A wireless communication tracking area code edge optimization method based on auxiliary positioning data

Abstract

The application discloses a wireless communication tracking area code edge optimization method based on auxiliary positioning data, steps are as follows: S1 respectively collects crowd measurement data and operator base station engineering parameter data, and carries out cleaning to the collected crowd measurement data, then is associated with the operator base station engineering parameter data, and the base station position information is supplemented; S2 divides a geographical area into a regular grid, in each grid, statistics different TAC density; then a screening strategy based on density is used to screen out hot spot grids; S3 extracts a service cell list according to the hot spot grid; for each TAC, the average distance between the measurement position reported on the crowd measurement data measurement report and the service cell engineering parameter position is calculated; based on the preset distance threshold and combined with the dominance of each TAC in the hot spot grid, a discrimination logic tree is established, and a discrimination result is obtained; S4 generates a specific optimization suggestion list according to the discrimination result. The problems of hysteresis, ambiguity and low efficiency of TAC optimization depending on network management KPI are solved.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication network optimization technology, specifically relating to a wireless communication tracking area code edge optimization method based on assisted positioning data. Background Technology

[0002] In mobile communication networks, the Tracking Area (TA) is the basic unit for mobility management, identified by a TAC (Tracking Area Code). When a user equipment (UE) moves within the same TAC, handover can be performed directly through the X2 / Xn interface (the interface for direct communication between two adjacent base stations), a simple and efficient process. However, when a UE moves between different TACs, handover requires the S1 / NG interface (the interface for indirect communication between two adjacent base stations via the core network as an information exchange node). The signaling path through the S1 / NG interface is longer and more susceptible to core network load and latency, resulting in a lower success rate for cross-TAC handovers compared to handovers within the same TAC, and higher latency, impacting user experience.

[0003] The boundary area of ​​the TAC zone is typically the edge of network coverage, where signal quality fluctuates greatly, easily triggering frequent handovers between base stations and further exacerbating the risk of handover failures. This situation has a more severe impact on user experience. Traditional TAC optimization methods mainly rely on handover performance indicators statistically analyzed by network management, adjusting parameters for base stations with high handover failure rates. This method has significant limitations: 1. Lagging nature: Intervention can only be carried out after problems occur and are reflected in the deterioration of KPI indicators (key statistical items for evaluating the health status of network operation), making it difficult to proactively discover potential network structural problems.

[0004] 2. Vague positioning: Network management KPI indicators can only locate the problematic base station, but cannot accurately reveal whether the problem stems from unreasonable wireless coverage or from unreasonable planning of the TAC area itself.

[0005] 3. Low efficiency: Problem identification relies heavily on the professional skills of engineers, making it impossible to perform batch and rapid analysis.

[0006] In recent years, the wireless network measurement data generated during GNSS-assisted positioning (e.g., network measurement data following the 3GPP TS36.355 LPP protocol, currently generally sourced from positioning map providers or positioning service processes of qualified companies) has become increasingly abundant and widely used in the field of wireless communication. This data contains massive amounts of user sampling location information and measurement reports from their serving cells. These data provide the user's actual movement trajectory within the network, offering a new data dimension and possibility for understanding TAC boundary configuration issues from the user's perspective. Based on this, combining GNSS-assisted positioning wireless network measurement data with IT computer technology makes a method for optimizing tracking area code edges in the field of wireless communication possible. This method can solve industry pain points such as the lag, ambiguous positioning, and low efficiency of current optimization methods that rely solely on network KPI indicators. It has the advantages of timely problem detection, accurate problem location, and efficient problem analysis.

[0007] Chinese patent document CN109996224A discloses an optimization method, system, device, and storage medium for tracking area code (TAC) boundaries. The optimization method includes: extracting updated TAU data for the tracking area and removing invalid data to retain valid TAU counts; performing grid quantization based on reported traffic volume and valid TAU counts to locate base stations with high traffic volume and high TAU counts within grids; using the base station with the highest traffic volume in the grid as the central base station, dividing the base stations within a predetermined distance range of the central base station into segments according to a predetermined gradient to obtain the initial TAC boundary for each segment; calculating the new TAC boundary for each segment through boundary simulation; comparing the total traffic volume of the new TAC boundary relative to the initial TAC boundary in each segment, and selecting the new TAC boundary in the segment with the lowest total traffic volume as the optimal TAC boundary. This prior art, by performing grid quantization on traffic volume and TAU counts, effectively solves the TAC boundary problem for grids with high traffic volume and high TAU counts. However, this existing technology relies solely on TAU data and traffic volume, failing to consider dynamic factors such as user movement trajectories and terrain occlusion. It lacks flexibility in gradient partitioning for complex scenarios like non-urban areas (e.g., a 1000m range + 200m gradient). Furthermore, its boundary calculation is simplistic, relying solely on the "TAC value corresponding to the minimum traffic volume + Thiessen polygon method," neglecting key network metrics such as paging efficiency and handover success rate, potentially leading to optimization imbalances.

[0008] Therefore, it is necessary to propose a wireless communication tracking area code edge optimization method based on auxiliary positioning data to solve the problems of lag, ambiguity, and low efficiency in the existing technology that relies on network management KPIs for TAC optimization. Summary of the Invention

[0009] The technical problem this invention aims to solve is to provide a wireless communication tracking area code edge optimization method based on assisted positioning data. This method acquires crowdsourced testing data, operator KPI data, and operational parameter data. It then performs outlier removal and correlation preprocessing on these data. Next, it identifies TAC interleaving hotspot areas based on the sampling density of different TACs. Following this, it intelligently determines the root cause of problems by combining the dominance of different TACs within the grid. Finally, it automatically outputs the directional optimization strategy results and visualizes them on a map, thus realizing wireless communication tracking area code edge optimization and display. This solves the problems of lag, ambiguity, and low efficiency in existing technologies that rely on network management KPIs for TAC optimization.

[0010] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a wireless communication tracking area code edge optimization method based on assisted positioning data, which specifically includes the following steps: S1. Data Acquisition and Preprocessing: Collect crowd-tested data and operator base station parameter data respectively, clean the collected crowd-tested data, and then associate it with the operator base station parameter data to complete the base station location information. S2. TAC Intertwined Hotspot Area Identification: The geographical area is divided into a regular grid, and different TAC densities are counted within each grid. Then, a density-based filtering strategy is used to filter out hotspot grids, which are the TAC intertwined hotspot areas. S3. Intelligent Root Cause Identification: Extract the list of serving cells based on the hotspot grid; for each TAC, calculate the average distance between the measurement location reported in the crowdsourced data measurement report and the service cell's parameter location; based on the preset distance threshold and combined with the dominance of each TAC within the hotspot grid, establish a discrimination logic tree to obtain the discrimination result; S4. Optimization Strategy Output and Visualization: Based on the judgment results, generate a specific list of optimization suggestions.

[0011] Preferably, the specific steps of step S1 are as follows: S11 Data Acquisition: First, the core fields to be used are extracted from the crowdsourced testing data generated during the positioning process. These core fields include network standard identifier, operator identifier, base station cell identifier, tracking area code, sampling longitude, and sampling latitude. The network management system then acquires the base station cell information to be used, which includes network standard, operator identifier, base station cell identifier, tracking area code, base station cell longitude, base station cell latitude, power, mechanical downtilt angle, and electronic downtilt angle. S12 Data Cleaning: Clean the acquired data and remove invalid or abnormal records; S13 Data Association: By associating Cell_Type, MNC, and ECI fields with the working parameters, each record in the crowdsourced testing data is associated with the working parameter data, so that each UE sampling record corresponds to the network management data of its serving cell.

[0012] Preferably, the removal of invalid and abnormal records in step S12 specifically includes: S121: Remove abnormal records in UE_Lon and UE_Lat, including null values, data with longitude or latitude of 0, and data with insufficient precision where the longitude or latitude is less than 5 digits; S122: Remove abnormal records whose ECI does not conform to the coding range, including domestic operators' coding rules that 4G ECI is a 7-digit hexadecimal number and 5G ECI is a 9-digit hexadecimal number; S123: Remove abnormal records where the TAC does not conform to the encoding range, including 4G TAC being a 4-digit hexadecimal number and 5 TAC being a hexadecimal number in the encoding rules of domestic operators.

[0013] Preferably, the specific steps of step S2 are as follows: S21 Spatial Rasterization: Adopting the mainstream Mercator GIS processing method in China, the latitude and longitude in the data source are converted into Mercator coordinates, and the target area is divided into N m × N m regular grids using Mercator projection, with each grid assigned a unique ID. S22 Grid TAC Density Statistics: For each grid, group and aggregate according to MNC, Cell_Type, and TAC, and count two indicators: TAC_Count, which is the total number of sampling points of this TAC in this grid, and TAC_Ratio, which is the percentage of the number of sampling points of this TAC to the total number of sampling points in this grid. S23 Hotspot Grid Filtering: A density-based filtering strategy is adopted to filter grids and automatically identify grids with multiple TACs and whose sampling density is greater than the density threshold as hotspot grids. These hotspot grids are TAC interleaving hotspot areas, which are also potential high-incidence areas of TAC boundary problems.

[0014] Preferably, the specific steps of step S21 are as follows: S211: Obtain the geographic boundary of the target city area, transform the geographic boundary to the Web Mercator projection coordinate system, and determine the coordinate range (X_min, Y_min, X_max, Y_max) of its outer rectangle; S212: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down, where... ; ; floor(x) represents the floor function; N is the side length of the grid. S213: Based on the origin (X_origin, Y_origin) and a fixed spacing of N meters, generate a grid network covering the entire outer rectangle, where each grid cell is identified by a unique row and column number (i, j), and its geographical extent is defined by coordinates (X_origin + i×N, Y_origin + j×N) and (X_origin + (i+1)×N, Y_origin + (j+1)×N); S214: Output a list of raster networks corresponding to the cities that need to be processed, and assign the corresponding raster correspondence to each sampled coordinate according to the coordinate range; S215: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down.

[0015] Preferably, the density screening conditions in step S23 are as follows: The number of remaining TACs in the raster is ≥2 (ensuring that TACs are interleaved within the raster) and the TAC_Count of a single TAC is >200 and the TAC_Ratio is ≥10% (ensuring that TACs have significant presence). Rasteres that meet the filtering criteria are identified as hotspot rasters, i.e., TAC interlaced hotspot areas. All rasters are traversed, and all raster data that meet the criteria are identified and filtered out.

[0016] Preferably, the specific steps of step S3 are as follows: S31 Data Compilation: Extract the list of serving cells for each hotspot grid, calculate the single-sample service distance, group the base station cell samples, and calculate the average service distance of the serving cells to obtain TAC feature parameters; repeat this process to traverse each hotspot grid and compile the results for all hotspot grids. S32 Root Cause Analysis: Based on a preset distance threshold and the dominance of each TAC within the hotspot grid, a discrimination logic tree is established to perform root cause analysis, classifying the problem as "unreasonable coverage" or "unreasonable TAC planning" to obtain the root cause analysis results.

[0017] Preferably, the specific steps of step S31 are as follows: S311 Extract Serving Cell List: Find all corresponding serving cells through the ECI of the sampling points within the hotspot grid, and list all serving cells (ECIs) that have appeared within the hotspot grid and their respective TACs; S312 calculates the single-sampling service distance: For each TAC, calculate the straight-line distance from all UE sampling points under it to the corresponding serving cell parameter location; the straight-line distance is calculated using the Great-circle distance formula, specifically through the Haversine formula, which is the recognized standard method for calculating the distance between two points on a sphere in geographic information systems; S313 grouped base station cell sampling: Grouping all TAC hotspot interleaved grid samples and their corresponding straight-line distances corresponding to a single serving cell; S314 Discrete Removal and Calculation of Average Serving Distance of Cells: For the sampled distances to the base station completed by each serving cell group, the three-standard-deviation method (3σ criterion) is used for data cleaning. For the standard deviation (σ) of all sampled distances to the base station dataset of the serving cell, outliers exceeding the range of [μ-3σ,μ+3σ] are removed, where μ is the mean of the dataset. The arithmetic mean is calculated to obtain the average distance of the serving cell, denoted as Avg_Distance_TAC, which is used as the feature parameter of this TAC. S315 Loop Calculation: Traverse each hotspot grid, execute steps S311-S314, and calculate the results for all hotspot grids.

[0018] Preferably, in step S32, "unreasonable coverage" means: if there are one or more TACs with Avg_Distance_TAC > 500 meters, then it is determined that the cells under these TACs have problems with excessively far coverage or unreasonable coverage; and optimization suggestions are proposed, wherein the optimization suggestions are to adjust the antenna feeder parameters (low tilt angle, azimuth angle) or power of these cells (especially the cells under the TAC with the largest distance) to reduce their coverage range; The "unreasonable TAC planning" is defined as follows: If the Avg_Distance_TAC of all TACs is less than or equal to 500 meters, then the coverage itself is considered reasonable. However, the TAC planning in this case leads to boundary overlap. Therefore, the TAC with the highest TAC_Ratio in the grid is selected as the "dominant TAC" and kept unchanged. An optimization suggestion is proposed, which is to uniformly plan (cut over) the cells under other TACs in the grid to this "dominant TAC" or another reasonable TAC, and update the relationships between surrounding neighboring cells simultaneously.

[0019] Preferably, the specific steps of step S4 are as follows: S41 Optimization Strategy Output: Based on the root cause analysis results, optimization strategies are output cyclically on a daily basis, divided into the following two cases: For unreasonable coverage, a specific list of cells and antenna / feeder adjustment suggestions are output; for unreasonable TAC planning, a list of cells suggesting TAC adjustment is output; and antenna / feeder or TAC adjustments are made as needed; specifically: Scenario A -- Antenna adjustment is required: i. The adjustment recommendations are determined according to the priority order: power, electronic downtilt angle, and mechanical downtilt angle. ii. If the power configuration value exceeds the default power configuration of the existing network by more than 1dB, it is recommended to reduce the power by 3dB; iii. If the electronic downtilt angle exceeds the default configuration by more than 1 degree, it is recommended to reduce the electronic downtilt angle by 3 degrees; iv. If neither the power nor the electronic downtilt angle is determined to require adjustment, it is recommended to adjust the mechanical downtilt angle downwards. The default adjustment range is 3 degrees, but the actual adjustment should be based on the results of the on-site survey. Scenario B -- TAC needs adjustment: i. Group all samples corresponding to the hotspot interleaving grid of the service cell that needs TAC adjustment by operator and network standard; ii. Identify the TAC with the highest percentage of TAC packets within the same operator among the grids that need adjustment; iii. Output the correspondence between the TAC before and after adjustment; S42 Issue Visualization: The 500m x 500m Mercator projection grid, the sampling points within the grid, and the locations of the service cells associated with the sampling are all included in the map, facilitating subsequent manual review of the rationality of optimization strategies; at the same time, hotspot areas and problem cells are visualized on the GIS map.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention effectively improves data quality and availability, achieving precise optimization of wireless communication tracking area code edges. The optimization results better match the needs of actual scenarios, effectively avoiding optimization imbalances. It can significantly improve optimization efficiency and reduce labor costs. Furthermore, it helps solve the problems of lag, ambiguity, and low efficiency associated with relying on network management KPIs for TAC optimization. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the overall business logic of the wireless communication tracking zone code edge optimization method based on assisted positioning data according to the present invention. Figure 2 This is a schematic diagram of the root cause discrimination logic tree in the wireless communication tracking area code edge optimization method based on auxiliary positioning data of the present invention; Figure 3 This is a schematic diagram of the TAC interleaved hotspot grid in the wireless communication tracking area code edge optimization method based on assisted positioning data of the present invention. Detailed Implementation

[0022] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solutions of the present invention more clearly, and should not be used to limit the scope of protection of the present invention.

[0023] Those skilled in the art will understand that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0024] Example: Figure 1 As shown, the wireless communication tracking area code edge optimization method based on assisted positioning data specifically includes the following steps: S1. Data Acquisition and Preprocessing: Collect crowd-tested data and operator base station parameter data respectively, clean the collected crowd-tested data, and then associate it with the operator base station parameter data to complete the base station location information. The specific steps of step S1 are as follows: S11 Data Acquisition: First, the core fields to be used are extracted from the crowdsourced testing data generated during the positioning process. These core fields include network standard identifier, operator identifier, base station cell identifier, tracking area code, sampling longitude, and sampling latitude. See Table 1 for the specific meanings of these fields. The network management system then acquires the base station cell information to be used. This information includes network standard, operator identifier, base station cell identifier, tracking area code, base station cell longitude, base station cell latitude, power, mechanical downtilt angle, and electronic downtilt angle. See Table 2 for the specific meanings of these fields. Table 1 Explanation of Crowdsourcing Data Fields

[0025] Table 2 Explanation of Operator Fields

[0026] S12 Data Cleaning: Clean the acquired data and remove invalid or abnormal records; The removal of invalid and abnormal records in step S12 specifically includes: S121: Remove abnormal records in UE_Lon and UE_Lat, including null values, data with longitude or latitude of 0, and data with insufficient precision where the longitude or latitude is less than 5 digits; S122: Remove abnormal records whose ECI does not conform to the coding range, including domestic operators' coding rules that 4G ECI is a 7-digit hexadecimal number and 5G ECI is a 9-digit hexadecimal number; S123: Remove abnormal records where the TAC does not conform to the encoding range, including 4G TAC being a 4-digit hexadecimal number and 5 TAC being a hexadecimal number in the encoding rules of domestic operators.

[0027] S13 Data Association: By associating Cell_Type, MNC, and ECI fields with the working parameters, each record in the crowdsourced testing data is associated with the working parameter data, so that each UE sampling record corresponds to the network management data of its serving cell, as shown in Table 3; Table 3: Example of table structure after data association Cell_Type MNC ECI TAC UE_Lon UE_Lat Cell_Lon Cell_Lat Power Mechanical_Downtilt Electrical_Downtilt 4 0 220361003 21002 118.921766 32.037216 118.921991 32.036526 21 3 3 5 1 220361004 21002 118.121766 32.137216 118.121991 32.136526 23 4 3 4 11 220361005 21002 118.521766 32.537216 118.521991 32.536526 23 3 4 … … … … … … … … … … … S2. TAC Intertwined Hotspot Area Identification: The geographical area is divided into a regular grid, and different TAC densities are counted within each grid. Then, a density-based filtering strategy is used to filter out hotspot grids, which are the TAC intertwined hotspot areas. The specific steps of step S2 are as follows: S21 Spatial Rasterization: The Mercator GIS processing method, which is relatively mainstream in China, is adopted to convert the latitude and longitude of the data source into Mercator coordinates, and the target area is divided into regular grids of Nm×Nm using Mercator projection. Each grid is assigned a unique ID. In this embodiment, a Mercator projection grid of 500m×500m is used. The grid size can be increased in cities with low population density. The following description uses 500m as the unit. The specific steps of step S21 are as follows: S211: Obtain the geographic boundary of the target city area, transform the geographic boundary to the Web Mercator projection coordinate system, and determine the coordinate range (X_min, Y_min, X_max, Y_max) of its outer rectangle; S212: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down, where... ; ; floor(x) represents the floor function; N is the side length of the grid.

[0028] S213: Based on the origin (X_origin, Y_origin) and a fixed spacing of 500 meters, generate a grid network covering the entire outer rectangle, where each grid cell is identified by a unique row and column number (i, j), and its geographical extent is defined by coordinates (X_origin + i × N, Y_origin + j × N) and (X_origin + (i+1) × N, Y_origin + (j+1) × N); In some specific embodiments, its geographical extent is defined by coordinates (X_origin + i × 500, Y_origin + j × 500) and (X_origin + (i+1) × 500, Y_origin + (j+1) × 500); S214: Output a list of raster networks corresponding to the cities that need to be processed, and assign the corresponding raster correspondence to each sampled coordinate according to the coordinate range; S215: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down.

[0029] S22 Grid TAC Density Statistics: For each grid, group and aggregate according to MNC, Cell_Type, and TAC, and count two indicators: TAC_Count, which is the total number of sampling points of this TAC in this grid, and TAC_Ratio, which is the percentage of the number of sampling points of this TAC to the total number of sampling points in this grid. S23 Hotspot Grid Filtering: A density-based filtering strategy is used to filter grids and automatically identify grids with multiple TACs and whose sampling density is greater than the density threshold as hotspot grids. These hotspot grids are TAC interleaving hotspot areas, which are also potential high-incidence areas of TAC boundary problems. The density screening criteria in step S23 are as follows: The number of remaining TACs in the raster is ≥2 (ensuring that TACs are interleaved within the raster) and the TAC_Count of a single TAC is >200 and the TAC_Ratio is ≥10% (ensuring that TACs have significant presence). Rasteres that meet the filtering criteria are identified as hotspot rasters, i.e. TAC interlaced hotspot areas. All rasters are traversed, and all raster data that meet the criteria are identified and filtered out. S3. Intelligent Root Cause Identification: Extract the list of serving cells based on the hotspot grid; for each TAC, calculate the average distance between the measurement location reported in the crowdsourced data measurement report and the service cell's parameter location; based on the preset distance threshold and combined with the dominance of each TAC within the hotspot grid, establish a discrimination logic tree to obtain the discrimination result; like Figure 2 As shown, the specific steps of step S3 are as follows: S31 Data Compilation: Extract the list of serving cells for each hotspot grid, calculate the single-sample service distance, group the base station cell samples, and calculate the average service distance of the serving cells to obtain TAC feature parameters; repeat this process to traverse each hotspot grid and compile the results for all hotspot grids. The specific steps of step S31 are as follows: S311 Extract Serving Cell List: Find all corresponding serving cells through the ECI of the sampling points within the hotspot grid, and list all serving cells (ECIs) that have appeared within the hotspot grid and their respective TACs; S312 calculates the single-sampling service distance: For each TAC, calculate the straight-line distance from all UE sampling points under it to the corresponding serving cell parameter location; the straight-line distance is calculated using the Great-circle distance formula, specifically through the Haversine formula, which is the recognized standard method for calculating the distance between two points on a sphere in geographic information systems; S313 grouped base station cell sampling: Grouping all TAC hotspot interleaved grid samples and their corresponding straight-line distances corresponding to a single serving cell; S314 Discrete Removal and Calculation of Average Serving Distance of Cells: For the sampled distances to the base station completed by each serving cell group, the widely used three-standard-deviation method (3σ criterion) is used for data cleaning. For the standard deviation (σ) of all sampled distances to the base station dataset of the serving cell, outliers exceeding the range of [μ-3σ,μ+3σ] are removed, where μ is the mean of the dataset. The arithmetic mean is calculated to obtain the average distance of the serving cell, denoted as Avg_Distance_TAC, which is used as the feature parameter of this TAC. S315 Loop Calculation: Traverse each hotspot grid, execute steps S311-S314, and calculate the results for all hotspot grids; S32 Root Cause Analysis: Based on a preset distance threshold and the dominance of each TAC within the hotspot grid, a discrimination logic tree is established to perform root cause analysis, classifying the problem as "unreasonable coverage" or "unreasonable TAC planning" to obtain the root cause analysis results.

[0030] In step S32, "unreasonable coverage" means: if there are one or more TACs with Avg_Distance_TAC > 500 meters, then it is determined that the cells under these TACs have problems with excessive or unreasonable coverage; and optimization suggestions are proposed, wherein the optimization suggestions are to adjust the antenna feeder parameters (low tilt angle, azimuth angle) or power of these cells (especially the cells under the TAC with the largest distance) to reduce their coverage range. The "unreasonable TAC planning" is defined as follows: If the Avg_Distance_TAC of all TACs is less than or equal to 500 meters, then the coverage itself is considered reasonable. However, the TAC planning in this case leads to boundary overlap. Therefore, the TAC with the highest TAC_Ratio in the grid is selected as the "dominant TAC" and kept unchanged. An optimization suggestion is proposed, which is to uniformly plan (cut over) the cells under other TACs in the grid to this "dominant TAC" or another reasonable TAC, and update the relationships between surrounding neighboring cells simultaneously.

[0031] S4. Optimization Strategy Output and Visualization: Generate a specific list of optimization suggestions based on the judgment results; The specific steps of step S4 are as follows: S41 Optimization Strategy Output: Based on the root cause identification results, optimization strategies are output in a daily cycle, divided into the following two cases: For unreasonable coverage, a specific list of cells and antenna adjustment suggestions are output; for unreasonable TAC planning, a list of cells with suggested TAC adjustments is output; and antennas or TACs are adjusted as needed. Case A – Antenna feeder adjustment is required: i. The adjustment recommendations are determined according to the priority order: power, electronic downtilt angle, and mechanical downtilt angle. ii. If the power configuration value exceeds the default power configuration of the existing network by more than 1dB, it is recommended to reduce the power by 3dB; iii. If the electronic downtilt angle exceeds the default configuration by more than 1 degree, it is recommended to reduce the electronic downtilt angle by 3 degrees; iv. If neither the power nor the electronic downtilt angle needs adjustment, it is recommended to adjust the mechanical downtilt angle. The default adjustment range is 3 degrees, but the actual adjustment should be based on the results of the on-site survey. Scenario B -- TAC needs adjustment: i. Group all samples corresponding to the hotspot interleaving grid of the service cell that needs TAC adjustment by operator and network standard; ii. Identify the TAC with the highest percentage of TAC packets within the same operator among the grids that need adjustment; iii. Output the correspondence between the TAC before and after adjustment; S42 Problem Visualization: The Mercator projection grid of N×N (m) (preferably 500m×500m), the sampling points within the grid, and the location of the service cells associated with the sampling are all included in the map to facilitate subsequent manual review of the rationality of the optimization strategy; at the same time, hot spots and problem cells are visualized on the GIS map.

[0032] Specific application example: The following describes the method of the present invention in detail using the optimization situation in H city, the capital city of a southern province. This wireless communication tracking area code edge optimization method based on assisted positioning data specifically includes the following steps: 1. Data Acquisition and Preprocessing: For the urban area of ​​H city, a one-day sampling of mobile base station reports was set up, collecting more than 350 million GNSS crowdsourced testing records. After removing various data anomalies, 250 million mobile sampling points were usable. After statistics, a total of 80,000 base station cells were involved. The collected data fields were associated with the fields required by the network management data for future use.

[0033] 2. Identification of TAC interlacing hotspot areas: The geographical area was divided into 500m × 500m Mercator projection grids. Within each grid, the number and proportion of sampling points for different TACs were counted. A density-based filtering strategy was used to automatically identify grids with multiple TACs and high sampling densities for each. One TAC interleaving hotspot grid (ID: 247_559) was identified in the urban area. A total of 17,687 sampling points for TAC 23456 (accounting for 45.5%) and densely interleaved TAC 17666 (accounting for 54.5%) were identified.

[0034] 3. Intelligent identification of root causes of problems: For the TAC interleaved hotspot grid (ID: 247_559), the average service distance of the cell under TAC 17666 (unique cell identifier: 10802366) is calculated to be 788 meters, exceeding the 500-meter distance threshold. The system automatically diagnoses this problem as "unreasonable coverage".

[0035] 4. Optimize strategy output and visualization: Based on the stored base station configuration data, the power is determined to be 22dB, lower than the default value of 23dB. Therefore, the power is considered normal and no adjustment is needed. Next, the electronic downtilt angle is determined to be 14 degrees, higher than the default configuration of 9 degrees. An optimization strategy is then implemented, reducing the angle by 3 degrees. This is visualized on the GIS interface for manual review. Figure 3 .

[0036] The system outputs an optimization strategy, as shown in Table 4.

[0037] Table 4: Optimization Strategy Output

[0038] For those skilled in the art, the specific embodiments are merely exemplary descriptions of the present invention. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution to other situations without modification, are all within the protection scope of the present invention.

Claims

1. A wireless communication tracking area code edge optimization method based on assisted positioning data, characterized in that, Specifically, the following steps are included: S1. Data Acquisition and Preprocessing: Collect crowd-tested data and operator base station parameter data respectively, clean the collected crowd-tested data, and then associate it with the operator base station parameter data to complete the base station location information. S2. TAC Intertwined Hotspot Area Identification: The geographical area is divided into a regular grid, and different TAC densities are counted within each grid. Then, a density-based filtering strategy is used to filter out hotspot grids, which are the TAC intertwined hotspot areas. S3. Intelligent Root Cause Identification: Extract the list of serving cells based on the hotspot grid; for each TAC, calculate the average distance between the measurement location reported in the crowdsourced data measurement report and the service cell's parameter location; based on the preset distance threshold and combined with the dominance of each TAC within the hotspot grid, establish a discrimination logic tree to obtain the discrimination result; S4. Optimization Strategy Output and Visualization: Based on the judgment results, generate a specific list of optimization suggestions.

2. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11 Data Acquisition: First, core fields are extracted from the crowdsourced testing data generated during the positioning process. These core fields include network standard identifier, operator identifier, base station cell identifier, tracking area code, sampling longitude, and sampling latitude. The network management system obtains the base station cell information that needs to be used. The base station cell information includes network type, operator identifier, base station cell identifier, tracking area code, base station cell longitude, base station cell latitude, power, mechanical downtilt angle, and electronic downtilt angle. S12 Data Cleaning: Clean the acquired data and remove invalid or abnormal records; S13 Data Association: By associating Cell_Type, MNC, and ECI fields with the working parameters, each record in the crowdsourced testing data is associated with the working parameter data, so that each UE sampling record corresponds to the network management data of its serving cell.

3. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 2, characterized in that, The removal of invalid and abnormal records in step S12 specifically includes: S121: Remove abnormal records in UE_Lon and UE_Lat, including null values, data with longitude or latitude of 0, and data with insufficient precision where the longitude or latitude is less than 5 digits; S122: Remove abnormal records whose ECI does not conform to the coding range, including domestic operators' coding rules that 4G ECI is a 7-digit hexadecimal number and 5G ECI is a 9-digit hexadecimal number; S123: Remove abnormal records where the TAC does not conform to the encoding range, including 4G TAC being a 4-digit hexadecimal number and 5 TAC being a hexadecimal number in the encoding rules of domestic operators.

4. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 2, characterized in that, The specific steps of step S2 are as follows: S21 Spatial Rasterization: The Mercator GIS processing method is adopted to convert the latitude and longitude in the data source into Mercator coordinates, and the target area is divided into Nm×Nm regular grids using Mercator projection, with each grid assigned a unique ID; S22 Grid TAC Density Statistics: For each grid, group and aggregate according to MNC, Cell_Type, and TAC, and count two indicators: TAC_Count, which is the total number of sampling points of this TAC in this grid, and TAC_Ratio, which is the percentage of the number of sampling points of this TAC to the total number of sampling points in this grid. S23 Hotspot Grid Filtering: A density-based filtering strategy is adopted to filter grids and automatically identify grids with multiple TACs and whose sampling density is greater than the density threshold as hotspot grids. The hotspot grids are TAC interleaved hotspot areas.

5. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 4, characterized in that, The specific steps of step S21 are as follows: S211: Obtain the geographic boundary of the target city area, transform the geographic boundary to the Web Mercator projection coordinate system, and determine the coordinate range (X_min, Y_min, X_max, Y_max) of its outer rectangle; S212: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down, where... ； ； S213: Based on the origin (X_origin, Y_origin) and a fixed spacing of N meters, generate a grid network covering the entire outer rectangle, where each grid cell is identified by a unique row and column number (i, j), and its geographical extent is defined by coordinates (X_origin + i×N, Y_origin + j×N) and (X_origin + (i+1)×N, Y_origin + (j+1)×N); S214: Output a list of raster networks corresponding to the cities that need to be processed, and assign the corresponding raster correspondence to each sampled coordinate according to the coordinate range; S215: Based on the coordinates of the lower left corner of the outer rectangle, a standardized grid system origin (X_origin, Y_origin) is calculated by rounding down.

6. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 4, characterized in that, The screening criteria in the density screening strategy in step S23 are as follows: The number of remaining TACs within a grid is ≥2, and the TAC_Count of a single TAC is >200 and the TAC_Ratio is ≥10%; Rasteres that meet the filtering criteria are identified as hotspot rasters, i.e., TAC interlaced hotspot areas. All rasters are traversed, and all raster data that meet the criteria are identified and filtered out.

7. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 4, characterized in that, The specific steps of step S3 are as follows: S31 Data Compilation: Extract the list of serving cells for each hotspot grid, calculate the single-sample service distance, group the base station cell samples, and calculate the average service distance of the serving cells to obtain TAC feature parameters; repeat this process to traverse each hotspot grid and compile the results for all hotspot grids. S32 Root Cause Analysis: Based on a preset distance threshold and the dominance of each TAC within the hotspot grid, a discrimination logic tree is established to perform root cause analysis, classifying the problem as "unreasonable coverage" or "unreasonable TAC planning" to obtain the root cause analysis results.

8. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 7, characterized in that, The specific steps of step S31 are as follows: S311 Extract Serving Cell List: Find all corresponding serving cells through the ECI of the sampling points in the hotspot grid, and list all serving cells that have appeared in the hotspot grid and their respective TACs; S312 calculates the single-sampling service distance: For each TAC, calculate the straight-line distance from all UE sampling points under it to the corresponding serving cell parameter location; S313 grouped base station cell sampling: Grouping all TAC hotspot interleaved grid samples and their corresponding straight-line distances corresponding to a single serving cell; S314 Discrete Removal and Calculation of Average Serving Distance of Cells: For the sampling distances to base stations completed by each serving cell group, the three-standard-deviation method is used for data cleaning. For the standard deviation of all sampling distance datasets to base stations of the serving cell, outliers exceeding the range of [μ-3σ,μ+3σ] are removed, where μ is the mean of the dataset. The arithmetic mean is calculated to obtain the average distance of the serving cell, denoted as Avg_Distance_TAC, which is used as the feature parameter of this TAC. S315 Loop Calculation: Traverse each hotspot grid, execute steps S311-S314, and calculate the results for all hotspot grids.

9. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 7, characterized in that, In step S32, "unreasonable coverage" means: if there are one or more TACs with Avg_Distance_TAC > 500 meters, then it is determined that the cells under these TACs have problems with excessive or unreasonable coverage; and optimization suggestions are proposed, wherein the optimization suggestions are to adjust the antenna feeder parameters or power of these cells to reduce their coverage range; "Unreasonable TAC planning" means: If the Avg_Distance_TAC of all TACs is ≤500 meters (planned coverage threshold, which can be modified according to the actual planning), then the coverage itself is considered reasonable. However, the planning of TACs has led to boundary overlap. Therefore, the TAC with the highest TAC_Ratio in the grid is selected as the "dominant TAC" and kept unchanged. An optimization suggestion is proposed, which is to uniformly plan the cells under other TACs in the grid to this "dominant TAC" or another reasonable TAC, and update the relationship of the surrounding neighboring cells at the same time.

10. The wireless communication tracking area code edge optimization method based on assisted positioning data according to claim 7, characterized in that, The specific steps of step S4 are as follows: S41 Optimization Strategy Output: Based on the root cause identification results, optimization strategies are output in a daily cycle, divided into the following two cases: For unreasonable coverage, a specific list of cells and antenna adjustment suggestions are output; for unreasonable TAC planning, a list of cells with suggested TAC adjustments is output; and antennas or TACs are adjusted as needed. S42 Problem Visualization: The N m × N m Mercator projection grid, the sampling points within the grid, and the location of the service cells associated with the sampling are all included in the map. At the same time, hot spots and problem cells are visualized on the GIS map.

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