CN113938954B - Load balancing optimization method, device and storage medium - Google Patents

Load balancing optimization method, device and storage medium Download PDF

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CN113938954B
CN113938954B CN202111185076.2A CN202111185076A CN113938954B CN 113938954 B CN113938954 B CN 113938954B CN 202111185076 A CN202111185076 A CN 202111185076A CN 113938954 B CN113938954 B CN 113938954B
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cell
target
indoor
load balancing
determining
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CN113938954A (en
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彭家立
吴争光
郑夏妍
徐毅
苏毅
胡羡
黎宏沃
张小健
张辉炯
宋兆斌
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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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
    • H04W28/09—Management thereof
    • H04W28/0958—Management thereof based on metrics or performance parameters
    • 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 disclosure provides a load balancing optimization method, a device and a storage medium, wherein the method comprises the following steps: acquiring network performance data and user perception data of an indoor distribution system, and then determining a first target indoor partition cell with load balancing optimization conditions according to the network performance data and the user perception data; then taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment; and finally, carrying out load balancing optimization on the target fragments so as to achieve the load balancing of the indoor distribution system. The present disclosure allows for the diversification of the conditions for load balancing decisions. In addition, by dividing the antenna into small fragments, load balancing optimization is performed on each fragment, so that the problems of limited optimized balancing parameters and limited equipment arrangement space can be solved relative to balanced parameter optimization and newly-added equipment, and waste of manpower and/or material resources is avoided.

Description

Load balancing optimization method, device and storage medium
Technical Field
The disclosure relates to the technical field of communication, and in particular relates to a load balancing optimization method, a device and a storage medium.
Background
In recent years, with the rapid development of wireless communication technology, the number of 4G and 5G users is rapidly increasing, and the complexity of network structure and service diversity make the enhancement of user perception more and more complex. The indoor distribution system has the advantages that complicated indoor scenes such as high-rise buildings, subway lines, large-scale venues and airport high-speed rail stations are more and more, wiring, construction, properties, equipment storage space and the like required by capacity expansion of the indoor distribution system are limited greatly, load balancing judgment and optimization difficulty is very high, but loads among cells in the indoor scenes are different greatly, and therefore the indoor distribution system has the opportunity and challenges.
At present, the traditional load balancing judgment and optimization means only performs load balancing judgment through network performance data analysis of a network management platform, and then performs load balancing optimization through manually adding equipment or adjusting balancing parameters on site. In the scheme, the condition for judging the load balance is single; in addition, aiming at indoor scenes, the arrangement space of newly-added equipment is very limited, and the equalization parameter optimization limitation is high, and a great amount of manpower and material resources are consumed by the optimization method.
Disclosure of Invention
In order to solve the above-mentioned problems, the present disclosure provides a load balancing optimization method. Not only saving manpower and material resources, but also being capable of more accurately carrying out load balancing optimization.
In a first aspect, the present disclosure provides a load balancing optimization method, including:
acquiring network performance data and user perception data of an indoor distribution system, wherein the indoor distribution system comprises at least two indoor sub-cells;
according to network performance data and user perception data, determining a first target indoor partition cell with a load balancing optimization condition, wherein the load balancing optimization condition is an indoor partition cell with high load characteristics and user perception difference characteristics in at least two indoor partition cells;
taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment;
and carrying out load balancing optimization on the target fragments so as to achieve the load balancing of the indoor distribution system.
In a possible implementation manner, load balancing optimization is performed for a target slice to achieve load balancing of an indoor distribution system, including: determining a user terminal in the target fragment as a sample point set; selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high load characteristics in at least two cells; after the optimal allocation, network performance data of the indoor distribution system are acquired again, and whether the load balance of the indoor distribution system is achieved is determined according to the acquired network performance data; and if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target room for cell division until the load balance of the indoor distribution system is achieved.
In one possible embodiment, determining whether load balancing of the indoor distribution system is achieved based on the retrieved network performance data includes at least one of:
if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, determining that the load balance of the indoor distribution system is not achieved; if the first target room sub-cell does not have the high load characteristic according to the re-acquired network performance data, and the second target room sub-cell has the high load characteristic, returning the target sample point to the first target room sub-cell, and determining that the load balance of the indoor distribution system is not achieved; if the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell does not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved; and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.
In a possible implementation manner, selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to the second target chamber partition cell includes: and after each time window is passed, selecting a preset threshold number of target sample points from the sample point set, and redirecting the first target sample points to the second target room partition cells.
In a possible implementation manner, determining a first target indoor partition with load balancing optimization conditions according to network performance data and user perception data includes: determining a first indoor partition with high load characteristics according to network performance data; determining a second indoor partition cell with user perception difference characteristics according to the user perception data; and determining a first target cell with load balancing optimization conditions according to the first cell and the second cell.
In a possible implementation manner, the network performance data includes a downlink traffic of a cell and a resource utilization rate of a downlink PRB of the cell, and determining, according to the network performance data, a first indoor partition cell with a high load characteristic includes: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell downlink PRB resource utilization greater than or equal to the cell downlink PRB resource utilization threshold is a first cell division cell with high load characteristics.
In one possible implementation manner, the network performance data includes a cell downlink traffic and a cell RRC connection user number, and determining, according to the network performance data, a first indoor partition cell with a high load feature includes: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold value, and the cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold value is a first cell with high load characteristics.
In one possible implementation, the user sensing data includes cell channel quality, cell stuck times, cell delay and cell average rate, and determining, according to the user sensing data, a second indoor partition cell with a user sensing difference feature includes: and determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to a cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.
In a possible embodiment, the load balancing optimization condition further includes: a second target cell is present in the neighborhood of the first target cell, the second target cell being a cell of the at least two cells that does not have the high load characteristic.
In a possible embodiment, the method further comprises: and if the second target room partition does not exist, ending the flow.
In a second aspect, the present disclosure provides a load balancing optimization apparatus, comprising:
the indoor distribution system comprises at least two indoor sub-cells;
The determining module is used for determining a first target indoor partition cell with a load balancing optimization condition according to network performance data and user perception data, wherein the load balancing optimization condition is an indoor partition cell with a high load characteristic and a user perception difference characteristic in at least two indoor partition cells;
the quantization module is used for quantizing the first target indoor partition cell by taking the indoor partition antenna coverage area corresponding to the first target indoor partition cell as a unit to obtain a target fragment;
and the optimizing module is used for carrying out load balancing optimization aiming at the target fragments so as to achieve the load balancing of the indoor distribution system.
In a possible implementation manner, the optimization module is specifically configured to: determining a user terminal in the target fragment as a sample point set; selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high load characteristics in at least two cells; after the optimal allocation, network performance data of the indoor distribution system are acquired again, and whether the load balance of the indoor distribution system is achieved is determined according to the acquired network performance data; and if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target room for cell division until the load balance of the indoor distribution system is achieved.
In a possible implementation manner, the determining module is specifically configured to at least one of the following:
if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, determining that the load balance of the indoor distribution system is not achieved; if the first target room sub-cell does not have the high load characteristic according to the re-acquired network performance data, and the second target room sub-cell has the high load characteristic, returning the target sample point to the first target room sub-cell, and determining that the load balance of the indoor distribution system is not achieved; if the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell does not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved; and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.
In a possible implementation manner, the optimization module is specifically configured to: and after each time window is passed, selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to the second target room partition.
In a possible implementation manner, the determining module is specifically configured to: determining a first indoor partition with high load characteristics according to network performance data; determining a second indoor partition cell with user perception difference characteristics according to the user perception data; and determining a first target cell with load balancing optimization conditions according to the first cell and the second cell.
In a possible implementation manner, the determining module is specifically configured to: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell downlink PRB resource utilization greater than or equal to the cell downlink PRB resource utilization threshold is a first cell division cell with high load characteristics.
In a possible implementation manner, the determining module is specifically configured to: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold value, and the cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold value is a first cell with high load characteristics.
In a possible implementation manner, the determining module is specifically configured to: and determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to a cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.
In a possible embodiment, the load balancing optimization condition further includes: a second target cell is present in the neighborhood of the first target cell, the second target cell being a cell of the at least two cells that does not have the high load characteristic.
In a possible implementation manner, the determining module is further configured to: and if the second target room partition does not exist, ending the flow.
In a third aspect, the present disclosure provides an electronic device comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored by the memory to implement the load balancing optimization method of the first aspect.
In a fourth aspect, the present disclosure is a computer-readable storage medium having stored therein computer-executable instructions for implementing the load balancing optimization method of the first aspect when executed by a processor.
In a fifth aspect, the present disclosure provides a computer program product comprising a computer program which, when executed by a processor, implements the load balancing optimization method of the first aspect.
The disclosure provides a load balancing optimization method, a device and a storage medium, wherein the indoor distribution system comprises at least two indoor sub-cells by acquiring network performance data and user perception data of the indoor distribution system; then, according to network performance data and user perception data, determining a first target indoor partition cell with a load balancing optimization condition, wherein the load balancing optimization condition is an indoor partition cell with high load characteristics and user perception difference characteristics in at least two indoor partition cells; then taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment; and finally, carrying out load balancing optimization on the target fragments so as to achieve the load balancing of the indoor distribution system. The method and the device judge whether the indoor partition cells meet the load balancing condition according to the network performance data and the user perception data, so that the load balancing judgment condition is diversified. In addition, by dividing the indoor antenna into small fragments, load balancing optimization is performed on each fragment area, and compared with the optimization of balancing parameters and newly-added equipment, the problems of limited optimized balancing parameters and limited equipment arrangement space can be solved, and waste of manpower and/or material resources is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of an indoor distribution system according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a load balancing optimization method according to an embodiment of the present disclosure;
FIG. 3 is a flow chart of load balancing optimization for target slices provided in another embodiment of the present disclosure;
FIG. 4 is a flow chart of load balancing optimization for target tiles provided by a further embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a load balancing optimization device according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.
First, some technical terms related to the present disclosure will be explained:
base Station (Base Station): that is, the public mobile communication base station is a form of a radio station, and means a radio transceiver station for transmitting information to and from a mobile phone terminal through a mobile communication switching center in a certain radio coverage area.
Cell ID: the identification code of the base station cell to which the service cell belongs.
Longitude: cell geographic longitude.
Latitude: cell geographical latitude.
Delay (Delay): refers to the time required for a message or packet to travel from one end of a network to another, and is a performance indicator of the level of latency characterized by the network operator.
Rate (Speed): the speed of network transmission is the speed or the change rate equivalent to file transmission, and the performance index of the network operator for representing the speed of transmission is provided.
Channel quality indicator (Chartered Quality Institute, CQI for short): is a measurement standard of the communication quality of a wireless channel, and is a performance index of a network operator for representing the channel quality.
Indoor distribution system: the indoor antenna distribution system is utilized to uniformly distribute signals of the base station at each indoor corner, so that an indoor area is ensured to have ideal signal coverage.
The method and the device for load balancing optimization and the storage medium are provided for solving the problems of single condition of load balancing judgment, long time consumption, high cost, low efficiency, poor feasibility and the like in the prior art, and the judgment of load balancing optimization is carried out from two dimensions of network performance data and user perception data, so that the condition of load balancing judgment is diversified; and load balancing optimization is performed through the redistribution of the coverage area, so that the load balancing optimization efficiency is improved, the resource utilization rate is improved, the cost is reduced, the feasibility of the load balancing optimization is improved, and the perception of a user can be improved.
Fig. 1 is a schematic diagram of an indoor distribution system according to an embodiment of the disclosure. As shown in fig. 1, the indoor distribution system includes a base station 100, where the indoor cells covered by the base station 100 include an indoor cell 101, an indoor cell 102, and an indoor cell 103. The indoor cell 101 includes a terminal device 4, and the indoor cell 102 includes a terminal device 1, a terminal device 2, and a terminal device 3.
In fig. 1, the cell 102 has three terminal devices, and network performance data and user awareness data of the cell 102 can be acquired from the base station 100. By the network performance data and the user perceived data of the cell 102, it is possible to determine whether the cell 102 is in a state of high load and poor perceived performance. If the cell 102 is in a state of high load and poor perceived performance, that is, has a high load characteristic and a user perceived difference characteristic, the cell 102 has a load balancing optimization condition.
Further, since the cell 101 contains 1 terminal device, the load is less, and the probability of being in a low load state is greater, so that the terminal device in the cell 102 can be redirected into the cell 101, for example, the terminal device 1 can be redirected into the cell 101.
It should be clear that whether the indoor partition has load balancing optimization conditions is not simply dependent on the number of terminal devices contained therein, but also related to services executed by the terminal devices, and is only illustrated for convenience of understanding, but not limited to the disclosure.
Since the present disclosure is optimized for load balancing for indoor scenarios, fig. 1 illustrates indoor cell division as an example.
After redirecting the terminal equipment in the cell 102 to the cell 101, the load of the cell 102 is reduced. In this case, if the cell 102 is in the low load state and the cell 101 is still in the low load state, the load balancing optimization can be considered to be completed.
It should be noted that fig. 1 is only a schematic diagram of an application scenario provided by an embodiment of the present disclosure, and the embodiment of the present disclosure does not limit the devices included in fig. 1, and does not limit the number of devices and the positional relationship between the devices in fig. 1. In addition, the terminal device may be a mobile phone, a computer or other communication devices, which is not limited in this disclosure.
Next, a load balancing optimization method is described by way of specific embodiments.
Fig. 2 is a flowchart of a load balancing optimization method according to an embodiment of the present disclosure. As shown in fig. 2, the load balancing optimization method includes:
s201, network performance data and user perception data of an indoor distribution system are obtained, and the indoor distribution system comprises at least two indoor sub-cells.
In particular, the indoor distribution system may include a base station, as shown in fig. 1. Further, the base station may be an indoor base station, which is not limited by the present disclosure.
For example, network performance data and user awareness data of the indoor distribution system may be obtained from the base station through a network management platform. The network data may represent the load condition of each cell, for example, the information such as the resource utilization rate of the downlink physical resource block (Physical Resource Block, abbreviated PRB) of the cell, the downlink traffic of the cell, and the average number of users of the cell. The user perception data can represent service use conditions of users in each cell, such as cell blocking times, cell blocking rate, cell time delay, cell channel quality, cell average rate and the like.
In addition, engineering physical parameters of the indoor distribution system may be obtained, where the engineering physical parameters may include longitude, latitude, cell number, cell name, coverage area, and the like of the indoor partition cell.
In some embodiments, the database may be built based on the network performance data, user awareness data, and engineering physical parameters described above. The database may include, but is not limited to, the following data:
Figure BDA0003298843410000091
wherein the database is composed of Q x Represents { C i Characterizing as a set of all room-partitioned cells contained in a network performance database; { PRB i Characterizing as a set of cell downlink PRB resource utilization; { User i Characterizing as a set of cell RRC connected user numbers; { Flow i Characterizing as a set of cell downlink traffic; { CQI i Characterizing as a set of cell channel qualities; { Caton i Characterizing as a set of cell stuck times; { Delay i Characterizing as a set of cell delays; { Speed i And is characterized as a set of cell average rates.
The key fields related to the network performance data in the database are shown in table 1:
TABLE 1
Figure BDA0003298843410000092
Key fields related to user perception data in the database are shown in table 2:
TABLE 2
Figure BDA0003298843410000093
Key fields related to engineering physical parameters in the database are shown in table 3:
TABLE 3 Table 3
Figure BDA0003298843410000101
S202, determining a first target indoor partition cell with a load balancing optimization condition according to network performance data and user perception data, wherein the load balancing optimization condition is an indoor partition cell with high load characteristics and user perception difference characteristics in at least two indoor partition cells.
By analyzing the network performance data and the user perception data, a cell having a high load characteristic and a user perception difference characteristic can be selected from a plurality of cells. It will be appreciated that the specific implementation corresponding to determining the first target ventricular zone with load balancing optimization conditions may also be different when the network performance data and/or the user awareness data are different. The following is an example illustration:
in one specific implementation, determining a first target cell with load balancing optimization conditions according to network performance data and user awareness data may include: determining a first indoor partition with high load characteristics according to network performance data; determining a second indoor partition cell with user perception difference characteristics according to the user perception data; and determining a first target cell with load balancing optimization conditions according to the first cell and the second cell.
Optionally, the first and second indoor sub-areas are subjected to 'and' algorithm matching operation, and the indoor sub-areas meeting the high-load characteristic and the user perception difference characteristic are screened and cleaned. The method for screening and cleaning the first target room partition cells with the load balancing optimization condition comprises the following steps:
Figure BDA0003298843410000102
Wherein, cell j Characterizing a first target compartmental Cell, with load balancing optimization conditions x Characterizing a compartmentalized cell having high load characteristics; cell y Characterizing a compartmentalized cell with user perceived difference characteristics.
For example, the first target cell with load balancing optimization conditions may also be represented by table 4:
TABLE 4 Table 4
Figure BDA0003298843410000111
In another specific implementation, determining, according to the network performance data and the user awareness data, the first target indoor partition cell with the load balancing optimization condition may include: determining a first indoor partition with high load characteristics according to network performance data; and determining the cell with the user perception difference characteristic from the first cell as a first target cell according to the user perception data.
In yet another specific implementation, determining, according to the network performance data and the user awareness data, the first target indoor partition cell having the load balancing optimization condition may include: determining a second indoor partition cell with user perception difference characteristics according to the user perception data; and determining the cell with the high load characteristic from the second cell as the first target cell according to the network performance data.
S203, quantifying the first target indoor partition cell by taking the indoor partition antenna coverage area corresponding to the first target indoor partition cell as a unit to obtain the target fragment.
Each cell includes at least one antenna therein and each antenna is responsible for covering an area of the cell. Partitioning a first target indoor cell determined before according to the coverage area of the indoor antenna:
and taking the area covered by each indoor antenna as a basic unit, and finally dividing the first target indoor subarea into a plurality of basic units to realize the target slicing operation. Thereby facilitating the fine management of user terminal devices under a plurality of base units.
S204, load balancing optimization is conducted on the target fragments so as to achieve load balancing of the indoor distribution system.
After the indoor sub-cells are thinned, the problem of load balance optimization is converted from the indoor sub-cells to the coverage area (target slicing) of one indoor antenna under the indoor sub-cells. Load balancing optimization is carried out on a plurality of target fragments, so that the load balancing optimization of the indoor distribution system is realized.
In some embodiments, the load balancing optimization conditions may further include: a second target cell is present in the neighborhood of the first target cell, the second target cell being a cell of the at least two cells that does not have the high load characteristic.
Optionally, the engineering physical parameters are combined to carry out algorithm matching with a base station down-hanging cell list, and after the algorithm matching is carried out, a first target cell with load balancing optimization conditions (the indoor base station is provided with the cell with high load characteristics and user perception difference characteristics and other cells without high load characteristics) is screened and cleaned. Screening and cleaning first target Cell with load balancing optimization condition m The method of (1) is as follows:
Figure BDA0003298843410000121
wherein, cell a Characterizing a cell with high load characteristic and user perception difference characteristic under the same indoor base station; cell a+1 Characterizing a cell which does not have high load characteristics under the same indoor base station; nodeB a Characterizing an indoor base station; a represents the number of indoor sub-cells under the same indoor base station。
In the above, nodeB a Cell comprising both high load and user perceived difference features a There are also Cell cells which do not have high load characteristics a+1 。
The first target room cell may be represented by table 5:
TABLE 5
Figure BDA0003298843410000122
Optionally, the load balancing optimization method of the present disclosure may further include: and if the second target room partition does not exist, ending the flow.
In the embodiment of the disclosure, network performance data and user perception data of an indoor distribution system are obtained, wherein the indoor distribution system comprises at least two indoor sub-cells; then, according to network performance data and user perception data, determining a first target indoor partition cell with a load balancing optimization condition, wherein the load balancing optimization condition is an indoor partition cell with high load characteristics and user perception difference characteristics in at least two indoor partition cells; then taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment; and finally, carrying out load balancing optimization on the target fragments so as to achieve the load balancing of the indoor distribution system. The method and the device judge whether the indoor partition cells meet the load balancing condition according to the network performance data and the user perception data, so that the load balancing judgment condition is diversified. In addition, by dividing the indoor antenna into small fragments, load balancing optimization is performed on each fragment area, and compared with the optimization of balancing parameters and newly-added equipment, the problems of limited optimized balancing parameters and limited equipment arrangement space can be solved, and waste of manpower and/or material resources is avoided.
Based on the above embodiment, load balancing optimization is performed for the target slices to achieve a specific implementation of load balancing of the indoor distribution system, which can be represented by a flow shown in fig. 3. As shown in fig. 3, S204 may further include:
s301, determining the user terminal in the target fragment as a sample point set.
In order to perform load balancing optimization operation, the number of user terminals in a target fragment needs to be quantized first, and the user terminals are formed into a sample point set. Each sample point in the set corresponds to a user terminal.
S302, selecting a preset threshold number of target sample points from a sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high load characteristics in at least two cells.
For load balancing optimization, at least one target sample point in the set of sample points needs to be redirected to the other compartmentalized cell.
Illustratively, the preset threshold is an integer greater than or equal to 1, and the preset threshold may be variable. The second target chamber cell may be in an adjacent relationship with the first target chamber cell.
The redirection refers to that a user terminal originally belonging to a certain target fragment in a first target cell is directed to a coverage area of a certain indoor antenna in a second target cell.
Further, in some embodiments, selecting a predetermined threshold number of target sample points from the set of sample points, redirecting the target sample points to the second target ventricular cell may include: and after each time window is passed, selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to the second target room partition.
Setting the time window may also be understood as setting a time period T, i.e. redirecting at least one target sample point to the second target ventricular partition after each lapse of the time period T. The reason for setting the time period T is that the frequency of redirection can be flexibly adjusted according to actual conditions. For example, for a plurality of cells, if each cell cannot accommodate more user terminals, it cannot perform a plurality of redirection operations, in which case the value of the set time period T needs to be large. For example, it may be 10 seconds. If there are a first target cell and a plurality of second target cells, the value of the time period T may be set to be smaller. In particular, it may be 5 seconds. In addition, the loss of the redirecting device needs to be considered, if the setting time period T is too small, the loss of the redirecting device can be increased, and therefore the service life is reduced.
As an example, the steps described above are as follows:
the coverage area of the Antenna after the quantization division and the small division is determined as a sample point set { Antenna ] p -a }; from a sample point set { Antenna p Selecting one or more target sample points, presetting a time period for determining a user adjustment strategy as T, redirecting all target sample points to a Cell of a Cell which is not provided with high load characteristics and is in the same-indoor base station in the neighborhood after each time period T is spaced a+1 Is a kind of medium. (the time period T may be set to 10, 20, 60 seconds, etc., as desired in the field).
In the embodiment of the disclosure, the time window is introduced, so that the redirecting frequency can be flexibly set, and further more application scenes are satisfied. In addition, the service life of the redirecting device is improved relative to not setting the time window.
S303, after optimizing distribution, network performance data of the indoor distribution system are acquired again, and whether load balance of the indoor distribution system is achieved is determined according to the acquired network performance data.
After load balancing optimization, the load state of the whole indoor distribution system needs to be judged again, and whether load balancing optimization can be further carried out is judged.
By way of example, the load status of each cell at this time can be determined by acquiring network performance data.
In some implementations, load balancing optimization is performed for the target slices to achieve load balancing of the indoor distribution system, and may include at least one of:
if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, determining that the load balance of the indoor distribution system is not achieved; if the first target room sub-cell does not have the high load characteristic according to the re-acquired network performance data, and the second target room sub-cell has the high load characteristic, returning the target sample point to the first target room sub-cell, and determining that the load balance of the indoor distribution system is not achieved; if the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell does not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved; and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.
Illustratively, the above steps may be written as:
if the conditions are satisfied:
Figure BDA0003298843410000151
and->
Figure BDA0003298843410000152
In the base station NodeB a The underhung indoor Cell includes a Cell m 、Cell a+1 A minimum of 2 compartmental cells. Wherein, cell m Belongs to a Cell with high load characteristic a+1 Cells are partitioned for chambers that do not feature high loads.
If the conditions are satisfied:
Figure BDA0003298843410000153
and->
Figure BDA0003298843410000154
The previous coverage area re-optimization allocation operation is returned, a new set of target sample points is selected from the sample point set instead, and the re-direction optimization allocation operation is re-executed.
In the base station NodeB a The underhung indoor Cell includes a Cell m 、Cell a+1 A Cell comprising a minimum of 2 Cell divisions, wherein the Cell m For cells without high load characteristics a+1 Is a cell with high load characteristics.
If:
case 1:
Figure BDA0003298843410000155
and->
Figure BDA0003298843410000156
Case 2:
Figure BDA0003298843410000157
and->
Figure BDA0003298843410000158
The redirection optimized allocation operation is stopped if one of the above cases is satisfied, both of which represent that each indoor partition (and each target partition inside the indoor partition) under the base station has reached a load-balanced state.
In the base station NodeB a The lower Cell comprises Cell m 、Cell a+1 A Cell comprising a minimum of 2 Cell divisions, wherein in case 1 m Belonging to a Cell which is not provided with high load characteristics a+1 Also belongs to a cell division cell without high load characteristics; in case 2, cell m Belongs to a Cell with high load characteristics and is a Cell a+1 And also belongs to a cell division cell with high load characteristics.
S304, if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target indoor partition cell until the load balance of the indoor distribution system is achieved.
In the embodiment of the disclosure, load balancing optimization is performed on a first target room partition cell with load balancing optimization conditions based on a time window. Specifically, load balancing optimization is performed on the user terminals under the target fragments. In the optimization process, when all cells are indoor division cells with high load characteristics; alternatively, all cells are compartmentalized cells that do not have high load characteristics. At this time, the load balancing optimization is finished, and the load balancing is realized by the indoor cells under the base station. In addition, for other cases, load balancing optimization is further performed. The method improves the comprehensiveness of optimization, and has high optimization speed and high accuracy.
Optionally, the network performance data may include a cell downlink traffic and a cell downlink PRB resource utilization, and determining, according to the network performance data, a first indoor partition cell with a high load feature may include:
and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell downlink PRB resource utilization greater than or equal to the cell downlink PRB resource utilization threshold is a first cell division cell with high load characteristics.
In other embodiments, the network performance data may further include a cell downlink traffic and a cell RRC connection user number, and determining, according to the network performance data, a first indoor partition cell with a high load feature may include: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold value, and the cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold value is a first cell with high load characteristics.
Illustratively, a Cell with high load characteristics x The following conditions need to be satisfied:
Figure BDA0003298843410000161
or->
Figure BDA0003298843410000162
Wherein, flow is i The method is characterized by the downlink flow of a cell; flow (Flow) th Characterized by a cell traffic threshold；PRB i The method is characterized by the utilization rate of the downlink PRB of a cell; PRB (physical resource block) th The method is characterized by a cell downlink PRB resource utilization threshold; user (User) i Characterized by the number of cell RRC connected users; user (User) th Characterized by a cell RRC connection user number threshold.
Specifically, the cell traffic threshold may be set to 8.4GB, the cell downlink PRB resource utilization threshold may be set to 80%, and the cell RRC connection user number threshold may be set to 160. The first compartmentalized cell with high load characteristics can be shown in table 6:
TABLE 6
Figure BDA0003298843410000171
Optionally, in the foregoing embodiment, the user sensing data may include cell channel quality, cell camping number, cell delay and cell average rate, and determining, according to the user sensing data, a second indoor partition cell with a user sensing difference feature may include:
and determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to a cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.
Illustratively, a Cell with user perceived difference features y The following conditions are required:
Figure BDA0003298843410000172
wherein CQI is i Characterized by cell channel quality; CQI (channel quality indicator) th Characterized by a cell channel quality threshold; caton i Characterizing as cell stuck; caton th Characterizing as a cell stuck threshold; delay of i Characterizing as cell delay; delay of th Characterized by a cell delay threshold; speed of i Characterized by a cell average rate; speed of th Characterized as a cell average rate threshold.
In addition, it should be noted that in some embodiments, the second compartmentalized cell with user perceived difference characteristics may be one or more of the above conditions met. The embodiments of the present disclosure are not limited thereto. Specifically, the cell channel quality threshold may be set to 10, the cell stuck threshold may be set to 70 times, the cell delay threshold may be set to 80MS, and the cell average rate threshold may be set to 10Mbps.
The second compartmentalized cell with user perceived difference characteristics can be represented by table 7:
TABLE 7
Figure BDA0003298843410000181
Next, a method for load balancing optimization on a target tile provided by an embodiment is described by using fig. 4, and as shown in fig. 4, the method includes:
s401, determining a first target room partition with load balancing optimization conditions.
This step is already described and will not be described here.
S402, quantifying the first target indoor partition cell by taking the indoor partition antenna coverage area corresponding to the first target indoor partition cell as a unit to obtain a target partition, and determining a user terminal in the target partition as a sample point set.
S403, selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high load characteristics in at least two cells.
S404, judging whether the indoor distribution system has the indoor subarea with high load characteristic according to the network performance data of the indoor distribution system.
If so, S406 is executed, and if not, S405 is executed.
S405, stopping the load balancing optimization operation.
S406, judging whether the second target cell is a cell with high load characteristics.
If yes, step S407 is executed, and if no, step S403 is returned.
S407, judging whether the first target cell is a cell with high load characteristics.
If yes, step S405 is executed, if not, step S408 is executed, and step S403 is returned.
The above can be seen that load balancing optimization is achieved only when all the indoor cells under one indoor subsystem have high load characteristics or all the indoor cells do not have high load characteristics, and then the load balancing optimization is stopped. For other cases, further execution of S403 is also required.
S408, marking the current target sample point and replacing the current target sample point in the original sample point set.
The method and the device aim at constructing a load balance judging model and a load balance optimizing model, outputting a cell list with load balance optimizing conditions, and then carrying out load balance optimizing adjustment to achieve the aims of load balance and user perception improvement. The determining of the indoor partition with the load balancing optimization condition comprises the following steps: firstly, judging a cell with high load characteristics according to network performance data; step two, judging a cell with a user perception difference characteristic according to the user perception data; and thirdly, judging the indoor partition cells with the high load characteristic and the user perception difference characteristic by combining engineering physical parameter base station data through algorithm operation, wherein the indoor partition cells with the load balance optimization conditions (the indoor partition cells with the high load characteristic and the user perception difference characteristic and the indoor partition scene of other indoor partition cells with no high load exist in the indoor partition cells hung under the same base station). And (3) screening by the algorithm to obtain the indoor partition cells with the load balancing optimization condition. And then carrying out quantitative division and fragmentation on the coverage area of the indoor partition antenna under the indoor partition cell, carrying out load balancing strategy optimization based on a time window on the coverage area after the division and fragmentation, and finally achieving the purposes of load balancing and user perception promotion optimization. Such as indoor scenes in high-rise buildings, underground railways, large indoor venues, airport high-speed rail stations, etc. The indoor scene points with high space limitation in the venue are all property difficulties, wiring difficulties, construction difficulties and the like. The indoor scene point has the characteristics of multiple user groups, user concentration in a certain area, complex wireless environment, large upsizing and optimizing difficulty, concentrated traffic and the like.
After the load balancing optimization method of the present disclosure is described, next, the load balancing optimization apparatus of the present disclosure is described by way of fig. 5. As shown in fig. 5, a load balancing optimization apparatus 500 of an embodiment of the present disclosure includes:
an obtaining module 501, configured to obtain network performance data and user perception data of an indoor distribution system, where the indoor distribution system includes at least two indoor cells;
the determining module 502 is configured to determine, according to the network performance data and the user perception data, a first target indoor partition cell having a load balancing optimization condition, where the load balancing optimization condition is an indoor partition cell having a high load characteristic and a user perception difference characteristic in at least two indoor partition cells;
a quantization module 503, configured to quantize the first target indoor partition cell with a coverage area of the indoor partition antenna corresponding to the first target indoor partition cell as a unit, to obtain a target partition;
and the optimizing module 504 is used for carrying out load balancing optimization aiming at the target fragments so as to achieve the load balancing of the indoor distribution system.
In a possible implementation manner, the optimization module 504 is specifically configured to: determining a user terminal in the target fragment as a sample point set; selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high load characteristics in at least two cells; after the optimal allocation, network performance data of the indoor distribution system are acquired again, and whether the load balance of the indoor distribution system is achieved is determined according to the acquired network performance data; and if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target room for cell division until the load balance of the indoor distribution system is achieved.
In a possible implementation manner, the determining module 502 is specifically configured to at least one of the following:
if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, determining that the load balance of the indoor distribution system is not achieved; if the first target room sub-cell does not have the high load characteristic according to the re-acquired network performance data, and the second target room sub-cell has the high load characteristic, returning the target sample point to the first target room sub-cell, and determining that the load balance of the indoor distribution system is not achieved; if the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell does not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved; and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.
In a possible implementation manner, the optimization module 504 is specifically configured to: and after each time window is passed, selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to the second target room partition.
In a possible implementation manner, the determining module 502 is specifically configured to: determining a first indoor partition with high load characteristics according to network performance data; determining a second indoor partition cell with user perception difference characteristics according to the user perception data; and determining a first target cell with load balancing optimization conditions according to the first cell and the second cell.
In a possible implementation manner, the determining module 502 is specifically configured to: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell downlink PRB resource utilization greater than or equal to the cell downlink PRB resource utilization threshold is a first cell division cell with high load characteristics.
In a possible implementation manner, the determining module 502 is specifically configured to: and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold value, and the cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold value is a first cell with high load characteristics.
In a possible implementation manner, the determining module 502 is specifically configured to: and determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to a cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.
In a possible embodiment, the load balancing optimization condition further includes: a second target cell is present in the neighborhood of the first target cell, the second target cell being a cell of the at least two cells that does not have the high load characteristic.
In a possible implementation manner, the determining module 502 is further configured to: and if the second target room partition does not exist, ending the flow.
The apparatus provided in the embodiments of the present disclosure may be used to perform the method of the foregoing embodiments, and the implementation principle and technical effects are similar, and are not described herein again.
It should be noted that, it should be understood that the division of the modules of the above apparatus is merely a division of a logic function, and may be fully or partially integrated into a physical entity or may be physically separated. And these modules may all be implemented in software in the form of calls by the processing element; or can be realized in hardware; the method can also be realized in a form of calling software by a processing element, and the method can be realized in a form of hardware by a part of modules. For example, the processing module may be a processing element that is set up separately, may be implemented in a chip of the above-mentioned apparatus, or may be stored in a memory of the above-mentioned apparatus in the form of program codes, and the functions of the above-mentioned processing module may be called and executed by a processing element of the above-mentioned apparatus. The implementation of the other modules is similar. In addition, all or part of the modules can be integrated together or can be independently implemented. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in a software form.
For example, the modules above may be one or more integrated circuits configured to implement the methods above, such as: one or more specific integrated circuits (application specific integrated circuit, ASIC), or one or more microprocessors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA), or the like. For another example, when a module above is implemented in the form of a processing element scheduler code, the processing element may be a general purpose processor, such as a central processing unit (central processing unit, CPU) or other processor that may invoke the program code. For another example, the modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present disclosure, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. For example, the electronic device may be provided as a server or network device, such as a base station. Referring to fig. 6, an electronic device 600 includes a processing component 601 that further includes one or more processors and memory resources represented by memory 602 for storing instructions, such as applications, executable by the processing component 601. The application program stored in the memory 602 may include one or more modules each corresponding to a set of instructions. Further, the processing component 601 is configured to execute instructions to perform any of the method embodiments described above.
The electronic device 600 may also include a power component 603 configured to perform power management of the electronic device 600, a wired or wireless network interface 604 configured to connect the electronic device 600 to a network, and an input output (I/O) interface 605. The electronic device 600 may operate based on an operating system stored in the memory 602, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, or the like.
The present disclosure also provides a computer-readable storage medium having stored therein computer-executable instructions that, when executed by a processor, implement the scheme of the load balancing optimization method as above.
The present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the aspects of the load balancing optimization method as above.
The computer readable storage medium described above may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk. A readable storage medium can be any available medium that can be accessed by a general purpose or special purpose computer.
An exemplary readable storage medium is coupled to the processor such the processor can read information from, and write information to, the readable storage medium. In the alternative, the readable storage medium may be integral to the processor. The processor and the readable storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuits, ASIC for short). The processor and the readable storage medium may, of course, also reside as discrete components in a load balancing optimization apparatus.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present disclosure, and not for limiting the same; although the present disclosure has been described in detail with reference to the foregoing embodiments,
those of ordinary skill in the art will appreciate that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present disclosure.

Claims (10)

1. A method for load balancing optimization, comprising:
acquiring network performance data and user perception data of an indoor distribution system, wherein the indoor distribution system comprises at least two indoor sub-cells;
Determining a first target indoor partition cell with a load balancing optimization condition according to the network performance data and the user perception data, wherein the load balancing optimization condition is an indoor partition cell with a high load characteristic and a user perception difference characteristic in the at least two indoor partition cells;
taking the coverage area of the indoor antenna corresponding to the first target indoor cell as a unit, and quantifying the first target indoor cell to obtain a target fragment;
load balancing optimization is carried out on the target fragments so as to achieve load balancing of the indoor distribution system;
the determining, according to the network performance data and the user perception data, a first target indoor partition cell with a load balancing optimization condition includes:
determining a first indoor partition with high load characteristics according to the network performance data;
determining a second indoor partition cell with a user perception difference characteristic according to the user perception data;
determining a first target cell with load balancing optimization conditions according to the first cell and the second cell;
the load balancing optimization for the target fragment to achieve the load balancing of the indoor distribution system comprises the following steps:
Determining the user terminal in the target fragment as a sample point set;
selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high-load characteristics in the at least two cells;
after optimizing distribution, re-acquiring network performance data of the indoor distribution system, and determining whether load balance of the indoor distribution system is achieved or not according to the re-acquired network performance data;
and if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target indoor partition cell until the load balance of the indoor distribution system is achieved.
2. The load balancing optimization method according to claim 1, wherein the determining whether the load balancing of the indoor distribution system is achieved according to the re-acquired network performance data comprises at least one of:
if the first target indoor partition cell is determined to have high load characteristics according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristics, determining that the load balance of the indoor distribution system is not achieved;
If the first target indoor partition cell does not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell has the high load characteristic, returning the target sample point to the first target indoor partition cell, and determining that the load balance of the indoor distribution system is not achieved;
if the first target indoor partition cell is determined to not have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to not have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved;
and if the first target indoor partition cell is determined to have the high load characteristic according to the re-acquired network performance data, and the second target indoor partition cell is determined to have the high load characteristic, the load balance of the indoor distribution system is determined to be achieved.
3. The load balancing optimization method according to claim 1, wherein the selecting a preset threshold number of target sample points from the set of sample points, redirecting the target sample points to a second target cell compartment, comprises:
and after each preset time window, selecting a preset threshold number of target sample points from the sample point set, and redirecting the first target sample points to the second target cell.
4. The method for load balancing optimization according to claim 1, wherein the network performance data includes a cell downlink traffic and a cell downlink physical resource block PRB resource utilization, and the determining the first indoor cell with the high load characteristic according to the network performance data includes:
and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell with the cell downlink PRB resource utilization rate greater than or equal to the cell downlink PRB resource utilization rate threshold is a first cell with high load characteristics.
5. The load balancing optimization method according to claim 1, wherein the network performance data includes a cell downlink traffic and a cell radio resource control RRC connection user number, and the determining, according to the network performance data, the first indoor partition cell with the high load feature includes:
and determining that the cell downlink flow is greater than or equal to a cell downlink flow threshold, and the cell division cell with the cell RRC connection user number greater than or equal to the cell RRC connection user number threshold is a first cell division cell with high load characteristics.
6. The method of claim 1, wherein the user perceived data includes cell channel quality, cell stuck times, cell delay and cell average rate, and the determining the second compartmental cell with the user perceived difference feature according to the user perceived data comprises:
And determining that the cell channel quality is smaller than or equal to a cell channel quality threshold, the cell blockage times is larger than or equal to a cell blockage times threshold, the cell delay is larger than or equal to the cell delay threshold, and the cell division cell with the cell average rate smaller than or equal to the cell average rate threshold is a second cell division cell with the user perception difference characteristic.
7. A load balancing optimization method according to any one of claims 1 to 3, wherein the load balancing optimization conditions further include: and a second target indoor partition cell exists in the neighborhood of the first target indoor partition cell, and the second target indoor partition cell is an indoor partition cell which does not have high load characteristics in the at least two indoor partition cells.
8. A load balancing optimization apparatus, characterized in that the load balancing optimization apparatus comprises:
the indoor distribution system comprises at least two indoor subareas;
the determining module is used for determining a first target indoor partition cell with a load balancing optimization condition according to the network performance data and the user perception data, wherein the load balancing optimization condition is an indoor partition cell with a high load characteristic and a user perception difference characteristic in the at least two indoor partition cells;
The quantization module is used for quantizing the first target indoor partition cell by taking the indoor partition antenna coverage area corresponding to the first target indoor partition cell as a unit to obtain a target partition;
the optimizing module is used for carrying out load balancing optimization aiming at the target fragments so as to achieve the load balancing of the indoor distribution system;
the determining module is specifically configured to:
determining a first indoor partition with high load characteristics according to the network performance data;
determining a second indoor partition cell with a user perception difference characteristic according to the user perception data;
determining a first target cell with load balancing optimization conditions according to the first cell and the second cell;
the optimizing module is specifically configured to:
determining the user terminal in the target fragment as a sample point set;
selecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target cell, wherein the second target cell is a cell which does not have high-load characteristics in the at least two cells;
after optimizing distribution, re-acquiring network performance data of the indoor distribution system, and determining whether load balance of the indoor distribution system is achieved or not according to the re-acquired network performance data;
And if the load balance of the indoor distribution system is not achieved, reselecting a preset threshold number of target sample points from the sample point set, and redirecting the target sample points to a second target indoor partition cell until the load balance of the indoor distribution system is achieved.
9. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored by the memory to implement the load balancing optimization method of any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein computer executable instructions for implementing the load balancing optimization method according to any one of claims 1 to 7 when being executed by a processor.
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