CN104113864A - Self-optimizing method and device of network - Google Patents

Abstract

The invention discloses a method and device for network self-optimization, relates to the communication field, and is invented in order to solve the problems of high optimization cost and low efficiency in the prior art. The method includes: obtaining a special optimization strategy, the special optimization strategy includes sub-optimization strategies and the execution order of the sub-optimization strategies; executing each sub-optimization strategy in turn according to the execution order, and obtaining a self-optimization result corresponding to each sub-optimization strategy; After the optimization strategy is executed, the self-optimization result is evaluated according to the preset effect index; if the effect evaluation is passed, the self-optimization process is ended; if the effect evaluation is not passed, the next sub-optimization strategy is executed. The present invention realizes network self-optimization through a special optimization strategy including sub-optimization strategies, and evaluates the effect after each sub-optimization strategy is executed, which can reduce the input of manpower and material resources and save costs; the degree of automation is high, and the optimization process can be shortened, Improve optimization efficiency.

Description

A method and device for network self-optimization

technical field

The present invention relates to the communication field, in particular to a method and device for network self-optimization.

Background technique

Currently, a user equipment (User Equipment, referred to as UE) is generally connected to a core network through a radio access network (Radio Access Network, referred to as RAN) to enjoy corresponding services. With the development of communication technology, users have higher and higher requirements on the quality of services provided by wireless access networks, so operators need to continuously optimize the network to ensure high-quality services.

The traditional optimization method uses methods such as manual on-site test data collection, parameter analysis, and hardware inspection to find the reasons that affect network quality, and optimizes the network through technical means such as parameter modification, network structure adjustment, and equipment configuration adjustment to ensure network quality and provide satisfactory Network services required by users.

However, this manual optimization method requires a large amount of manpower and material resources investment due to the existence of a large amount of manual labor, so the optimization cost is high; secondly, the optimization process of manually discovering problems, analyzing problems, and solving problems has a long cycle and high efficiency. Relative to the ever-changing network environment, there is a delay in time, resulting in the optimization result may not fully meet the new network requirements, that is, the optimal network performance is not obtained through optimization.

Contents of the invention

Embodiments of the present invention provide a method and device for network self-optimization, which can solve the problems of high optimization cost and low efficiency in the prior art.

In order to achieve the above object, embodiments of the present invention adopt the following technical solutions:

On the one hand, the present invention provides a method for network self-optimization, comprising:

Obtain a special optimization strategy, the special optimization strategy includes sub-optimization strategies, and the execution order of the sub-optimization strategies;

According to the execution sequence, each sub-optimization strategy is executed sequentially, and the self-optimization result corresponding to each sub-optimization strategy is obtained;

After the execution of the current sub-optimization strategy is completed, the corresponding self-optimization results obtained are evaluated according to the preset effect indicators;

If the result of self-optimization passes the effect evaluation, the self-optimization process ends;

If the result of self-optimization fails the effect evaluation, execute the next sub-optimization strategy according to the order of execution.

On the other hand, the present invention also provides a device for network self-optimization, including:

The obtaining unit is used to obtain a special optimization strategy, the special optimization strategy includes sub-optimization strategies, and the execution order of the sub-optimization strategies;

The execution unit is configured to sequentially execute each sub-optimization strategy acquired by the acquisition unit according to the execution order acquired by the acquisition unit, and obtain a self-optimization result corresponding to each sub-optimization strategy;

The effect evaluation unit is used to evaluate the effect of the corresponding self-optimization result obtained according to the preset effect index after the execution unit completes the execution of the current sub-optimization strategy;

The execution unit is also used to end the self-optimization process when the self-optimization result passes the effect evaluation of the effect evaluation unit;

The executing unit is further configured to execute the next sub-optimization strategy according to the execution order when the result of the self-optimization fails the effect evaluation of the effect evaluation unit.

A method and device for network self-optimization provided by the present invention firstly obtains a special optimization strategy, then sequentially executes each sub-optimization strategy included in the special optimization strategy, and evaluates the effect of each sub-optimization strategy after execution To judge whether the preset effect index is reached, when it is reached, the entire self-optimization process is completed, and the self-optimization process is ended; otherwise, continue to execute the next sub-optimization strategy and repeat the above steps. Compared with the manual optimization method in the prior art, the present invention realizes network self-optimization through a special optimization strategy including a series of sub-optimization strategies, and evaluates the effect after each sub-optimization strategy is executed. The strategy-oriented self-optimization method of the evaluation mechanism, compared with the optimization process of manually finding problems, analyzing problems, and solving problems, has less manual labor, so it can reduce the input of corresponding manpower and material resources in the optimization process and save costs. The self-optimization method can be automatically executed after being triggered, and has a high degree of automation, which can shorten the optimization process and improve optimization efficiency.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

FIG. 1 is a flowchart of a method for network self-optimization provided in this embodiment;

Fig. 2 is the execution flow diagram of the network self-optimization provided by this embodiment;

Fig. 3 is the execution flowchart of the sub-optimization strategy provided by this embodiment;

Fig. 4 is the schematic diagram of the database that this embodiment provides;

FIG. 5 is a network architecture diagram provided by application scenario 1 of this embodiment;

FIG. 6 is a network architecture diagram provided by application scenario 2 of this embodiment;

FIG. 7 is a schematic structural diagram of a device for network self-optimization provided in this embodiment;

FIG. 8 is a schematic structural diagram of a device for network self-optimization provided in this embodiment;

FIG. 9 is a schematic structural diagram of a device for network self-optimization provided in this embodiment;

FIG. 10 is a schematic structural diagram of an apparatus for network self-optimization provided in this embodiment.

Detailed ways

The following will clearly and completely describe the technical solution in this embodiment with reference to the drawings in this embodiment. Obviously, the described embodiment is only a part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

As shown in Figure 1, an embodiment of the present invention provides a method for network self-optimization, including:

S11: Obtain a special optimization strategy, where the special optimization strategy includes sub-optimization strategies and the execution order of the sub-optimization strategies.

In this method, the self-optimization of the network is realized through a special optimization strategy, which is a self-optimization strategy formulated to improve the performance of a certain special network, such as the downlink signal quality for the network performance of the wireless network. Special quality optimization strategy, call drop performance special optimization strategy for network performance such as call drop in the voice communication process, etc.

In an implementation of this method, the special optimization strategy is triggered by a predefined event, and different events correspond to different special optimization strategies, such as triggering a load balancing special optimization strategy to start the process of network self-optimization when the cell load exceeds a threshold .

In another implementation of the method, the special optimization strategy is triggered periodically, and the corresponding special optimization strategy is triggered every preset time to start the process of network self-optimization.

The triggering method of special optimization strategies can be comprehensively determined according to optimization requirements and optimization goals. It can be either event-triggered or periodic triggering for all of them, or periodic triggering for some special optimization strategies, and the other part of special optimization strategies. The methods of event triggering are not listed in this method.

Obtaining a special optimization strategy is the process of automatically selecting a corresponding special optimization strategy triggered by a certain event or time period.

Special optimization strategies include sub-optimization strategies and the execution order of sub-optimization strategies;

A certain special network performance may be jointly affected by multiple individual network performances, and correspondingly, each special optimization strategy includes multiple sub-optimization strategies. The sub-optimization strategy is a self-optimization strategy formulated for a single network index, such as the sub-optimization strategy for the received signal code power (Received Signal Code Power, referred to as RSCP) and the sub-optimization strategy for the downlink channel quality (Ec/Io). For the same single network performance, there may be multiple sub-optimization strategies to achieve different optimization requirements and optimization goals. For example, for the network performance index of downlink RSCP, the levels that need to be achieved in the downlink signal quality self-optimization and call drop self-optimization are different. , so there are multiple downlink RSCP sub-optimization strategies, which can be distinguished by labels, such as the downlink RSCP sub-optimization strategy m.

Specifically, according to the optimization requirements and optimization goals, select one or several sub-optimization strategies in the optimization strategy library to establish a special optimization strategy, and coordinate each sub-optimization strategy to determine the execution order of each sub-optimization strategy to avoid The adjustment of the same parameter conflicts; for example, the special optimization strategy for downlink signal quality includes the downlink RSCP sub-optimization strategy m and the downlink Ec/Io sub-optimization strategy n, and the execution order is to execute the downlink RSCP sub-optimization strategy m first, and then execute the downlink Ec/Io sub-optimization strategy n.

If the optimization goal or optimization requirement changes, the corresponding special optimization strategy is modified by adding sub-optimization strategies, deleting sub-optimization strategies, or adjusting the execution order of existing sub-optimization strategies.

S12: Execute each sub-optimization strategy sequentially according to the execution sequence, and obtain a self-optimization result corresponding to each sub-optimization strategy.

The execution of the special optimization strategy is realized by sequentially executing a series of sub-optimization strategies contained in it. After each sub-optimization strategy is executed, a self-optimization result indicating the current network status is obtained. The self-optimization result can be completed by executing Relevant network data collected after this sub-optimization strategy is represented.

S13: After the execution of the current sub-optimization strategy is completed, perform an effect evaluation on the obtained corresponding self-optimization result according to the preset effect index.

In this method, after executing a sub-optimization strategy each time, it is necessary to perform network performance evaluation on the self-optimization result corresponding to the sub-optimization strategy according to the effect index corresponding to the special optimization strategy in the performance index database, To judge whether the optimized network performance meets the standard and whether the network problem is solved.

S131: If the self-optimization result passes the effect evaluation, end the self-optimization process.

If the optimized network performance meets the requirements of the above effect indicators, there is no need to execute subsequent sub-optimization strategies, the entire special optimization strategy is executed, the entire self-optimization process ends, and the network self-optimization is successful.

S132: If the self-optimization result fails the effect evaluation, execute the next sub-optimization strategy according to the execution order.

If the optimized network performance cannot meet the requirements of the above effect indicators, continue to execute the next sub-optimization strategy, and repeat steps S12 and S13.

Compared with the manual optimization method in the prior art, the present invention realizes network self-optimization through a special optimization strategy including a series of sub-optimization strategies, and evaluates the effect after each sub-optimization strategy is executed. The strategy-oriented self-optimization method of the evaluation mechanism, compared with the optimization process of manually finding problems, analyzing problems, and solving problems, has less manual labor, so it can reduce the input of corresponding manpower and material resources in the optimization process and save costs. The self-optimization method can be automatically executed after being triggered, and has a high degree of automation, which can shorten the optimization process and improve optimization efficiency.

As a refinement of the method shown in Figure 1, the embodiment of the present invention also provides a method for network self-optimization, including:

1): Obtain a special optimization strategy.

2): According to the execution order, each sub-optimization strategy is executed sequentially, and the self-optimization result corresponding to each sub-optimization strategy is obtained.

Since the direct execution result after executing a certain sub-optimization strategy includes two results of adjusting the network or not making any adjustments, in one implementation of this method, after the current sub-optimization strategy is executed Finally, the effect evaluation of the self-optimization results according to the preset effect indicators is as follows:

3): After the execution of the current sub-optimization strategy is completed, first analyze the execution result of the sub-optimization strategy;

31): If the execution result of the sub-optimization strategy is to adjust the network, the corresponding changes in the parameters related to the current network brought about by the network adjustment will be obtained, so it is necessary to perform an effect on the current self-optimization results obtained Evaluate;

32): If the execution result of the sub-optimization strategy is that no adjustment is made to the network, no effect evaluation is performed, and the next sub-optimization strategy can be continued.

Further, in one implementation of the method, the method performs the following processing for the different results obtained after evaluating the effect of the adjusted network:

311): If the result of the self-optimization passes the effect evaluation, then end the self-optimization process.

If the optimized network performance meets the requirements of the above effect indicators, there is no need to execute subsequent sub-optimization strategies, the entire special optimization strategy is executed, the entire self-optimization process ends, and the network self-optimization is successful.

312): If the self-optimization result fails the effect evaluation, it is judged whether the special optimization strategy still includes unexecuted sub-optimization strategies.

3121): If there is an unexecuted sub-optimization strategy, continue to execute the next sub-optimization strategy.

3122): If there are no unexecuted sub-optimization strategies, that is, if the result of self-optimization fails the effect evaluation, and the special optimization strategy does not contain unexecuted sub-optimization strategies, then execute the sub-optimization strategies in the special optimization strategy again according to the order of execution. Optimizing strategies for execution. Executing the special optimization strategy again is not simply repetition. In fact, each time the special optimization strategy is executed, the implementation process of each sub-optimization strategy (described in detail below) will change.

Further, in order to prevent the execution of the special optimization strategy from falling into an infinite infinite loop, this method limits the execution counter of the special optimization strategy. The special optimization execution counter is used to record the execution times of the special self-optimization process. Each time the special optimization strategy is executed, the special optimization strategy execution counter is incremented by one. Before re-executing the special optimization strategy, it is first judged whether the execution counter of the special optimization strategy reaches the threshold value. If so, that is, the effect evaluation has not been passed until the execution counter of the special optimization strategy reaches the set threshold value, the self-optimization process is ended, and the network self-optimization Failed; if not, repeat step 2) and step 3) and subsequent steps. The threshold of the special optimization policy execution counter is defined according to the optimization goal and optimization requirement.

Wherein, the implementation process of the above step 1) and step 2) is the same as that of steps S11 and S21 of the method shown in FIG. 1 , and this method will not be repeated here.

Since the special optimization strategy includes sub-optimization strategies and the execution order of the sub-optimization strategies, the execution of the special optimization strategy starts from the sub-optimization strategy in the first order. As shown in FIG. 2 , in a specific implementation manner of this embodiment, the execution process of the entire special optimization strategy is as follows.

S201: Obtain a special optimization strategy; execute step S202.

S202: Execute sub-optimization strategy 1 to obtain an execution result of sub-optimization strategy 1; execute step S203.

Sub-optimization strategy 1 is a sub-optimization strategy in the first order.

S203: Analyze the execution result of the current sub-optimization strategy; if the execution result of the current sub-optimization strategy is that the network is adjusted, then perform step S204; if the execution result of the current sub-optimization strategy is that the network is not adjusted, then perform step S207 . If the current sub-optimization strategy is sub-optimization strategy 1, the execution result of sub-optimization strategy 1 is analyzed accordingly; similarly, if the current sub-optimization strategy is sub-optimization strategy 2, its corresponding execution result is analyzed, and so on.

S204: Evaluate the effect of the self-optimization result, and execute step S205.

S205: Determine whether the self-optimization result passes the effect evaluation.

If the self-optimization result passes the effect evaluation, execute step S210; if the self-optimization result fails the effect evaluation, execute step S206.

S206: Determine whether there is still an unexecuted sub-optimization strategy.

If there is an unexecuted sub-optimization strategy, execute step S207; if there is no unexecuted sub-optimization strategy, execute step S208.

S207: Execute the next sub-optimization strategy.

S208: Add 1 to the special optimization strategy execution counter, and execute step S209.

S209: Determine whether the special optimization execution counter reaches a threshold.

If the threshold is reached, step S210 is performed; if the threshold is not reached, step S202 is performed.

S210: End the execution of the special optimization strategy.

As a refinement of the method shown in Figure 1, this method adds the analysis of the execution results of the sub-optimization strategy before the effect evaluation. If the execution result of the sub-optimization strategy is that the network has not been adjusted, no effect evaluation is required, which can avoid unnecessary waste of time and shorten the process; when the entire special optimization strategy is executed once but still cannot meet the requirements of the effect indicators, the special optimization strategy is executed multiple times to realize network self-optimization as much as possible.

As a supplement to the methods shown in Figures 1 and 2, the embodiment of the present invention also provides a method for network self-optimization, which specifically introduces the implementation process of any sub-optimization strategy:

For any sub-optimization strategy, its execution process includes:

1): Collect data from preset data sources.

During the execution of the sub-optimization strategy, data must first be collected from the current network for subsequent processing.

The self-optimization process of this method is a self-optimization process based on multiple data sources, and the multiple data sources referred to mainly include:

Network management data: measurement data of mobile base station (Node B) or radio network controller (Radio Network Controller, RNC for short) (such as load level, number of users in soft handover/softer handover) and radio resource control (Radio Resource Control, (referred to as RRC) layer signaling, etc.;

Measurement Report (MR for short): The measurement report (such as pilot RSCP, Ec/Io, etc.) reported by the terminal to the RNC;

Drive Test (DT) data: measurement data and location information obtained through actual drive tests.

Complaint data: User complaint information about network performance and problems collected by the operator's customer service system.

When the sub-optimization strategy collects data, select the corresponding preset data source for data collection. The corresponding preset data source can be one or more of the above data sources. For example, when executing the downlink RSCP sub-optimization strategy n, the collected MR provides DT data; when executing the sub-optimization strategy m of neighbor relation, collect DT data, etc. When the sub-optimization strategy is modified, the corresponding data source is correspondingly modified; for example, when the neighbor relation sub-optimization strategy m is modified, its preset data source is changed from MR to DT data.

2): According to the preset optimization performance indicators and the preset current network configuration, analyze the collected data source data and obtain the analysis results.

Analyzing the above-mentioned data collected based on multiple data sources with reference to the optimization performance index corresponding to the sub-optimization strategy in the network index database, obtaining an analysis result, and determining whether there is a network problem corresponding to the sub-optimization strategy according to the analysis result; If there is a problem, combine the current network configuration saved in the network configuration database to determine the cause of the problem.

21): If the analysis result shows that the collected data does not meet the requirements of the optimization performance index, then select the corresponding optimization plan, and pre-evaluate the possible effects of the optimization plan. If the pre-evaluation passes, execute the optimization plan, otherwise restart Choose an optimization scheme.

The implementation of this step, specifically, includes selection of an optimization scheme, pre-evaluation of the optimization scheme, and execution of the optimization scheme, as described below.

If the analysis result shows that the collected data does not meet the requirements of the optimization performance index, then an optimization scheme suitable for the sub-optimization strategy is selected from the optimization scheme library according to the network problem and the cause of the problem obtained through the analysis.

For the same network problem and network reason, since different sub-optimization strategies have different optimization objectives and optimization requirements, when selecting an optimization scheme, sub-optimization strategies should also be combined.

Before implementing the selected optimization scheme, it is necessary to pre-evaluate the possible effects of the optimization scheme. By analyzing the implementation process of the optimization scheme, the beneficial effects and negative effects that may be brought about after the implementation of the optimization scheme are obtained. Comprehensively measure the beneficial effect and negative impact, if it can bring about the beneficial effect of optimizing network performance, and the negative impact is within the acceptable range defined by the sub-optimization strategy, then it is determined that the optimization scheme has passed the pre-evaluation; otherwise, the optimization scheme is determined If the pre-evaluation is not passed, it is necessary to reselect the optimization plan from the optimization plan library and repeat the above pre-evaluation process. It should be noted that when re-selecting the optimization scheme, the optimization scheme that has been selected but failed the pre-evaluation will be excluded first, and the optimization scheme will be re-determined from the remaining alternative optimization schemes according to the reasons for the failure of the scheme pre-evaluation.

In order to prevent the scheme selection from falling into an infinite loop, in one implementation of this method, the scheme selection counter is used to limit the threshold value of the scheme selection counter according to the optimization goals and optimization requirements of the sub-optimization strategy. , the program chooses to add one to the counter. If the solution pre-evaluation passes, the solution selection counter is cleared; if the selected solution does not pass the pre-evaluation when it reaches the threshold, the execution of the sub-optimization strategy ends, and the solution selection counter is cleared.

After the selected optimization scheme passes the pre-evaluation, execute the optimization scheme. The process of implementing the optimization scheme is the process of adjusting the network, such as optimizing wireless parameters such as handover and reselection parameters or adjusting mechanical and physical optimizations such as antenna downtilt angles.

After the optimization scheme is executed, since the current network is adjusted, the adjusted network configuration needs to be updated into the network configuration database.

22): If the analysis result shows that the collected data meets the requirements of the optimization performance index, then the execution of the sub-optimization strategy ends.

If the analysis result shows that the collected data meets the requirements of the optimization performance index, that is, the current network performance is good and meets the optimization performance index corresponding to the sub-optimization strategy, then there is no need to make any adjustments to the current network, and the sub-optimization process ends.

As shown in FIG. 3 , in an implementation manner of this embodiment, the execution flow of the sub-optimization strategy is as follows.

S301: Collect data from a preset data source, where the preset data source is one or more of MR, network management data, drive test data, and complaint data; execute step S302.

S302: Analyze the collected data according to the preset optimization performance index and the preset current network configuration, and obtain the analysis result;

If the analysis result is that the collected data meets the requirements of the optimized performance index, that is, up to the standard, then perform step S308;

S303: Select an optimization scheme suitable for the sub-optimization strategy from the optimization scheme library, and execute step S304.

S304: Add 1 to the scheme selection counter, and execute step S305.

S305: Pre-evaluate the selected optimization scheme to obtain a pre-evaluation result;

If the result of the pre-evaluation is passed, execute step S306; if the result of the pre-evaluation is not passed, execute step S307.

S306: Execute the selected optimization solution.

S307: Determine whether the solution selection counter reaches a threshold.

If the threshold is not reached, step S303 is performed; if the threshold is reached, step S308 is performed.

S308: End the execution of the sub-optimization strategy.

The sub-optimization strategy provided by this method is a self-optimization strategy based on multiple data sources with a pre-evaluation mechanism, combined with a flexible trigger mechanism and a self-evaluation mechanism during the execution of the special optimization strategy, so that the network self-evaluation provided by this embodiment The optimization method has complete structure, flexible implementation and high degree of automation, so it can greatly improve the efficiency of network self-optimization. At the same time, the entire execution process takes a short time, which can greatly reduce the delay of network optimization and provide users with the best network experience in a timely manner.

As a supplement to the methods shown in Figure 1, Figure 2, and Figure 3, this article will focus on the explanations of the above-mentioned databases.

As shown in FIG. 4 , the aforementioned optimization policy database 401 , performance index database 402 , optimization scheme database 403 and network configuration database 404 mentioned in this embodiment are managed by a centralized database 40 .

Among them, the optimization strategy library 401 centrally manages and stores the sub-optimization strategies, such as the downlink RSCP sub-optimization strategy m, the downlink RSCP sub-optimization strategy n, the downlink Ec/Io sub-optimization strategy n, the switching sub-optimization strategy p, etc. are located in this database. According to optimization requirements and optimization goals, sub-optimization strategies are selected from the optimization strategy library 401 to establish special optimization strategies, and their execution sequence is defined. If the sub-optimization strategy in the optimization strategy library 401 is modified, the corresponding special optimization strategy is automatically updated. The corresponding special optimization strategy includes the above-mentioned modified sub-optimization strategy, and there is no need to separately modify each special optimization strategy containing the sub-optimization strategy. strategy, and will not affect the implementation process of self-optimization.

The performance index database 402 centrally defines and manages the performance index requirements of each level. Each special optimization strategy has its corresponding level of effect performance index requirements, which are used to evaluate the network performance after the execution of each sub-optimization strategy to judge the optimization Whether the final network performance is up to standard and whether the network problem is solved. Each sub-optimization strategy has its corresponding level of optimization performance index requirements, which are used to analyze the collected data, and then determine whether the network indicators meet the requirements and whether there are network problems.

The optimization scheme library 403 centrally manages and stores the optimization schemes. Different optimization schemes correspond to different network problems and problem causes. During the execution of the sub-optimization strategy, an optimization solution is selected from the optimization solution library 403 according to the analyzed network problem and the cause of the problem. The network administrator updates the optimization solution library 403 according to the development of the network optimization technology, and the update of the optimization solution library 403 does not affect the self-optimization process.

The network configuration database 404 stores the basic information of each base station in the network and configuration information of its parameters. During the execution of the sub-optimization strategy, refer to the optimization performance index requirements in the performance index database 402 and read the base station basic information and parameter configuration information in the network configuration database 404 to analyze the collected data to determine whether there is a problem and the cause of the problem; After the solution is executed, the adjusted network configuration information is updated to the network configuration database 404 .

In general, in the self-optimization process, the special optimization strategy and the sub-optimization strategies that make up the special optimization strategy read and modify the network configuration database 404, read the performance index database 402 and read the optimization scheme library 401, making the network The configuration database 404, the performance index database 402, and the optimization scheme library 401 cooperate with the self-optimization process to proceed normally.

With the popularization of the third-generation mobile communication technology (3rd-Generation, referred to as 3G) network, wideband code division multiple access network (WCDMA for short) is more and more widely used, the following through the common downlink in WCDNA system The implementation of the network self-optimization method provided in this embodiment is introduced in three application scenarios of signal quality self-optimization, handover self-optimization, and call drop self-optimization:

Scenario 1: Self-optimization of downlink signal quality

As shown in Figure 5, a network structure comprising an operation and maintenance server (O&M), a radio network controller, base stations 1, 2, and 3, and cells a, b, and c located in each base station is provided, wherein the radio network controller deploys There is a self-optimization system, which includes various special optimization strategies and sub-optimization strategies. The network needs to perform self-optimization of downlink signal quality, and the special optimization strategy corresponding to the self-optimization is a special optimization strategy for downlink signal quality, and the special optimization strategy includes downlink RSCP sub-optimization strategy m and downlink Ec/Io sub-optimization strategy n. When the number of user complaints about the poor signal quality of a certain cell (cell a of base station 2 in this example) reaches a certain threshold, the self-optimization process is triggered.

Step 501: Execute the downlink RSCP sub-optimization strategy m in the first order.

Collect the RSCP of base station 2 cell a in the past 24 hours provided by MR;

To analyze the collected data, first compare the collected RSCP value with the index requirement of level k in the performance index database (the index requirement of level k is that the proportion of RSCP greater than -85dBm is greater than 50%). The analysis result shows that 60% of the RSCPs are lower than -85dBm, and it is judged that there is a downlink RSCP difference problem. Analyze the cause of the problem, read the base station basic information and downlink power in the network configuration database, judge that the distance between stations is large, and the downlink transmit power value is 33dBm, and judge that the cause of the problem may be low downlink transmit power.

According to the analyzed network problem and the cause of the problem, select the downlink RSCP optimization scheme x corresponding to the downlink RSCP sub-optimization strategy m (the downlink transmit power is increased by 3dBm).

Pre-evaluate the downlink RSCP optimization scheme x, and obtain statistical indicators to analyze the cell code resource utilization rate, channel element (Channel Element, CE) resource occupancy rate, and cell carrier frequency transmit power utilization rate are relatively low. The evaluation results show that the downlink The increase of the transmit power from 3dBm to 36dBm will not cause capacity problems, and the interference to neighboring cells is also within the acceptable range, and the evaluation result is passed.

Execute the downlink RSCP optimization scheme x, increase the downlink transmission power of cell a of base station 2 by 3dBm, and update the increased downlink transmission power value (36dBm) to the network configuration database.

Step 502: collect the RSCP of base station 2 cell a provided by MR data according to the special optimization strategy for downlink signal quality for 24 hours, and compare it with the index requirements of grade m in the performance index database (the ratio of RSCP greater than -85dBm required by the grade m index is greater than 60%) , the analysis result shows that the number of sampling points with RSCP greater than -85dBm is 90%, and the downlink coverage problem has been solved.

Step 503: This self-optimization process ends.

Scenario 2: Call drop self-optimization

As shown in Figure 6, a network structure including base station I, base station J, cells in the base station (not shown), wireless network controller, and operation and maintenance server is provided. When there is a certain number, arrange testers to conduct tests in the corresponding area according to the test specifications to find out the problematic area between base station I and base station J, and trigger a special optimization strategy for call drop, which is an event trigger. Then in this self-optimization process, the special optimization strategy is the call drop special optimization strategy, and the sub-optimization strategy and its execution sequence included are: neighbor cell relationship sub-optimization strategy m, downlink RSCP sub-optimization strategy n, downlink Ec/Io sub-optimization strategy o, switch the sub-optimization strategy p.

Step 601: First execute the sub-optimization strategy m of the neighbor relationship in the first order:

Collect the Ec/Io of each call drop point activation set, monitoring set, and detection set in the drive test data and the strongest Ec/Io information recorded by the scanner.

Analyze the strongest Ec/Io information recorded by the Scanner and the Ec/Io of the activation set, monitoring set, and detection set before the call drop according to the sub-optimization strategy m of the neighbor cell relationship, and judge that there is no missing configuration of the neighbor cell.

Neighborhood relationship sub-optimization strategy m is executed.

Step 602: Execute the downlink RSCP sub-optimization strategy n in the second order:

Collect the RSCP of the best cell before the call drop in the drive test data.

Analyze the RSCP of the best cell before the call drop, and compare and analyze the RSCP of the best cell with the index k in the performance index database. The RSCP of 80% of the call drop points is lower than the index k (RSCP is lower than -100dBm) , judging that there is a coverage problem. Through the analysis of the Global Positioning System (Global Positioning System, referred to as GPS) information of the call drop point, it is concluded that the call drop is concentrated in area A, and the base station basic information and base station configuration information in the network configuration database are used to judge the problem as the distance between base station I and base station J is too big.

According to the problem, the cause of the problem, and the geographical location of the problem area A, select the downlink RSCP optimization scheme x (decrease the elevation angle of base station I by 5 degrees, and reduce the elevation angle of base station J by 7 degrees).

Evaluate the downlink RSCP optimization scheme x, obtain statistical indicators and analyze the cell code resource utilization rate, CE resource occupancy rate, and cell carrier frequency transmit power utilization rate are relatively low. The interference of the cell is also within the acceptable range, and the evaluation result is passed.

Execute the downlink RSCP optimization scheme x, adjust the antenna angles of cell I and cell J respectively, and update the adjustment results to the network configuration database.

The execution of the downlink RSCP sub-optimization strategy n is completed.

Step 603: Evaluate the network performance after executing the downlink RSCP sub-optimization strategy n, analyze the signaling call drop rate within 24 hours, compare it with the index i in the performance index database, and the analysis result meets the requirements.

Step 604: This self-optimization process ends.

Scenario 3: Switching to Self-Optimization

In this self-optimization process, the special optimization strategy is to switch the special optimization strategy, the sub-optimization strategy is to switch the sub-optimization strategy m, the trigger method is periodic triggering, and the trigger period is two weeks.

Step a: Execute switching sub-optimization strategy m.

Collect radio link failure (RadioLink Failure, referred to as RLF) related data and related signaling within 24 hours of all cells applying the handover sub-optimization strategy;

The number of RLF failures related to the statistical handover is compared with the index of level k in the performance index database. The result of the comparison is that the number of RLFs related to the handover of cell a under the base station N exceeds the index requirement. Analyze the handover event and judge that there is a handover problem. The cause of the problem is that the handover from cell a under base station N to cell c under base station M is not timely.

According to the existing network problem (handover problem) and the cause of the problem (handover is not timely), choose the untimely handover solution x: adjust the reporting range of 1A event from 3dB to 5dB, and adjust the hysteresis from 3.5dB to 2dB; at the same time, the reporting range of 1B event is changed from 7dB is adjusted to 6dB, hysteresis is adjusted from 3.5dB to 4dB.

Pre-evaluate the untimely handover optimization scheme x. Since the load of cell a under base station N has reached a certain threshold, adjusting the 1B event reporting range may cause the cell to be overloaded, and the pre-evaluation result fails.

Reselect the untimely handover optimization scheme y according to the reasons for the failure of the pre-evaluation: adjust the 1A event reporting range from 3dB to 5dB, and adjust the hysteresis from 3.5dB to 2dB; and pre-evaluate the untimely handover optimization scheme y.

Pre-evaluation of the untimely handover optimization scheme y, the evaluation results show that the adjustment of the 1A event reporting range from 3dB to 5dB and the hysteresis from 3.5dB to 2dB will cause cell loads in cell a under base station N and cell c under base station M The impact is within the acceptable range, and the plan is passed.

Implement the untimely handover optimization scheme y; according to the untimely handover optimization scheme y, adjust the 1A event reporting range of cell a under base station N from 3dB to 5dB, adjust the hysteresis from 3.5dB to 2dB, and update the adjusted parameters to the network configuration database.

Step b: Collect the 24-hour RLF-related data of cell a of base station N, compare and analyze it with the index i in the performance index database, evaluate the network performance after completing the handover sub-optimization strategy m, and the number of handover-related RLF reaches the index i Require.

Step c: This self-optimization process ends.

Furthermore, as an implementation of the methods and applications shown in the above figures, this embodiment also provides a device for network self-optimization.

As shown in FIG. 7, the device includes: an acquisition unit 701, configured to acquire a special optimization strategy, the special optimization strategy includes sub-optimization strategies, and the execution sequence of the sub-optimization strategies;

The execution unit 702 is configured to sequentially execute each sub-optimization strategy acquired by the acquisition unit 701 according to the execution order acquired by the acquisition unit 701, and obtain a self-optimization result corresponding to each sub-optimization strategy;

An effect evaluation unit 703, configured to evaluate the corresponding self-optimization result obtained according to a preset effect index after the execution unit 702 finishes executing the current sub-optimization strategy;

The execution unit 702 is further configured to end the self-optimization process when the self-optimization result passes the effect evaluation of the effect evaluation unit 703;

The execution unit 702 is further configured to execute the next sub-optimization strategy according to the execution sequence when the self-optimization result fails the effect evaluation by the effect evaluation unit 703 .

Further, as shown in FIG. 8, the execution unit 702 further includes a data collection module 801, a data analysis module 802, a judgment module 803, a solution selection module 804, a solution pre-evaluation module 805, and a solution execution module 806, wherein:

A data collection module 801, configured to collect data from preset data sources;

The data analysis module 802 is configured to analyze the data source data collected by the data collection module 801 according to the preset optimization performance index and the preset current network configuration;

Judging module 803, used to judge whether the analysis result obtained by data analysis module 802 is up to standard;

The scheme selection module 804 is used to select a corresponding optimization scheme when the judging module 803 judges that the analysis result is not up to standard;

The scheme pre-evaluation module 805 is used to pre-evaluate the possible effect of the optimization scheme selected by the scheme selection module 804;

The scheme execution module 806 is used for executing the optimized scheme when passing the pre-evaluation of the scheme pre-evaluation module 805;

The scheme selection module 804 is also used for reselecting the optimization scheme when the pre-evaluation of the scheme pre-evaluation module 805 is not passed;

The executing unit 702 is further configured to end the execution of the sub-optimization strategy when the judging module 803 judges that the analysis result meets the standard.

Further, the execution unit 702 is further configured to, after the last sub-optimization strategy is executed, execute the sub-optimization strategies in the special optimization strategy again according to the execution order when the self-optimization result has not passed the effect evaluation.

Further, as shown in FIG. 9, the device further includes an establishment unit 901 and a modification unit 902, wherein:

Establishing unit 901, configured to select at least one sub-optimization strategy according to the optimization objective and optimization requirements, and establish a special optimization strategy in a certain order;

The modification unit 902 is configured to modify the special optimization strategy established by the establishment unit 901 by adding sub-optimization strategies, deleting sub-optimization strategies or adjusting the execution order of existing sub-optimization strategies when the optimization goal or optimization requirement changes.

Further, as shown in FIG. 10, the device further includes: an updating unit 101, configured to automatically update the corresponding special optimization strategy when the sub-optimization strategy in the optimization strategy library is modified, and the corresponding special optimization strategy includes the modified sub-optimization strategy.

In the device for network self-optimization provided by the present invention, the acquisition unit acquires a special optimization strategy, the execution unit sequentially executes each sub-optimization strategy included in the special optimization strategy, and the effect evaluation unit evaluates the effect of each sub-optimization strategy that has been executed. To judge whether the preset effect index is reached, when it is reached, the entire self-optimization process is completed, and the self-optimization process is ended; otherwise, the execution unit continues to execute the next sub-optimization strategy. This device automatically completes the execution of each sub-optimization strategy and special optimization strategy through the signal transmission between each unit, with less manual labor, which can reduce the input of corresponding manpower and material resources in the optimization process and save costs; the degree of automation is high, and it can shorten Optimize processes and improve optimization efficiency.

Through the description of the above embodiments, those skilled in the art can clearly understand that the present invention can be realized by means of software plus necessary general-purpose hardware, and of course also by hardware, but in many cases the former is a better embodiment . Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product is stored in a readable storage medium, such as a floppy disk of a computer , a hard disk or an optical disk, etc., including several instructions for enabling a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

The above is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Anyone skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present invention. Should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims (10)

1. a method for network self-organization, is characterized in that, comprising:

Obtain special optimisation strategy, described special optimisation strategy comprises sub-optimisation strategy, and the execution sequence of described sub-optimisation strategy;

According to described execution sequence, carry out successively each sub-optimisation strategy, obtain the result of the self-optimizing corresponding with every individual sub-optimisation strategy;

After current sub-optimisation strategy is finished, according to default effectiveness indicator, the result of the corresponding described self-optimizing obtaining is carried out to recruitment evaluation;

If the result of described self-optimizing, by described recruitment evaluation, finishes self-optimizing flow process;

If the result of described self-optimizing, not by described recruitment evaluation, is carried out next son optimisation strategy according to described execution sequence.

2. method according to claim 1, is characterized in that, for any one sub-optimisation strategy, its implementation comprises:

Collect the data in preset data source;

According to default optimality criterion and default current network configuration, the data source data of collecting is analyzed, obtain analysis result;

If described analysis result is described data do not meet described optimality criterion requirement, choose corresponding prioritization scheme, and the issuable effect of described prioritization scheme is carried out to Pre-Evaluation, if Pre-Evaluation passes through, carry out described prioritization scheme, otherwise again choose prioritization scheme;

If described analysis result is the requirement that described data meet described optimality criterion, finish the execution of described sub-optimisation strategy.

3. method according to claim 1, it is characterized in that, after in the end a sub-optimisation strategy is finished, if the result of described self-optimizing is not yet by described recruitment evaluation, again the sub-optimisation strategy in described special optimisation strategy is carried out according to described execution sequence.

4. method according to claim 1, is characterized in that, described obtain special optimisation strategy before, described method further comprises:

According to optimization aim and optimization demand, select at least one sub-optimisation strategy, set up described special optimisation strategy according to a definite sequence;

After foundation completes described special optimisation strategy, described method further comprises:

If described optimization aim or optimization demand change,, by increasing sub-optimisation strategy, deleting sub-optimisation strategy or adjust the execution sequence that has sub-optimisation strategy, revise described special optimisation strategy.

5. according to the method described in any one in claim 1 to 4, it is characterized in that, described method further comprises:

If the sub-optimisation strategy in optimisation strategy storehouse is modified, automatically upgrade corresponding special optimisation strategy, the sub-optimisation strategy being modified described in the special optimisation strategy of described correspondence comprises.

6. a device for network self-organization, is characterized in that, described device comprises:

Acquiring unit, for obtaining special optimisation strategy, described special optimisation strategy comprises sub-optimisation strategy, and the execution sequence of described sub-optimisation strategy;

Performance element, for the execution sequence obtaining according to described acquiring unit, carries out each the sub-optimisation strategy that described acquiring unit obtains successively, obtains the result of the self-optimizing corresponding with every individual sub-optimisation strategy;

Recruitment evaluation unit, after current sub-optimisation strategy being finished at described performance element, carries out recruitment evaluation according to default effectiveness indicator to the result of the corresponding described self-optimizing obtaining;

Described performance element, also, in the time that the result of described self-optimizing is passed through the recruitment evaluation of described recruitment evaluation unit, finishes self-optimizing flow process;

Described performance element, also, in the time that the result of described self-optimizing is not passed through the recruitment evaluation of described recruitment evaluation unit, carries out next son optimisation strategy according to described execution sequence.

7. device according to claim 6, is characterized in that, described performance element also comprises data collection module, data analysis module, judge module, Scheme Choice module, scheme Pre-Evaluation module, scheme Executive Module, wherein:

Described data collection module, for collecting the data in preset data source;

Described data analysis module, for according to default optimality criterion and default current network configuration, analyzes the data source data of described data collection module collection;

Described judge module, whether up to standard for judging that described data analysis module obtains analysis result;

Described Scheme Choice module, in the time that described judge module judges that described analysis result is not up to standard, chooses corresponding prioritization scheme;

Described scheme Pre-Evaluation module, carries out Pre-Evaluation for the issuable effect of described prioritization scheme that described Scheme Choice module is chosen;

Described scheme Executive Module, for when the described Pre-Evaluation by described scheme Pre-Evaluation module, carries out described prioritization scheme;

Described Scheme Choice module, also, for when the described Pre-Evaluation by described scheme Pre-Evaluation module not, chooses prioritization scheme again;

Described performance element, also, in the time that described judge module judges that described analysis result is up to standard, finishes the execution of described sub-optimisation strategy.

8. device according to claim 6, it is characterized in that, described performance element also for, after in the end a sub-optimisation strategy is finished, in the time that the result of described self-optimizing is not passed through described recruitment evaluation yet, again the sub-optimisation strategy in described special optimisation strategy is carried out according to described execution sequence.

9. device according to claim 6, is characterized in that, described device also comprises sets up unit, amendment unit, wherein:

The described unit of setting up, for according to optimization aim and optimization demand, selects at least one sub-optimisation strategy, sets up described special optimisation strategy according to a definite sequence;

Described amendment unit, in the time that described optimization aim or optimization demand change, by increasing sub-optimisation strategy, delete sub-optimisation strategy or adjust the execution sequence of existing sub-optimisation strategy, revises the described described special optimisation strategy that unit is set up of setting up.

10. according to the device described in any one in claim 6 to 9, it is characterized in that, described device also comprises:

Updating block, in the time that the sub-optimisation strategy in optimisation strategy storehouse is modified, upgrades corresponding special optimisation strategy automatically, the sub-optimisation strategy being modified described in the special optimisation strategy of described correspondence comprises.