CN117835249A - Communication network load adjustment method, electronic device, and storage medium - Google Patents

Communication network load adjustment method, electronic device, and storage medium Download PDF

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Publication number
CN117835249A
CN117835249A CN202211189531.0A CN202211189531A CN117835249A CN 117835249 A CN117835249 A CN 117835249A CN 202211189531 A CN202211189531 A CN 202211189531A CN 117835249 A CN117835249 A CN 117835249A
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cell
information
communication
cells
target communication
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裴帆
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ZTE Corp
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ZTE Corp
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Priority to CN202211189531.0A priority Critical patent/CN117835249A/en
Priority to PCT/CN2023/114037 priority patent/WO2024066801A1/en
Publication of CN117835249A publication Critical patent/CN117835249A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/12—Wireless traffic scheduling
    • H04W72/1263—Mapping of traffic onto schedule, e.g. scheduled allocation or multiplexing of flows
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides a communication network load adjustment method, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring neighbor relation and cell resource information of a plurality of communication cells, and determining cell mass centers of the communication cells; dividing the areas of each communication cell according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminal and the mass center of each cell; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell. According to the scheme provided by the embodiment of the invention, the load adjustment effect of the communication cell can be improved, and the load adjustment effect of the whole area is improved, so that the user experience is improved.

Description

Communication network load adjustment method, electronic device, and storage medium
Technical Field
The present invention relates to, but not limited to, the field of communications technologies, and in particular, to a method for adjusting a load of a communications network, an electronic device, and a storage medium.
Background
With the development of mobile communication, gaps are sometimes formed between the design capacity and the actual traffic of part of sites in a wireless communication network, so that user experience is affected. If the redundancy design capacity is increased, the cost of operators is increased, so that the coverage of cells is dynamically coordinated, the cost can be saved, the sudden high traffic can be relieved, and the user experience is improved.
At present, for a communication cell with load to be coordinated, only the adjacent cell closest to the communication cell is selected for load adjustment, other adjacent cells with more user interaction with the communication cell are ignored, the load adjustment effect of the communication cell is poor, in addition, for an area formed by a plurality of communication cells, load adjustment can only be carried out one by one, so that the load adjustment effect of the whole area is poor, and the user experience is poor.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a communication network load adjustment method, electronic equipment and a storage medium, which can improve the load adjustment effect of a communication cell and the load adjustment effect of the whole area, thereby improving the user experience.
In a first aspect, an embodiment of the present invention provides a method for adjusting a communication network load, including: acquiring neighbor relation and cell resource information of a plurality of communication cells, and determining cell mass centers of the communication cells, wherein the cell resource information is determined by terminals positioned in the communication cells; dividing the communication cells into areas according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of the terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminals and the mass centers of the cells; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell.
In some embodiments, the cell resource information comprises a capacity ratio; the step of dividing the communication cells into areas according to the neighbor cell relationship and the cell resource information to determine a hot spot balancing area comprises the following steps: determining the load attribute of the communication cell according to the capacity ratio; and according to the neighbor cell relation and the load attribute, carrying out region division on each communication cell to obtain a hot spot balance region.
In some embodiments, the hotspot balancing area comprises a core cell; the core cell is determined by: under the condition that the capacity duty ratio is larger than a preset first threshold value, determining the load attribute of the communication cell as a high-load cell; and taking any one of the high-load cells as a core cell based on the neighbor relation, and taking all the high-load cells adjacent to the core cell as the core cell.
In some embodiments, the hotspot balancing area further comprises a load sharing cell; the load sharing cell is determined by the steps of: determining the load attribute of the communication cell as a medium-low load cell under the condition that the capacity duty ratio is smaller than or equal to a preset second threshold value, wherein the second threshold value is smaller than or equal to the first threshold value; and traversing each medium-low load cell based on the neighbor relation, and taking all the medium-low load cells adjacent to the core cell as load sharing cells.
In some embodiments, the hotspot balancing area includes a core cell and a load sharing cell, and the cell resource information includes a capacity duty ratio and a capacity gap; the determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information comprises the following steps: traversing the core cell based on the capacity duty ratio and the ordering information of the capacity gap; for any core cell, the terminal in the core cell is used as a terminal to be processed, and the load sharing cell adjacent to the core cell is used as a cell to be processed based on the neighbor cell relation; and inputting the distance information of the terminal to be processed, the neighbor relation, the capacity ratio of the core cell, the capacity ratio of the cell to be processed and the capacity gap of the core cell into the machine learning algorithm, and determining the edge adjustment information of each target communication cell.
In some embodiments, the cell resource information includes a capacity ratio and a capacity gap, and the machine learning algorithm includes a classification model or a clustering model; the determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information comprises the following steps: acquiring initial attribution labels of the terminals, wherein the initial attribution labels are used for representing the target communication cells corresponding to the terminals; inputting the distance information, the neighbor cell relation, the capacity ratio and the capacity gap into the classification model or the clustering model to determine target attribution labels of the terminals, wherein the target attribution labels are used for representing the target communication cells corresponding to the terminals; and determining the edge adjustment information of each target communication cell according to the distance information, the initial home label, the target home label and the neighbor relation.
In some embodiments, the determining the edge adjustment information of each of the target communication cells according to the distance information, the initial home label, the target home label, and the neighbor relation includes: for any terminal, when the target attribution label is different from the initial attribution label, determining a first target communication cell according to the initial attribution label, and determining a second target communication cell according to the target attribution label, wherein the first target communication cell is adjacent to the second target communication cell; and determining the edge adjustment information according to the distance information, the first target communication cell, the second target communication cell and the neighbor relation, wherein the edge adjustment information is used for adjusting the coverage area of the first target communication cell or the second target communication cell so as to minimize the difference of capacity occupation ratios of the first target communication cell and the second target communication cell.
In some embodiments, before the step of obtaining the neighbor relation and the cell resource information of each communication cell, the method further includes: acquiring measurement reports of the communication cells, wherein the measurement reports are generated by the terminals located in the communication cells; and determining the neighbor relation and the distance information according to the measurement report.
In some embodiments, the cell resource information includes a capacity ratio and a capacity gap; before the step of obtaining the neighbor relation and the cell resource information of each communication cell, the method further comprises the following steps: acquiring resource utilization information and resource capacity information of the communication cells and initial attribution labels of the terminals, wherein the initial attribution labels are used for representing the target communication cells corresponding to the terminals; determining the capacity ratio according to the resource utilization information; and determining the capacity gap according to the resource capacity information and the initial attribution label.
In some embodiments, the resource utilization information includes traffic resource utilization information and hardware resource utilization information; the service resource utilization information at least comprises one of the following: frequency domain utilization rate and downlink power utilization rate; the hardware resource utilization information includes at least one of: the ratio of the number of connection users to the maximum number of support users, and the CPU utilization.
In a second aspect, the present invention also provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the communication network load adjustment method as described in the first aspect above when the computer program is executed.
In a third aspect, the present invention also provides a computer-readable storage medium storing a computer-executable program for causing a computer to execute the communication network load adjustment method according to the first aspect above.
The embodiment of the invention comprises the following steps: acquiring neighbor relation and cell resource information of a plurality of communication cells, and determining cell mass centers of the communication cells, wherein the cell resource information is determined by terminals positioned in the communication cells; dividing the communication cells into areas according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of the terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminals and the mass centers of the cells; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell. According to the scheme provided by the embodiment of the invention, the plurality of target communication cells are divided into the hot spot balancing areas according to the neighbor relation and the cell resource information of the communication cells, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the edge adjustment information of each target communication cell in the hot spot balancing areas is determined by using the distance between the terminal and the mass center of each cell and the neighbor relation and the cell resource information of the target communication cells through a machine learning algorithm, the coverage area of each target communication cell is adjusted, dynamic adjustment of the edges of the target communication cells is realized, the load adjustment effect of the adjacent target communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimum is more easily achieved, the overall load adjustment effect of the areas is improved, and the user experience is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate and do not limit the invention.
FIG. 1 is a flow chart of a method for communication network load adjustment provided by one embodiment of the present invention;
FIG. 2 is a flow chart of a method for determining a hotspot equalization area provided by another embodiment of the present invention;
fig. 3 is a flow chart of a method for determining a core cell according to another embodiment of the present invention;
fig. 4 is a flow chart of a method of determining a load sharing cell provided by another embodiment of the present invention;
FIG. 5 is a flow chart of a method for determining edge adjustment information according to another embodiment of the present invention;
FIG. 6 is a flow chart of another method for determining edge adjustment information according to another embodiment of the present invention;
FIG. 7 is a flowchart of a specific method for determining edge adjustment information according to another embodiment of the present invention;
FIG. 8 is a flow chart of a method of determining neighbor relation and distance information provided by another embodiment of the present invention;
FIG. 9 is a flow chart of a method of determining capacity occupancy and capacity gap provided by another embodiment of the present invention;
FIG. 10 is a schematic diagram of a hotspot equalization area provided by an embodiment of the present invention;
fig. 11 is a schematic diagram of a coverage area of a communication cell according to an embodiment of the present invention;
fig. 12 is a block diagram of an electronic device according to another embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description, in the claims and in the above-described figures, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
At present, for a communication cell with load to be coordinated, only the adjacent cell closest to the communication cell is selected for load adjustment, other adjacent cells with more user interaction with the communication cell are ignored, the load adjustment effect of the communication cell is poor, in addition, for an area formed by a plurality of communication cells, load adjustment can only be carried out one by one, so that the load adjustment effect of the whole area is poor, and the user experience is poor.
Aiming at the problems that the load adjustment effect of a communication cell is poor and the load adjustment effect of the whole area is poor, the invention provides a communication network load adjustment method, electronic equipment and a storage medium, wherein the communication network load adjustment method comprises the following steps: acquiring neighbor relation and cell resource information of a plurality of communication cells, and determining cell centroids of the communication cells, wherein the cell resource information is determined by terminals positioned in the communication cells; dividing the areas of each communication cell according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminal and the mass center of each cell; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell. According to the scheme provided by the embodiment of the invention, the plurality of target communication cells are divided into the hot spot balancing areas according to the neighbor relation and the cell resource information of the communication cells, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the edge adjustment information of each target communication cell in the hot spot balancing areas is determined by using the distance between the terminal and the mass center of each cell and the neighbor relation and the cell resource information of the target communication cells through a machine learning algorithm, the coverage area of each target communication cell is adjusted, dynamic adjustment of the edges of the target communication cells is realized, the load adjustment effect of the adjacent target communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimum is more easily achieved, the overall load adjustment effect of the areas is improved, and the user experience is improved.
Embodiments of the present invention will be further described below with reference to the accompanying drawings.
The communication network load adjusting method provided by the embodiment of the invention can be applied to a terminal, a server and software running in the terminal or the server.
As shown in fig. 1, fig. 1 is a flowchart of a communication network load adjustment method according to an embodiment of the present invention. The communication network load adjustment method includes, but is not limited to, the steps of:
step S110, obtaining neighbor relation and cell resource information of a plurality of communication cells, and determining cell mass centers of the communication cells, wherein the cell resource information is determined by terminals positioned in the communication cells;
step S120, dividing the areas of each communication cell according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells;
step S130, obtaining the distance information of the terminal positioned in each target communication cell, wherein the distance information is used for representing the distance between the terminal and the mass center of each cell;
step S140, determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relationship and the cell resource information, so as to adjust the coverage of the target communication cell.
It can be understood that the wireless communication sub-network may include a plurality of communication cells, each communication cell may be accessed by a plurality of terminals, the terminal accessing the communication cell corresponds to the terminal being located in the communication cell, the cell resource information of the communication cell is associated with the terminal located in the communication cell, the cell resource information may refer to a capacity ratio and a capacity gap, specifically, the more the number of terminals located in the communication cell, the less the available resources of the communication cell, the higher the capacity ratio in the communication cell, the higher the load of the communication cell, the capacity gap will occur when the number of terminals exceeds the terminal capacity of the cell, and the more the available resources of the communication cell, the lower the capacity ratio in the communication cell, and the lower the load of the communication cell; the neighbor relation is used for determining neighbor cells of the communication cell, for example, if the communication cell A is a neighbor cell of the communication cell B, the neighbor relation of the communication cell A and the communication cell B is adjacent, and if the communication cell A is not a neighbor cell of the communication cell B, the neighbor relation of the communication cell A and the communication cell B is not adjacent; dividing a hot spot balancing area by utilizing the neighbor cell relationship and the cell resource information, wherein the hot spot balancing area comprises target communication cells with higher load and lower load; the communication cell can be set as a cell centroid, and the distance between the terminal and each cell centroid is acquired; then determining edge adjustment information of each target communication cell through a machine learning algorithm and the distance between a terminal and the center of mass of each cell and the cell resource information of each target communication cell, adjusting the coverage area of each target communication cell through the edge adjustment information, shrinking the edge of a target communication cell with higher load, correspondingly expanding the edge of a target communication cell with lower load, switching the terminal positioned at the edge of the target communication cell with higher load to the target communication cell with lower load, reducing the number of terminals positioned at the target communication cell with higher load, increasing the number of terminals positioned at the target communication cell with lower load, realizing the load balance of each target communication cell in a hot spot balance area, avoiding increasing redundant design capacity and reducing the cost of operators; based on the method, a plurality of target communication cells are divided into hot spot balancing areas according to the neighbor relation and the cell resource information of the communication cells through area division, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the distance between a terminal and the mass center of each cell and the neighbor relation and the cell resource information of the target communication cells are utilized through a machine learning algorithm to determine the edge adjustment information of each target communication cell in the hot spot balancing areas, and then the coverage area of the target communication cells is adjusted, so that dynamic adjustment of the edges of the target communication cells is achieved, load adjustment of the adjacent target communication cells is carried out, the load adjustment effect of the communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimum is achieved more easily, the overall load adjustment effect of the areas is improved, and user experience is improved.
It should be noted that, the coverage area of the communication cell is not required to be determined, only the edge adjustment information of the communication cell is required to be determined through a machine learning algorithm, the data processing amount is less, the adjustment efficiency can be improved, and the edge adjustment information refers to the edge variation of the communication cell, for example, the edge variation is 10db or-10 db; if the edge variation is positive, it represents the edge of the communication cell that needs to be expanded, and if the edge variation is negative, it represents the edge of the communication cell that needs to be contracted.
It should be noted that, the coverage area of the communication cell is adjusted by the cell edge management module, and after the cell edge management module receives the edge adjustment information, the coverage area of the corresponding target communication cell is adjusted, and the adjustment mode of the cell edge management module to the target communication cell includes but is not limited to: adjusting a switching threshold, adjusting an antenna direction angle, adjusting a row power, adjusting a PRACH detection threshold, adjusting an access level threshold, adjusting an uplink maximum transmitting power, and adjusting a reselection parameter; in addition, whether the current coverage is reasonable or not can be judged cell by cell according to expert experience, and the coverage of the target communication cell is manually adjusted through a cell edge management module.
It is noted that, the manner of adjusting the coverage of the target communication cell at least includes two manners, in which the first adjustment manner is to modify a handover parameter or other parameters so as to make a terminal accessing other communication cells switch to the own cell in advance or delay, thereby indirectly realizing the result of changing the coverage of the communication cell, but the actual coverage of the communication cell is unchanged; the second way of adjustment is to adjust the communication cell coverage parameters to change the actual coverage of the communication cell.
Notably, the capacity ratio and capacity gap can be determined by the number of RRC connected users; the distance between the terminal and the mass center of each cell can be determined through the data such as path loss distribution, terminal positioning data and the like; the delay of the terminal from the communication cell can be used to replace the distance of the terminal from the centroid of the cell.
It should be noted that if a crowd emergency event or a fire accident occurs, for the decision center, the scheduling process of the fire truck or the emergency truck also faces the problems of capacity and distance, and the scheduling process of the fire truck or the emergency truck can be completed by adopting the machine learning algorithm in the communication network load adjustment method provided by the embodiment of the invention.
In addition, referring to fig. 2, in an embodiment, the cell resource information includes a capacity ratio; step S120 in the embodiment shown in fig. 1 includes, but is not limited to, the following steps:
step S210, determining the load attribute of the communication cell according to the capacity ratio;
step S220, according to the neighbor relation and the load attribute, dividing the areas of the communication cells, and determining the hot spot balancing areas.
It can be understood that the capacity ratio of the communication cell is determined by the number of terminals located in the communication cell, the capacity ratio refers to the utilization ratio of the resource capacity of the communication cell, the larger the capacity ratio is, the higher the load of the communication cell is, the smaller the capacity ratio is, the lower the load of the communication cell is, and therefore, the load attribute of the communication cell can be determined by the capacity ratio, and then the hot spot balancing area is divided in each communication cell by combining the neighbor relation and the load attribute.
In addition, referring to fig. 3, in an embodiment, the hot spot balancing area in step S220 in the embodiment shown in fig. 2 includes a core cell; a method of determining a core cell includes, but is not limited to, the steps of:
step S310, under the condition that the capacity ratio is larger than a preset first threshold value, determining the load attribute of the communication cell as a high-load cell;
Step S320, any high-load cell is used as a core cell based on the neighbor relation, and all high-load cells adjacent to the core cell are used as the core cells.
It can be understood that by setting the first threshold, the capacity ratio of the communication cell is judged, and then the high-load cell is distinguished, when a hot spot balancing area needs to be determined, starting from any high-load cell, the high-load cell is used as a core cell, and in combination with the neighbor relation, all the high-load cells having the neighbor relation with the core cell are used as core cells.
In addition, referring to fig. 4, in an embodiment, the hotspot balancing area in step S220 in the embodiment shown in fig. 2 further includes a load sharing cell; a method of determining a load sharing cell, including but not limited to the steps of:
step S410, determining the load attribute of the communication cell as a medium-low load cell under the condition that the capacity duty ratio is smaller than or equal to a preset second threshold value, wherein the second threshold value is smaller than or equal to the first threshold value;
step S420, traversing each medium-low load cell based on the neighbor relation, and taking all medium-low load cells adjacent to the core cell as load sharing cells.
It can be understood that by setting the second threshold, the capacity ratio of the communication cell is judged, and then the middle-low load cell is distinguished, when a hot spot balancing area needs to be determined, after the core cells are determined, all the middle-low load cells adjacent to each core cell are taken as load sharing cells in combination with the neighbor relation, and finally all the core cells and the load sharing cells are taken as an integral area, namely the hot spot balancing area; in the hot spot balancing area, the load of the core cell is too high, so that the user experience can be influenced, the load of the core cell needs to be adjusted, and part of terminals in the core cell are accessed to adjacent load sharing cells, so that the load of the core cell is reduced, and the user experience is ensured.
It is worth noting that the hot spot balancing area is used as a load adjusting unit, global optimization is easier to achieve, the overall load adjusting effect of the area is improved, and therefore user experience is improved.
It should be noted that, the second threshold may be set manually, or obtained by repeated experiment tuning; the first threshold and the second threshold may be the same or different.
In addition, referring to fig. 5, in an embodiment, the hotspot balancing area includes a core cell and a load sharing cell, and the cell resource information includes a capacity ratio and a capacity gap; step S140 in the embodiment shown in fig. 1 includes, but is not limited to, the following steps:
Step S510, traversing the core cell based on the capacity occupation ratio and the ordering information of the capacity gap;
step S520, regarding any core cell, taking a terminal positioned in the core cell as a terminal to be processed, and taking a load sharing cell adjacent to the core cell as a cell to be processed based on a neighbor cell relation;
and step S530, inputting the distance information of the terminal to be processed, the neighbor relation, the capacity ratio of the core cell, the capacity ratio of the cell to be processed and the capacity gap of the core cell into a machine learning algorithm, and determining the edge adjustment information of each target communication cell.
It can be understood that during the processing of the machine learning algorithm, the core cells are traversed according to the capacity occupation ratio and the capacity gap, each core cell is sequentially processed, the capacity gap of each core cell is further judged, if the core cell with the capacity gap exists in the core cell to be processed, the core cell with the larger capacity gap is preferentially processed, if the capacity gap in the core cell to be processed is zero, the core cell with the larger capacity occupation ratio is preferentially processed, the load of each target communication cell in the hot spot balancing area can be dynamically balanced, the global optimum is achieved, and the user experience is improved.
It should be noted that the ranking information based on the capacity ratio and the capacity gap specifically refers to ranking information based on the capacity ratio and the capacity gap from high to low.
In addition, referring to fig. 6, in an embodiment, the cell resource information includes a capacity ratio and a capacity gap, and the machine learning algorithm includes a classification model or a clustering model; step S140 in the embodiment shown in fig. 1 includes, but is not limited to, the following steps:
step S610, obtaining initial attribution labels of all terminals, wherein the initial attribution labels are used for representing target communication cells corresponding to the terminals;
step S620, inputting the distance information, the neighbor cell relation, the capacity occupation ratio and the capacity gap into a classification model or a clustering model, and determining target attribution labels of all terminals, wherein the target attribution labels are used for representing target communication cells corresponding to the terminals;
step S630, determining the edge adjustment information of each target communication cell according to the distance information, the initial home label, the target home label and the neighbor relation.
It can be understood that the initial home label of the terminal is acquired first, and the initial home label is used for representing the target communication cell to which the terminal is currently connected; then, based on the processing of the classification model or the clustering model, determining the target attribution label of each terminal through the distance information, the neighbor cell relation, the capacity ratio of each communication cell and the capacity gap of each core cell, wherein the target attribution label is used for representing the target communication cell to which the terminal should be connected; the initial edge information of each target communication cell can be determined through the initial attribution label and the distance information of each terminal and the neighbor relation of the target communication cell, the target edge information of each target communication cell can be determined through the target attribution label and the distance information of each terminal and the neighbor relation of the target communication cell, the edge adjustment information of each target communication cell can be determined through the initial edge information and the target edge information, and then the coverage area of each target communication cell can be adjusted through the edge adjustment information, so that the terminal can access the target communication cell corresponding to the target attribution label, and when each terminal accesses the target communication cell corresponding to the target attribution label, the load of each target communication cell in the hot spot balancing area can be dynamically balanced, the global optimum is achieved, and the user experience is improved.
The method comprises the steps of determining target attribution labels of all terminals through a classification model, specifically, determining N terminals which can be contained in a core cell at maximum when the terminals are classified in any core cell, then pre-classifying the N terminals which are closest to the cell centroid of the core cell by combining distance information, classifying attributions of all terminals through the classification model by utilizing the cell resource information of the core cell and adjacent load sharing cells thereof, determining communication cells to which all terminals belong, dividing a plurality of terminals which are far away from the cell centroid of the core cell into the load sharing cells to which the targets belong, and further determining the target attribution labels of all the terminals.
It should be noted that, the target attribution labels of the terminals can also be determined through the clustering model, specifically, according to the distance information of the terminals, the terminals located in each communication cell are clustered, the terminals close to the cell centroid of a certain communication cell are clustered to the same communication cell, the clustering result is adjusted by combining the neighbor cell relationship, the capacity ratio and the capacity gap, and the target attribution labels of the terminals are further determined through the communication cells to which the targets of the terminals belong.
In addition, referring to fig. 7, in an embodiment, step S630 in the embodiment shown in fig. 6 includes, but is not limited to, the following steps:
step S710, for any terminal, when the target attribution label is different from the initial attribution label, determining a first target communication cell according to the initial attribution label, and determining a second target communication cell according to the target attribution label, wherein the first target communication cell is adjacent to the second target communication cell;
step S720, according to the distance information, the first target communication cell, the second target communication cell and the adjacent cell relationship, edge adjustment information is determined, wherein the edge adjustment information is used for adjusting the coverage of the first target communication cell or the second target communication cell so as to minimize the difference between the capacity occupation ratios of the first target communication cell and the second target communication cell.
It can be understood that the initial edge information of each target communication cell can be determined through the initial attribution label and the distance information of each terminal and the neighbor relation of the target communication cell; the target edge information of each target communication cell can be determined through the target attribution label and the distance information of each terminal and the neighbor relation of the target communication cell; when the target home label is different from the initial home label, which is equivalent to that of the target edge information and the initial edge information, the edge of the communication cell needs to be adjusted, and when two adjacent target communication cells are adjusted, the two target communication cells can be adjusted simultaneously, for example, the coverage area of the first target communication cell is expanded and the coverage area of the second target communication cell is contracted, or the coverage area of the first target communication cell is contracted and the coverage area of the second target communication cell is expanded, and only one of the target communication cells can be adjusted, so that the coverage area of the target communication cell is contracted or expanded, and the terminal needing to be adjusted to access the cell is quickly switched to a new communication cell, thereby ensuring user experience.
In addition, referring to fig. 8, in an embodiment, before step S110 in the embodiment shown in fig. 1, the following steps are included, but not limited to:
step S810, obtaining measurement reports of all communication cells, wherein the measurement reports are generated by terminals located in the communication cells;
step S820, determining the neighbor relation and the distance information according to the measurement report.
It can be understood that the measurement report (Measurement Report, MR) refers to measurement information reported by the terminal in the cell overlapping area, and the measurement report includes the identifier of the serving cell to which the terminal belongs, the identifier of the neighbor cell detected by the terminal, the RSRP information of the reference signal received power of the distance from the terminal to the serving cell, and the RSRP information of the neighbor cell from the terminal; therefore, the measurement report summarizing module is used for acquiring the measurement report of each communication cell, the relationship between the neighbor cells of each communication cell can be determined through the association relationship between the service cell and the neighbor cells in the measurement report, and the distance information of the terminal can be determined through the RSRP information; therefore, by analyzing the measurement report, static or dynamic adjustment of the coverage area of each communication cell can be realized, and the load of each target communication cell is balanced, so that the user experience is improved.
In addition, referring to fig. 9, in an embodiment, the cell resource information includes a capacity ratio and a capacity gap; prior to step S110 in the embodiment shown in fig. 1, the method further includes, but is not limited to, the following steps:
step S910, obtaining resource utilization information and resource capacity information of a communication cell and initial attribution labels of all terminals, wherein the initial attribution labels are used for representing target communication cells corresponding to the terminals;
step S920, determining the capacity ratio according to the resource utilization information;
step S930, determining a capacity gap according to the resource capacity information and the initial attribution label.
It can be understood that, the resource utilization information of the communication cell is used to characterize the resource utilization, the resource capacity information of the communication cell is used to characterize the resource capacity, the resource capacity in the communication cell is fixed, the capacity occupation ratio can be determined by the resource utilization, in addition, in the hot spot balancing area, since the upper limit of the capacity occupation ratio is 100%, in order to accurately determine the load state of the communication cell, the capacity gap of the target communication cell needs to be determined, the capacity gap is used to characterize the value that the capacity exceeds the resource capacity, the capacity gap can be calculated by the initial attribution label and the resource capacity information, specifically, each terminal is regarded as having the same resource occupation amount, the resource capacity of each target communication cell is fixed, the number of terminals corresponding to each target communication cell is determined by the initial attribution label, the resource exceeding amount of each target terminal is calculated, the resource exceeding amount of all terminals in the target communication cell is the sum of the resource occupation amount of all terminals and the resource capacity difference of the target communication cell, if the resource exceeding amount is zero, and if the resource exceeding amount is greater than zero, the capacity gap is the resource exceeding amount.
In addition, in an embodiment, the resource utilization information includes traffic resource utilization information and hardware resource utilization information; the service resource utilization information includes at least one of: frequency domain utilization rate and downlink power utilization rate; the hardware resource utilization information includes at least one of: the ratio of the number of connection users to the maximum number of support users, and the CPU utilization.
It can be understood that the load attribute of the communication cell can be determined through both the service resource utilization information and the hardware resource utilization information, and the high-load cell can be a high load on the service resource or a high load on the hardware resource, and the hardware resource is used for representing the performance of the communication cell; if the service resources such as the frequency domain resources are about to be exhausted or reach the upper limit, the service demands of the terminal are limited, and the user experience is affected, so that the high load on the service resources is relieved, the flow of the communication cell of the wireless communication sub-network can be increased, and the user experience is ensured; if the hardware resources such as the CPU or the memory are about to reach the upper limit or reach the upper limit, the system is unstable, so that the high load on the hardware resources is relieved, and the communication cell can be prevented from entering an abnormal state.
In addition, referring to fig. 10, fig. 10 is a schematic diagram of a hotspot balancing area according to an embodiment of the present invention.
It can be understood that when the area division is performed, each communication cell is set as a cell centroid, and if any two communication cells are adjacent, the cell centroids corresponding to the two communication cells are connected by a dotted line; firstly, identifying all high-load cells of a wireless communication subnet, for example, sequentially marking cell centroids corresponding to the high-load cells as H1, H2, H3 and H4, and then starting from any high-load cell, taking the high-load cell as a core cell, for example, starting from H1, and taking the high-load cell corresponding to H1 as the core cell; then according to the neighbor relation, H2 is adjacent to H1, H3 and H4 are adjacent to H2 respectively, so that high-load cells corresponding to H2, H3 and H4 are all used as core cells; then, according to the adjacent cell relation, determining all the medium and low load cells adjacent to each core cell, taking the medium and low load cells as load sharing cells, and marking the centroids corresponding to the load sharing cells as L1, L2, L3, L4 and L5 in sequence; finally, taking the communication cells corresponding to H1, H2, H3, H4, L1, L2, L3, L4 and L5 as an integral area, namely a hot spot balancing area; the cell centroids outside the hot spot balancing area are marked as O1, O2, O3 and O4 in sequence.
It should be noted that, the dashed rectangular box corresponding to the hot spot balancing area is only used as an illustration, and does not represent the coverage area corresponding to the hot spot balancing area, and because the coverage area of each communication cell is usually a sector area, the coverage area of the hot spot balancing area may be formed by stacking a plurality of sector areas.
In addition, referring to fig. 11, fig. 11 is a schematic diagram of a coverage area of a communication cell according to an embodiment of the present invention.
It can be understood that when the area division is performed, each communication cell is set as a cell centroid, and if a dotted line connection exists between the terminal and the cell centroid, it is indicated that the terminal can report a measurement report to the communication cell; for any cell centroid, there is a sector formed by a dash-dot line, which is the coverage of the communication cell; adjusting the coverage of the communication cell is to adjust the direction or the size of the sector area; according to the method, a plurality of target communication cells are divided into hot spot balancing areas according to the neighbor relation of the communication cells and the cell resource information, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the edge adjustment information of each target communication cell in the hot spot balancing areas is determined by using the distance between a terminal and each cell centroid and the neighbor relation and the cell resource information of the target communication cells through a machine learning algorithm, the coverage area of the target communication cells is adjusted, dynamic adjustment of the edges of the target communication cells is achieved, load adjustment of the adjacent target communication cells is carried out, the load adjustment effect of the communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimization is achieved more easily, the overall load adjustment effect of the areas is improved, and therefore user experience is improved.
In addition, referring to fig. 12, an embodiment of the present invention further provides an electronic device.
Specifically, the electronic device includes: one or more processors and memory, one processor and memory being illustrated in fig. 12. The processor and the memory may be connected by a bus or otherwise, for example in fig. 12.
The memory is used as a non-transitory computer readable storage medium for storing a non-transitory software program and a non-transitory computer executable program, such as the communication network load adjustment method in the above-described embodiments of the present invention. The processor implements the communication network load adjustment method in the above-described embodiments of the present invention by running a non-transitory software program stored in a memory and the program.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data and the like necessary for performing the communication network load adjustment method in the above-described embodiment of the present invention. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some implementations, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and the program required to implement the communication network load adjustment method in the above-described embodiments of the present invention are stored in the memory, and when executed by one or more processors, the communication network load adjustment method in the above-described embodiments of the present invention is executed, for example, the method steps S110 to S140 in fig. 1, the method steps S210 to S220 in fig. 2, the method steps S310 to S320 in fig. 3, the method steps S410 to S420 in fig. 4, the method steps S510 to S530 in fig. 5, the method steps S610 to S630 in fig. 6, the method steps S710 to S720 in fig. 7, the method steps S810 to S820 in fig. 8, and the method steps S910 to S930 in fig. 9 are executed, by acquiring the neighbor relation and the cell resource information of a plurality of communication cells, and determining the cell centroid of each communication cell, wherein the cell resource information is determined by the terminal located within the communication cell; dividing the areas of each communication cell according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminal and the mass center of each cell; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell. Based on the method, a plurality of target communication cells are divided into hot spot balancing areas according to the neighbor relation and the cell resource information of the communication cells through area division, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the distance between a terminal and the mass center of each cell and the neighbor relation and the cell resource information of the target communication cells are utilized through a machine learning algorithm to determine the edge adjustment information of each target communication cell in the hot spot balancing areas, and then the coverage area of the target communication cells is adjusted, so that dynamic adjustment of the edges of the target communication cells is achieved, load adjustment of the adjacent target communication cells is carried out, the load adjustment effect of the communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimum is achieved more easily, the overall load adjustment effect of the areas is improved, and user experience is improved.
Furthermore, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions that are executed by a processor or a controller, for example, by one of the processors in the above-described electronic device embodiment, and that cause the processor to perform the communication network load adjustment method in the above-described embodiment, for example, by performing the above-described method steps S110 to S140 in fig. 1, the method steps S210 to S220 in fig. 2, the method steps S310 to S320 in fig. 3, the method steps S410 to S420 in fig. 4, the method steps S510 to S530 in fig. 5, the method steps S710 to S720 in fig. 6, the method steps S810 to S820 in fig. 8, and the method steps S910 to S930 in fig. 9, and determining that each cell is located within a centroid of a communication cell by acquiring cell relationship and cell communication resource information, wherein the cell is located within the terminal; dividing the areas of each communication cell according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells; acquiring distance information of terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminal and the mass center of each cell; and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell. Based on the method, a plurality of target communication cells are divided into hot spot balancing areas according to the neighbor relation and the cell resource information of the communication cells through area division, the hot spot balancing areas are areas needing to be subjected to load adjustment, then the distance between a terminal and the mass center of each cell and the neighbor relation and the cell resource information of the target communication cells are utilized through a machine learning algorithm to determine the edge adjustment information of each target communication cell in the hot spot balancing areas, and then the coverage area of the target communication cells is adjusted, so that dynamic adjustment of the edges of the target communication cells is achieved, load adjustment of the adjacent target communication cells is carried out, the load adjustment effect of the communication cells is effectively improved, in addition, the load of each target communication cell in the hot spot balancing areas can be dynamically balanced, the whole hot spot balancing areas are used as load adjustment units, global optimum is achieved more easily, the overall load adjustment effect of the areas is improved, and user experience is improved.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
While the preferred embodiment of the present invention has been described in detail, the present invention is not limited to the above embodiment, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.

Claims (12)

1. A method of communication network load adjustment, comprising:
acquiring neighbor relation and cell resource information of a plurality of communication cells, and determining cell mass centers of the communication cells, wherein the cell resource information is determined by terminals positioned in the communication cells;
dividing the communication cells into areas according to the neighbor cell relationship and the cell resource information, and determining a hot spot balancing area, wherein the hot spot balancing area comprises a plurality of target communication cells;
acquiring distance information of the terminals located in each target communication cell, wherein the distance information is used for representing the distance between the terminals and the mass centers of the cells;
and determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information so as to adjust the coverage range of the target communication cell.
2. The method of claim 1, wherein the cell resource information comprises a capacity ratio; the step of dividing the communication cells into areas according to the neighbor cell relationship and the cell resource information to determine a hot spot balancing area comprises the following steps:
determining the load attribute of the communication cell according to the capacity ratio;
and according to the neighbor cell relation and the load attribute, carrying out region division on each communication cell to obtain a hot spot balance region.
3. The method of claim 2, wherein the hotspot equalization region comprises a core cell; the core cell is determined by:
under the condition that the capacity duty ratio is larger than a preset first threshold value, determining the load attribute of the communication cell as a high-load cell;
and taking any one of the high-load cells as a core cell based on the neighbor relation, and taking all the high-load cells adjacent to the core cell as the core cell.
4. A method according to claim 3, wherein the hotspot balancing area further comprises a load sharing cell; the load sharing cell is determined by the steps of:
Determining the load attribute of the communication cell as a medium-low load cell under the condition that the capacity duty ratio is smaller than or equal to a preset second threshold value, wherein the second threshold value is smaller than or equal to the first threshold value;
and traversing each medium-low load cell based on the neighbor relation, and taking all the medium-low load cells adjacent to the core cell as load sharing cells.
5. The method of claim 1, wherein the hotspot balancing area comprises a core cell and a load sharing cell, and wherein the cell resource information comprises a capacity ratio and a capacity gap; the determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information comprises the following steps:
traversing the core cell based on the capacity duty ratio and the ordering information of the capacity gap;
for any core cell, the terminal in the core cell is used as a terminal to be processed, and the load sharing cell adjacent to the core cell is used as a cell to be processed based on the neighbor cell relation;
And inputting the distance information of the terminal to be processed, the neighbor relation, the capacity ratio of the core cell, the capacity ratio of the cell to be processed and the capacity gap of the core cell into the machine learning algorithm, and determining the edge adjustment information of each target communication cell.
6. The method of claim 1, wherein the cell resource information comprises a capacity ratio and a capacity gap, and wherein the machine learning algorithm comprises a classification model or a clustering model; the determining edge adjustment information of each target communication cell based on a preset machine learning algorithm according to the distance information, the neighbor cell relation and the cell resource information comprises the following steps:
acquiring initial attribution labels of the terminals, wherein the initial attribution labels are used for representing the target communication cells corresponding to the terminals;
inputting the distance information, the neighbor cell relation, the capacity ratio and the capacity gap into the classification model or the clustering model to determine target attribution labels of the terminals, wherein the target attribution labels are used for representing the target communication cells corresponding to the terminals;
And determining the edge adjustment information of each target communication cell according to the distance information, the initial home label, the target home label and the neighbor relation.
7. The method of claim 6, wherein said determining edge adjustment information for each of said target communication cells based on said distance information, said initial home label, said target home label, and said neighbor relation comprises:
for any terminal, when the target attribution label is different from the initial attribution label, determining a first target communication cell according to the initial attribution label, and determining a second target communication cell according to the target attribution label, wherein the first target communication cell is adjacent to the second target communication cell;
and determining the edge adjustment information according to the distance information, the first target communication cell, the second target communication cell and the neighbor relation, wherein the edge adjustment information is used for adjusting the coverage area of the first target communication cell or the second target communication cell so as to minimize the difference of capacity occupation ratios of the first target communication cell and the second target communication cell.
8. The method of claim 1, wherein prior to the step of obtaining neighbor relation and cell resource information for each communication cell, further comprising:
acquiring measurement reports of the communication cells, wherein the measurement reports are generated by the terminals located in the communication cells;
and determining the neighbor relation and the distance information according to the measurement report.
9. The method of claim 1, wherein the cell resource information comprises a capacity ratio and a capacity gap; before the step of obtaining the neighbor relation and the cell resource information of each communication cell, the method further comprises the following steps:
acquiring resource utilization information and resource capacity information of the communication cells and initial attribution labels of the terminals, wherein the initial attribution labels are used for representing the target communication cells corresponding to the terminals;
determining the capacity ratio according to the resource utilization information;
and determining the capacity gap according to the resource capacity information and the initial attribution label.
10. The method of claim 9, wherein the resource utilization information comprises traffic resource utilization information and hardware resource utilization information; the service resource utilization information at least comprises one of the following: frequency domain utilization rate and downlink power utilization rate; the hardware resource utilization information includes at least one of: the ratio of the number of connection users to the maximum number of support users, and the CPU utilization.
11. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the communication network load adjustment method according to any of claims 1 to 10 when the computer program is executed.
12. A computer-readable storage medium storing a computer-executable program for causing a computer to execute the communication network load adjustment method according to any one of claims 1 to 10.
CN202211189531.0A 2022-09-28 2022-09-28 Communication network load adjustment method, electronic device, and storage medium Pending CN117835249A (en)

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