CN112566143B - Load balancing method, device and computing device - Google Patents

Abstract

The embodiment of the invention relates to the technical field of wireless communication, and discloses a load balancing method, a load balancing device and computing equipment. The method comprises the following steps: determining a problem cell from the cell to be balanced according to a load balancing result of the cell to be balanced; calculating an actual azimuth angle of the problem cell; and if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to a preset deviation threshold value, after the preset azimuth angle is adjusted to the actual azimuth angle, carrying out load balancing again on the cell to be balanced. Through the mode, the embodiment of the invention can judge the load imbalance caused by the antenna feeder coverage difference, and the optimization effect is improved.

Description

Load balancing method, device and computing device

technical field

Embodiments of the present invention relate to the field of wireless communication technologies, and in particular, to a load balancing method, apparatus, and computing device.

Background technique

With the continuous development of Long Term Evolution (Long Term Evolution, LTE) services, hot spots and sudden high load areas frequently appear. The problem of insufficient capacity is generally solved by measures such as cell expansion and new site construction. By monitoring the key performance indicators (Key Performance Indicators, KPI) of the existing network, it will be found that the problem of capacity difference between the same coverage cells is becoming more and more serious, and the number of users or the utilization rate of physical resource blocks (PRB) in some cells has already It is close to the capacity limit, while the resource utilization rate of other cells is very low, resulting in a waste of investment resources. Therefore, how to balance the load between cells with the same coverage or overlapping coverage areas is of great significance.

At present, the load balancing method is mainly optimized through the user's autonomous switching of cells, and the load imbalance caused by the difference in the coverage of the antenna and feeder cannot be determined, and the optimization effect is limited.

SUMMARY OF THE INVENTION

In view of the above problems, embodiments of the present invention provide a load balancing method, apparatus, and computing device, which overcome the above problems or at least partially solve the above problems.

According to an aspect of the embodiments of the present invention, a load balancing method is provided, the method includes: determining a problem cell from the to-be-balanced cell according to a load balancing result of the to-be-balanced cell; calculating the actual value of the problem cell Azimuth; if the difference between the actual azimuth and its corresponding preset azimuth is greater than or equal to a preset deviation threshold, after adjusting the preset azimuth to the actual azimuth, The cell performs load balancing again.

In an optional manner, the calculating the actual azimuth angle of the problem cell further includes: obtaining the minimum drive test data of the problem cell; and obtaining user location data according to the minimum drive test data; According to the user location data, the coverage center of the problem cell is determined; according to the coverage center, the actual azimuth of the problem cell is calculated.

In an optional manner, the method further includes: if the difference between the actual azimuth and its corresponding preset azimuth is less than the preset deviation threshold, calculating the coverage distance of each of the cells to be equalized ; Determine the cells to be calibrated according to the coverage distances of the cells to be equalized; and perform load balancing on the cells to be equalized again after performing antenna calibration on the cells to be calibrated.

In an optional manner, the calculating the coverage distance of each cell to be equalized further includes: acquiring the antenna height and downtilt angle of the cell to be equalized; according to the antenna height and downtilt angle of the cell to be equalized , and calculate the coverage distance of the cells to be equalized.

In an optional manner, before determining the problem cell from the cell to be balanced according to the load balancing result of the cell to be balanced, the method further includes: acquiring historical load data of the cell to be balanced; The historical load data is substituted into a preset prediction model to determine a preset prediction function, wherein the preset prediction function is related to time; load balancing is performed on the cells to be balanced according to the preset prediction function.

In an optional manner, the preset prediction model includes a load trend function model, a periodic function model and a holiday function model; then, the historical load data is substituted into the preset prediction model to determine the preset prediction function, further comprising: substituting the historical load data into the load trend function model, the periodic function model and the holiday function model respectively, and fitting the load trend function, the periodic function and the holiday according to the L-BFGS quasi-Newton method function; the preset prediction function is determined according to the load trend function, the periodic function and the holiday function.

According to another aspect of the embodiments of the present invention, a load balancing apparatus is provided, the apparatus includes: a problem cell determination module, configured to determine a problem cell from the to-be-balanced cell according to the load balancing result of the to-be-balanced cell; an azimuth angle calculation module, used for calculating the actual azimuth angle of the problem cell; the first re-optimization module is used for if the difference between the actual azimuth angle and its corresponding preset azimuth angle is greater than or equal to a preset deviation threshold, then After the preset azimuth angle is adjusted to the actual azimuth angle, load balancing is performed on the cells to be balanced again.

According to yet another aspect of the embodiments of the present invention, a computing device is provided, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface complete each other through the communication bus The memory is used for storing at least one executable instruction, and the executable instruction enables the processor to perform the operations of the load balancing method as described above.

According to another aspect of the embodiments of the present invention, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction causes a processor to execute the above load balancing method.

In the embodiment of the present invention, after performing load balancing in the cells to be balanced, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced, and the actual azimuth angle of the problem cell is calculated. If the difference is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth angle to the actual azimuth angle, the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed. The load imbalance caused by the abnormal feed coverage improves the optimization effect.

The above description is only an overview of the technical solutions of the embodiments of the present invention. In order to understand the technical means of the embodiments of the present invention more clearly, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and The advantages can be more clearly understood, and the following specific embodiments of the present invention are given.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are for the purpose of illustrating preferred embodiments only and are not to be considered limiting of the invention. Also, the same components are denoted by the same reference numerals throughout the drawings. In the attached image:

FIG. 1 shows a flowchart of a load balancing method provided by an embodiment of the present invention;

Figure 2 shows a flowchart of step 150 in Figure 1;

Fig. 3 shows the flow chart of step 150 in Fig. 1;

Fig. 4 shows the schematic diagram of DBSCAN algorithm;

FIG. 5 shows a flowchart of a load balancing method provided by another embodiment of the present invention;

FIG. 6 shows a schematic diagram of antenna height and downtilt angle;

FIG. 7 shows a flowchart of a load balancing method provided by another embodiment of the present invention;

FIG. 8 shows a flowchart of generating an optimization work order provided by an embodiment of the present invention;

FIG. 9 shows a schematic structural diagram of a load balancing apparatus provided by an embodiment of the present invention;

FIG. 10 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present invention will be more thoroughly understood, and will fully convey the scope of the present invention to those skilled in the art.

The load balancing method, apparatus, and computing device provided in this embodiment can be used in mobile communication networks such as Long Term Evolution (Long Term Evolution, LTE).

FIG. 1 shows a flowchart of a load balancing method provided by an embodiment of the present invention. The method is applied in a computing device, such as a server in a communication network. As shown in Figure 1, the method includes the following steps:

Step 140: Determine a problem cell from the cells to be balanced according to the load balancing result of the cells to be balanced.

Among them, the cells to be balanced are several cells that need to perform load balancing. For example, if the base station covers signals in all directions, every 120 degrees is used as a cell, covering three cells in total, then these three cells can be used as the cells to be equalized.

When the cell load is unbalanced, it is necessary to perform load balancing optimization on the cell. However, due to some cell resource management data errors, unreasonable azimuth configuration, or the deviation of the main coverage direction from the actual user distribution area, these cells cannot be improved after multiple rounds of optimization, so the load balancing optimization cannot be completed.

In step 140, the problem cell is a cell that cannot be improved after several rounds of load balancing optimization, wherein the problem cell may be one or more. Then, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced. The specific implementation may be: after three load balancing optimizations are performed in the cells to be balanced, the load balancing results of the cells to be balanced are obtained, and the load balancing results of the cells are compared. Various indicators before and after load balancing are used to identify problem cells. For example, if the m index of cell A is 20 before load balancing, and the m index is 22 after load balancing, and the variation range is less than the preset variation threshold of 10, then cell A is determined to be a problem cell.

Step 150: Calculate the actual azimuth of the problem cell.

Wherein, the actual azimuth is the azimuth between the coverage center of the cell and the connection between the base station and the designated direction in the actual environment. For example, if the specified direction is due north and the coverage center of the cell is located 30 degrees east of the north of the base station, the azimuth angle is 30 degrees.

In step 150, as shown in FIG. 2, the actual azimuth angle of the problem cell is calculated, which further includes:

Step 151: Obtain the minimum drive test data of the problem cell;

Step 152: Obtain user location data according to the minimized drive test data;

Step 155: Determine the coverage center of the problem cell according to the user location data;

Step 156: Calculate the actual azimuth of the problem cell according to the coverage center.

Wherein, the minimization drive test data is the data obtained by the Minimization of Drive Tests (MDT) technology. The MDT technology mainly obtains the relevant parameters required for network optimization through the measurement report that carries the position information of the global navigation satellite system reported by the mobile phone. Compared with traditional MR data, MDT data comes with accurate longitude and latitude information, which can accurately locate the coverage of the existing network. The amount of MDT data is very large. The daily data volume of a single base station is more than GB, which cannot be parsed by ordinary tools. Therefore, Pandas big data processing tools can be used to process MDT data. Wherein, in step 151, acquiring the minimum drive test data of the problem cell may specifically be: acquiring the MDT data of all the problem cells for one day.

In step 152, the user location data is specifically the user's longitude and latitude information data, which can be obtained from the minimization drive test data. For example, the MDT data is exported through the maos platform, and the python gzip tool is used to decompress, decode and splicing the MDT data, extract the latitude and longitude information data, and remove the null values in the data.

In step 155, after acquiring the user location data, the distribution center of the user is determined according to the distribution location of the user, that is, the coverage center of the problem cell.

In step 156, the specific implementation may be as follows: obtaining the base station position corresponding to the problem cell, and determining the actual azimuth angle of the problem cell according to the angle between the line connecting the base station position and the coverage center and the designated direction. For example, the sphere geometry analytical function geodirection can be written to solve for the actual azimuth.

In order to more accurately calculate the actual azimuth of the problem cell, as shown in FIG. 3 , step 150 further includes:

Step 153: Obtain network coverage parameters according to the minimized drive test data;

Step 154, performing noise removal on the user location data according to the DBSCAN algorithm based on density clustering;

Step 155: Calculate the coverage center of the problem cell by weighted average according to the user location data and the network coverage parameter.

The network coverage parameter is the network coverage strength of each user, for example, the reference signal receiving power (Reference Signal Receiving Power, RSRP) of the sampling point that can be obtained from the minimized drive test data.

Due to the rapid fading effect of wireless signals, abnormal GPS reception, inaccurate indoor positioning, or MDT platform acquisition, some MDT sampling points deviate from the actual coverage area and become noise points, which interfere with the calculation of the actual azimuth angle. Then, in step 154, the user location data is denoised by using the DBSCAN algorithm based on density clustering, so as to obtain accurate user location data.

Among them, the basic idea of the DBSCAN algorithm is to calculate whether the number of sampling points within a certain eps radius is greater than the set value minPts. Among them, eps and minPts are the two most important parameters in the DBSCAN algorithm, which respectively limit the area radius of the algorithm and the minimum number of sample points. As shown in Figure 4, set minPts=11, the data set X={xi} is the sampling point, the eps neighborhood of x1 contains 12 quality difference points, then x1 is the core quality difference point; and the eps neighborhood of x2 contains 9 The quality difference point, but x2 is a boundary quality difference point because it is in the neighborhood of x1; x3 is not in the neighborhood of other sample points, and the number of sample points in its own neighborhood is less than 11, which is a noise point.

Since the level of the MDT sampling point in the direction of the main wave plate of the antenna is stronger than that of the side lobe at the same distance, a higher weight can be given to the high-level MDT sampling point. The specific implementation of step 155 may be: according to the longitude and latitude information data of the sampling point and the RSRP value, weighted average calculation of the coverage center of the problem cell.

Step 160: If the difference between the actual azimuth and the corresponding preset azimuth is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth to the actual azimuth, load balance the cells to be balanced again.

Wherein, the preset azimuth is the preset azimuth of a certain cell, and the preset azimuth can be recorded in the resource management system, and adjusting the preset azimuth to the actual azimuth can be: setting the preset azimuth in the resource management system The azimuth data is adjusted to the actual azimuth value.

The specific implementation of step 160 may be: obtaining the preset azimuth angle of the problem cell, comparing the actual azimuth angle of the problem cell with the preset azimuth angle, and if the difference between the two is greater than or equal to the preset deviation threshold, the preset After the azimuth angle is adjusted to the actual azimuth angle, load balancing is performed on all cells to be balanced again. For example, if the preset azimuth angle of cell A is 78 degrees, the preset deviation threshold is 10, and the actual azimuth angle is calculated to be 89 degrees, then the deviation between the preset azimuth and the actual azimuth is greater than the preset deviation threshold, then After the preset azimuth angle is adjusted to 89 degrees, load balancing is performed on all cells to be balanced again.

In the embodiment of the present invention, after performing load balancing in the cells to be balanced, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced, and the actual azimuth angle of the problem cell is calculated. If the difference is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth angle to the actual azimuth angle, the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed. The load imbalance caused by the abnormal feed coverage improves the optimization effect.

FIG. 5 shows a flowchart of a load balancing method provided by another embodiment of the present invention. As shown in Figure 5, the method further includes:

Step 170: If the difference between the actual azimuth and the corresponding preset azimuth is smaller than the preset deviation threshold, calculate the coverage distance of each cell to be equalized.

If the difference between the actual azimuth and the corresponding preset azimuth is less than the preset deviation threshold, it indicates that the azimuth of the cell is set reasonably, and the problem needs to be further determined.

Wherein, calculating the coverage distance of each cell to be equalized further includes: acquiring the antenna height and downtilt angle of the cell to be equalized; and calculating the coverage distance of the cell to be equalized according to the antenna height and downtilt angle of the cell to be equalized. The antenna height and downtilt angle of the cell can be input by the user or obtained from other systems. For example, as shown in Figure 6, assuming that the height of the antenna of the acquired cell is H, the down-tilt angle is B, and the half-power angle of the vertical plane of the antenna is A/2, then according to the trigonometric function relationship, tg(B-A/2)=H/R , so that the coverage distance R of the cell can be obtained.

Step 180: Determine the cells to be calibrated according to the coverage distances of the cells to be equalized.

The specific implementation of step 180 may be as follows: after obtaining the coverage distance of each cell to be equalized in the sector group that is covered by the entire network, calculate the ratio of the maximum value of the coverage distance to the minimum value of the coverage distance, if the ratio is greater than a preset value Distance ratio, then determine the cell to be corrected. For example, the preset distance ratio is 2, and the calculated coverage distances of cells A, B, C, and D in the sector group covered by the entire network are 100, 201, 150, and 130, respectively, and the maximum coverage distance is 201 and the minimum coverage distance. If the ratio of 100 is 2.01, which is greater than the preset distance ratio of 2, and the coverage distance of cell A is close to that of cells C and D, it is determined that the cell with a large coverage difference is cell B, that is, cell B is the cell to be corrected.

Step 190: After performing antenna calibration on the cells to be calibrated, perform load balancing again on the cells to be balanced.

The antenna correction may be to adjust the antenna hanging height or downtilt angle.

In the embodiment of the present invention, when the difference between the actual azimuth angle and the corresponding preset azimuth angle is smaller than the preset deviation threshold, the coverage distance of each cell to be equalized is calculated, the cell to be corrected is determined, and after antenna calibration is performed on the cell to be corrected , the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed, and the unbalanced load caused by the abnormal coverage of the antenna feeder can be judged, which improves the optimization effect.

FIG. 7 shows a flowchart of a load balancing method provided by another embodiment of the present invention. As shown in FIG. 7 , the difference from the above embodiment is that before step 140, the method further includes:

Step 110: Obtain historical load data of the cells to be balanced.

The historical load data may be historical traffic data, and the corresponding time is recorded, for example, at least including whether the current day is a holiday, the season to which the current day belongs, and the like. For example, the historical load data is the daily traffic data from 2016 to 2018.

Step 120: Substitute the historical load data into a preset prediction model to determine a preset prediction function, wherein the preset prediction function is related to time.

The preset prediction model is a preset calculation model, and the preset prediction model is provided with several unknown parameters. By substituting historical load data into the preset prediction model to determine the unknown parameters, the preset prediction function is determined.

In this embodiment, since the load has the characteristics of an obvious upward trend, periodic changes with seasons, and significant holiday effects, the forecasting model may include a load trend function model, a periodic function model, and a holiday function model.

In this embodiment, the calculation of the prediction prediction model follows the following formula:

y(t)=g(t)+s(t)+h(t)+ε _t

Among them, y(t) is the forecast prediction model, g(t) is the load trend function model, s(t) is the periodic function model, h(t) is the holiday function model, t is the date of the day, ε _t is the offset amount of error.

Among them, the population and consumption patterns show that there is a certain upper limit for the growth of load data in the foreseeable future, so a logistic regression model can be used to fit the load growth trend. The load trend function model g(t) can be expressed as:

Among them, C is the carrying capacity, which defines the maximum value that can be increased, k is the growth rate, and m is the offset. C, k, and m are all unknown parameters, which are determined by substituting historical load data into a preset prediction model.

Among them, because the time series will show seasonal changes with the changes of days, weeks, months, years, etc., or periodic transformations, Fourier series can be used to simulate the seasonal components of each year. The periodic function model s(t) can be expressed as:

Among them, P represents a fixed period, which can be set by the user as required. For example, in the statistical data in days, P=365.25 for annual data and P=7 for weekly data. N represents the number of such cycles that you want to use in the model. A larger N value can fit a more complex periodic function, which can be set by the user as needed. For example, annual period N=10 and weekly period N=3.

Among them, since different holidays can be regarded as independent models, and different holidays can affect the load data for a period of time before and after, different window values before and after can be set for different holidays. The holiday function model h(t) can be expressed as:

Among them, for holiday i, D _i represents the window time before and after the holiday, the whole year holiday sequence can be represented by _Z (t) matrix, DL is the last holiday in the year, in the actual implementation process, the holiday can be set in advance It is marked in the program; κ is the influence factor of the holiday on the load, and κ is the unknown parameter, which is determined by substituting the historical load data into the preset prediction model.

In step 120, it further includes: step 121, substituting the historical load data into the load trend function model, the periodic function model and the holiday function model respectively, and fitting the load trend function, the periodic function and the holiday function according to the L-BFGS quasi-Newton method; Step 122: Determine a preset prediction function according to the load trend function, the period function and the holiday function.

Among them, the preset prediction function is determined according to the load trend function, the period function and the holiday function, which may be specifically as follows: first, the load trend function g(t), the period function s(t) and the holiday function h(t) are obtained by fitting, and then y(t) is calculated and predicted by g(t), s(t) and h(t), and then a predetermined preset prediction function is obtained.

Step 130: Perform load balancing of the cells to be balanced according to a preset prediction function.

After the determined preset prediction function is obtained, the future load value calculated by the preset prediction function can be used to perform load balancing of the cells to be balanced in advance according to this value. According to the preset prediction function, load balancing is performed on the cells to be balanced. The specific implementation may be: according to the preset prediction function, increasing or decreasing the frequency of performing load balancing in the cells to be balanced. For example, assuming that cell A and cell B have roughly the same load on the day, if the load of cell A will increase by 20% and the load of cell B will decrease by 20% in the next three days according to the preset prediction function, the Load balancing to keep the load of cells A and B roughly the same. For another example, assuming that the load balancing operation is performed every other week, if the load amount in the next week is very uneven according to the preset prediction function, the frequency of the load balancing operation will be increased in the next week.

In some implementations, the method further includes: receiving whitelist information, and determining cells to be equalized according to the whitelist information. The whitelist information can be any frequency band (for example, FDD900 frequency band), room division 3DMIMO and other network element information that does not require equalization, and can be freely set according to user needs. By receiving whitelist information, network elements that do not need balancing can be freely configured, improving the flexibility of load balancing.

In some embodiments, the method further includes: receiving parameter configuration information, and performing load balancing on the cells to be balanced according to the parameter configuration information. Wherein, the parameter configuration information may be information such as weighting parameters of each frequency band utilization rate, threshold parameters of PRB utilization rate difference between high and low load cells of unbalanced problem sector groups, etc., which can be freely set according to the needs of users. For example, set the utilization weighting parameter of frequency band FDD900 to 1.2, set the utilization weighting parameter of frequency band FDD1800 to 0.9, and set the utilization weighting parameter of frequency band F2 to 1.2. By performing load balancing on the cells to be balanced according to the parameter configuration information, refined load balancing can be achieved.

In some embodiments, the method further includes: generating an optimization work order, and performing load balancing of the cells to be balanced according to the optimization work order. Among them, optimization work orders can be generated in the following order: 1. Two-way neighbor relationship check and supplementary work order generation; 2. Cell power verification in the same frequency band and generation of leveling work orders; 3. Mobility Load Balancing (MLB) Parameter set verification and optimization work order generation; 4. Interoperability parameter set verification and optimization work order generation; 5. Fine optimization of peer-to-peer switching neighborhood parameters (CIO). For example, an optimization work order can be generated with reference to the process shown in FIG. 8 . Among them, when calculating MLB parameters and interoperability parameters (A2, A4, A5, CIO, etc.), discard the original "small step and fast run" mode (each round of optimization uses a conservative output adjustment scheme with small steps of 2dB, for example), Instead, the proportional control idea in the PID control algorithm is adopted, namely: M=K*e. Among them, M is the output adjustment step size, e is the difference between the current PRB utilization rate of the cell and the preset target PRB utilization rate, and K is the proportional coefficient (freely set according to user experience). Through the proportional control algorithm, the convergence speed of the PRB utilization rate of the target cell can be greatly improved, the number of iterative optimizations can be reduced, and the efficiency of load balancing can be improved.

The embodiment of the present invention obtains the historical load data of the cells to be balanced, substitutes the historical load data into a preset prediction model to determine a preset prediction function, and performs load balancing optimization on the cells to be balanced according to the preset prediction function, so that the load data can be predicted and the load balance can be optimized. Optimized in advance to improve the degree of automation.

FIG. 9 shows a schematic structural diagram of a load balancing apparatus provided by an embodiment of the present invention. As shown in FIG. 9 , the apparatus 200 includes: a problem cell determination module 210 , an actual azimuth angle calculation module 220 and a first re-optimization module 230 .

The problem cell determination module 210 is used to determine the problem cell from the cells to be balanced according to the load balancing result of the cells to be balanced; the actual azimuth calculation module 220 is used to calculate the actual azimuth of the problem cell; the first re-optimization Module 230 is configured to, if the difference between the actual azimuth and its corresponding preset azimuth is greater than or equal to a preset deviation threshold, after adjusting the preset azimuth to the actual azimuth, perform a The balancing cell performs load balancing again.

In an optional manner, the actual azimuth calculation module is specifically configured to: obtain the minimum drive test data of the problem cell; obtain user location data according to the minimized drive test data; and obtain user location data according to the user location data , determine the coverage center of the problem cell; calculate the actual azimuth of the problem cell according to the coverage center.

In an optional manner, the actual azimuth calculation module is further configured to: remove noise from the user location data according to the DBSCAN algorithm based on density clustering; obtain network coverage parameters according to the minimized drive test data ; the determining the coverage center of the problem cell according to the user location data specifically includes: calculating the coverage center of the problem cell by weighted average according to the user position data and the network coverage parameter.

In an optional manner, the apparatus 200 further includes: a coverage distance calculation module, a cell to be corrected determination module, and a second re-optimization module. The coverage distance calculation module is used to calculate the coverage distance of each of the cells to be equalized if the difference between the actual azimuth and its corresponding preset azimuth is less than the preset deviation threshold; The coverage distance of each of the cells to be balanced determines the cells to be calibrated; the second re-optimization module is configured to perform load balancing on the cells to be balanced again after performing antenna calibration on the cells to be calibrated.

In an optional manner, the coverage distance calculation module is specifically configured to obtain the antenna height and downtilt angle of the cell to be equalized; calculate the coverage of the cell to be equalized according to the antenna height and downtilt angle of the cell to be equalized distance.

In an optional manner, the apparatus 200 further includes: a historical data acquisition module, a prediction function determination module, and an optimization module. The historical data acquisition module is used to acquire the historical load data of the cells to be balanced; the prediction function determination module is used to substitute the historical load data into a preset prediction model to determine a preset prediction function, wherein the preset prediction function It is related to time; the optimization module is configured to perform load balancing on the cells to be balanced according to the preset prediction function.

In an optional manner, the preset prediction model includes a load trend function model, a periodic function model and a holiday function model; the prediction function determination module is specifically configured to: substitute the historical load data into the load trend respectively The function model, the periodic function model and the holiday function model are fitted according to the L-BFGS quasi-Newton method to obtain a load trend function, a periodic function and a holiday function; according to the load trend function, the periodic function and the holiday function to determine the preset prediction function.

It should be noted that, the load balancing apparatus provided by the embodiment of the present invention is a device capable of executing the above load balancing method, and all embodiments of the above load balancing method are applicable to the apparatus, and can achieve the same or similar beneficial effects.

In the embodiment of the present invention, after performing advance load balancing in the cells to be balanced, the cells in question and the cells to be corrected are acquired, and after azimuth adjustment or antenna correction is performed, the load balancing of the cells to be balanced is performed again, which can not only predict the load data and perform advance optimization, The degree of automation is improved, and the cells that cannot be improved after multiple rounds of optimization can be analyzed, and the antenna feeder optimization can be performed, and the load balancing optimization can be performed again, thereby improving the quality of the load balancing optimization.

An embodiment of the present invention provides a computer storage medium, where at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to execute the load balancing method in any of the foregoing method embodiments.

In the embodiment of the present invention, after performing load balancing in the cells to be balanced, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced, and the actual azimuth angle of the problem cell is calculated. If the difference is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth angle to the actual azimuth angle, the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed. The load imbalance caused by the abnormal feed coverage improves the optimization effect.

The embodiments of the present invention provide a computer program product through the embodiments of the present invention. The computer program product includes a computer program stored on a computer storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer , causing the computer to execute the load balancing method in any of the foregoing method embodiments.

In the embodiment of the present invention, after performing load balancing in the cells to be balanced, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced, and the actual azimuth angle of the problem cell is calculated. If the difference is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth angle to the actual azimuth angle, the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed. The load imbalance caused by the abnormal feed coverage improves the optimization effect.

FIG. 10 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention. The specific embodiment of the present invention does not limit the specific implementation of the computing device.

As shown in FIG. 10 , the computing device may include: a processor (processor) 302 , a communications interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

The processor 302 , the communication interface 304 , and the memory 306 communicate with each other through the communication bus 308 . The communication interface 304 is used for communicating with network elements of other devices such as clients or other servers. The processor 302 is configured to execute the program 310, and specifically may execute the load balancing method in any of the foregoing method embodiments.

Specifically, the program 310 may include program code including computer operation instructions.

The processor 302 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the computing device may be the same type of processors, such as one or more CPUs; or may be different types of processors, such as one or more CPUs and one or more ASICs.

The memory 306 is used to store the program 310 . Memory 306 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

In the embodiment of the present invention, after performing load balancing in the cells to be balanced, according to the load balancing results of the cells to be balanced, the problem cells are determined from the cells to be balanced, and the actual azimuth angle of the problem cell is calculated. If the difference is greater than or equal to the preset deviation threshold, after adjusting the preset azimuth angle to the actual azimuth angle, the load balancing of the cells to be balanced can be performed again, and the cells that cannot be improved after multiple rounds of optimization can be analyzed. The load imbalance caused by the abnormal feed coverage improves the optimization effect.

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 with teaching based on this. The structure required to construct such a system is apparent from the above description. Furthermore, embodiments of the present invention are not directed to any particular programming language. It is to be understood that various programming languages may be used to implement the inventions described herein, and that the descriptions of specific languages above are intended to disclose the best mode for carrying out the invention.

In the description provided herein, numerous specific details are set forth. It will be understood, however, 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 in order not to obscure an understanding of this description.

Similarly, it is to be understood that, in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together into a single implementation in order to simplify the invention and to aid in the understanding of one or more of the various aspects of the invention. examples, figures, or descriptions thereof. Those skilled in the art will understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and further they may be divided into multiple sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method so disclosed may be employed in any combination, unless at least some of such features and/or procedures or elements are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, it will be understood by those skilled in the art that although some of the embodiments herein include certain features, but not others, included in other embodiments, that combinations of features of the different embodiments are intended to be within the scope of the present invention And form different embodiments. For example, any of the embodiments claimed in the claims may be used in any combination.

It should be noted that the above-described embodiments illustrate rather than limit the invention, and that alternative embodiments may be devised by those skilled in the art without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, and third, etc. do not denote any order. These words can be interpreted as names. The steps in the above embodiments should not be construed as limitations on the execution order unless otherwise specified.

Claims (10)

1. A method of load balancing, the method comprising:

determining a problem cell from the cell to be balanced according to a load balancing result of the cell to be balanced;

calculating an actual azimuth angle of the problem cell;

and if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to a preset deviation threshold value, after the preset azimuth angle is adjusted to the actual azimuth angle, carrying out load balancing again on the cell to be balanced.

2. The method of claim 1, wherein the calculating an actual azimuth of the problem cell further comprises:

acquiring minimization of drive test data of the problem cell;

acquiring user position data according to the MDT data;

determining a coverage center of the problem cell according to the user position data;

and calculating the actual azimuth angle of the problem cell according to the coverage center.

3. The method of claim 2, wherein the calculating the actual azimuth of the problem cell further comprises:

noise removal is carried out on the user position data according to a density clustering-based DBSCAN algorithm;

acquiring network coverage parameters according to the minimization of drive test data;

the determining the coverage center of the problem cell according to the user location data specifically includes:

and calculating the coverage center of the problem cell by weighted average according to the user position data and the network coverage parameters.

4. The method of claim 1, further comprising:

if the difference value between the actual azimuth and the corresponding preset azimuth is smaller than the preset deviation threshold, calculating the coverage distance of each cell to be equalized;

determining a cell to be corrected according to the coverage distance of each cell to be equalized;

and after the antenna correction is carried out on the cell to be corrected, carrying out load balancing again on the cell to be balanced.

5. The method according to claim 4, wherein said calculating the coverage distance of each of the cells to be equalized further comprises:

acquiring the antenna height and the downward inclination angle of the cell to be balanced;

and calculating the coverage distance of the cell to be equalized according to the antenna height and the downward inclination angle of the cell to be equalized.

6. The method according to any of claims 1-5, wherein before determining the problem cell from the cell to be balanced according to the load balancing result of the cell to be balanced, the method further comprises:

acquiring historical load data of the cell to be balanced;

substituting the historical load data into a preset prediction model to determine a preset prediction function, wherein the preset prediction function is related to time;

and carrying out load balancing on the cell to be balanced according to the preset prediction function.

7. The method of claim 6, wherein the predetermined predictive models include a load trend function model, a periodic function model, and a holiday function model;

then, the substituting the historical load data into a preset prediction model to determine a preset prediction function further includes:

respectively substituting the historical load data into the load trend function model, the periodic function model and the holiday function model, and fitting according to an L-BFGS quasi-Newton method to obtain a load trend function, a periodic function and a holiday function;

and determining the preset prediction function according to the load trend function, the periodic function and the holiday function.

8. A load balancing apparatus, the apparatus comprising:

the problem cell determining module is used for determining a problem cell from the cell to be balanced according to the load balancing result of the cell to be balanced;

the actual azimuth angle calculation module is used for calculating an actual azimuth angle of the problem cell;

and the first re-optimization module is used for performing load balancing again on the cell to be balanced after the preset azimuth angle is adjusted to the actual azimuth angle if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to a preset deviation threshold value.

9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;

the memory is configured to store at least one executable instruction that causes the processor to perform the operations of the load balancing method of any one of claims 1-7.

10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform a method of load balancing according to any one of claims 1 to 7.

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