; The sampling point data in the network measurement data is mapped to the fan-ring grid model to determine the total number of users distributed within each fan-ring grid; wherein, the sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and time advance of the corresponding sampling point; Cluster analysis is performed on the total number of users within each of the aforementioned fan-shaped grid cells to identify hotspot areas with dense user distribution. Based on the hotspot area, the antenna weights are adjusted using a preset antenna weight library.

[0006] As an improvement to the above scheme, the step of establishing a fan-shaped grid model using horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes includes: Based on the measurement accuracy of the horizontal angle of arrival, the measurement step size of the horizontal angle of arrival is determined; Based on the measurement accuracy of the horizontal arrival angle, the measurement step size of the vertical arrival angle is determined; The measurement step size of the time advance is determined based on the measurement accuracy of the time advance. A three-dimensional fan-shaped grid model is established using the horizontal arrival angle, the vertical arrival angle, and the time advance as coordinate axes; wherein, the size of the grid unit in the fan-shaped grid model is determined according to the measurement step size of the horizontal arrival angle, the vertical arrival angle, and the time advance.

[0007] As an improvement to the above scheme, the step of mapping the sampling point data in the pre-acquired network measurement data to the sector-ring grid model and determining the total number of users distributed within each sector-ring grid includes: Based on the measurement step size of the horizontal angle of arrival, the vertical angle of arrival, and the time advance, the data of multiple sampling points in each network measurement data are transformed to obtain the grid position data of each sampling point. Map the grid position data of each sampling point to the corresponding grid unit of the fan-ring grid model; The number of mapping points in each grid unit is aggregated according to a preset time period and angle step size to obtain the total number of user distributions in each fan ring grid for multiple time periods. The fan-shaped grid is obtained by dividing the grid units within the fan-shaped grid model according to the angular step size.

[0008] As an improvement to the above scheme, the number of mapping points in each of the grid units is aggregated according to a preset time period and angle step size to obtain the total number of user distributions within each fan-shaped grid in multiple time periods, including: For each sampling time, the number of sampling points mapped to the fan-ring grid is counted in the form of slices to obtain the total number of users in each fan-ring grid at the corresponding sampling time; The total number of users in each sector grid at all sampling times within each time period is counted to obtain the total number of users distributed in each sector grid within the corresponding time period.

[0009] As an improvement to the above scheme, the step of mapping the number of sampling points of the fan-ring grid in the form of slices for each sampling time to obtain the total number of users in each fan-ring grid at the corresponding sampling time includes: For each sampling time, the plane formed by the coordinate axes corresponding to the horizontal and vertical arrival angles within the fan ring grid is the slicing plane, and the measurement step size of the time advance is the slicing step size. The number of sampling points mapped to the slicing plane under each slicing step size is counted to obtain the total number of sampling points of the corresponding slicing plane. The total number of sampling points in all slice planes within the fan-ring grid is counted to obtain the total number of users within the fan-ring grid at the corresponding sampling time.

[0010] As an improvement to the above scheme, the step of performing cluster analysis on the total number of users within each of the aforementioned fan-shaped grids to determine hotspot areas with dense user distribution includes: For each time period, the total number of users within all the fan-ring grids is clustered according to the preset clustering neighborhood radius and minimum number of neighborhood points to determine the hotspot area corresponding to the corresponding time period.

[0011] As an improvement to the above solution, the step of adjusting the antenna weights according to the hotspot area using a preset antenna weight library includes: For each time period, determine the three-dimensional boundary of the hotspot region for that time period; The three-dimensional boundary of the hotspot area is matched with different antenna weight combinations in the antenna weight library to find the antenna weight combination corresponding to the beam coverage range that matches the three-dimensional boundary of the hotspot area. Based on the found antenna weight combinations, determine the antenna weight strategy for the corresponding time period; The antenna weights are adjusted based on the antenna weight strategy for multiple time periods.

[0012] As an improvement to the above scheme, the step of adjusting the antenna weights according to the antenna weight strategy for multiple time periods includes: Sort the antenna weighting strategies for multiple time periods in chronological order; The antenna weighting strategy is compared between two adjacent time periods; When the maximum value of the difference in horizontal bandwidth, the maximum value of the difference in electronic direction angle, the maximum value of the difference in vertical bandwidth, and the maximum value of the difference in electronic downtilt angle between the antenna weighting strategies of two adjacent time periods are all less than their respective threshold values, the antenna weighting strategy of the later time period is updated to the antenna weighting strategy of the previous time period, thus obtaining the final antenna weighting strategy for each time period. The antenna weights are adjusted based on the final antenna weight strategy for each time period.

[0013] As an improvement to the above scheme, the step of determining the antenna weight strategy for the corresponding time period based on the found antenna weight combination includes: For the found antenna weight combinations, based on the total number of users distributed within each of the said sector grids, calculate the number of users that can be covered within a set time period under the current mechanical direction angle and different combinations of electronic azimuth and horizontal beamwidth in the antenna weight combinations; Based on the number of users that can be covered by different combinations of electronic azimuth and horizontal bandwidth, determine the optimal values ​​of electronic azimuth and horizontal bandwidth under the antenna weight combination. Based on the total number of users distributed within each of the aforementioned sector ring grids, calculate the cumulative number of users within a set time period under the current mechanical downtilt angle and different electronic downtilt angles of the antenna weight combination; The optimal value of the electron downtilt angle under the antenna weight combination is determined based on the cumulative number of users with different electron downtilt angles. Based on the optimal values ​​of the electronic azimuth, horizontal beamwidth, electronic downtilt angle, and vertical beamwidth under the aforementioned antenna weight combination, the antenna weight strategy for the corresponding time period is obtained.

[0014] Secondly, embodiments of the present invention provide an antenna weight adaptive adjustment device, comprising: The grid model building module is used to build a fan-shaped grid model with horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes. The user distribution determination module is used to map the sampling point data in the network measurement data to the sector ring grid model and determine the total number of users in each sector ring grid; wherein, the sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and time advance of the corresponding sampling point; The hotspot area determination module is used to perform cluster analysis on the total number of users within each of the fan-ring grids to determine hotspot areas with dense user distribution. The antenna weight adjustment module is used to adjust the antenna weights according to the hotspot area using a preset antenna weight library.

[0015] Thirdly, embodiments of the present invention provide an antenna weight adaptive adjustment device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the antenna weight adaptive adjustment method as described in any one of the first aspects.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the antenna weight adaptive adjustment method as described in any one of the first aspects.

[0017] Fifthly, embodiments of the present invention provide a computer program product, including a computer program / instruction that, when executed by a processor, implements the antenna weight adaptive adjustment method as described in any one of the first aspects.

[0018] Compared to existing technologies, the present invention provides an antenna weight adaptive adjustment method, apparatus, device, storage medium, and product. This method establishes a fan-ring grid model using horizontal angle of arrival, vertical angle of arrival, and timing advance as coordinate axes. Sampling point data from network measurement data is mapped to the fan-ring grid model to determine the total number of users within each fan-ring grid. The sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and timing advance of the corresponding sampling point. Then, cluster analysis is performed on the total number of users within each fan-ring grid to identify hotspot areas with dense user distribution. Subsequently, antenna weights are adjusted based on the hotspot areas using a preset antenna weight library. The present invention, through the established fan-ring grid model, first aggregates sampling points to the grid before analyzing user distribution, effectively improving computational efficiency, reducing computing power requirements, enabling rapid hotspot identification, and enhancing user perception in hotspot areas. Attached Figure Description

[0019] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of an antenna weight adaptive adjustment method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system implementation for adaptive adjustment of antenna weights based on a sector ring grid provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the fan-ring grid model provided in an embodiment of the present invention; Figure 4 This is a three-dimensional schematic diagram of the fan-shaped grid of user distribution density provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the latitude and longitude conversion calculation of MR sampling points provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of MR sampling point height calculation provided in an embodiment of the present invention; Figure 7 This is a three-dimensional schematic diagram of the fan-shaped grid of the aggregated user hotspot area provided in an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the calculation of horizontal wavewidth and electronic azimuth angle provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of electron downtilt angle calculation provided in an embodiment of the present invention; Figure 10 This is another flowchart of the adaptive adjustment of antenna weights provided in an embodiment of the present invention; Figure 11 This is a structural block diagram of an antenna weight adaptive adjustment device provided in an embodiment of the present invention; Figure 12 This is a structural block diagram of an antenna weight adaptive adjustment device provided in an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] It is understood that the various numerical designations used in the embodiments of this invention are merely for descriptive convenience and are not intended to limit the scope of this application. The order of the process numbers does not imply the order of execution; the execution order of each process should be determined by its function and internal logic.

[0023] In embodiments of the invention, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without necessarily requiring or implying any such actual relationship or order between these entities or operations. 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..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element. The term "a plurality or several" refers to two or more.

[0024] Please see Figure 1 , Figure 1 This is a flowchart of an antenna weight adaptive adjustment method provided by an embodiment of the present invention. The antenna weight adaptive adjustment method specifically includes: S11: Establish a fan-shaped grid model with horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes; Measurement is a crucial function of 5G New Radio (NR) systems. Network measurement data reported by the physical layer can be used by the radio resource control sublayer to trigger events such as cell selection / reselection and handover, and can also be used for system operation and maintenance to observe the system's operational status. Network devices should have the capability to measure the specified network measurement data.

[0025] The network measurement data includes: Measurement Reports (MRs) reported by user equipment and / or Minimum Driver Test (MDT) data.

[0026] The network measurement data possesses three-dimensional measurement capabilities, specifically including horizontal angle of arrival (hAOA), vertical angle of arrival (vAOA), and timing advance (TA). In implementation, network measurement data reported by user equipment is pushed from the network parameter management platform of the operator's Intelligent Centralized Optimization System (ICOS) to the system executing the antenna weight adaptive adjustment described in this embodiment (hereinafter referred to as "this system"). The network measurement data is then parsed to obtain relevant key field information, including sampling point information. The standard fields for the key field information are shown in the table below:

[0027] The basic engineering parameters (also known as base station planning data) of the wireless cell obtained from the asset management platform are shown in the table below:

[0028] Based on the specifications of key data fields in the aforementioned network measurement data, this embodiment of the invention extracts three key field indicators: hAOA, vAOA, and TA, and constructs a fan-ring grid model. For example, using the horizontal angle of arrival (hAOA), vertical angle of arrival (vAOA), and time advance (TA) as coordinate axes, and the base station location of the cell as the center, a cell-level fan-ring grid model is established based on the measurement step size of the horizontal angle of arrival (hAOA), vertical angle of arrival (vAOA), and time advance (TA).

[0029] S12: Map the sampling point data in the network measurement data to the fan-ring grid model to determine the total number of users distributed in each fan-ring grid; wherein, the sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and time advance of the corresponding sampling point; In this embodiment of the invention, network measurement data can be reported by the base station or by the user equipment itself. The sampling period for the base station (e.g., 5G GNodeB) instructing the user equipment to measure or for the user equipment to measure can be 2048ms, 5120ms, 10240ms, 1min, 6min, 12min, 30min, or 60min. In this embodiment of the invention, the base station or user equipment performs measurements according to a sampling period of 2048ms and uploads the network measurement data periodically. For example, the measurement results are statistically reported to the ICOS system according to a set reporting period (e.g., 15 minutes or an integer multiple of 15 minutes). For example, if the user equipment performs measurements every 2048ms and obtains the measurement result of one sampling point (including the data of the above sampling point), then the measurement results of multiple sampling points can be obtained within a 15-minute reporting period.

[0030] To minimize the impact of MR measurement on user experience, this embodiment of the invention employs a sampling acquisition mode. The system collects network measurement data daily at preset time granularities (e.g., half an hour, one hour, two hours) and based on preset time intervals, thereby obtaining network measurement data across multiple time periods within a day. For example, it collects one hour of network measurement data every four hours throughout the 24 hours of the day, for a total collection time of six hours, resulting in six one-hour network measurement data sets for subsequent user calculations.

[0031] Furthermore, the sampled network measurement data can be cleaned and calculated, such as identifying and discarding null or invalid values, to obtain key field information from the network measurement data, thereby reducing the amount of subsequent computation.

[0032] In the process of user location geolocation, the sampling point data in the key field information of network measurement data is mapped to the corresponding fan ring grid of the fan ring grid model, so as to realize the accurate identification of user location. This avoids the traditional complex calculation process of performing a series of geometric relationship calculations and geodetic coordinate system transformations on the sampling point data, simplifies the calculation process, and improves calculation efficiency.

[0033] S13: Perform cluster analysis on the total number of users within each of the aforementioned fan-shaped grid cells to determine hotspot areas with dense user distribution; In this embodiment of the invention, for the fan-shaped grid model that aggregates sampling point data, by aggregating and analyzing the hotspot areas with dense user distribution in the model, the deconstruction and calculation of massive data into public geographic grids are avoided, which greatly improves the calculation efficiency related to location analysis and enables rapid identification of hotspots.

[0034] S14: Adjust the antenna weights according to the hotspot area using a preset antenna weight library.

[0035] The antenna weight library includes multiple different antenna weight combinations. Each antenna weight combination indicates the adjustment range of the horizontal beamwidth, vertical beamwidth, electronic azimuth angle, and electronic downtilt angle under the corresponding antenna weight combination.

[0036] like Figure 2 As shown, this system stores network measurement data such as MR / MDT data, an antenna weight library, base station planning data, and a GIS model. Subsequently, a sector-ring grid model is established to map user locations to sector-ring grid points, essentially mapping sampling point data from the network measurement data to the sector-ring grid. Through aggregation calculations, user hotspot distribution—i.e., densely populated user hotspot areas—is determined. Finally, by matching the hotspot areas with the beam coverage range of the antenna weight library, the optimal antenna weight scheme is determined, and the Massive MIMO antenna weights of the base stations within the cell are adjusted. In this embodiment, matching and mapping hotspot areas with the antenna weight library is performed using hAOA, vAOA, and TA measurement step sizes. This yields the optimal antenna weight scheme with a preset time granularity, used to adjust the antenna weights of the base stations within the cell. This achieves adaptive matching between the antenna beam coverage range and the user hotspot distribution (i.e., hotspot areas), significantly improving the efficiency of wireless network antenna weight calculation and analysis, saving computing power and time, reducing computing power configuration requirements, and enhancing user perception in hotspot areas.

[0037] Meanwhile, the hAOA, vAOA, and TA fields used in this invention can come from MDT or MR data, which avoids the drawback of using only MDT data and having a small amount of data due to poor user device support. It makes full use of the sufficient amount of MR data and avoids the massive calculation tasks and calculation errors caused by the conversion process of using MR data and then using positioning algorithms to locate the user's location in traditional methods.

[0038] Furthermore, the establishment of the fan-shaped grid model using horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes includes: Based on the measurement accuracy of the horizontal angle of arrival, the measurement step size of the horizontal angle of arrival is determined; Based on the measurement accuracy of the horizontal arrival angle, the measurement step size of the vertical arrival angle is determined; The measurement step size of the time advance is determined based on the measurement accuracy of the time advance. A three-dimensional fan-shaped grid model is established using the horizontal arrival angle, the vertical arrival angle, and the time advance as coordinate axes; wherein, the size of the grid unit in the fan-shaped grid model is determined according to the measurement step size of the horizontal arrival angle, the vertical arrival angle, and the time advance.

[0039] like Figure 3 As shown, the fan-ring grid model consists of three coordinate axes: hAOA, vAOA, and TA, centered on the cell's base station location. The step size unit for each coordinate axis is set to ΔhAOA, ΔvAOA, and ΔTA, respectively, ultimately constructing a three-dimensional fan-ring grid model (hAOA×vAOA×TA). It can be understood that a sampling point in the network measurement data includes the horizontal angle of arrival (hAOA), vertical angle of arrival (vAOA), and time advance (TA), corresponding to one sampling point. The coordinates of the i-th sampling point in the network measurement data are represented as uAOA. i (hAOA) i vAOA i TA i This allows the sampling points to be mapped onto the sector ring grid model, thereby introducing four remotely adjustable weighting factors: horizontal wavelength, vertical wavelength, electron azimuth angle, and electron downtilt angle. The construction process of the sector ring grid model is as follows: Determine the measurement step size. Based on the specifications of network measurement data such as MR / MDT, determine the measurement step sizes ΔhAOA, ΔvAOA, and ΔTA for hAOA, vAOA, and TA.

[0040] hAOA: The measurement reference direction is due north, counterclockwise, and is simply called the horizontal angle of arrival. hAOA can help determine the user's location with a measurement accuracy of 0.5 degrees and a total of 720 units; for example, [0 degrees, 0.5 degrees) is an interval corresponding to hAOA.0; [359.5 degrees, 360 degrees) is an interval corresponding to hAOA.719, and so on.

[0041] vAOA: The measurement reference direction is due west, counterclockwise, and is also known as the Vertical Angle of Arrival. vAOA helps determine the user's location, with a measurement accuracy of 1 degree and a total of 360 units; for example, [0 degrees, 1 degree) is one interval, corresponding to vAOA.0; [359 degrees, 360 degrees) is another interval, corresponding to vAOA.359, and so on. In NR systems, when the number of antennas is less than or equal to 16, this measurement item takes a null value.

[0042] TA: In 5G networks, the TA value is used to adjust the uplink transmission time of user equipment to ensure synchronization between the user equipment and the base station, also known as timing advance. Current 5G systems typically set the subcarrier spacing u to 15kHz, so one TA equals 39.0625 meters (78.125 meters round trip). That is, TA0 corresponds to 0-39 meters, TA1 corresponds to 39-78 meters, TA2 corresponds to 78-117 meters, and so on. Therefore, the measurement accuracy of TA is taken as 39 meters.

[0043] Establish a sector-to-ring grid model. Using hAOA, vAOA, and TA as coordinate axes, establish a three-dimensional sector-to-ring grid model. The size of each grid unit (i.e., the smallest computational unit) is ΔhAOA × ΔvAOA × ΔTA. Where ΔhAOA = 0.5 degrees, ΔvAOA = 1 degree, and ΔTA = 39 meters.

[0044] For each wireless cell, a fan-shaped grid model centered on the cell can be formed. Considering scenarios involving antenna weight adjustment in fixed site and cell antenna configurations, and given that the physical structure of a 5G MIMO antenna determines its basic radiation characteristics, such as envelope shape, antenna weight adjustment can optimize beamforming within the antenna, making energy more concentrated towards the target direction, or more effectively allocating resources in a multi-user environment. Therefore, antenna weight adjustment can be carried out based on each cell itself, without needing to align all cells, thus eliminating the need for conversion to public geographic data through latitude and longitude calculations. Therefore, this embodiment of the invention changes the traditional Mercator projection coordinate system (5m×5m×5m) grid unit and adopts a fan-shaped grid polar coordinate system with hAOA, vAOA, and TA as coordinate axes. The grid unit is redefined as (ΔhAOA×ΔvAOA×ΔTA) = (0.5 degrees × 1 degree × 39 meters). Based on the measurement step size of hAOA, vAOA, and TA, a cell-level fan-shaped grid model is constructed. This avoids the complex process of transformation calculation using the traditional Mercator projection coordinate system, reduces the calculation difficulty when locating user hotspots, and thus reduces the computational requirements.

[0045] In an optional embodiment, mapping the sampling point data from the pre-acquired network measurement data to the sector-ring grid model and determining the total number of users within each sector-ring grid includes: Based on the measurement step size of the horizontal angle of arrival, the vertical angle of arrival, and the time advance, the data of multiple sampling points in each network measurement data are transformed to obtain the grid position data of each sampling point. Map the grid position data of each sampling point to the corresponding grid unit of the fan-ring grid model; The number of mapping points in each grid unit is aggregated according to a preset time period and angle step size to obtain the total number of user distributions in each fan ring grid for multiple time periods. The fan-shaped grid is obtained by dividing the grid units within the fan-shaped grid model according to the angular step size.

[0046] For example, for 6 hours of network measurement data collected each day, the sampling point data in the network measurement data collected at each time point can be mapped. The specific mapping process is as follows: Initialize grid units: Initialize the user count for each grid unit to 0.

[0047] User location mapping: (1) Sampling point sorting: After the network measurement data (such as MR data) is parsed, it becomes sampling point level data. The measurement step size (ΔhAOA×ΔvAOA×ΔTA) = (0.5 degrees × 1 degree × 39 meters) and it is directly used as the grid unit.

[0048] For hAOA, it is arranged from 0 to 719. For example, if the hAOA value is 20 degrees, the value in the model is 20 / 0.5=10. If the hAOA value is 700 degrees, the value in the model is 700 / 0.5=350.

[0049] For vAOA, it is arranged from 0 to 359. For example, if the vAOA value is 20 degrees, the value in the model is 20 / 1=20. If the vAOA value is 350 degrees, the value in the model is 3500 / 1=350.

[0050] For a TA, it is processed according to 39 meters, for example, TA i (meters) = (2) TA i-1 (index value) + 1) / 2 39. TA i (meters) represents the physical distance corresponding to the i-th TA value, in meters; TA i-1 (Index value) represents the (i-1)th TA value. The index value of the TA can be converted into physical distance through the above calculation.

[0051] (2) Sampling point mapping: The raster position data obtained by converting the hAOA, vAOA, and TA values ​​of each sampling point as described above is mapped to the corresponding sector raster model. For example, the raster position data (10, 350, 39) is mapped to the corresponding raster unit.

[0052] (3) Sampling point count update: When a sampling point is mapped to a grid unit, the user count of that grid unit is incremented by 1, the aggregated number is counted and recorded as the number of users of the grid unit, that is, the number of mapped sampling points of the grid unit is equal to the number of users of that grid unit.

[0053] (4) Sampling Point Convergence: Considering the current antenna beamwidth accuracy, based on ΔhAOA / ΔvAOA, and according to a preset angular step size (e.g., a 5-degree angular step size, i.e., forming a fan-shaped grid with 10 measurement steps on the ΔhAOA axis and 5 measurement steps on the ΔvAOA axis), the total number of users in each fan-shaped grid is mapped to obtain the convergence. Figure 4 As shown, after sorting all the fan-ring grids according to the number of users from most to least, these fan-ring grids are then divided into different percentage intervals (i.e., the range of the total number of users in a certain fan-ring grid compared to the total number of users in all fan-ring grids). In the figure, different gray levels represent different percentage intervals of the number of users in that fan-ring grid. For example, the percentage intervals of the total number of users in the fan-ring grids are sorted from top to bottom, and the fan-ring grids are distinguished in sequence by different gray levels in intervals of 10%, 15%, 25%, and 50%. That is, the 10% fan-ring grids (i.e., the top 10% of the fan-ring grids), the 15% fan-ring grids (i.e., the fan-ring grids sorted between 10% and 25%), the 25% fan-ring grids (i.e., the fan-ring grids sorted between 25% and 50%), and the 50% fan-ring grids (i.e., the fan-ring grids sorted between 50% and 100%) are distinguished by different gray levels. In other embodiments, different colors can also be used for differentiation. For example, similarly, the fan ring grid can be differentiated in sequence according to the intervals of 10%, 15%, 25%, and 50%, using different colors. That is, the 10% fan ring grid (i.e., the top 10% of the fan ring grid), the 15% fan ring grid (i.e., the fan ring grid between 10% and 25%), the 25% fan ring grid (i.e., the fan ring grid between 25% and 50%), and the 50% fan ring grid (i.e., the fan ring grid between 50% and 100%) are distinguished by red, yellow, blue, and green, respectively. In this case, red represents the top 10% of the fan ring grids with a higher percentage of total users, and green represents the bottom 50% of the fan ring grids with a lower percentage of total users.

[0054] Through the above mapping process, the network measurement data sampled at each moment can be mapped to the sector grid, and the total number of users in each sector grid at that moment can be obtained. After further mapping and accumulating the sampling point data of the network measurement data over 6 hours to the sector grid model, the total number of users distributed in each sector grid of the network measurement data for 6 hours each day can be obtained.

[0055] This invention maps sampling points of network measurement data such as MR / MDT to grid cells expressed as (ΔhAOA×ΔvAOA×ΔTA), forming a mapping relationship between MR / MDT sampling points and a three-dimensional fan-ring grid. This mapping is then aggregated to obtain the number of sampling points within each fan-ring grid unit, i.e., the number of users. By traversing all sampled network measurement data and aggregating them according to hAOA, vAOA, and TA, the total number of users distributed within the fan-ring grid over the corresponding time period can be obtained. This invention's mapping and aggregation of sampling points according to hAOA, vAOA, and TA further simplifies calculations and improves computational efficiency.

[0056] Specifically, the number of mapping points in each of the grid units is aggregated according to a preset time period and angle step size to obtain the total number of user distributions within each fan-shaped grid across multiple time periods, including: For each sampling time, the number of sampling points mapped to the fan-ring grid is counted in the form of slices to obtain the total number of users in each fan-ring grid at the corresponding sampling time; The total number of users in each sector grid at all sampling times within each time period is counted to obtain the total number of users distributed in each sector grid within the corresponding time period.

[0057] Specifically, for each sampling time, the number of sampling points mapped to the fan-ring grid in the form of slices is counted to obtain the total number of users in each fan-ring grid at the corresponding sampling time, including: For each sampling time, the plane formed by the coordinate axes corresponding to the horizontal and vertical arrival angles within the fan ring grid is the slicing plane, and the measurement step size of the time advance is the slicing step size. The number of sampling points mapped to the slicing plane under each slicing step size is counted to obtain the total number of sampling points of the corresponding slicing plane. The total number of sampling points in all slice planes within the fan-ring grid is counted to obtain the total number of users within the fan-ring grid at the corresponding sampling time.

[0058] In this embodiment of the invention, for the network measurement data at each time point, based on ΔhAOA / ΔvAOA, an aggregation radius is set, such as the number of sampling points. Aggregation is performed within a 2.5-degree interval, that is, within a 5-degree angular step centered on the sampling point, the grid units are aggregated. The specific process is as follows: For each sampling point, select points in the model centered on their value on the hAOA axis. A grid unit with a 2.5-degree interval is used to form a fan-shaped grid, for example, the hAOA of sampling point i. i of A 2.5-degree interval, from the model center point to the sampling point vAOA i The interval (i.e., from 0 to vAOA along the vAOA axis) i The interval, or the area containing vAOA along the vAOA axis. i (within a 5-degree interval), from the model center point to the sampling point TA i The interval (i.e., from 0 to TA along the TA axis) i The interval, or the area containing TA along the vAOA axis. i The region consisting of the set step size interval (including the range of the set step size) is a fan-shaped grid.

[0059] For each sector ring grid, in the angular dimension, the hAOA of sampling point i is... i of A 2.5-degree interval, from the model center point (i.e., 0 value) along the vAOA axis to the sampling point vAOA. i The plane formed by the interval is the fan-shaped grid section (i.e., the slice plane). Using the measurement step size of the TA coordinate axis as the statistical step size, the total number of sampling points corresponding to the fan-shaped grid section at each statistical step size is counted, denoted as . AOA i The total number of sampling points in the fan-shaped grid section is obtained by counting the number of mapping sampling points of the grid cells in the section. i represents the index of the hAOA value in the collected sampling points, such as i=0, 1, 2, ..., 71.

[0060] It is understandable that the maximum angle of hAOA / vAOA is 360 degrees. When indexing starts from 0 and aggregating in units of 5-degree grids, the index is 360 degrees / 5 degrees - 1 = 71, that is, the maximum value of i is 71.

[0061] Aggregate TA value dimension data, that is, calculate the total number of sampling points on the corresponding sector-ring grid cross-section under all TA values ​​within the sector-ring grid, to obtain the total number of users within each sector-ring grid, denoted as . TA j , where j represents the index of the collected TA value, j=0,1,2,...,3846.

[0062] Comprehensive AOA i Interval, TA j The total number of users within the interval can be used to obtain the total number of users within each sector grid, such as... Figure 4 As shown.

[0063] In the case of a non-fan-shaped raster model, sampling points need to undergo latitude and longitude conversion and Mercator coordinate rasterization before being output to a Geographic Information System (GIS) for relevant analysis. At this point, MR data and the cell's latitude and longitude are used, such as... Figure 5 As shown, the conversion calculation can be performed as follows: Sampling point longitude calculation: Sampling point longitude = cell longitude + (TA) cos(vAOA+downtilt)) sin(zonal azimuth + hAOA) / (111320) cos(local latitude)).

[0064] Latitude calculation of sampling points: Sampling point latitude = cell latitude + (TA) cos(vAOA+downtilt)) sin(zonal azimuth + hAOA) / 110540.

[0065] Sampling point height calculation: Sampling point height = station height TA sin(vAOA + downtilt). Where, station height represents the base station height, used to indicate the height of the antenna on the base station. The sampling point height is calculated as follows: Figure 6 As shown.

[0066] The latitude and longitude coordinate system (WGS84 coordinate system) provides spherical geographic information. In the process of georeferencing this information, the longitude, latitude, and altitude of the sampling points obtained through the latitude and longitude transformation are typically projected onto a plane for processing. The commonly used Mercator transformation formula is: (1); (2); Where R represents the Earth's radius, usually taken as 6,378,137 meters; Ln is the natural logarithm used to calculate a given positive real number. The natural logarithm is the logarithm with the constant e (approximately 2.71828) as its base.

[0067] In the implementation of sampling point convergence and antenna weight adjustment, the sampling points are mapped using the fan-ring grid model and angle as a reference. This avoids the complex calculations of latitude and longitude conversion and Mercator coordinate mapping, reduces the amount of computation, and improves efficiency.

[0068] In one optional embodiment, the step of performing cluster analysis on the total number of users within each of the fan-shaped grid cells to determine hotspot areas with dense user distribution includes: For each time period, the total number of users within all the fan-ring grids is clustered according to the preset clustering neighborhood radius and minimum number of neighborhood points to determine the hotspot area corresponding to the corresponding time period.

[0069] For example, the DBSCAN clustering algorithm can be used to perform clustering analysis on the sampling points of the model mapping. DBSCAN is a density-based clustering algorithm that, by traversing each sampling point and based on a set clustering neighborhood radius (also called the search radius) and the minimum number of neighborhood points within the search range, can cluster regions with sufficiently high density into clusters of arbitrary shapes. This embodiment of the invention uses the grid unit step size as a basis and sets the clustering neighborhood radius, for example, a grid unit with a clustering neighborhood radius of 5 degrees. Clustering analysis is performed on the cumulative total number of users within the fan-shaped grid for each time period. For example, network measurement data is collected for 1 hour every 4 hours. Clustering the collected 1-hour data using a 5-degree grid yields the corresponding hotspot areas for that hour. Traversing 6 hours of data yields 6-hour hotspot areas. The clustering process for sampling points in each time period (e.g., 1 hour) is as follows: Set the clustering neighborhood radius and minimum number of neighborhood points: Considering the correspondence with the antenna horizontal beam adjustment step size of 5 degrees, the clustering neighborhood radius is set to 5 grid units and the minimum number of neighborhood points is set to 100 sampling points. Start traversing the data: Begin with any unvisited 5-degree grid unit; Similar clustering: Check the number of sampling points of the adjacent 5-degree grid units of the current 5-degree grid unit. If the number of sampling points is ≥100, the adjacent 5-degree grid unit is marked as a core point and a new cluster is formed; otherwise, the 5-degree grid unit is marked as a noise point. Expanding clusters: Starting from the core point, recursively add all 5-degree raster units adjacent to it to the current cluster until it can no longer be expanded; Continue traversing: Repeat the above steps for any unvisited 5-degree grid units until all 5-degree grid units have been visited; Noise point classification: 5-degree grid units that do not belong to the core point are uniformly marked as noise points.

[0070] Through the above clustering process, multiple clusters can be obtained, namely, hotspot areas where users are densely distributed within this time period, such as... Figure 7The grayscale area shown is darker. In other embodiments, hotspot areas can also be represented in red. By clustering hourly sampling points, this embodiment of the invention avoids processing at the sampling point level, reduces the workload of clustering calculations, further reduces the complexity of system operations, and further enhances the possibility of real-time deployment and operation.

[0071] In one optional embodiment, adjusting the antenna weights according to the hotspot area using a preset antenna weight library includes: For each time period, determine the three-dimensional boundary of the hotspot region for that time period; The three-dimensional boundary of the hotspot area is matched with different antenna weight combinations in the antenna weight library to find the antenna weight combination corresponding to the beam coverage range that matches the three-dimensional boundary of the hotspot area. Based on the found antenna weight combinations, determine the antenna weight strategy for the corresponding time period; The antenna weights are adjusted based on the antenna weight strategy for multiple time periods.

[0072] Considering the principle that all possible weight combinations of MIMO antennas in a base station can be adjusted within a certain range, this embodiment of the invention establishes an antenna weight library to define antenna adjustment parameters and ranges under different antenna weight combinations. The main parameters include: horizontal beamwidth, vertical beamwidth, electronic azimuth angle, and electronic downtilt angle. The antenna adjustment parameters and ranges can be maintained according to the antenna type and parameters of the equipment manufacturer accessing the network. The antenna weight library is shown in the table below.

[0073]

[0074] Among them, hbw m vbw m , edt m eat m This represents the antenna weight parameters, namely, horizontal beamwidth, vertical beamwidth, electron downtilt angle, and electron azimuth angle, for the antenna weight combination designated by the symbol m.

[0075] In this embodiment of the invention, for network measurement data in each time period, the beam coverage range corresponding to different antenna weight combinations in the antenna weight library is matched with the three-dimensional boundary of the hotspot area by combining the base station location of the cell and the three-dimensional boundary of the hotspot area, and the antenna weight combination with the highest overlap is found as the optimal solution for antenna weight adjustment.

[0076] Understandably, the 3D boundary of a hotspot region can be determined based on the maximum and minimum values ​​of the hotspot region along each coordinate axis. For example, the 3D boundary of the hotspot region can be obtained by sequentially connecting the maximum and minimum values ​​of the hotspot region along each coordinate axis in the connected model. Alternatively, the smallest ellipse / circle formed by connecting the maximum and minimum values ​​of the hotspot region along each coordinate axis can be used as the 3D boundary of the hotspot region. Connecting the 3D boundary of the hotspot region to the center point of the model can achieve a cone-shaped space.

[0077] In the process of calculating the optimal antenna weights and performing adjustments, the antenna weight library is searched based on the three-dimensional boundary of the hotspot area. The beam coverage range formed by the angular step size of the electronic downtilt angle, electronic azimuth angle, horizontal beamwidth and vertical beamwidth of different antenna weight combinations is used as the basis to find the antenna weight combination with the highest overlap with the area surrounded by the three-dimensional boundary of the hotspot area. For example, the antenna weight combination corresponding to the smallest beam coverage range that can cover the area surrounded by the three-dimensional boundary of the hotspot area is found.

[0078] The process of finding the antenna weight combination is as follows: (3); n represents the number of antenna weight combinations in the antenna weight library.

[0079] The antenna weight combination with the highest overlap with the region enclosed by the three-dimensional boundary of the hotspot area is found by searching and matching the antenna weight combination with the code 0. In this embodiment of the invention, for the found antenna weight combination, its electronic downtilt angle and electronic azimuth angle combination can correspond to multiple different combinations of horizontal and vertical beamwidths. Based on the principle of not changing or changing the beamwidth as little as possible, the parameters are adjusted to obtain the optimal antenna weight strategy. For example, if the electronic azimuth angle of the current horizontal beamwidth cannot be adjusted, the horizontal beamwidth is gradually reduced until the electronic azimuth angle is adjustable, the horizontal beamwidth is updated to the reduced horizontal beamwidth, and the combination of the vertical beamwidth, the reduced horizontal beamwidth, the electronic downtilt angle of the reduced horizontal beamwidth, and the electronic azimuth angle is used as the final antenna weight strategy.

[0080] Specifically, the strategy for determining the antenna weights for the corresponding time period based on the found antenna weight combinations includes: For the found antenna weight combinations, based on the total number of users distributed within each of the said sector grids, calculate the number of users that can be covered within a set time period under the current mechanical direction angle and different combinations of electronic azimuth and horizontal beamwidth in the antenna weight combinations; Based on the number of users that can be covered by different combinations of electronic azimuth and horizontal bandwidth, determine the optimal values ​​of electronic azimuth and horizontal bandwidth under the antenna weight combination. Based on the total number of users distributed within each of the aforementioned sector ring grids, calculate the cumulative number of users within a set time period under the current mechanical downtilt angle and different electronic downtilt angles of the antenna weight combination; The optimal value of the electron downtilt angle under the antenna weight combination is determined based on the cumulative number of users with different electron downtilt angles. Based on the optimal values ​​of the electronic azimuth, horizontal beamwidth, electronic downtilt angle, and vertical beamwidth under the aforementioned antenna weight combination, the antenna weight strategy for the corresponding time period is obtained.

[0081] In this embodiment of the invention, the determination of the optimal antenna weighting strategy includes the calculation of the optimal values ​​of the horizontal beamwidth and the electronic azimuth angle, and the optimal value of the electronic downtilt angle.

[0082] For example, for the first antenna weight combination A combination of (electron azimuth angle, horizontal wave width) is denoted as (eat) m hbw m The current mechanical azimuth angle is denoted as at. Calculate the number of users that can be covered per hour, denoted as . Then we have: .

[0083] (4); (5); (6); Where MOD represents the modulo operation, for example INT represents the integer operation. max(angle) m ), min (angle) m ) represent the maximum and minimum coverage angles of the antenna in the horizontal direction, respectively.

[0084] The above calculations yield the following results. for Figure 8 The number of users within all grid cells within the cone-shaped area surrounding the hotspot region is shown. The sum. At this point, the optimal solution is the electronic azimuth angle. m and horizontal wave width hbw m The maximum value is denoted as Here, the argmax function is used to return... The electron azimuth angle corresponding to the maximum value is eat m and horizontal wave width hbw m As the optimal electronic azimuth angle, eat m and horizontal wave width hbw m .

[0085] Calculate the total number of users sampled per hour on the TA dimension (i.e., the distance dimension), denoted as . (j=0, 1, 2, ..., 3846), the corresponding electron downtilt angle is denoted as When the total number of users accumulates to a preset threshold, the corresponding electronic downtilt angle represents the ideal coverage area of ​​the cell. For example, if the threshold is set to 80% of the cell's capacity, when the number of users reaches 80% of the cell's capacity, the electronic downtilt angle represents the optimal coverage area. Figure 9 As shown, electron downtilt angle for: (7); in, This indicates the current mechanical undertilt angle of the residential area. Indicates vertical wavewidth, It indicates a high position.

[0086] The electron downtilt angle calculated using the above formula (7) As the optimal electron downtilt angle The above calculations determine the optimal values ​​for horizontal bandwidth, electron azimuth, and electron downtilt angle in the antenna weight combination for that time period. Combined with the original vertical bandwidth, the optimal antenna weight strategy for that time period can be obtained. Repeating the above calculations for all time periods yields the optimal antenna weight strategy for all time periods.

[0087] Furthermore, the adjustment of antenna weights based on antenna weighting strategies across multiple time periods includes: Sort the antenna weighting strategies for multiple time periods in chronological order; The antenna weighting strategy is compared between two adjacent time periods; When the maximum value of the difference in horizontal bandwidth, the maximum value of the difference in electronic direction angle, the maximum value of the difference in vertical bandwidth, and the maximum value of the difference in electronic downtilt angle between the antenna weighting strategies of two adjacent time periods are all less than their respective threshold values, the antenna weighting strategy of the later time period is updated to the antenna weighting strategy of the previous time period, thus obtaining the final antenna weighting strategy for each time period. The antenna weights are adjusted based on the final antenna weight strategy for each time period.

[0088] In this embodiment of the invention, the process is traversed sequentially over time to obtain the optimal antenna weighting strategy for each time period. For example, for a 6-hour sampling period, the process is traversed sequentially from time 0 to time 23 to obtain the antenna weighting strategy for each of the 6 sampling hours per day. To avoid network fluctuations caused by ineffective adjustments, this embodiment of the invention merges the antenna weighting strategies for two adjacent time periods to reduce fluctuations caused by network adjustments. Specifically, the weights corresponding to adjacent beams at the antenna ports are used as a reference, and their differences are calculated. Two sets of antenna weighting strategies that are less than a threshold value can be merged, that is, the antenna weighting strategy for the next hour remains unchanged from the previous hour. The specific process is as follows: Determine whether the maximum value among the differences in horizontal bandwidth, electron orientation angle, vertical bandwidth, and electron downtilt angle of the antenna weighting strategy in two adjacent time periods is less than their respective threshold values; for example, if the horizontal bandwidths of the antenna weighting strategies in two adjacent time periods are hbw... m and hbw m+1 It can be calculated by hbw m The upper limit value and hbw m+1 The difference between the lower limit value and hbw m+1 The upper limit value and hbw m The difference between the lower limits, and the maximum value between the two differences is hbw. m and hbw m+1 The maximum value of the difference, the maximum value of the difference in electron orientation angle, the difference in vertical wavelength, and the difference in electron downtilt angle are calculated similarly.

[0089] If the maximum value among the differences in horizontal bandwidth, electron direction angle, vertical bandwidth, and electron downtilt angle of the antenna weighting strategies in the two time periods is less than their respective threshold values, then the antenna weighting strategies of the two time periods are merged, for example, the antenna weighting strategy of the next time period is maintained as the antenna weighting strategy of the previous time period.

[0090] The threshold values ​​for horizontal beamwidth, electronic direction angle, vertical beamwidth, and electronic downtilt angle are denoted as d_hbw, d_eat, d_vbw, and d_edt, respectively. The values ​​of d_hbw, d_eat, d_vbw, and d_edt are related to the antenna type and number of beams indicated by the antenna weight library. For example, for a typical 8-beam SSB antenna, the difference in horizontal beamwidth between two beams is usually less than 25, the difference in electronic direction angle is usually less than 10, the difference in vertical beamwidth is usually less than 9, and the difference in electronic downtilt angle is usually less than 6. Therefore, d_hbw=15, d_eat=10, d_vbw=9, and d_edt=6 can be set. When the maximum value of the difference in horizontal beamwidth, the maximum value of the difference in electronic direction angle, the maximum value of the difference in vertical beamwidth, and the maximum value of the difference in electronic downtilt angle in the antenna weight strategy of two time periods are all less than their respective threshold values, the antenna weight strategies of the two time periods are merged.

[0091] After traversing the antenna weighting strategies for all time periods, the antenna weighting strategies for all time periods are aggregated and output according to the number of time periods and the duration of time, and used to adjust the antenna weights of cell base stations in chronological order.

[0092] Compared with the prior art, the beneficial effects of the embodiments of the present invention are as follows: (1) Construct a cell-level sector-ring grid model using the hAOA, vAOA, and TA wireless network measurement units in the MR / MDT data specification as coordinate axes. Map the sampling points of the MR / MDT data reported by user equipment to the sector-ring grid model based on hAOA, vAOA, and TA. Determine the densely populated hotspot areas for each time period based on the total number of users in the grid on an hourly basis. Finally, determine the optimal antenna weight optimization scheme based on the hotspot areas and the total number of users in the grid, and perform hourly antenna weight adjustments, such as... Figure 10As shown, this method can achieve accurate user location identification while avoiding the complex deconstruction and calculation process of converting massive amounts of data into public geographic rasters, reducing coordinate transformation calculations by 80%. This greatly simplifies the calculation complexity of wireless network antenna weights, improves the calculation efficiency related to location analysis, and supports the real-time deployment requirements of adjustment schemes. Taking the data processing of a medium-sized province as an example, using the traditional method to process and clean and normalize the historical user distribution MR sampling point data of the entire province requires at least 20 servers with 32 CPU cores / 300G memory / 10T hard disk for data acquisition and storage; while the scheme described in this embodiment only requires 3 CPU cores / 32G memory / 10T hard disk. A cloud resource processor with 6 cores, 64GB of memory, and 1TB of hard drive can handle the task, greatly improving processing efficiency. It also enhances integration with cloud resource pool services, reduces computing power requirements, and facilitates network deployment. Furthermore, utilizing a fan-ring grid for aggregation and analysis of cumulative user distribution—by first aggregating sampling points to the grid and then analyzing user distribution—allows for rapid identification of hotspot areas. Compared to traditional solutions that analyze each sampling point individually, this method improves analysis efficiency by 30 times, enabling rapid hotspot identification and dynamic matching of coverage and capacity. This allows for rapid adjustment of antenna weights, improving user experience in hotspot areas and achieving optimal overall wireless network performance. This enhances network deployment efficiency and user experience. Additionally, it can determine tidal time periods based on the time-of-day changes in the total number of users, allowing antenna weight adjustments to follow user distribution across different time periods, further improving coverage performance.

[0093] (2) The fan-ring grid model is established based on the hAOA, vAOA, and TA fields in the MR / MDT data. This is an extension of the traditional two-dimensional coordinate plane, which makes the user location identification more accurate. At the same time, the three-dimensional coordinate system of the fan-ring grid model and the antenna weight coefficients are similar in terms of measurement values. This makes it easier to use the antenna beam characteristics and the angle similarity of the fan-ring grid model for subsequent modeling and calculation. This enables the adaptive adjustment of antenna weights in the "one site, one scenario, one policy" approach to adaptively match the antenna beam coverage with the user distribution. This achieves real-time output and adjustment of the antenna weight scheme, further improving network performance and user perception.

[0094] Specifically, the adaptive antenna weight adjustment approach, described as "one policy per site, one scenario," is suitable for scenarios where dynamic service hotspots change in real time with user distribution. It enables rapid antenna weight adjustment for each cell, overcoming the limitations of existing network optimization solutions that typically provide fixed adjustment schemes for static scenarios (such as stadiums and office buildings). Furthermore, in terms of real-time adjustment, the data analysis cycle has been improved from offline analysis at a monthly granularity (typically 7 days) to analysis that can be integrated with network management systems and analyze real-time data. The output granularity of the antenna weight strategy has been improved from daily to hourly. Regarding implementation effectiveness: This embodiment of the invention selects two university scenarios as test subjects, with the start of the school year as the test period. Similar scenario characteristics avoid potential deviations caused by natural business growth. The method of this embodiment was used as a pilot group (145 cells in University A) and a control group (188 cells in University B) using the traditional method. Compared to the control group, the pilot group showed a 5.05% increase in business volume after adopting the method of this embodiment, with a significant increase in traffic and other key indicators remaining stable, as shown in the table below.

[0095]

[0096] After implementing this embodiment of the invention in a certain city, the monthly data of the city for that year was statistically analyzed. A total of 3,276 communities were adjusted, with an average traffic increase of 1% per community (excluding natural growth, but the combined effect of other optimization methods cannot be ruled out). The revenue of a single community increased by 4,938.83 yuan, resulting in an overall revenue increase of approximately 16.18 million yuan.

[0097] (3) By using the parameter difference analysis in the antenna weight strategy at adjacent times, similar weight strategies are merged, which simplifies weight adjustment, avoids frequent network adjustments and possible fluctuations, and can save the manpower costs of high-end optimization personnel and tower workers. At the same time, the use of beam focusing reduces the workload of adjustment and maintenance, such as maintenance costs.

[0098] (4) Using the measurement step size of hAOA, vAOA, and TA as the grid unit avoids conversion calculation, simplifies geographic coordinate calculation, and improves calculation efficiency.

[0099] (4) Focus on user location and associate service hotspots (i.e., hotspot areas where users are densely distributed) with antenna weight library, thereby providing a basic data source of service hotspots for the best coverage performance of cell antennas. Still using the fan ring grid as the reference system, the hotspot area is mapped to the antenna weight library in units of ΔhAOA, ΔvAOA, and ΔTA measurement steps, and the optimal antenna weight scheme is output to achieve fast adaptive beam adjustment.

[0100] See Figure 11 , Figure 11This is a structural block diagram of an antenna weight adaptive adjustment device provided in an embodiment of the present invention. The antenna weight adaptive adjustment device includes: The grid model building module 11 is used to build a fan-shaped grid model with the horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes. The user distribution determination module 12 is used to map the sampling point data in the network measurement data to the fan ring grid model and determine the total number of users in each fan ring grid; wherein, the sampling point data includes the horizontal arrival angle, vertical arrival angle and time advance of the corresponding sampling point. The hotspot area determination module 13 is used to perform cluster analysis on the total number of users within each of the fan-ring grids to determine hotspot areas with dense user distribution. The antenna weight adjustment module 14 is used to adjust the antenna weights according to the hotspot area using a preset antenna weight library.

[0101] In an optional embodiment, the grid model building module 11 includes: The first step length determination unit is used to determine the measurement step length of the horizontal arrival angle based on the measurement accuracy of the horizontal arrival angle; The second step length determination unit is used to determine the measurement step length of the vertical arrival angle based on the measurement accuracy of the horizontal arrival angle. The third step length determination unit is used to determine the measurement step length of the time advance based on the measurement accuracy of the time advance. The model building unit is used to build a three-dimensional fan-shaped grid model with the horizontal arrival angle, the vertical arrival angle, and the time advance as coordinate axes; wherein, the size of the fan-shaped grid in the fan-shaped grid model is determined according to the measurement step size of the horizontal arrival angle, the vertical arrival angle, and the time advance.

[0102] In an optional embodiment, the user distribution determination module 12 includes: The transformation unit is used to transform the data of multiple sampling points in each of the network measurement data according to the measurement step size of the horizontal arrival angle, the vertical arrival angle, and the time advance, so as to obtain the grid position data of each sampling point. A mapping unit is used to map the grid position data of each of the sampling points to the corresponding fan-ring grid of the fan-ring grid model; The aggregation unit is used to aggregate the number of mapping points of each grid unit according to a preset time period and angle step size to obtain the total number of user distributions in each fan ring grid of multiple time periods. Specifically, the total number of users in each fan-ring grid at all sampling times within each time period is counted to obtain the total number of user distributions in each fan-ring grid within the corresponding time period.

[0103] In one optional embodiment, the aggregation unit includes: The first statistical subunit is used to perform a number of mappings of sampling points on the fan-ring grid in the form of slices for each sampling time, so as to obtain the total number of users in each fan-ring grid at the corresponding sampling time. The second statistical subunit is used to count the total number of users in each fan-ring grid at all sampling times within each time period, so as to obtain the total number of user distributions in each fan-ring grid within the corresponding time period.

[0104] In an optional embodiment, the first statistical subunit includes: The third statistical subunit is used to, for each sampling time, take the plane formed by the coordinate axes corresponding to the horizontal and vertical arrival angles within the fan ring grid as the slicing plane, and take the measurement step size of the time advance as the slicing step size, and count the number of sampling points mapped to the slicing plane under each slicing step size to obtain the total number of sampling points of the corresponding slicing plane. The fourth statistical subunit is used to count the total number of sampling points of all slice planes within the fan-ring grid, and to obtain the total number of users within the fan-ring grid at the corresponding sampling time.

[0105] In an optional embodiment, the hotspot area determination module 13 includes: The clustering unit is used to cluster the total number of users within all the fan-ring grids for each time period according to the preset clustering neighborhood radius and minimum number of neighborhood points, and to determine the hotspot area corresponding to the corresponding time period.

[0106] In an optional embodiment, the antenna weight adjustment module 14 includes: Boundary determination unit, used to determine the three-dimensional boundary of the hotspot region for each time period; The matching unit is used to match the three-dimensional boundary of the hot spot area with different antenna weight combinations in the antenna weight library, and find the antenna weight combination corresponding to the beam coverage range that matches the three-dimensional boundary of the hot spot area. The first antenna weight strategy determination unit is used to determine the antenna weight strategy for the corresponding time period based on the found antenna weight combination. The first adjustment unit is used to adjust the antenna weights according to the antenna weight strategy for multiple time periods.

[0107] In an optional embodiment, the antenna weight adjustment module 13 further includes: The sorting unit is used to sort the antenna weighting strategies of multiple time periods in chronological order. A comparison unit is used to compare the antenna weighting strategy in two adjacent time periods; The second antenna weight strategy determination unit is used to update the antenna weight strategy of the next time period to the antenna weight strategy of the previous time period when the maximum value of the difference in horizontal bandwidth, the maximum value of the difference in electronic direction angle, the maximum value of the difference in vertical bandwidth, and the maximum value of the difference in electronic downtilt angle between the antenna weight strategies of two adjacent time periods are all less than their respective threshold values, so as to obtain the final antenna weight strategy for each time period. The second adjustment unit is used to adjust the antenna weights according to the final antenna weight strategy for each time period.

[0108] In an optional embodiment, the first antenna weighting strategy determination unit includes: The first user number determination subunit is used to calculate the number of users that can be covered within a set time period under the current mechanical direction angle and different electronic azimuth angles and horizontal beamwidth combinations in the antenna weight combinations, based on the total number of users distributed in each of the fan ring grids, according to the total number of users distributed in each of the found antenna weight combinations. The first parameter determination subunit is used to determine the optimal values ​​of the electronic azimuth angle and horizontal bandwidth under the antenna weight combination based on the number of users that can be covered according to different combinations of electronic azimuth angle and horizontal bandwidth. The second user count determination subunit is used to calculate the cumulative number of users within a set time period under the current mechanical downtilt angle and the different electronic downtilt angles of the antenna weight combination, based on the total number of users distributed in each of the fan ring grids. The second parameter determination subunit is used to determine the optimal value of the electron downtilt angle under the antenna weight combination based on the cumulative number of users with different electron downtilt angles. The weighting strategy determination subunit is used to obtain the antenna weighting strategy for the corresponding time period based on the optimal values ​​of the electronic azimuth, horizontal beamwidth, electronic downtilt angle and vertical beamwidth under the antenna weighting combination.

[0109] It should be noted that the working process of each module in the antenna weight adaptive adjustment device described in the embodiments of the present invention can refer to the working process of the antenna weight adaptive adjustment method described in the above embodiments, and the technical effect achieved is the same as that of the antenna weight adaptive adjustment method described in the above embodiments, which will not be repeated here.

[0110] See Figure 12 , Figure 12This is a structural block diagram of an antenna weight adaptive adjustment device provided in an embodiment of the present invention. The antenna weight adaptive adjustment device includes a processor 21, a memory 22, and a computer program stored in the memory 22 and executable on the processor 21. When the processor 21 executes the computer program, it implements the steps in the various antenna weight adaptive adjustment method embodiments described above, such as steps S11 to S14.

[0111] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the antenna weight adaptive adjustment device.

[0112] The antenna weight adaptive adjustment device may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of an antenna weight adaptive adjustment device and does not constitute a limitation on the device. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the antenna weight adaptive adjustment device may also include input / output devices, network access devices, buses, etc.

[0113] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the antenna weight adaptive adjustment device, connecting all parts of the device via various interfaces and lines.

[0114] The memory 22 can be used to store the computer program and / or modules. The processor 21 implements various functions of the antenna weight adaptive adjustment device by running or executing the computer program and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0115] If the modules / units integrated in the antenna weight adaptive adjustment device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 21, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0116] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0117] The above description represents the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An adaptive antenna weight adjustment method, characterized in that, include: A fan-shaped grid model is established using the horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes; The sampling point data in the network measurement data is mapped to the fan-ring grid model to determine the total number of users distributed within each fan-ring grid; wherein, the sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and time advance of the corresponding sampling point; Cluster analysis is performed on the total number of users within each of the aforementioned fan-shaped grid cells to identify hotspot areas with dense user distribution. Based on the hotspot area, the antenna weights are adjusted using a preset antenna weight library.

2. The antenna weight adaptive adjustment method as described in claim 1, characterized in that, The process of establishing a fan-shaped grid model using horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes includes: Based on the measurement accuracy of the horizontal angle of arrival, the measurement step size of the horizontal angle of arrival is determined; Based on the measurement accuracy of the horizontal arrival angle, the measurement step size of the vertical arrival angle is determined; The measurement step size of the time advance is determined based on the measurement accuracy of the time advance. A three-dimensional fan-shaped grid model is established using the horizontal arrival angle, the vertical arrival angle, and the time advance as coordinate axes; wherein, the size of the grid unit in the fan-shaped grid model is determined according to the measurement step size of the horizontal arrival angle, the vertical arrival angle, and the time advance.

3. The antenna weight adaptive adjustment method as described in claim 2, characterized in that, The step of mapping the sampling point data from the pre-acquired network measurement data to the sector-ring grid model and determining the total number of users within each sector-ring grid includes: Based on the measurement step size of the horizontal angle of arrival, the vertical angle of arrival, and the time advance, the data of multiple sampling points in each network measurement data are transformed to obtain the grid position data of each sampling point. Map the grid position data of each sampling point to the corresponding grid unit of the fan-ring grid model; The number of mapping points in each grid unit is aggregated according to a preset time period and angle step size to obtain the total number of user distributions in each fan ring grid for multiple time periods. The fan-shaped grid is obtained by dividing the grid units within the fan-shaped grid model according to the angular step size.

4. The antenna weight adaptive adjustment method as described in claim 3, characterized in that, The mapping number of sampling points in each of the grid units is aggregated according to a preset time period and angle step size to obtain the total number of user distributions within each fan-shaped grid in multiple time periods, including: For each sampling time, the number of sampling points mapped to the fan-ring grid is counted in the form of slices to obtain the total number of users in each fan-ring grid at the corresponding sampling time; The total number of users in each sector grid at all sampling times within each time period is counted to obtain the total number of users distributed in each sector grid within the corresponding time period.

5. The antenna weight adaptive adjustment method as described in claim 4, characterized in that, For each sampling time, the number of sampling points mapped to the fan-ring grid in slice form is counted to obtain the total number of users in each fan-ring grid at the corresponding sampling time, including: For each sampling time, the plane formed by the coordinate axes corresponding to the horizontal and vertical arrival angles within the fan ring grid is the slicing plane, and the measurement step size of the time advance is the slicing step size. The number of sampling points mapped to the slicing plane under each slicing step size is counted to obtain the total number of sampling points of the corresponding slicing plane. The total number of sampling points in all slice planes within the fan-ring grid is counted to obtain the total number of users within the fan-ring grid at the corresponding sampling time.

6. The antenna weight adaptive adjustment method as described in claim 4, characterized in that, The cluster analysis of the total number of users within each of the aforementioned sector-ring grids to determine hotspot areas with dense user distribution includes: For each time period, the total number of users within all the fan-ring grids is clustered according to the preset clustering neighborhood radius and minimum number of neighborhood points to determine the hotspot area corresponding to the corresponding time period.

7. The antenna weight adaptive adjustment method as described in claim 6, characterized in that, The step of adjusting the antenna weights according to the hotspot area using a preset antenna weight library includes: For each time period, determine the three-dimensional boundary of the hotspot region for that time period; The three-dimensional boundary of the hotspot area is matched with different antenna weight combinations in the antenna weight library to find the antenna weight combination corresponding to the beam coverage range that matches the three-dimensional boundary of the hotspot area. Based on the found antenna weight combinations, determine the antenna weight strategy for the corresponding time period; The antenna weights are adjusted based on the antenna weight strategy for multiple time periods.

8. The antenna weight adaptive adjustment method as described in claim 7, characterized in that, The adjustment of antenna weights based on antenna weighting strategies across multiple time periods includes: Sort the antenna weighting strategies for multiple time periods in chronological order; The antenna weighting strategy is compared between two adjacent time periods; When the maximum value of the difference in horizontal bandwidth, the maximum value of the difference in electronic direction angle, the maximum value of the difference in vertical bandwidth, and the maximum value of the difference in electronic downtilt angle between the antenna weighting strategies of two adjacent time periods are all less than their respective threshold values, the antenna weighting strategy of the later time period is updated to the antenna weighting strategy of the previous time period, thus obtaining the final antenna weighting strategy for each time period. The antenna weights are adjusted based on the final antenna weight strategy for each time period.

9. The antenna weight adaptive adjustment method as described in claim 7, characterized in that, The strategy for determining antenna weights for a given time period based on the found antenna weight combinations includes: For the found antenna weight combinations, based on the total number of users distributed within each of the said sector grids, calculate the number of users that can be covered within a set time period under the current mechanical direction angle and different combinations of electronic azimuth and horizontal beamwidth in the antenna weight combinations; Based on the number of users that can be covered by different combinations of electronic azimuth and horizontal bandwidth, determine the optimal values ​​of electronic azimuth and horizontal bandwidth under the antenna weight combination. Based on the total number of users distributed within each of the aforementioned sector ring grids, calculate the cumulative number of users within a set time period under the current mechanical downtilt angle and different electronic downtilt angles of the antenna weight combination; The optimal value of the electron downtilt angle under the antenna weight combination is determined based on the cumulative number of users with different electron downtilt angles. Based on the optimal values ​​of the electronic azimuth, horizontal beamwidth, electronic downtilt angle, and vertical beamwidth under the aforementioned antenna weight combination, the antenna weight strategy for the corresponding time period is obtained.

10. An antenna weight adaptive adjustment device, characterized in that, include: The grid model building module is used to build a fan-shaped grid model with horizontal arrival angle, vertical arrival angle, and time lead as coordinate axes. The user distribution determination module is used to map the sampling point data in the network measurement data to the sector ring grid model and determine the total number of users in each sector ring grid; wherein, the sampling point data includes the horizontal angle of arrival, vertical angle of arrival, and time advance of the corresponding sampling point; The hotspot area determination module is used to perform cluster analysis on the total number of users within each of the fan-ring grids to determine hotspot areas with dense user distribution. The antenna weight adjustment module is used to adjust the antenna weights according to the hotspot area using a preset antenna weight library.

11. An antenna weight adaptive adjustment device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the antenna weight adaptive adjustment method as described in any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the antenna weight adaptive adjustment method as described in any one of claims 1 to 9.

13. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the antenna weight adaptive adjustment method according to any one of claims 1 to 9.

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