CN113133058B - Load balancing method, device and system - Google Patents

Abstract

The present application discloses a load balancing method, device and system, which belongs to the field of communication technology. The method includes: obtaining first information of a base station, the first information includes measurement reports, minimized road tests, antenna azimuth deviation and base station configuration information; identifying load-unbalanced cells according to the base station configuration information and preset imbalance conditions; determining the coverage scenario of the load-unbalanced cells according to the measurement reports, minimized road tests, antenna azimuth deviation, base station configuration information and preset scenario conditions; sending second information to a second server, the second information includes the first information, the load-unbalanced cells and the coverage scenario of the load-unbalanced cells. According to the embodiments of the present application, the problem that the accuracy of the load-unbalanced cells manually screened in the related technology is low, which leads to the inability to optimize the imbalance existing in the network in a timely manner, affects the network quality, and leads to low utilization of network resources can be solved.

Description

Load balancing method, device and system

Technical Field

The present application belongs to the field of communication technology, and specifically relates to a load balancing method, device and system.

Background technique

With the full coverage of Long Term Evolution (LTE) networks, New Radio (NR) base stations have gradually penetrated and embedded into LTE networks, which has led to an increasingly prominent problem of load imbalance under multi-frequency networking.

At present, in the relevant technology, manual methods are usually used to determine load-unbalanced cells. However, since the network is dynamically changing and the effectiveness of manually screening load-unbalanced cells is delayed for a long time, the accuracy of manually screened load-unbalanced cells is low, which makes it impossible to optimize the imbalance in the network in a timely manner, affects the network quality, and leads to low utilization of network resources.

Summary of the invention

The purpose of the embodiments of the present application is to provide a load balancing method, device and system, which can solve the problem of low accuracy of manually screened load imbalance cells in related technologies, which leads to the inability to optimize the imbalance existing in the network in a timely manner, affects the network quality, and leads to low utilization of network resources.

In a first aspect, an embodiment of the present application provides a load balancing method, which is applied to a first server deployed in an upper layer of a base station, and the method includes:

Acquire first information of a base station, where the first information includes a measurement report, minimization of drive tests, antenna azimuth deviation, and base station configuration information;

Identify cells with unbalanced loads according to base station configuration information and preset unbalanced conditions;

Determine the coverage scenario of the unbalanced load cell based on the measurement report, minimized drive test, antenna azimuth deviation, base station configuration information and preset scenario conditions;

Second information is sent to the second server, where the second information includes the first information, the load imbalance cell, and the coverage scenario of the load imbalance cell.

In a second aspect, an embodiment of the present application provides a load balancing method, which is applied to a second server deployed on a base station, including:

receiving second information sent by the first server, where the second information includes the first information, the unbalanced load cell and the coverage scenario of the unbalanced load cell, and the first information includes the measurement report, minimized drive test, antenna azimuth deviation and base station configuration information;

Calculate the current load of the unbalanced cell in each coverage scenario based on the measurement report, the lowest roadside, antenna azimuth deviation, and base station configuration information;

Calculate the target bearer load of the unbalanced load cell in each coverage scenario according to the current bearer load of the unbalanced load cell in each coverage scenario;

According to the Markov process and the preset state return value distribution matrix, the target load parameter when the load of the load-unbalanced cell is the target load is calculated;

The target load parameters of the load-unbalanced cells are sent to the operation and maintenance center OMC, so that the OMC can load balance the load-unbalanced cells according to the target load parameters.

In a third aspect, an embodiment of the present application provides a load balancing device, characterized in that a first server deployed on a base station upper layer includes:

An acquisition module, configured to acquire first information of a base station, the first information including a measurement report, minimization of drive tests, antenna azimuth deviation, and base station configuration information;

The identification module is used to identify the load imbalance cell according to the base station configuration information and the preset imbalance condition.

A determination module, used to determine the coverage scenario of the load imbalance cell based on the measurement report, minimization of drive test, antenna azimuth deviation, base station configuration information and preset scenario conditions;

The first sending module is used to send second information to the second server, where the second information includes the first information, the load imbalance cell and the coverage scenario of the load imbalance cell.

In a fourth aspect, an embodiment of the present application provides a load balancing device, which is applied to a second server deployed on an upper layer of a base station, including:

A receiving module, configured to receive second information sent by the first server, where the second information includes the first information, a load imbalance cell and a coverage scenario of the load imbalance cell, and the first information includes a measurement report, minimization of drive test, antenna azimuth deviation and base station configuration information;

A calculation module is used to calculate the current load of the unbalanced cell in each coverage scenario based on the measurement report, the lowest road side, the antenna azimuth deviation, and the base station configuration information;

The calculation module is further used to calculate the target bearer load of the unbalanced load cell in each coverage scenario according to the current bearer load of the unbalanced load cell in each coverage scenario;

The calculation module is further used to calculate the target load parameter when the load of the load-unbalanced cell is the target load according to the Markov process and the preset state return value distribution matrix;

The second sending module is used to send the target load parameter of the load-unbalanced cell to the operation and maintenance center OMC, so that the OMC can load balance the load-unbalanced cell according to the target load parameter.

In a fifth aspect, an embodiment of the present application provides a load balancing system, which is deployed at an upper layer of a base station and includes:

A first server, configured to execute the load balancing method in the first aspect and/or any possible implementation manner of the first aspect;

A second server, used to execute the load balancing method in the second aspect and/or any possible implementation manner of the second aspect;

The Operation Maintenance Center (OMC) is used to load balance the cells with unbalanced loads according to the target load parameters.

In a sixth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method in the first aspect and/or any possible implementation method of the first aspect are implemented.

In the embodiment of the present application, by deploying the first server at the upper layer of the base station, the first server can obtain the base station configuration information, and can automatically identify the load-unbalanced cell according to the configuration information of the base station. In this way, there is no need to manually screen the load-unbalanced cell through historical traffic statistics data and imbalance thresholds, and the load-unbalanced cell can be automatically identified, which ensures timeliness and improves the accuracy of the identified load-unbalanced cell. The first server sends the second information to the second server deployed at the upper layer of the base station, and the second information includes the first information, the load-unbalanced cell and the coverage scenario of the load-unbalanced cell. The second server can calculate the target load of the unbalanced load cell in each coverage scenario, and then calculate the target load parameter when the load-unbalanced cell is the target load according to the Markov process, and send the target load parameter to the operation and maintenance center (OMC). The OMC can automatically load balance the unbalanced load cell in each coverage scenario according to the target load parameter. In this way, the load-unbalanced cell can be load-balanced in time, improving the network quality and network resource utilization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic diagram of the architecture of a load balancing system provided in an embodiment of the present application;

FIG2 is a schematic diagram of the structure of a load balancing system provided in an embodiment of the present application;

FIG3 is a flow chart of a load balancing method provided in an embodiment of the present application;

FIG4 is a schematic diagram of the distribution of cells in different coverage scenarios provided by an embodiment of the present application;

FIG5 is a schematic diagram of the structure of a load balancing device provided in an embodiment of the present application;

FIG6 is a schematic diagram of the structure of another load balancing device provided in an embodiment of the present application;

FIG. 7 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

Detailed ways

The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar elements, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the elements distinguished by "first", "second", etc. are usually a class, and the number of elements is not limited. For example, the first element can be one or more. In addition, "and/or" in the specification and claims represents at least one of the connected elements, and the character "/" generally indicates that the front and back associated elements are in an "or" relationship.

With the comprehensive coverage of LTE networks, NR base stations are gradually embedded in LTE networks, which leads to the increasingly prominent problem of load imbalance under multi-frequency networking.

At present, in the related technology, load imbalance cells are usually determined manually. However, since the network is dynamically changing, the effectiveness of manually selecting load imbalance cells has a long delay, which also makes the accuracy of manually selected load imbalance cells low.

In order to solve the problems of the prior art, the embodiments of the present invention provide a load balancing method, device and system. In order to fully understand the embodiments of the present application, firstly, the data of different dimensions in the first information in the embodiments of the present application are described in detail.

The measurement report includes the Reference Signal Receiving Power (RSRP) and the LTE Reference Signal Receiving Quality (RSRQ).

Minimized drive testing includes the user's latitude and longitude, and the user's altitude.

The antenna arrival angle includes the antenna azimuth deviation.

The base station configuration information includes base station information, such as the latitude and longitude of the base station, broadcast beam width, number of cell users, cell utilization, cell load, cell frequency, cell neighboring area relationship, and sector coverage distance of the cell user stationed base station.

The following first introduces the load balancing system provided in the embodiment of the present application.

1 is a schematic diagram of the architecture of a load balancing system 100 provided in an embodiment of the present application. The load balancing system 100 is deployed on the upper layer of the base station, that is, the load balancing system 100 is deployed between the core network and the base station. The load balancing system 100 may include a first server 101 and a second server 102.

Here, the first server can be a pool management server, which can divide the base station into zones for management, monitor the load of the base station in real time, and collect the first information reported by the base station, wherein the first information includes measurement reports, minimized road tests, antenna azimuth deviations, and base station configuration information. Among them, the first server can also perform periodic inspections, for example, perform periodic inspections at an hourly granularity, obtain the first information, and thereby determine the unbalanced load cell based on the first information. The coverage scenario of the unbalanced load cell is located based on the four-dimensional correlation of the base station longitude and latitude, sector coverage distance, antenna azimuth deviation, and broadcast beam width in the first information.

In some embodiments, as shown in Figure 2, the load balancing system provided in the embodiment of the present application may include a data collection module 201 and an identification module 202, wherein the data collection module 201 is used to collect various data in the first information, and the identification module 202 is used to determine the unbalanced cell and the coverage scenario where the unbalanced cell is located based on the data in the first information.

The first server can report the first information, the load imbalance cell, and the coverage scenario corresponding to the load imbalance cell to the second server.

The second server can be a cloud server. The second server can iteratively optimize according to the first information to determine the target load parameters for load balancing under the same coverage scenario, and send the target load parameters to the OMC. The OMC load balances the load-unbalanced cells according to the target load parameters.

In some embodiments, as shown in Figure 2, the second server includes a cluster balancing module 203, which is used to determine the load balance under the same load scenario, and iterate based on the Markov process to determine the target load parameter corresponding to the optimal load balancing solution. The second server also includes a solution output module 204, which is used to send the target load parameter to the OMC, and the OMC can automatically perform load balancing on the unbalanced cells according to the target load parameter.

Here, the load balancing system provided in the embodiment of the present application can execute the load balancing method provided in the embodiment of the present application. The load balancing method provided in the embodiment of the present application is introduced below.

Fig. 3 is a flow chart of a load balancing method 300 provided in this embodiment. As shown in Fig. 3, the load balancing method 300 provided in this embodiment is applied to a load balancing system, and the load balancing method may include S301-S309.

S301: A first server obtains first information of a base station, where the first information includes a measurement report, minimization of drive tests, antenna azimuth deviation, and base station configuration information.

S302: The first server identifies a load imbalance cell according to the base station configuration information and a preset imbalance condition.

S303: The first server determines the coverage scenario of the load imbalance cell according to the measurement report, minimization of drive test, antenna azimuth deviation, base station configuration information and preset scenario conditions.

S304: The first server sends second information to the second server, where the second information includes the first information, the load imbalance cell, and the coverage scenario of the load imbalance cell.

S305: The second server calculates the current bearing load of the unbalanced load cell in each coverage scenario according to the measurement report, the lowest road side, the antenna azimuth deviation, and the base station configuration information.

S306: The second server calculates the target bearer load of the load-unbalanced cell in each coverage scenario according to the current bearer load of the load-unbalanced cell in each coverage scenario.

S307: The second server calculates the target load parameter when the load of the load-unbalanced cell is the target load according to the Markov process and the preset state reward value distribution matrix;

S308: The second server sends the target load parameter of the load-unbalanced cell to the operation and maintenance center OMC.

S309: The OMC performs load balancing on the cells with unbalanced loads according to the target load parameters.

The following is a detailed introduction to S301-S309.

In S301, the first information refers to data that can reflect network quality and is generated during the interaction between the base station and the user, such as measurement reports, minimization of drive tests, antenna azimuth deviation, and base station configuration information.

In some embodiments, the first server may collect the first information from the base station. To avoid resource occupation, the first server may collect the first information periodically. For example, the first information may be collected at an hourly granularity.

In S302, the first server may read the cell utilization and cell frequency of the cell from the configuration information of the base station, and then compare the cell utilization with the utilization threshold in the imbalance condition to obtain the cell utilization difference. Then, according to the cell frequency, the utilization difference at the cell frequency is compared with the difference threshold in the imbalance condition, thereby determining the load imbalance cell.

Specifically, in S302, first, the cell utilization and cell frequency of the cell are read from the base station configuration information; then, the cell utilization is compared with the minimum cell utilization to obtain the cell utilization difference; then, the cell utilization is compared with the utilization threshold in the imbalance condition to obtain the cell utilization result; finally, the load imbalance cell is determined based on the cell frequency, utilization result, utilization difference and difference threshold in the imbalance condition.

Unequalized conditions are set for multiple frequency bands.

For example, in the D band: when the wireless utilization rate of the cell with the highest utilization rate in the band group exceeds 50%, and the difference between the wireless utilization rate of the cell with the lowest utilization rate exceeds 20%, the band group is unbalanced.

In the F band: When the wireless utilization rate of the cell with the highest utilization rate in the frequency band group exceeds 50%, and F1 is higher than F2 by more than 30% or F2 is higher than F1 by more than 10%, the frequency band group is unbalanced.

D/F: When the utilization of the D band or F band in the band group exceeds 50% and the difference exceeds 20%, the band group is unbalanced (the utilization of the D or F band is calculated based on the average value within the band).

TDD/FDD1800: When the FDD1800 cell utilization rate exceeds 70% or the TDD cell utilization rate exceeds 50% within the frequency band group, and the FDD rate is more than 20% higher than the TDD rate or the TDD rate is higher than the FDD rate, the frequency band group is unbalanced (TDD utilization rate takes the average value within the frequency band).

If a cell has a load imbalance in any frequency band, the load imbalance of the cell can be determined, and the imbalance type of the load imbalance cell can also be determined. When the cell has an unbalanced load in a frequency band, the load imbalance type of the cell is unbalanced load in the frequency band, as shown in Table 1.

Table I

In this way, there is no need to manually screen the load-unbalanced cells through historical traffic statistics data and imbalance thresholds, and the load-unbalanced cells can be automatically identified, which ensures timeliness and improves the accuracy of the identified load-unbalanced cells.

In order to determine the load balancing solution, the first server also needs to identify the coverage scenario of the cells with unbalanced loads, so as to perform load balancing for the cells in the same coverage scenario.

Specifically, in S303, first, the location information of the base station corresponding to the load-unbalanced cell is determined according to the measurement report and the base station configuration information; then, the sector coverage distance and the broadcast beam width of the load-unbalanced cell in the base station configuration information are read; finally, according to the location information, the antenna azimuth deviation of the load-unbalanced cell, the sector coverage distance, the broadcast beam width and the scene conditions, the coverage scenario of the load-unbalanced cell is determined.

Here, a coverage scenario model can be established based on the location information, the antenna azimuth deviation of the load-unbalanced cell, the sector coverage distance, and the broadcast beam width, wherein the coverage scenario model satisfies the following formula (1):

N＝o×LL+p×Dist+q×(AOA+BEAMWIDTH) (1)

Among them, LL is the latitude and longitude of the base station, Dist is the sector coverage size, AOA is the antenna azimuth deviation, BEAMWIDTH is the broadcast beam width; o, p, q are weight factor coefficients;

The coverage scenario types include the same site same coverage scenario, the same site large and small coverage scenario, the same site overlapping coverage scenario, and different site overlapping coverage scenario. As shown in Figure 4, taking F1 and F2 as two cells as an example, A1 is the same site same coverage scenario, A2 is the same site large and small coverage scenario, A3 is the same site overlapping coverage scenario, and A4 is the different site overlapping coverage scenario.

The scenario conditions for judgment are different for different coverage scenario types. Specifically, the scenario conditions for the same-site same-coverage scenario are that the sector coverage distance deviation between the co-site frequency bands is within 10 meters and the azimuth deviation is within 10°. The scenario conditions for the same-site large and small coverage scenario are that the sector coverage distance deviation between the co-site frequency bands is greater than 10 meters and the antenna azimuth deviation is within 10°. The scenario conditions for the same-site overlapping coverage scenario are that the coverage distance deviation between the co-site frequency bands is within 10 meters, the antenna azimuth deviation is within 120°, and the side lobe angle in the antenna azimuth deviation is greater than 0°. The scenario conditions for the different-site overlapping coverage scenario are that the azimuth deviation is within 120° and the side lobe angle in the antenna azimuth deviation is greater than 0° between non-co-site frequency bands.

Here, when judging the coverage scenario type, it is also necessary to combine the location information of the base station (the longitude and latitude of the base station) to minimize the drive test (the longitude and latitude of the user), the broadcast beam width, and other scenario conditions for analysis, as shown in Table 2.

Table II

After determining the coverage scenario of the load-unbalanced cell, the first server sends second information to the second server, the second information includes the first information, the load-unbalanced cell and the coverage scenario of the load-unbalanced cell, and the first information includes measurement report, minimized road test, antenna azimuth deviation and base station configuration information.

After receiving the second information, the second server determines the target load parameter corresponding to the load balancing optimization solution.

Specifically, in S305, first, for each load-unbalanced cell in each coverage scenario, the product of the cell coverage level of the load-unbalanced cell in the measurement report and the first weight is calculated to obtain a first result value; then, the product of the cell bandwidth of the load-unbalanced cell in the minimized road test and the second weight is calculated to obtain a second result value; then, the product of the cell utilization of the load-unbalanced cell in the base station configuration information and the third weight is calculated to obtain a third result value; finally, the product of the first result value, the second result value and the third result value is calculated to obtain the current carrying load.

Here, the cell coverage level can be obtained from the measurement report, and the cell bandwidth can be obtained from the base station configuration information. The first weight, the second weight, and the third weight are all weights pre-set by the staff according to the network conditions. Among them, the current load W _Ni of the i-th load imbalance cell in the N scenario satisfies the following formula (2):

W _Ni =q×Cov+j×+k×PRB (2)

Wherein, Cov is the cell coverage level, Ban is the cell bandwidth, and PRB is the cell utilization rate; q, j, k are weight factor coefficients, i=1, 2, 3, ...

In S306, first, the number of load-unbalanced cells in each coverage scenario is calculated; then, the sum of the current carrying loads of the load-unbalanced cells in each coverage scenario is calculated to obtain the current carrying sum in each coverage scenario; finally, the quotient between the current carrying sum in each coverage scenario and the number of load-unbalanced cells in each coverage scenario is calculated to obtain the target carrying load of the load-unbalanced cells in each coverage scenario.

Here, the target load of the unbalanced cell in each coverage scenario is Satisfies the following formula (3):

In S307, first, the target load state is randomly obtained from the state reward value distribution matrix; then, the load parameters and load state are randomly selected; then, based on the Markov process, the expected value of the target load state is calculated according to the target load state, load parameters and load state; finally, the load parameter corresponding to the maximum expected value of the target load state is determined as the target load parameter.

Specifically, let R = {load state, load parameter} = {S, A}, and the state return value distribution matrix R satisfies the following formula (4):

The expected value Qt ₊₁ of the target load state satisfies the following formula (5):

Q _t+1 ＝R _t +γ×maxQ(S _t+1 ,A _t ) (5)

Among them, Q _t+1 represents the final expected value from the current state to the next state, Rt is the return value obtained from the current state, S _t represents the load state at time t, S _t+1 represents the load state at time t+1, A _t represents the load parameter at time t, γ represents the conversion factor, maxQ represents the maximum expected value from time t to the next moment, and the Q _t+1 value is calculated through Markov iteration to determine the expected value of the maximum target load state. The load parameter corresponding to the expected value of the maximum target load state is the target load parameter in the optimal load balancing solution.

The load balancing method provided in the embodiment of the present application, by deploying the first server on the upper layer of the base station, so that the first server can obtain the base station configuration information, and can automatically identify the load-unbalanced cell according to the configuration information of the base station, so that there is no need to manually screen the load-unbalanced cell through historical traffic statistics data and imbalance thresholds, and the load-unbalanced cell can be automatically identified, ensuring timeliness and improving the accuracy of the identified load-unbalanced cell. The first server sends the second information to the second server deployed on the upper layer of the base station, and the second information includes the first information, the load-unbalanced cell and the coverage scenario of the load-unbalanced cell. The second server can calculate the target load of the unbalanced load cell in each coverage scenario, and then calculate the target load parameter when the load-unbalanced cell is the target load according to the Markov process, and send the target load parameter to the operation and maintenance center (Operation Maintenance Center, OMC), OMC can automatically load balance the unbalanced load cell in each coverage scenario according to the target load parameter, so that the load-unbalanced cell can be load-balanced in time, improving network quality and network resource utilization.

Based on the load balancing method provided by the present application, the present application accordingly provides a load balancing device according to an embodiment. The load balancing device provided by the embodiment of the present application is described below.

FIG5 is a schematic diagram of the structure of the load balancing device provided in the embodiment of the present application. As shown in FIG5, the load balancing device provided in the embodiment of the present application is applied to the first server deployed on the upper layer of the base station, and may include: an acquisition module 501, an identification module 502, a determination module 503, and a first sending module 504.

An acquisition module 501 is used to acquire first information of a base station, where the first information includes a measurement report, minimization of drive tests, antenna azimuth deviation, and base station configuration information;

The identification module 502 is used to identify a load imbalance cell according to the base station configuration information and a preset imbalance condition.

The determination module 503 is used to determine the coverage scenario of the unbalanced load cell according to the measurement report, minimization of drive test, antenna azimuth deviation, base station configuration information and preset scenario conditions;

The first sending module 504 is configured to send second information to the second server, where the second information includes the first information, the load imbalance cell and the coverage scenario of the load imbalance cell.

In a possible implementation, the identification module includes:

A reading unit, used to read the cell utilization rate and cell frequency of the cell from the base station configuration information;

A comparison unit, used to compare the cell utilization with the utilization threshold in the imbalance condition to obtain a utilization difference of the cell;

The determination unit is used to determine the load imbalance cell according to the cell frequency, the utilization result, the utilization difference and the difference threshold in the imbalance condition.

In a possible implementation, the determining module includes:

A determination unit, configured to determine location information of a base station corresponding to a load imbalance cell according to the measurement report and the base station configuration information;

A reading unit, used to read the sector coverage distance and broadcast beam width of the load imbalance cell in the base station configuration information;

The determination unit is further used to determine the coverage scenario of the unbalanced load cell according to the location information, the antenna azimuth deviation of the unbalanced load cell, the sector coverage distance, the broadcast beam width and the scenario conditions.

The load balancing device provided in the embodiment of the present application can execute the method steps executed by the first server side in the embodiment shown in Figure 3 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

The load balancing device provided in the embodiment of the present application deploys a first server on the upper layer of the base station so that the first server can obtain the base station configuration information, and can automatically identify the load-unbalanced cell according to the configuration information of the base station. In this way, there is no need to manually screen the load-unbalanced cell through historical traffic statistics data and imbalance thresholds, and the load-unbalanced cell can be automatically identified, ensuring timeliness and improving the accuracy of the identified load-unbalanced cell. The first server sends the second information to the second server deployed on the upper layer of the base station, and the second information includes the first information, the load-unbalanced cell and the coverage scenario of the load-unbalanced cell. The second server can calculate the target load of the unbalanced load cell in each coverage scenario, and then calculate the target load parameter when the load-unbalanced cell is the target load according to the Markov process, and send the target load parameter to the operation and maintenance center (OMC). The OMC can automatically load balance the unbalanced load cell in each coverage scenario according to the target load parameter. In this way, the load-unbalanced cell can be load-balanced in time, improving the network quality and network resource utilization.

FIG6 is a schematic diagram of the structure of the load balancing device provided in the embodiment of the present application. As shown in FIG6 , the load balancing device provided in the embodiment of the present application is applied to the second server deployed on the upper layer of the base station, and may include a receiving module 601 , a calculating module 602 , and a second sending module 603 .

The receiving module 601 is used to receive second information sent by the first server, where the second information includes the first information, the load imbalance cell and the coverage scenario of the load imbalance cell, and the first information includes the measurement report, the minimization of drive test, the antenna azimuth deviation and the base station configuration information;

The calculation module 602 is used to calculate the current load of the unbalanced cell in each coverage scenario according to the measurement report, the lowest road side, the antenna azimuth deviation, and the base station configuration information;

The calculation module 602 is further used to calculate the target bearer load of the unbalanced load cell in each coverage scenario according to the current bearer load of the unbalanced load cell in each coverage scenario;

The calculation module 602 is further used to calculate the target load parameter when the load of the load-unbalanced cell is the target load according to the Markov process and the preset state reward value distribution matrix;

The second sending module 603 is used to send the target load parameter of the load-unbalanced cell to the operation and maintenance center OMC, so that the OMC can load balance the load-unbalanced cell according to the target load parameter.

In a possible implementation, the computing module is used to:

For each unbalanced load cell in each coverage scenario, calculate the product of the cell coverage level of the unbalanced load cell in the measurement report and the first weight to obtain a first result value;

Calculate the product of the cell bandwidth of the cell with unbalanced load in the minimized drive test and the second weight to obtain a second result value;

Calculate the product of the cell utilization rate of the load-unbalanced cell in the base station configuration information and the third weight to obtain a third result value;

The product of the first result value, the second result value and the third result value is calculated to obtain the current bearing load.

In a possible implementation, the computing module is used to:

Calculate the number of cells with unbalanced load in each coverage scenario;

Calculate the sum of the current loads of the unbalanced cells in each coverage scenario to obtain the sum of the current loads in each coverage scenario;

The quotient between the sum of the current loads in each coverage scenario and the number of cells with unbalanced loads in each coverage scenario is calculated to obtain the target load of the cells with unbalanced loads in each coverage scenario.

In a possible implementation, the computing module is used to:

Randomly obtain the target load state from the state return value distribution matrix;

Randomly select load parameters and load states;

Based on the Markov process, the expected value of the target load state is calculated according to the target load state, the load parameter and the load state;

It is determined that the load parameter corresponding to the maximum expected value of the target load state is the target load parameter.

The load balancing device provided in the embodiment of the present application can execute the method steps executed by the second server side in the embodiment shown in Figure 3 and achieve the same technical effect. To avoid repetition, it will not be described in detail here.

The load balancing device provided in the embodiment of the present application deploys a first server on the upper layer of the base station so that the first server can obtain the base station configuration information, and can automatically identify the load-unbalanced cell according to the configuration information of the base station. In this way, there is no need to manually screen the load-unbalanced cell through historical traffic statistics data and imbalance thresholds, and the load-unbalanced cell can be automatically identified, ensuring timeliness and improving the accuracy of the identified load-unbalanced cell. The first server sends the second information to the second server deployed on the upper layer of the base station, and the second information includes the first information, the load-unbalanced cell and the coverage scenario of the load-unbalanced cell. The second server can calculate the target load of the unbalanced load cell in each coverage scenario, and then calculate the target load parameter when the load-unbalanced cell is the target load according to the Markov process, and send the target load parameter to the operation and maintenance center (OMC). The OMC can automatically load balance the unbalanced load cell in each coverage scenario according to the target load parameter. In this way, the load-unbalanced cell can be load-balanced in time, improving the network quality and network resource utilization.

FIG. 7 shows a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

The electronic device may include a processor 701 and a memory 702 storing computer program instructions.

Specifically, the processor 701 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

The memory 702 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 702 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 702 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 702 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 702 is a non-volatile solid-state memory.

The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical/tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of the present application.

The processor 701 implements any one of the load balancing methods in the above embodiments by reading and executing computer program instructions stored in the memory 702 .

In one example, the electronic device may further include a communication interface 707 and a bus 710. As shown in Fig. 7, the processor 701, the memory 702, and the communication interface 707 are connected via the bus 710 and communicate with each other.

The communication interface 707 is mainly used to implement communication between various modules, devices, units and/or equipment in the embodiments of the present application.

Bus 710 includes hardware, software or both, and the parts of electronic equipment are coupled to each other.For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 710 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

In addition, in combination with the load balancing method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the load balancing methods in the above embodiment is implemented.

It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and/or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and/or the block diagram and the combination of each square box in the flow chart and/or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function/action specified in one or more square boxes of the flow chart and/or the block diagram.Such a processor can be but is not limited to a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.It can also be understood that each square box in the block diagram and/or the flow chart and the combination of the square boxes in the block diagram and/or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims (10)

1. The load balancing method is characterized by being applied to a first server deployed on an upper layer of a base station, and comprising the following steps of:

Acquiring first information of a base station, wherein the first information comprises a measurement report, a minimization of drive test, an antenna azimuth deviation and base station configuration information;

Identifying a load imbalance cell according to the base station configuration information and a preset imbalance condition;

determining a coverage scene of the unbalanced load cell according to the measurement report, the minimization of drive tests, the antenna azimuth deviation, the base station configuration information and preset scene conditions;

the second information is sent to a second server, the second information comprises the first information, the load imbalance cells and coverage scenes of the load imbalance cells are used for the second server to calculate current load bearing of the load imbalance cells under each coverage scene according to the measurement report, the minimization of drive test, the antenna azimuth deviation and the base station configuration information, calculate target load bearing of the load imbalance cells under each coverage scene according to the current load bearing of the load imbalance cells under each coverage scene, calculate target load parameters when the load bearing of the load imbalance cells is the target load according to a Markov process and a preset state return value distribution matrix, and send the target load parameters of the load imbalance cells to an operation maintenance center OMC for the OMC to perform load balancing on the load imbalance cells according to the target load parameters.

2. The method according to claim 1, wherein the identifying a load imbalance cell according to the base station configuration information and a preset imbalance condition comprises:

reading the cell utilization rate and the cell frequency point of the cell from the base station configuration information;

Comparing the cell utilization rate with the lowest cell utilization rate to obtain a utilization rate difference value of the cell;

Comparing the cell utilization rate with a utilization rate threshold value in the unbalanced condition to obtain a utilization rate result of the cell;

And determining the unbalanced load cell according to the cell frequency point, the utilization rate result, the utilization rate difference value and a difference value threshold value in an unbalanced condition.

3. The method of claim 1, wherein the determining the coverage scenario of the unbalanced load cell according to the measurement report, the minimization of drive tests, the antenna azimuth bias, the base station configuration information, and a preset scenario condition comprises:

determining the position information of the base station corresponding to the load imbalance cell according to the measurement report and the base station configuration information;

reading the sector coverage distance and the broadcast beam width of the unbalanced load cell in the base station configuration information;

And determining the coverage scene of the unbalanced load cell according to the position information, the antenna azimuth deviation of the unbalanced load cell, the sector coverage distance, the broadcast beam width and the scene condition.

4. The load balancing method is characterized by being applied to a second server deployed on an upper layer of a base station, and comprising the following steps of:

Receiving second information sent by a first server, wherein the second information comprises first information, a load imbalance cell and a coverage scene of the load imbalance cell, and the first information comprises a measurement report, a minimization of drive test, an antenna azimuth deviation and base station configuration information;

Calculating the current load of the load imbalance cell under each coverage scene according to the measurement report, the minimization of drive tests, the antenna azimuth deviation and the base station configuration information;

calculating the target load of the load imbalance cell under each coverage scene according to the current load of the load imbalance cell under each coverage scene;

Calculating a target load parameter when the load of the load imbalance cell is the target load according to a Markov process and a preset state return value distribution matrix;

And sending the target load parameter of the load imbalance cell to an operation maintenance center OMC, so that the OMC can carry out load balancing on the load imbalance cell according to the target load parameter.

5. The method of claim 4, wherein calculating the current load of the unbalanced load cell in each coverage scenario according to the measurement report, the minimization of drive tests, the antenna azimuth deviation, and the base station configuration information comprises:

calculating the product of the cell coverage level and the first weight of the load imbalance cells in the measurement report for each load imbalance cell under each coverage scene to obtain a first result value;

calculating the product of the cell bandwidth of the load imbalance cell and the second weight in the minimization drive test to obtain a second result value;

calculating the product of the cell utilization rate of the load imbalance cell and a third weight in the base station configuration information to obtain a third result value;

And calculating the product of the first result value, the second result value and the third result value to obtain the current bearing load.

6. The method of claim 4, wherein calculating the target load for each coverage scenario of the load imbalance cell based on the current load for each coverage scenario of the load imbalance cell comprises:

calculating the number of unbalanced load cells in each coverage scene;

calculating the sum of the current load of the load imbalance cells in each coverage scene to obtain the current load sum in each coverage scene;

And calculating the quotient between the current bearing sum in each coverage scene and the number of the unbalanced load cells in each coverage scene to obtain the target bearing load of the unbalanced load cells in each coverage scene.

7. The method according to claim 4, wherein calculating the target load parameter when the load of the unbalanced load cell is the target load according to the markov process and a preset state report value distribution matrix includes:

Randomly acquiring a target load state from the state return value distribution matrix;

randomly selecting a load parameter and a load state;

Calculating an expected value of the target load state according to the target load state, the load parameter and the load state based on the Markov process;

and determining the corresponding load parameter when the expected value of the target load state is maximum as the target load parameter.

8. A load balancing apparatus, applied to a first server deployed at an upper layer of a base station, comprising:

The acquisition module is used for acquiring first information of the base station, wherein the first information comprises a measurement report, a minimization of drive test, an antenna azimuth deviation and base station configuration information;

The identification module is used for identifying a load imbalance cell according to the base station configuration information and a preset imbalance condition;

the determining module is used for determining a coverage scene of the load imbalance cell according to the measurement report, the minimization of drive tests, the antenna azimuth deviation, the base station configuration information and preset scene conditions;

The first sending module is configured to send second information to a second server, where the second information includes the first information, the load imbalance cell and a coverage scenario of the load imbalance cell, so that the second server calculates a current load of the load imbalance cell in each coverage scenario according to the measurement report, the minimization of drive test, the antenna azimuth deviation, and the base station configuration information, calculates a target load of the load imbalance cell in each coverage scenario according to the current load of the load imbalance cell in each coverage scenario, calculates a target load parameter when the load of the load imbalance cell is the target load according to a markov process and a preset state report value distribution matrix, and sends the target load parameter of the load imbalance cell to an operation maintenance center OMC, so that the OMC performs load balancing on the load imbalance cell according to the target load parameter.

9. A load balancing device, applied to a second server deployed by an upper layer of a base station, comprising:

The receiving module is used for receiving second information sent by the first server, wherein the second information comprises first information, a load imbalance cell and a coverage scene of the load imbalance cell, and the first information comprises a measurement report, a minimization of drive test, an antenna azimuth angle deviation and base station configuration information;

The calculation module is used for calculating the current load of the load imbalance cell under each coverage scene according to the measurement report, the minimization of drive tests, the antenna azimuth deviation and the base station configuration information;

The calculating module is further used for calculating the target load of the unbalanced load cell under each coverage scene according to the current load of the unbalanced load cell under each coverage scene;

The calculation module is further configured to calculate, according to a markov process and a preset state return value distribution matrix, a target load parameter when the load of the load imbalance cell is the target load;

And the second sending module is used for sending the target load parameter of the load imbalance cell to an operation maintenance center OMC so as to be used for carrying out load balancing on the load imbalance cell according to the target load parameter by the OMC.

10. A load balancing system, wherein the load balancing system is deployed at an upper layer of a base station, the load balancing system comprising:

a first server for performing the load balancing method according to any of claims 1-3;

a second server for performing the load balancing method according to any of claims 4-7;

And the operation maintenance center OMC is used for carrying out load balancing on the unbalanced load cells according to the target load parameters.