A method, apparatus and device for optimizing a cell

Abstract

The application discloses a cell optimization method, device and equipment, and the method comprises the following steps: acquiring cell data information of at least one time length level of a cell to be optimized; determining an optimization scheme corresponding to the cell data information of at least one time length level; and performing optimization processing on the cell to be optimized according to the optimization scheme to obtain a processing result. In the foregoing manner, the application realizes that different time level data is targetedly subjected to a cell optimization scheme of a corresponding scene, and the compatibility of different time level data in different scenes is achieved, and the application has high practicability.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically to a method, apparatus, and device for optimizing a cell. Background Technology

[0002] Massive MIMO (Multiple-Input Multiple-Output) is a key technology for improving the coverage and spectrum efficiency of 5G systems. It relies on a larger antenna array to achieve beam scanning of the broadcast / control channel and a narrower transmission beam for the service channel, thereby achieving the goal of improving coverage and spectrum efficiency.

[0003] In the development of mobile communication technology, the rapid pace of technological evolution and the gradual changes in business models have led to most 4G cells in the current network using Massive MIMO antennas for wireless signal coverage. This means that 5G Massive MIMO antennas are essentially converted into 4G Massive MIMO antennas. Furthermore, to facilitate remote electrical adjustment of the antennas, manufacturers have standardized the default beamforming scenarios for 4G and 5G. While this improves the flexibility of 4G antennas and facilitates future upgrades to 5G, it also increases the complexity of adjusting current 4G antennas, making Massive MIMO antenna devices ineffectively compatible in different scenarios. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method, apparatus and device for optimizing cells to overcome or at least partially solve the above problems.

[0005] According to one aspect of the present invention, a cell optimization method is provided, the method comprising:

[0006] Obtain cell data information of at least one time length level for the cell to be optimized;

[0007] Determine the optimization scheme corresponding to cell data information at least one time length level;

[0008] The cell to be optimized is optimized according to the optimization scheme to obtain the processing result.

[0009] According to another aspect of the present invention, a cell optimization apparatus is provided, the apparatus comprising:

[0010] The acquisition module is used to acquire cell data information of at least one time length level of the cell to be optimized.

[0011] The determination module is used to determine the optimization scheme corresponding to cell data information at least one time length level;

[0012] The processing module is used to optimize the cell to be optimized according to the optimization scheme and obtain the processing result.

[0013] According to another aspect of the present invention, a computing device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus;

[0014] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the optimization method of the above-mentioned cell.

[0015] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the optimization method of the cell described above.

[0016] According to the solution provided in the above embodiments of the present invention, cell data information of at least one time length level of the cell to be optimized is obtained; an optimization scheme corresponding to the cell data information of at least one time length level is determined; and the cell to be optimized is optimized according to the optimization scheme to obtain the processing result. This achieves targeted application of cell optimization schemes for different time length levels of data, achieving compatibility between different time length levels of data in different scenarios, and has high practicality.

[0017] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0019] Figure 1 A flowchart of a cell optimization method provided in an embodiment of the present invention is shown;

[0020] Figure 2 A flowchart of historical data processing in a cell optimization method provided in another embodiment of the present invention is shown;

[0021] Figure 3This diagram illustrates the load-sharing cell search and weight optimization scheme generation in a high-load optimization embodiment of the present invention.

[0022] Figure 4 This diagram illustrates the conversion of MDT raster data to TA data in an embodiment of the present invention.

[0023] Figure 5 A schematic diagram illustrating the calculation of 5G cell grid MDT data according to an embodiment of the present invention is shown;

[0024] Figure 6 A flowchart illustrating a specific implementation of the optimization method for a cell is shown.

[0025] Figure 7 A schematic diagram of the structure of the cell optimization device provided in an embodiment of the present invention is shown;

[0026] Figure 8 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0027] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0028] Figure 1 A flowchart illustrating a cell optimization method provided in an embodiment of the present invention is shown. Figure 1 As shown, the method includes the following steps:

[0029] Step 11: Obtain cell data information of at least one time length level for the cell to be optimized. Here, MDT data information of the cell to be optimized at a first time length level and MDT data at a second time length level can be obtained; the first time length level and the second time length are different. Specifically, at least one time length level for the cell to be optimized can include: hourly level and daily level, that is, cell data information for one hour or cell data information for one day can be obtained; the cell data information here can include the cell's MDT (Mobile Data Terminal) data, that is, the minimum drive test data of the cell, which is collected by the terminal, and the terminal can collect data once every 15 minutes;

[0030] Step 12: Determine the optimization scheme corresponding to cell data information at least one time length level; the optimization scheme here may include optimization schemes for high load scenarios and optimization schemes for tidal scenarios, but is not limited to these two schemes, such as scenarios that are not optimized;

[0031] Step 13: Optimize the cell to be optimized according to the optimization scheme to obtain the processing result.

[0032] The solution in this embodiment can achieve targeted cell optimization schemes for different time levels of data, thus achieving compatibility between different time levels of data in different scenarios and has high practicality.

[0033] In practice, the community data information can be preprocessed, which may include MDT data cleaning, data aggregation and other processing.

[0034] Here, MDT data cleaning includes: filtering the input integrated grid-level MDT data (i.e., data without cell classification) or cell grid-level MDT data (i.e., data whose cell classification is known). The filtering range is within a 180° positive coverage area with the cell's mechanical azimuth as the normal direction, and a radius of [missing information].

[0035] The MDT data within a semicircle of meters (H is the station height of the cell) is used as the MDT data for all grid cells in the cell, while the MDT data outside the semicircle is discarded.

[0036] It should be noted that for grid MDT data, the processing method is to count the number of MDT data entries in each grid. In the live network, MDT data is reported at regular intervals. Therefore, the same user may report multiple MDT data entries in a certain area within a certain period of time. However, the number of MDT data entries can be directly approximated as the number of users.

[0037] The aggregation of hourly and daily MDT data includes: hourly MDT data can be used to calculate hourly plans, meaning that a cell can have different weighted plans at different times of the day, achieving "multiple adjustments per day"; at the same time, daily MDT data can be used to calculate daily plans, meaning that each cell can have different plans every day from Monday to Sunday, achieving "one plan per day".

[0038] Specifically, assuming there are N days (records) of data, since a week can be divided into two main scenarios: weekdays (Monday to Friday) and weekends (Saturday and Sunday), therefore, for hourly MDT data... We can divide it into two scenarios and handle them separately. For weekdays, we have:

[0039]

[0040]

[0041] in Indicates the community In the grid The total amount of MDT data at time k on day j. and , Indicates the first The day data corresponds to the weekday dates.

[0042] For weekends,

[0043]

[0044]

[0045] This involves calculating the mean of the hourly MDT data distribution for weekdays and weekends, thereby improving the accuracy of the data.

[0046] Typically, when hourly MDT data is insufficient or incomplete, it is aggregated into daily MDT data for use. However, the daily MDT data for the seven days of the week may vary. To ensure data accuracy and implement a "one-day-one-policy" approach, the data for each day of the week is statistically summarized for the daily MDT data. ,have

[0047]

[0048]

[0049] in Indicates the community In the grid The MDT data for week d in the middle, here Indicates the number of days in the week and , This indicates whether the weekday date of the j-th data item is equal to d.

[0050] In the process of processing the aforementioned MDT data, weight optimization is achieved by collecting current live MDT data. Therefore, the accuracy of the MDT data directly affects the quality of the optimization scheme. However, in the live network, due to various factors (network latency, GPS drift of terminal devices, etc.), the collected MDT data often exhibits a certain degree of fluctuation. Furthermore, in the live network, unexpected events frequently occur, such as holidays and important gatherings, causing significant changes in user distribution compared to normal times. In such cases, using MDT data from a few days prior to the unexpected event cannot generate a weight optimization scheme that provides good coverage of the unexpected scenario. Because the MDT data volume is large, storing a large amount of historical MDT data will accumulate over time, consuming significant storage resources and causing considerable difficulties during use (data reading, caching, and computation consume substantial computational, storage, and time resources).

[0051] Taking into account the above-mentioned problems, in the embodiments of the present invention, the processing of MDT data is as follows: Figure 2 As shown, the current MDT data can be classified into two scenarios: regular data and abnormal data, based on historical data. Then, the data for the corresponding scenario can be updated. This solves the problems of storing and using historical data while improving the reliability and flexibility of the model.

[0052] Specifically, for the hourly raster MDT data obtained above Hourly raster MDT data is divided into two main scenarios: weekdays and weekends. Anomaly detection thresholds are set for all raster data. (Here it is set) For the currently acquired N days (records) of hourly raster MDT data Each case is evaluated individually; if the change in the MDT data volume is no greater than... If it is, then it is considered regular data and updated accordingly.

[0053]

[0054]

[0055] in , indicating the th record in the Nth day (of data) obtained so far. strip, express The number of days corresponding to the data, for example, a certain The data consists of 6 corresponding data points processed by a formula. The average calculation shown is generated as follows: Therefore, whenever After the update, there are = And corresponding updates will be made. When The change exceeded the threshold In this case, there are two scenarios: a sudden decrease or an increase in the amount of data.

[0056] A sudden decrease in the amount of raster MDT data can be caused not only by normal changes in user distribution, but also by many network faults. Therefore, for In this case, directly dealing with the data Discarding data that suddenly increases in volume often indicates a shift in user distribution hotspots, signifying the emergence of new user distribution hotspots. This abnormal data distribution should be addressed accordingly. Updates are being made.

[0057]

[0058]

[0059] in , indicating the th record in the Nth day (of data) obtained so far. similar, express The number of days corresponding to the data. For The daily MDT data shown follows the same processing method.

[0060] In the case of abnormal data When using this data, some raster types may not exist. Therefore, when using abnormal raster data... When calculating the weighting scheme, for rasters without this type of data, simply set the value of their raster MDT data to 0 and include it in the weighting scheme calculation. .

[0061] For regular data If a sufficient amount of data (30 days or more) cannot be obtained initially, then, for the sake of both efficiency and practicality, an anomaly data judgment threshold can be set. The value is set to a large value (e.g., 500%) so that initially, no abnormal data is checked in the acquired data; all data is accumulated and updated as normal. When the data has accumulated for a sufficient number of days (greater than 30 days), the abnormal data detection function can be enabled (i.e., set...). ), and begin filtering the data. In addition, abnormal data. This often reflects the distribution of users in similar scenarios. Therefore, if MDT data for holidays or major events can be obtained, such as MDT data for the Spring Festival, Lantern Festival, Qingming Festival, and Dragon Boat Festival, it can be combined with formula (5) to obtain the initial abnormal data distribution. Then, updating it on a regular basis based on the data will yield more stable results.

[0062] In an optional embodiment of the present invention, step 12 may include:

[0063] Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at at least one time length level, at least one optimization scheme is determined. Specifically, this step may include:

[0064] Step 121: Based on the average Physical Resource Block (PRB) utilization rate of the cell to be optimized at the first time length level and the first preset threshold, determine the first optimization scheme and / or the second optimization scheme; or

[0065] Step 122: Determine the second optimization scheme based on the physical resource block (PRB) utilization rate of the cell to be optimized at the second time length level and the second preset threshold.

[0066] Wherein, the first time length level is hour level, and step 121 may include:

[0067] Step 1211, if the average PRB utilization rate of the cell per hour per day within a week If the value exceeds the first preset threshold, the first optimization scheme is determined to be the optimization scheme for high-load scenarios; otherwise, the value at that moment is recorded. of The number of times the utilization rate exceeds the threshold in a week according to With the second preset threshold The first optimization scheme is determined to be the optimization scheme for high-load scenarios; or

[0068] Step 1212: Obtain the average value of the PRB utilization rate of the cell for each hour of each day within a week. The average PRB utilization rate of the community is greater than For each time period, the second optimization scheme is determined to be the tidal scenario optimization scheme; where i is the cell number and k is the time in hours.

[0069] In this embodiment, hourly PRB utilization data can be processed to determine the cells to be optimized. ,set up The PRB utilization rate of this cell in the k-th hour of the j-th data point.

[0070] For hourly MDT data, hourly calculations need to be performed on the PRB data to filter out the hours that require high-load optimization.

[0071] Specifically, the week is divided into two main scenarios: weekdays and weekends. The PRB data for weekdays is then calculated hourly.

[0072]

[0073]

[0074] That is, the data from each hour is aggregated and then averaged over the number of days, and , Represents the nth data point in the nth day's data. Data for the day. Similarly, PRB data for the weekend is available.

[0075]

[0076]

[0077] After that, if it's during weekday hours of Average utilization Greater than the threshold ( If the threshold is met, it indicates that there is a stable high load phenomenon during that hour of the week, therefore this time is determined to be a regular high load time and output; if the threshold is not met, then the time needs to be statistically analyzed. of The number of times the utilization rate exceeds the threshold during a week. That is, for weekdays,

[0078]

[0079] If the number of days is less than the threshold (i.e.) If so, it indicates that the community The high load issue that occurred this week is sporadic and lacks statistical regularity; therefore, the timing of the event is uncertain. For non-high load hours; conversely, if The value is greater than the threshold. This indicates that the community is usually a high-occupancy community, and the situation only occurred occasionally this week. A decrease in utilization does not affect its status as a high-load period; conversely, for the weekend, a direct judgment is necessary. Is it greater than the threshold? If the load is greater than a certain value, it indicates a high-load period; otherwise, it indicates a low-load period. This results in a set of high-load hours. .

[0080] In an optional embodiment of the present invention, the second time length level is days, and step 122 may include:

[0081] Based on the average daily PRB utilization rate of the community over a week With the third preset threshold The second optimization scheme was determined to be the tidal scenario optimization scheme; where i is the cell number and d is any day within a week.

[0082] In practice, the PRB data is statistically analyzed to identify the times when tidal optimization is needed. Specifically, for hourly MDT data, the hourly PRB data is averaged, resulting in...

[0083]

[0084] Then, all hours with values ​​greater than the mean are considered as periods requiring tidal adjustment, while periods with values ​​less than the mean are considered non-hotspot periods. In these cases, the distribution of the MDT data is highly random, so adjustment is not considered. Therefore, we have...

[0085]

[0086] For daily MDT data, if hourly KPI data can be obtained, then...

[0087]

[0088]

[0089] Similarly, among them Represents the nth data point in the nth day's data. Data from today, here Indicates the day number of the week. This indicates whether the weekday date of the j-th data point is equal to d. Then, it applies this to its hourly KPI data. The judgment is made according to the threshold, that is:

[0090]

[0091] if If neither the value nor the quantity of the above conditions are met, it indicates that the traffic volume on that day (d) is low and there are no high traffic events. Tidal adjustments can be made based on the daily MDT data (meeting the threshold conditions proves that the cell's traffic volume is normal and no adjustment is needed). Alternatively, for daily MDT data, tidal optimization can be performed directly without relying on KPI data.

[0092] In the above embodiments of the present invention, the first optimization scheme includes: for cell i, at each high-load time of the day, determining whether its current horizontal beamwidth can be further reduced; if so, outputting the horizontal beamwidth after reduction by one level; otherwise, outputting the original horizontal beamwidth; for high-load cells that can be reduced, finding high-load sharing cells from its neighboring cells to absorb the services released by the high-load cells; or

[0093] The second optimization scheme includes: sorting the hourly grid MDT data of cell i from largest to smallest to obtain a set of angle of arrival (AOA) and tracking area (TA) data corresponding to all hot zone grids with a heat value greater than a preset heat value in the hourly grid; determining the angle boundary and distance boundary of the hot zone based on the set of AOA and TA data; and obtaining an antenna weight optimization scheme based on the angle boundary and distance boundary.

[0094] The above optimization scheme requires calculating the user heat map of the cell. When optimizing a cell for high load, it is necessary to calculate the user heat map of the high-load cell and adjust the weights of nearby neighboring cells to cover the heat map, thereby achieving service load sharing for the high-load cell. Specifically, for the high-load cell... Processed hourly raster MDT data Sort from largest to smallest to get [ ] indicates the MDT data volume of the [number]th [item]. For large grids, the area comprised of 80% of the total data volume is defined as the hot zone.

[0095]

[0096] in, Indicates high load cell In the The total amount of all raster MDT data at each time point This indicates that the raster MDT data is summed in descending order of data size. Then, the center point of the hot zone is summed. Perform calculations, where

[0097]

[0098]

[0099] here This indicates the number of grid cells in the hot zone. At this point, high-load cells can be identified. The center of the hot zone Locate the eligible communities for load sharing, and then, based on the heat zone... To optimize the weighting of neighboring communities.

[0100] In specific implementation, in the first optimization scheme mentioned above, namely the high-load scenario optimization scheme, for the cell The set of peak load times of the day can be calculated based on its hourly PRB utilization data. For high-load moments, it is determined whether the current horizontal beamwidth can be reduced. As shown in Table 1, which is the default weight parameter table of a certain model of Huawei's Massive MIMO antenna device for 5G, it is determined whether the horizontal beamwidth in the current weight scenario can be further reduced based on the default weight table corresponding to each device. If it can, the horizontal beamwidth after being reduced by one level is output; otherwise, the original horizontal beamwidth is output.

[0101] Table 1. Default Weights of Manufacturer A's 5G Maxive MIMO

[0102]

[0103] When addressing high load issues, if a cell can resolve the issue by shrinking itself, ideally, there should be a nearby cell that can absorb the released traffic, i.e., there should be a high load-sharing cell.

[0104] like Figure 3 First, the coverage hotspots of high-load cells are identified, and then the grid set of user hotspots in high-load cells is determined. and the approximate center point of the hot zone Then, the following criteria were considered: electrically adjustable cells, cells that cannot be carrier aggregation cells (corresponding to multiple cells, so they cannot be changed), cells meeting the inter-site spacing threshold (less than 1000m), and load sharing. Utilization threshold (less than or equal to 40%) and the mechanical azimuth angle of the adjacent cell is the center point of the hot zone from the normal. Find all the cells whose angles are within the range of [-30°, 30°]. The strongest neighborhood, if its If the utilization rate is less than the threshold (40%), it indicates that the current traffic volume is still low, given its proximity to the cell and good signal quality. Therefore, it can be considered an ideal cell for load sharing. In the The thermal flow rate at each moment is distributed. Then, its horizontal and vertical wavelengths are fixed, the electron orientation angle is adjusted, and all areas that can cover the thermal region are identified. The set of electron azimuth angles covering 70% or more of the area Similarly, by adjusting the electron downtilt angle, all areas that can be covered by the hot zone can be identified. A collection of electrons with an undertilt angle of 50%-95% (to prevent interference) of the area. Finally, the M electron azimuth angles and N electron downtilt angles obtained are arranged and combined to obtain M A set of N weight adjustment schemes ,in, This represents the combination that maximizes the number of users covered by the electron azimuth angle and has the largest electron downtilt angle value. This represents the combination with the second largest number of users covered by the electron azimuth angle and the largest value of the electron downtilt angle, ..., This represents the combination with the largest number of users covered by the electronic azimuth angle and the second largest electronic downtilt angle, and so on. This completes the high-load optimization process and outputs the weight adjustment scheme for high-load cells and their sharing cells.

[0105] In practical implementation, in the second optimization scheme mentioned above, namely the tidal optimization scheme, both hourly and daily MDT data can be optimized through the tidal branch. For hourly MDT data, it can be divided into two main scenarios: weekdays and weekends. Then, based on the processed hourly PRB data, the tidal times for each day of the weekdays and weekends are filtered and optimized to generate a "multiple adjustments per day" hourly weighting scheme. For daily MDT data, if hourly PRB data is available, it can be used to determine whether each day of the week meets the adjustment conditions, and then optimize it. If not, it can be optimized directly to uncover the user distribution pattern over the seven days of the week and generate a "one-site-one-policy" daily weighting scheme. Since the types and accuracy of the data currently used are limited (only a certain amount of two-dimensional MDT data), the tidal optimization branch only optimizes the electron azimuth, electron downtilt angle, and horizontal wave width in the weight quadruple based on the MDT data. The specific process is as follows:

[0106] Fast grid density clustering algorithms are used because the MDT (Mean Distribution Data) raster data here has a natural raster structure. The number of MDT data points in each raster represents the raster density. Therefore, a network density-based clustering algorithm can be used to find user hotspots in the raster MDT data. Furthermore, it's not necessary to find the exact shape of the hotspot; only the angular and radial boundaries of the hotspot relative to the cell's polar coordinates need to be located. The specific process is as follows:

[0107] Step 1: For the community Hourly raster MDT data (or daily raster MDT data) Sort the data in descending order, then find the set of AOA and TA data corresponding to all hot zone grids.

[0108]

[0109]

[0110] Step 2: From the AOA set of hot zones Find the maximum and minimum angle boundaries , This leads to the angular boundary of the hot zone. , ;

[0111] Step 3: Similarly, from the AOA set of hot regions Find the maximum and minimum angle boundaries , This leads to the angular boundary of the hot zone. , ;

[0112] Horizontal beamwidth optimization: Based on the default weight table of the manufacturer to which the current cell belongs, find the value that is closest to (…). Take the horizontal wave width with the closest value As the final output.

[0113] Electronic direction angle optimization: Similarly, based on the calculated horizontal wavewidth and current vertical beamwidth Within the adjustable range of the electronic azimuth angle confirmed by the constructed beam scene, find the... The closest electron azimuth angle As output.

[0114] Horizontal beamwidth optimization: Finally, Theoretical electron downtilt angle calculation is performed using the antenna normal direction, which has... ,in This indicates the mechanical downtilt angle of the cell. The calculated horizontal wavewidth... and current vertical beamwidth Within the adjustable range of electron downtilt angles confirmed by the constructed beam pattern, find the electron downtilt angle closest to that value. Output the results.

[0115] After the calculation is completed, a set of weight optimization schemes is obtained, denoted as follows: For hourly-level solutions, to ensure compatibility during subsequent solution integration, the following approach is adopted: The solutions corresponding to 75% and 70% of the user hot zones were calculated and denoted as follows: , .

[0116] like Figure 4 As shown, this is the processed MDT data for each grid cell in the cell. or Calculate its polar coordinates relative to the cell:

[0117]

[0118]

[0119] in,

[0120]

[0121]

[0122]

[0123] This indicates that all polar coordinates are located at the polar angle. The total number of MDT users in the angle interval, such as Figure 4 As shown, for hourly and daily data:

[0124]

[0125]

[0126]

[0127] in This indicates that hourly data has , This indicates that daily data has .

[0128] Similarly, This indicates that all polar coordinate distances are located at... The total number of MDT users in the meter range, such as Figure 4 As shown, for hourly and daily data:

[0129]

[0130]

[0131]

[0132] in This indicates that hourly data has , This indicates that daily data has Here, the downslope angle is calculated directly using the number of users within the annular area.

[0133] like Figure 5 As shown, when optimizing the antenna weights of 5G cells, the distribution patterns of 5G users can be approximated using the MDT data of 4G users. This involves identifying the coverage area of ​​each 5G cell, which generates the 5G cell coverage label; then, based on the 5G coverage label, the corresponding AOA / TA data is calculated to facilitate the statistical analysis of user distribution.

[0134] First, convert the latitude and longitude coordinates of the 5G cell and the MDT grid into planar coordinates. Let the latitude and longitude of a certain coordinate point be lon and lat, then its Mercator coordinates x and y are:

[0135]

[0136]

[0137] The above process is defined as Next, the grid MDT data within the 5G cell coverage area is calculated as the user distribution of the 5G cell, thereby optimizing the weights of the 5G cell. Specifically, each grid cell is transformed relative to the cell cell for the same data time period using polar coordinates:

[0138]

[0139]

[0140]

[0141]

[0142]

[0143]

[0144] Where grid.lon and grid.lat are the grid latitude and longitude, cell.lon and cell.lat are the cell latitude and longitude, and ctm is the Mercator function for latitude and longitude as defined above. For a cell, the set of cell coverage grids is:

[0145]

[0146]

[0147]

[0148] Where azimuth is the mechanical azimuth of the cell, hbw is the horizontal wavewidth of the cell, and % is the modulo operation. This represents the cell height. All grids within the grids represent the 5G coverage tag data for the current cell, which is the calculated 4GMDT data considered as the distribution of 5G cell users. The calculation result of the above cell coverage grids set is as follows: Figure 5 As shown. Because the engineering parameters for the community do not include electronic azimuth, only mechanical azimuth, the calculation here only considers the mechanical azimuth. The part after "or" in the square brackets of the above formula considers the difference. The calculation method when the value is negative.

[0149] like Figure 6 The diagram shows the overall flowchart of the antenna weight optimization scheme for the cell. It can be divided into a high-load optimization branch and a tidal optimization branch. For hourly MDT data, the model can be optimized through both the high-load branch and the tidal branch to generate an hourly scheme with "multiple adjustments per day". For daily MDT data, it is optimized directly through the tidal branch to output a weight optimization scheme for each day of the week, achieving "one policy per station".

[0150] In the implementation of the above-described scheme: due to factors such as the current data quantity and data quality, the hourly MDT data may have large fluctuations. In addition, when the antenna equipment adjusts the weights, it will also have a brief impact on the existing wireless environment. Therefore, in order to avoid excessively frequent adjustments of antenna weights throughout the day, the hourly schemes calculated for each cell are merged.

[0151] Specifically, the first step is to merge adjustment schemes that are similar and occur at adjacent times. This involves iterating through the time sequence (from time 0 to time 23) and determining whether the weighting schemes of two adjacent times can be merged. For example, for... Moment Each electron azimuth angle and Moment Combine the azimuth angles of the electrons to find the current position. The difference between the maximum and minimum values ​​of the azimuth angle of each electron; similarly, for Moment downtilt angle of each electron and Moment By combining the electron downtilt angle and horizontal wavelength, we can find the value at this point. The difference between the maximum and minimum values ​​of the electron downtilt angle and horizontal pulse width, if both differences are less than the threshold ( , , If the two time periods are combined, then the schemes at the two time points will be merged, meaning that at this time, the schemes at the two time points will be merged. The most in Each electron azimuth angle and One electron downtilt angle. Next, considering the number of time periods, the time periods are further merged. Currently, it is assumed that the number of time periods cannot exceed 4 (typically, there are two busy periods in the morning and evening, and two idle periods; this value is configurable). The specific steps are as follows: traverse in chronological order (from 0:00 to 23:00), find the shortest time period, compare it with the lengths of the time periods before and after it, and merge it with the shorter ones (if the lengths of the two time periods before and after it are the same, merge it with the previous time period). Repeat this process until the number of time periods meets the threshold. Finally, the length of the time periods is judged to solve the problem of time period lengths being too short (currently, the threshold for time period length is not less than 6, which is configurable). The solution is as above: traverse in chronological order (from 0:00 to 23:00), find the shortest time period, compare it with the lengths of the time periods before and after it, and merge it with the shorter ones (if the lengths of the two time periods before and after it are the same, merge it with the previous time period). Repeat this process until the length of all time periods meets the threshold.

[0152] Finally, for each moment... The M electron azimuth angles, N electron downtilt angles, and P horizontal wavelengths obtained at this point are then arranged and combined to obtain M N A set of P weight adjustment schemes ,in, This represents the combination of maximum horizontal wavewidth, maximum electron downtilt angle, and maximum electron azimuth angle. This represents the combination with the largest horizontal wavewidth, the largest electron downtilt angle, and the second largest electron azimuth angle, ... This represents the combination with the largest horizontal wave width, the second largest electron downtilt angle, and the largest electron azimuth angle, ... The combination representing the second largest horizontal wave width, the largest electron downtilt angle, and the largest electron azimuth angle is deduced sequentially.

[0153] The same principle applies to the celestial-level solution; since there's no need to calculate redundant schemes, the final output is obtained directly. (3) Integration of expert experience model optimization schemes and final output.

[0154] For daily MDT data, directly use the generated set of tidal optimization schemes. The optimal solution from the expert experience model can be output.

[0155] For hourly MDT data, after a cell completes the optimization of its expert experience model, for certain cells at certain times, it may undergo both tidal optimization and high-load optimization as a load-sharing cell among neighboring cells. Therefore, it is necessary to merge the optimization schemes for each cell at each time. Specifically, since solving the high-load problem is more urgent than solving the tidal optimization (traffic triggering in a single cell) in the current network, and load-sharing cells can also bring considerable traffic by absorbing the traffic released by high-load cells, high-load optimization has a higher priority than tidal optimization. Therefore, if a cell exist A set of high-load optimization solutions at all times Collection of tidal optimization schemes Then only the set of high-load optimization schemes will be retained. Finally, the final set of hourly solutions is obtained. Finally, in each time period, the first scheme is selected as the optimal scheme for output. For the tidal period, this is the combination with the largest horizontal wave width, the largest electron azimuth angle, and the largest electron downtilt angle. For the load-sharing scheme, this is the combination with the largest electron azimuth angle coverage of AOA data and the largest electron downtilt angle.

[0156] Rapid optimization of response weights for sudden events is crucial. Normally, network metrics can be monitored using various indicators. However, if a sudden surge in network metrics is detected in a particular cell or multiple cells within a region—specifically, a sudden increase in traffic—it's likely due to unusual user aggregation, causing a significant shift in user hotspots. In such cases, following the traditional process of "collecting MDT data -> expert experience model calculation -> deriving an optimization solution" is insufficient due to the latency in MDT data collection (periodic push notifications), hindering rapid weight optimization. Using manual drive test data further lengthens the optimization cycle, making it unsuitable for handling sudden traffic spikes. Therefore, in such situations, [the following is a separate, unrelated point:] ... By using abnormal distribution data obtained from processing historical data as input, a corresponding weight optimization scheme is derived, thereby enabling responses to emergencies.

[0157] The above embodiments of the present invention can solve the high load problem of the existing network while discovering tidal value periods. Specifically, the cell optimization method includes both a tidal optimization branch and a high load optimization branch. The tidal optimization branch can be used to discover tidal value periods, realize traffic stimulation, and improve economic efficiency; the high load branch can solve the high load problem of the existing network through high load cell self-optimization and neighborhood load sharing optimization, thereby improving user experience.

[0158] The methods described in the above embodiments of the present invention have high compatibility in many aspects, and therefore have high practicality in existing networks. Specifically, in terms of input data, it can be compatible with existing network input data of different granularities. For more granular hourly MDT data, it can be divided into two main scenarios: weekdays and weekends, to achieve hourly weight optimization. For coarser-granular daily MDT data, it can output a periodic (weekly) daily scheme to achieve effective data utilization. In terms of hardware, it can be compatible with Massive MIMO antenna devices from different manufacturers (ZTE / Huawei) and different network standards (4G / 5G). Furthermore, through database table expansion and a clever generalized weight optimization algorithm, it can quickly become compatible with devices from other manufacturers, facilitating subsequent upgrades to weight optimization capabilities, thus demonstrating high practicality.

[0159] The method described in the above embodiments of the present invention uses MDT data as input data and designs data processing algorithms and cell optimization algorithms. Therefore, it can automate the entire process of data acquisition, data processing, scheme calculation, and weight adjustment, greatly improving production efficiency. It can be used for daily large-scale weight optimization work across the entire network. The designed data processing method solves the problem of low efficiency in storing and using historical data. The output conventional distribution data has high accuracy, while the output abnormal distribution data can be used to calculate weight optimization schemes when user distribution is abnormal, thereby quickly responding to abnormal situations while taking into account the stability of optimization results and the flexibility of application scenarios.

[0160] Figure 7 A schematic diagram of the cell optimization device provided in an embodiment of the present invention is shown. Figure 7 As shown, the device includes:

[0161] The acquisition module 71 is used to acquire cell data information of at least one time length level of the cell to be optimized;

[0162] The determination module 72 is used to determine the optimization scheme corresponding to cell data information of at least one time length level;

[0163] The processing module 73 is used to optimize the cell to be optimized according to the optimization scheme and obtain the processing result.

[0164] Obtain cell data information of at least one time length level for the cell to be optimized, including:

[0165] Obtain mobile data terminal MDT data information at a first time length level and MDT data at a second time length level for the cell to be optimized; the first time length level and the second time length level are different.

[0166] Optionally, an optimization scheme is generated corresponding to cell data information at at least one time length level, including:

[0167] Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at at least one time length level, at least one optimization scheme is determined.

[0168] Optionally, based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at at least one time length level, at least one optimization scheme is determined, including:

[0169] Based on the average Physical Resource Block (PRB) utilization rate of the cell to be optimized at the first time length level and the first preset threshold, determine the first optimization scheme and / or the second optimization scheme; or

[0170] The second optimization scheme is determined based on the physical resource block (PRB) utilization rate of the cell to be optimized at the second time length level and the second preset threshold.

[0171] Optionally, the first time length level is hourly. Based on the average Physical Resource Block (PRB) utilization rate of the cell to be optimized at the first time length level and a first preset threshold, a first optimization scheme and / or a second optimization scheme are determined, including:

[0172] If the average PRB utilization rate of the community per hour per day within a week If the value exceeds the first preset threshold, the first optimization scheme is determined to be the optimization scheme for high-load scenarios; otherwise, the value at that moment is recorded. of The number of times the utilization rate exceeds the threshold in a week ,according to With the second preset threshold The first optimization scheme is determined to be the optimization scheme for high-load scenarios; or

[0173] Obtain the mean of the average PRB utilization rate of the community per hour for each day of the week. The average PRB utilization rate of the community is greater than For each time period, the second optimization scheme is determined to be the tidal scenario optimization scheme; where i is the cell number and k is the time in hours.

[0174] Optionally, the second time length level is on the day level. Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at the second time length level and the second preset threshold, a second optimization scheme is determined, including:

[0175] Based on the average daily PRB utilization rate of the community over a week With the third preset threshold The second optimization scheme was determined to be the tidal scenario optimization scheme; where i is the cell number and d is any day within a week.

[0176] Optionally, the first optimization scheme includes: for cell i, at each high-load time of the day, determining whether its current horizontal beamwidth can be further reduced; if so, outputting the horizontal beamwidth after reduction by one level; otherwise, outputting the original horizontal beamwidth; for high-load cells that can be reduced, finding high-load sharing cells from its neighboring cells to absorb the services released by the high-load cells; or

[0177] The second optimization scheme includes: sorting the hourly grid MDT data of cell i from largest to smallest to obtain a set of angle of arrival (AOA) and tracking area (TA) data corresponding to all hot zone grids with a heat value greater than a preset heat value in the hourly grid; determining the angle boundary and distance boundary of the hot zone based on the set of AOA and TA data; and obtaining an antenna weight optimization scheme based on the angle boundary and distance boundary.

[0178] It should be noted that the embodiments of this device are devices corresponding to the above-described method embodiments. All implementation methods in the above-described method embodiments are applicable to the embodiments of this device and can achieve the same technical effect.

[0179] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the cell optimization method in any of the above method embodiments.

[0180] Figure 8 The diagram shows a structural schematic of a computing device provided in an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0181] like Figure 8 As shown, the computing device may include a processor, a communications interface, memory, and a communications bus.

[0182] The processor, communication interface, and memory communicate with each other via a communication bus. The communication interface is used to communicate with other network elements, such as clients or other servers. The processor executes programs, specifically the relevant steps in the above-described optimization method embodiment for the cell used in the computing device.

[0183] Specifically, the program may include program code, which includes computer operation instructions.

[0184] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0185] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device.

[0186] Specifically, the program can be used to cause the processor to execute the cell optimization method in any of the above method embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units in the above cell optimization method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0187] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the embodiments of the present invention.

[0188] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0189] Similarly, it should be understood that, in order to streamline the embodiments of the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0190] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0191] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0192] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0193] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. Embodiments of the present invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for optimizing a residential community, characterized in that, The method includes: Obtain cell data information of at least one time length level for the cell to be optimized, wherein the time length level includes hourly and daily levels, and the cell data information includes: the cell's MDT data; Determine optimization schemes corresponding to cell data information at least one time length level, wherein the hourly MDT data is used to calculate hourly schemes, and the hourly schemes are schemes in which cells have different weights at different times of the day, so as to achieve multiple adjustments per day; The daily MDT data is used to calculate the daily scheme, which has a different scheme for each cell every day from Monday to Sunday, achieving a one-plan-per-day approach; the hourly scheme is a weighted optimization scheme calculated through the high-load optimization branch and the tidal optimization branch; the daily scheme is a weighted optimization scheme calculated through the tidal optimization branch. The cell to be optimized is optimized according to the optimization scheme to obtain the processing result.

2. The cell optimization method according to claim 1, characterized in that, Obtain cell data information of at least one time length level for the cell to be optimized, including: Obtain mobile data terminal MDT data information at a first time length level and MDT data at a second time length level for the cell to be optimized; the first time length level and the second time length level are different.

3. The cell optimization method according to claim 1, characterized in that, Generate optimization schemes corresponding to cell data information at at least one time length level, including: Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at at least one time length level, at least one optimization scheme is determined.

4. The cell optimization method according to claim 3, characterized in that, Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at at least one time length level, at least one optimization scheme is determined, including: Based on the average Physical Resource Block (PRB) utilization rate of the cell to be optimized at the first time length level and the first preset threshold, determine the first optimization scheme and / or the second optimization scheme; or The second optimization scheme is determined based on the physical resource block (PRB) utilization rate of the cell to be optimized at the second time length level and the second preset threshold.

5. The cell optimization method according to claim 4, characterized in that, The first time length level is hourly. Based on the average Physical Resource Block (PRB) utilization rate of the cell to be optimized at the first time length level and the first preset threshold, a first optimization scheme and / or a second optimization scheme are determined, including: If the average PRB utilization rate of the community per hour per day within a week If the value exceeds a first preset threshold, the first optimization scheme is determined to be the optimization scheme for high-load scenarios; otherwise, the statistical time is... of The number of times the utilization rate exceeds the threshold in a week ,according to With the second preset threshold The first optimization scheme is determined to be the optimization scheme for high-load scenarios; or Obtain the mean of the average PRB utilization rate of the community per hour for each day of the week. The average PRB utilization rate of the community is greater than For each time period, the second optimization scheme is determined to be the tidal scenario optimization scheme; where i is the cell number and k is the time in hours.

6. The cell optimization method according to claim 4, characterized in that, The second time length level is days. Based on the Physical Resource Block (PRB) utilization rate of the cell to be optimized at the second time length level and the second preset threshold, a second optimization scheme is determined, including: Based on the average daily PRB utilization rate of the community over a week With the third preset threshold The second optimization scheme was determined to be the tidal scenario optimization scheme; where i is the cell number and d is any day within a week.

7. The cell optimization method according to claim 4, characterized in that, The first optimization scheme includes: for cell i, at each high-load time of the day, determine whether its current horizontal beamwidth can be further reduced; if so, output the horizontal beamwidth after reducing it by one level; otherwise, output the original horizontal beamwidth; for high-load cells that can be reduced, find high-load sharing cells from its neighboring cells to absorb the services released by the high-load cells; or The second optimization scheme includes: sorting the hourly grid MDT data of cell i from largest to smallest to obtain a set of angle of arrival (AOA) and tracking area (TA) data corresponding to all hot zone grids with a heat value greater than a preset heat value in the hourly grid; determining the angle boundary and distance boundary of the hot zone based on the set of AOA and TA data; and obtaining an antenna weight optimization scheme based on the angle boundary and distance boundary.

8. A community optimization device, characterized in that, The device includes: The acquisition module is used to acquire cell data information of the cell to be optimized at least at least one time length level, wherein the time length level includes hourly and daily levels, and the cell data information includes: the cell's MDT data; The determination module is used to determine optimization schemes corresponding to cell data information at least one time length level. Specifically, the hourly MDT data is used to calculate hourly schemes, where each cell has different weighting schemes at different times of the day, enabling multiple adjustments per day. The daily MDT data is used to calculate daily schemes, where each cell has a different scheme each day from Monday to Sunday, enabling one policy per day. The hourly schemes are weighted optimization schemes generated through high-load optimization branches and tidal optimization branches; the daily schemes are weighted optimization schemes generated through tidal optimization branches. The processing module is used to optimize the cell to be optimized according to the optimization scheme and obtain the processing result.

9. A computing device, comprising: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the cell optimization method as described in any one of claims 1-7.

10. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the cell optimization method as described in any one of claims 1-7.

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