CN112566143B - Load balancing method and device and computing equipment - Google Patents
Load balancing method and device and computing equipment Download PDFInfo
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- CN112566143B CN112566143B CN201910854702.9A CN201910854702A CN112566143B CN 112566143 B CN112566143 B CN 112566143B CN 201910854702 A CN201910854702 A CN 201910854702A CN 112566143 B CN112566143 B CN 112566143B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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
Technical Field
The embodiment of the invention relates to the technical field of wireless communication, in particular to a load balancing method, a load balancing device and computing equipment.
Background
With the continuous development of 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 capacity expansion, site new construction and the like. By monitoring Key Performance Indicators (KPIs) of the existing network, it is found that the problem of capacity difference between the cells covered by the network is increasingly serious, the utilization rate of Physical Resource Blocks (PRBs) or the number of users in some cells is close to the capacity limit, and the Resource utilization rate of other cells is very low, thereby causing waste of investment resources. Therefore, how to balance the load among cells with the same coverage or overlapping coverage areas is of great significance.
At present, a load balancing method is mainly optimized by autonomous cell switching of users, load imbalance caused by antenna feeder coverage difference cannot be judged, and the optimization effect is limited.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present invention provide a load balancing method, apparatus, and computing device, which overcome the foregoing problems or at least partially solve the foregoing problems.
According to an aspect of an embodiment of the present invention, there is provided a load balancing method, including: determining a problem cell from the cell to be balanced according to the 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.
In an optional manner, the calculating an actual azimuth of the problem cell further includes: 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.
In an optional manner, the method further comprises: 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.
In an optional manner, the calculating a coverage distance of each cell to be equalized further includes: 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.
In an optional manner, before the determining, according to the load balancing result of the cell to be balanced, a problem cell from the cell to be balanced, the method further includes: acquiring historical load data of a 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.
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 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.
According to another aspect of the embodiments of the present invention, there is provided a load balancing apparatus, including: 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.
According to still another aspect of an embodiment of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface are communicated with each other through the communication bus; the memory is used for storing at least one executable instruction which causes the processor to execute the operation of the load balancing method.
According to another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to execute the load balancing method as described above.
According to the embodiment of the invention, after the load balancing is carried out on the cell to be balanced, the problem cell is determined from the cell to be balanced according to the load balancing result of the cell to be balanced, the actual azimuth angle of the problem cell is calculated, if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to the preset deviation threshold value, the load balancing is carried out on the cell to be balanced again after the preset azimuth angle is adjusted to the actual azimuth angle, the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by the antenna feeder coverage abnormity is judged, and the optimization effect is improved.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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 only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 shows a flowchart of a load balancing method according to an embodiment of the present invention;
FIG. 2 shows a flow chart of step 150 in FIG. 1;
FIG. 3 shows a flowchart of step 150 in FIG. 1;
FIG. 4 shows a schematic diagram of the DBSCAN algorithm;
fig. 5 is a flowchart illustrating a load balancing method according to another embodiment of the present invention;
FIG. 6 shows a schematic diagram of antenna height and downtilt angle;
fig. 7 is a flowchart illustrating a load balancing method according to another embodiment of the present invention;
FIG. 8 illustrates a flow chart for generating an optimized work order provided by an embodiment of the present invention;
fig. 9 is a schematic structural diagram illustrating a load balancing apparatus according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The load balancing method, the load balancing device, and the computing device provided in this embodiment may be used in a mobile communication network such as Long Term Evolution (LTE).
Fig. 1 shows a flowchart of a load balancing method according to 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 fig. 1, the method comprises the steps of:
The cell to be balanced is a plurality of cells needing load balancing. For example, the base station covers signals all around, and covers three cells every 120 degrees as one cell, and then the three cells can be used as cells to be equalized.
When the cell load is unbalanced, the cell needs to be optimized for load balancing. However, due to the fact that there may be some cells with wrong resource management data, unreasonable azimuth configuration, or deviation of the main coverage direction from the actual distribution area of the user, these cells still cannot be improved through multiple rounds of optimization, and thus load balancing optimization cannot be completed.
In step 140, the problem cell is a cell that cannot be improved through several rounds of load balancing optimization, wherein the problem cell may be one or more cells. Then, according to the load balancing result of the cell to be balanced, the problem cell is determined from the cell to be balanced, and the specific implementation may be: and after the cell to be balanced is subjected to load balancing optimization for three times, acquiring a load balancing result of the cell to be balanced, and comparing each index of each cell before load balancing and after load balancing so as to determine the problem cell. For example, if m index of the cell a is 20 before load balancing, m index of the cell a is 22 after load balancing, and the variation amplitude is smaller than the preset variation threshold 10, the cell a is determined to be a problem cell.
And 150, calculating the actual azimuth angle of the problem cell.
The actual azimuth is the azimuth between the connecting line of the coverage center of the cell and the base station in the actual environment and the designated direction. For example, if the specified direction is the true north direction and the coverage center of the cell is 30 degrees north of the base station, then the azimuth is 30 degrees.
In step 150, as shown in fig. 2, calculating an actual azimuth of the problem cell further includes:
152, acquiring user position data according to the minimization of drive test data;
The Minimization of Drive Tests (MDT) data is acquired by MDT (Minimization of Drive Tests) technology. The MDT technology mainly obtains relevant parameters required by network optimization through a measurement report which is reported by a mobile phone and carries global navigation satellite system position information. Compared with the traditional MR data, the MDT data carries accurate longitude and latitude information, and can accurately position the existing network coverage. The data volume of the MDT is very large, the data volume of a single base station per day is more than GB, and a common tool cannot analyze the data volume at all, so that a Pandas large data processing tool can be used for processing the MDT data. In step 151, the obtaining of the minimization of drive test data of the problem cell may specifically be: and acquiring MDT data of all the problem cells in one day.
In step 152, the user location data is specifically latitude and longitude information data of the user, and may be obtained from the minimization of drive test data. For example, MDT data is exported through a maos platform, decompressed, decoded and data spliced by using a python gzip tool, latitude and longitude information data is extracted, and null values in the data are removed.
In step 155, after the user location data is obtained, 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 is determined.
In step 156, the specific implementation may be: and acquiring the position of the base station corresponding to the problem cell, and determining the actual azimuth angle of the problem cell according to the included angle between the connecting line of the position of the base station and the coverage center and the designated direction. For example, a sphere geometry resolving function, geodesiction, may be written to solve for the actual azimuth.
In order to calculate the actual azimuth of the problem cell more accurately, as shown in fig. 3, step 150 further includes:
and step 155, calculating the coverage center of the problem cell by weighted average according to the user position data and the network coverage parameters.
The network coverage parameter is a network coverage strength of each user, for example, a Reference Signal Receiving Power (RSRP) of a sampling point that can be obtained from the minimization of drive test data.
Due to the problems of wireless signal fast attenuation effect, abnormal GPS receiving, inaccurate indoor positioning or MDT platform acquisition and the like, a part of MDT sampling points deviate from an actual coverage area to become noise points, and the interference is caused to the calculation of an actual azimuth angle. Then in step 154, the DBSCAN algorithm based on density clustering is used to perform noise removal on the user location data to obtain accurate user location data.
The basic idea of the DBSCAN algorithm is to calculate whether the number of sampling points within a certain eps radius range is greater than a set value minPts. Wherein eps and minPts are the two most important parameters in the DBSCAN algorithm, respectively defining the area radius and the minimum number of sample points of the algorithm. As shown in fig. 4, if minPts is set to 11, the data set X { xi } is a sampling point, and 12 quality difference points are included in the eps neighborhood of X1, X1 is a core quality difference point; while the eps neighborhood of x2 contains 9 quality difference points, but x2 is a boundary quality difference point because the eps neighborhood of x 1; 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.
Because the level of the MDT sampling point in the main wave plate direction of the antenna is stronger than that of the side lobe under the same distance, higher weight can be given to the high-level MDT sampling point. Then the specific implementation of step 155 may be: and calculating the coverage center of the problem cell by weighted average according to the longitude and latitude information data and the RSRP value of the sampling point.
And step 160, if the difference value between the actual azimuth and the corresponding preset azimuth is greater than or equal to the preset deviation threshold, performing load balancing again on the cell to be balanced after the preset azimuth is adjusted to the actual azimuth.
The preset azimuth is a preset azimuth of a certain cell, and the preset azimuth can be recorded in the resource management system, and then the adjustment of the preset azimuth to the actual azimuth can be: and adjusting the data of the preset azimuth angle in the resource management system to the numerical value of the actual azimuth angle.
The specific implementation of step 160 may be: and obtaining a preset azimuth angle of the cell with the problem, comparing the actual azimuth angle with the preset azimuth angle of the cell with the problem, and if the difference value between the actual azimuth angle and the preset azimuth angle is greater than or equal to a preset deviation threshold value, adjusting the preset azimuth angle to the actual azimuth angle and then carrying out load balancing on all cells to be balanced again. For example, if the preset azimuth angle of the cell a is 78 degrees, the preset deviation threshold is 10, and the calculated actual azimuth angle is 89 degrees, the deviation between the preset azimuth angle and the actual azimuth angle is greater than the preset deviation threshold, after the preset azimuth angle is adjusted to 89 degrees, the load balancing is performed again on all the cells to be balanced.
According to the embodiment of the invention, after the load balancing is carried out on the cell to be balanced, the problem cell is determined from the cell to be balanced according to the load balancing result of the cell to be balanced, the actual azimuth angle of the problem cell is calculated, if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to the preset deviation threshold value, the load balancing is carried out on the cell to be balanced again after the preset azimuth angle is adjusted to the actual azimuth angle, the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by the antenna feeder coverage abnormity is judged, and the optimization effect is improved.
Fig. 5 is a flowchart illustrating a load balancing method according to another embodiment of the present invention. As shown in fig. 5, the method further includes:
If the difference value between the actual azimuth angle and the corresponding preset azimuth angle is smaller than the preset deviation threshold value, the azimuth angle of the cell is set reasonably, and then the problem needs to be further determined.
Wherein, the coverage distance of each cell to be equalized is calculated, further comprising: acquiring the antenna height and the downward inclination angle of a 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. The antenna height and the downward inclination angle of the cell can be input by a user or acquired from other systems. For example, as shown in fig. 6, assuming that the antenna height of the acquired cell is H, the downtilt angle is B, and the half-power angle of the antenna vertical plane is a/2, the coverage distance R of the cell can be obtained by having tg (B-a/2) equal to H/R according to the trigonometric function relationship.
And step 180, determining the cell to be corrected according to the coverage distance of each cell to be equalized.
The specific implementation of step 180 may be: after the coverage distance of each cell to be equalized in the whole network common coverage sector group is obtained, the ratio of the maximum value of the coverage distance to the minimum value of the coverage distance is calculated, and if the ratio is larger than the preset distance ratio, the cell to be corrected is determined. For example, the preset distance ratio is 2, the coverage distances of A, B, C, D cells in the total network common coverage sector group are calculated to be 100, 201, 150 and 130, respectively, the ratio of the maximum value 201 of the coverage distance to the minimum value 100 of the coverage distance is 2.01, and is greater than the preset distance ratio 2, and the coverage distance of the cell a is closer to the cell C, D, then the cell with the larger coverage difference is determined to be the cell B, that is, the cell B is the cell to be corrected.
And 190, after the antenna correction is carried out on the cell to be corrected, carrying out load balancing again on the cell to be balanced.
The antenna calibration may be adjusting the antenna elevation or downtilt.
According to the embodiment of the invention, when the difference value between the actual azimuth angle and the corresponding preset azimuth angle is smaller than the preset deviation threshold value, the coverage distance of each cell to be balanced is calculated, the cell to be corrected is determined, and after the cell to be corrected is subjected to antenna correction, the cell to be balanced is subjected to load balancing again, so that the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by abnormal antenna feeder coverage is judged, and the optimization effect is improved.
Fig. 7 is a flowchart illustrating a load balancing method according to 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 comprises:
and step 110, acquiring historical load data of the cell to be balanced.
The historical load data is traffic data which can be historical, and corresponding time is recorded, for example, whether the current time is a holiday, a season of the day, and the like are included at least. For example, the historical load data is traffic data for each day from 2016 to 2018.
And step 120, 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.
The preset prediction model is a preset calculation model, a plurality of unknown parameters are arranged in the preset prediction model, and the unknown parameters are determined by substituting the historical load data into the preset prediction model, so that a preset prediction function is determined.
In this embodiment, since the load has characteristics of a significant rising trend, a periodic variation with seasons, a significant holiday effect, and the like, the prediction and prediction model may include a load trend function model, a periodic function model, and a holiday function model.
In this embodiment, the calculation of the predictive model follows the following equation:
y(t)＝g(t)+s(t)+h(t)+εt
wherein y (t) is a prediction model, g (t) is a load trend function model, s (t) is a periodic function model, h (t) is a holiday function model, t is the date of the day, epsilontIs an offset error amount.
The population and consumption patterns show that a certain upper limit can be predicted for the future increase of load data, so that a logistic regression model can be used for fitting the load increase trend. The load trend function model g (t) can be expressed as:
where C is the loading capacity, which defines the maximum value that can be increased, k is the rate of increase, and m is the offset. C. k and m are unknown parameters, and the historical load data are substituted into a preset prediction model to be determined.
Because the time series can show seasonal changes along with the change of days, weeks, months, years and the like, or periodic transformation, seasonal components of each year can be simulated by Fourier series. The periodic function model s (t) can be expressed as:
where P denotes a certain fixed period and can be set by the user as needed, for example, in the statistical data in units of days, P of the annual data is 365.25 and P of the weekly data is 7. N represents the number of such cycles that one wishes to use in the model, and larger values of N can fit more complex periodic functions, which can be set by the user as desired. For example, the annual cycle N is 10 and the periodic cycle N is 3.
Different holidays can be regarded as mutually independent models, and load data of a period of time before and after the influence of the different holidays can be set, so that different front and back window values can be set for the different holidays. The holiday function model h (t) can be expressed as:
wherein, for holidays i, DiRepresenting the window time before and after the holiday, the holiday sequence of the whole year can be represented by a Z (t) matrix, DLFor the last holiday in the year, in the actual implementation process, the holiday can be marked in the program in advance; and k is an influence factor of holidays on the load, is an unknown parameter, and is determined by substituting historical load data into a preset prediction model.
In step 120, further comprising: step 121, respectively substituting the historical load data into a load trend function model, a periodic function model and a 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 step 122, determining a preset prediction function according to the load trend function, the periodic function and the holiday function.
The preset prediction function is determined according to the load trend function, the periodic function and the holiday function, and specifically may be: firstly, a load trend function g (t), a periodic function s (t) and a holiday function h (t) are obtained through fitting, and then y (t) is calculated and predicted through g (t), s (t) and h (t), so that a determined preset prediction function is obtained.
And step 130, carrying out load balancing on the cell to be balanced according to a preset prediction function.
After the determined preset prediction function is obtained, the future load value calculated according to the preset prediction function can be used for carrying out load balancing on the cell to be balanced in advance according to the future load value. According to a preset prediction function, performing load balancing on a cell to be balanced, wherein the specific implementation mode can be as follows: and increasing or decreasing the frequency of load balancing of the cell to be balanced according to a preset prediction function. For example, assuming that the load capacity of the cells a and B is approximately the same on the same day, if the load of the cell a will increase by 20% and the load of the cell B will decrease by 20% on the next three days according to the preset prediction function, load balancing is performed on the A, B cells in advance to maintain the load capacity of the cell A, B to be approximately the same. For another example, if the load balancing operation is performed every other week, and the load amounts in the future week are quite uneven according to the preset prediction function, the frequency of performing the load balancing operation is increased in the future week.
In some implementations, the method further comprises: and receiving the white list information, and determining the cell to be balanced according to the white list information. The white list information may be network element information that does not need to be balanced, such as any frequency band (e.g., FDD900 frequency band), indoor division 3DMIMO, and the like, and may be freely set according to the needs of the user. By receiving the white list information, the network elements which do not need to be balanced can be freely configured, and the flexibility of load balancing is improved.
In some embodiments, the method further comprises: and receiving the parameter configuration information, and carrying out load balancing on the cell to be balanced according to the parameter configuration information. The parameter configuration information can be information such as utilization rate weighting parameters of each frequency band, high and low load cell PRB utilization rate difference threshold parameters of the unbalanced problem sector group, and can be freely set according to the needs of users. For example, the utilization rate weighting parameter of the frequency band FDD900 is set to 1.2, the utilization rate weighting parameter of the frequency band FDD1800 is set to 0.9, and the utilization rate weighting parameter of the frequency band F2 is set to 1.2. By performing load balancing on the cell to be balanced according to the parameter configuration information, refined load balancing can be realized.
In some embodiments, the method further comprises: and generating an optimized work order, and carrying out load balancing on the cell to be balanced according to the optimized work order. Wherein, the optimized work order can be generated according to the following sequence: firstly, two-way neighbor relation verification and supplementary work order generation; checking the power of the cells in the same frequency band and generating a leveling work order; thirdly, checking a Mobility Load Balancing (MLB) parameter set and generating an optimized work order; checking the interoperation parameter set and generating an optimized work order; and fifthly, point-to-point switching neighbor cell parameter (CIO) fine optimization. For example, an optimized work order may be generated with reference to the flow shown in FIG. 8. When calculating the MLB parameters and the interoperation parameters (a2, a4, a5, CIO, etc.), the original "small-step fast-running" mode (output adjustment scheme conserved in small steps of, for example, 2dB per round of optimization) is abandoned, and the proportional control concept in the PID control algorithm is adopted, that is: m ═ K × e. Wherein, M is the output adjustment step, e is the difference between the current PRB utilization rate of the cell and the preset target PRB utilization rate, and K is a scaling factor (freely set according to user experience). Through the proportional control algorithm, the convergence rate of the target cell PRB utilization rate can be greatly improved, the iterative optimization times are reduced, and the load balancing efficiency is improved.
According to the embodiment of the invention, the historical load data of the cell to be balanced is obtained, the historical load data is substituted into the preset prediction model to determine the preset prediction function, and the load balancing optimization is carried out on the cell to be balanced according to the preset prediction function, so that the load data can be predicted and optimized in advance, and the automation degree is improved.
Fig. 9 is a schematic structural diagram illustrating a load balancing apparatus according to an embodiment of the present invention. As shown in fig. 9, the apparatus 200 includes: a problem cell determination module 210, an actual azimuth calculation module 220, and a first re-optimization module 230.
The problem cell determining module 210 is configured to determine a problem cell from a cell to be balanced according to a load balancing result of the cell to be balanced; the actual azimuth calculation module 220 is configured to calculate an actual azimuth of the problem cell; the first re-optimization module 230 is configured to, if a difference between the actual azimuth and a corresponding preset azimuth is greater than or equal to a preset deviation threshold, perform load balancing again on the cell to be balanced after the preset azimuth is adjusted to the actual azimuth.
In an alternative manner, the actual azimuth calculation module is specifically configured to: 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.
In an optional manner, the actual azimuth calculation module is further specifically configured to: 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 MDT 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.
In an optional manner, the apparatus 200 further comprises: the device comprises a covering distance calculation module, a cell to be corrected determination module and a second re-optimization module. The coverage distance calculation module is used for calculating the coverage distance of each cell to be equalized if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is smaller than the preset deviation threshold; the cell to be corrected determining module is used for determining the cell to be corrected according to the coverage distance of each cell to be equalized; and the second re-optimization module is used for performing load balancing again on the cell to be balanced after performing antenna correction on the cell to be corrected.
In an optional manner, the coverage distance calculation module is specifically configured to obtain an antenna height and a 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 the downward inclination angle of the cell to be equalized.
In an optional manner, the apparatus 200 further comprises: the device comprises a historical data acquisition module, a prediction function determination module and an optimization module. The historical data acquisition module is used for acquiring historical load data of the cell to be balanced; the prediction function determination module is used for 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 the optimization module is used for carrying out load balancing on the cell 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: 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.
It should be noted that, the load balancing apparatus provided in the embodiments of the present invention is an apparatus capable of executing the load balancing method, and all embodiments of the load balancing method are applicable to the apparatus and can achieve the same or similar beneficial effects.
According to the embodiment of the invention, after the cells to be balanced are subjected to load balancing in advance, the problem cells and the cells to be corrected are obtained, and after azimuth adjustment or antenna correction is carried out, the cells to be balanced are subjected to load balancing again, so that not only can load data be predicted and optimized in advance, but also the automation degree is improved, the cells which cannot be improved after multiple rounds of optimization can be analyzed, antenna feeder optimization processing is carried out, and load balancing optimization is carried out again, so that the quality of load balancing optimization is improved.
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 causes a processor to execute the load balancing method in any of the above method embodiments.
According to the embodiment of the invention, after the load balancing is carried out on the cell to be balanced, the problem cell is determined from the cell to be balanced according to the load balancing result of the cell to be balanced, the actual azimuth angle of the problem cell is calculated, if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to the preset deviation threshold value, the load balancing is carried out on the cell to be balanced again after the preset azimuth angle is adjusted to the actual azimuth angle, the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by the antenna feeder coverage abnormity is judged, and the optimization effect is improved.
Embodiments of the present invention provide, by way of embodiments of the present invention, a computer program product comprising a computer program stored on a computer storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform a load balancing method in any of the above-described method embodiments.
According to the embodiment of the invention, after the load balancing is carried out on the cell to be balanced, the problem cell is determined from the cell to be balanced according to the load balancing result of the cell to be balanced, the actual azimuth angle of the problem cell is calculated, if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to the preset deviation threshold value, the load balancing is carried out on the cell to be balanced again after the preset azimuth angle is adjusted to the actual azimuth angle, the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by the antenna feeder coverage abnormity is judged, and the optimization effect is improved.
Fig. 10 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and 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 communication Interface 304, a memory 306, and a communication bus 308.
Wherein: the processor 302, communication interface 304, and memory 306 communicate with each other via a communication bus 308. A communication interface 304 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 may specifically execute the load balancing method in any of the method embodiments described above.
In particular, program 310 may include program code comprising computer operating 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 an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 306 for storing a program 310. Memory 306 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
According to the embodiment of the invention, after the load balancing is carried out on the cell to be balanced, the problem cell is determined from the cell to be balanced according to the load balancing result of the cell to be balanced, the actual azimuth angle of the problem cell is calculated, if the difference value between the actual azimuth angle and the corresponding preset azimuth angle is greater than or equal to the preset deviation threshold value, the load balancing is carried out on the cell to be balanced again after the preset azimuth angle is adjusted to the actual azimuth angle, the cell which cannot be improved after multi-round optimization can be analyzed, the load imbalance caused by the antenna feeder coverage abnormity is judged, and the optimization effect is improved.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is 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 should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, any of the embodiments claimed in the claims can be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments 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 may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
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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