CN106507398B - A kind of network self-organization method based on continuous learning - Google Patents
A kind of network self-organization method based on continuous learning Download PDFInfo
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Abstract
The invention discloses a kind of network self-organization methods based on continuous learning；Include continuous learning process and network optimization process；Network self-organization method provided by the invention can greatly reduce the investment of manpower and material resources, save the cost, shorten Optimizing Flow, improve optimization efficiency, while it is tediously long to solve the foregoing invention optimization time, may not be the defect of optimum optimization strategy;It rapidly finds the problem in network, and the duration of network failure can be shortened, restore the normal working condition of network in time, achieve the purpose that optimize network performance.
Description
Technical field
The invention belongs to network optimization field more particularly to a kind of network self-organization methods based on continuous learning.
Background technique
With the extensive growth of mobile device, wireless traffic is also grown rapidly, and user is for wireless network cut-in quality
Requirement also increasingly increase, so if obtain any user can with quality assurance at any time and any place
Service, it is necessary to which network is constantly optimized.
Wireless communication system becomes network management and optimization very due to the explosive growth of user and business
It is difficult.The matter of utmost importance that network operator faces is that there are thousands of a network elements (such as base station) in network, and wherein each
Network element is likely to go wrong or failure, some in these problems may be not obvious at its initial stage, if will rely on
Manually go find and check if, that workload will be it is huge even not possible with.Until now, the network optimization still needs
A large amount of professional and superior equipment, this considerably increases the costs of operator, reduce profit space.Operator spends at present
The great number operation maintenance cost of expense has been difficult to improve bring income by extra-service performance to compensate, because of each user
Average yield constantly decline.
Such as application No. is a kind of method, apparatus of 201410371501.0 network self-organization；The one of invention offer
The method, apparatus of kind network self-organization obtains special optimisation strategy first, and then sequence, which executes in special project optimisation strategy, includes
The sub- optimisation strategy of each single item, and recruitment evaluation is carried out to judge whether to have reached pre- to the sub- optimisation strategy of each single item being finished
If effectiveness indicator, just complete the process of entire self-optimizing when reached, terminate the self-optimizing process, otherwise continue to execute down
One sub- optimisation strategy, repeats the above steps.Compared with artificial optimization's method in the prior art, send elsewhere bright by the inclusion of a series of
The special optimisation strategy of sub- optimisation strategy realizes network self-organization, and carries out effect after the completion of each sub- optimisation strategy executes
Assessment, for it is a kind of with self-evaluating mechanism towards strategy self-organization method, relative to artificial discovery problem, problem analysis,
The Optimizing Flow solved the problems, such as, hand labor it is less thus can reduce the investment of corresponding manpower and material resources in optimization process, save
Cost, simultaneously because the method for self-optimizing provided by the invention is that can execute automatically after triggering, the degree of automation is higher, can
Shorten Optimizing Flow, improve optimization efficiency.But only simply sequence executes optimisation strategy for the invention, until effect of optimization changes
It is kind.Defect present in it has two o'clock: 1. selecting sequences execute the mode of optimisation strategy, and it is tediously long to will lead to the optimization time；2. working as
Effect of optimization just terminates entire self-optimizing process after being improved, may result in the optimisation strategy is not optimal policy.
Summary of the invention
The technical problem to be solved by the present invention is to propose that one kind is based on holding to solve shortcoming and defect in the prior art
The network self-organization method of continuous study；Its investment for reducing manpower and material resources, save the cost shorten Optimizing Flow, improve optimization effect
Rate, at the same solve foregoing invention optimization the time it is tediously long, may not be the defect of optimum optimization strategy.
The present invention uses following technical scheme to solve above-mentioned technical problem
A kind of network self-organization method based on continuous learning includes continuous learning process and network optimization process；
The continuous learning process specifically includes the following steps；
Step 1, prepare the network optimization problem classified；
Step 2, the characteristic information for the network optimization problem that extraction step 1 prepares, and the characteristic information extracted is trained
At a prior model；
Step 3, classified using prior model to subsequent network problem；
Step 4, if there is new network optimization problem, new network optimization problem is extracted using unsupervised learning
Characteristic information；
Step 5, the characteristic information obtained according to step 4 is by Active Learning come the priori mould of renolation step 2 training
Type；
The network optimization process specifically includes the following steps:
Step 6, after the classification of known network optimization problem, the optimisation strategy for belonging to such network optimization problem is obtained；
Step 7, the optimisation strategy of such network optimization problem is executed；
Step 8, effect of optimization corresponding with the optimisation strategy executed is obtained；
Step 9, it is evaluated according to each effect of optimization of preset Indexes of Evaluation Effect to acquisition；
Step 10, all evaluations are compared, select optimal Network Optimization Strategy.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, in step 2,
The characteristic information of the network optimization problem of step 1 preparation is extracted using deep learning method.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the optimization
Strategy is respectively the optimisation strategy of covering class problem and the optimisation strategy of signal quality class problem；
Wherein, covering class problem optimisation strategy includes adjustment antenna feeder, adjustment pilot power, adjustment system ovelay range, inspection
Whether normal look into neighbor stations RxLev；
The optimisation strategy of signal quality class includes PCI optimization, adjustment antenna feeder, the leading covering of enhancing, adjustment pilot power.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the effect is commented
Valence index includes covering class index, call setup class index, calling holding class index and time delay class index
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the covering class
Index includes RSRP, RSSI, RSRQ, SINR.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the call setup
Vertical class index includes that RRC connection is created as that power, RRC connection is created as power, E-RAB is created as power, wireless percent of call completed.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the calling is protected
Holding class index includes RRC connection abnormal cutting off rate, E-RAB cutting off rate.
As a kind of further preferred scheme of the network self-organization method based on continuous learning of the present invention, the time delay class
Index include UE converted from Idle state to Active state time delay, Attach time delay, user plane time delay, in system in X2 service switchover
S1 service switchover interrupts time delay in disconnected time delay, system, different system service switchover interrupts time delay.
The invention adopts the above technical scheme compared with prior art, has following technical effect that
Network self-organization method provided by the invention can greatly reduce the input cost of manpower and material resources, reduce network construction
With maintenance cost；Automatically, it rapidly finds the problem in network, and the duration of network failure can be shortened, in time
Restore the normal working condition of network, achievees the purpose that optimize network performance.
Detailed description of the invention
Fig. 1 is continuous learning process of the invention；
Fig. 2 is prior model schematic diagram of the present invention；
Fig. 3 is the updated prior model schematic diagram of the present invention；
Fig. 4 is network optimization process of the invention；
Fig. 5 is the database that present example 1 provides.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing:
A kind of network self-organization process based on continuous learning is as follows: including continuous learning process and network optimization process；
As shown in Figure 1, the process of continuous learning is as follows:
Step 1, prepare the network optimization problem classified；The network of step 1 preparation is extracted using deep learning method
The characteristic information of optimization problem.
Step 2, the characteristic information for the network optimization problem that extraction step 1 prepares, and the characteristic information extracted is trained
At a prior model；
Step 3, classified using prior model to subsequent network problem；
Step 4, if there is new network optimization problem, new network optimization problem is extracted using unsupervised learning
Characteristic information；
Step 5, the characteristic information obtained according to step 4, come progressive updating sophisticated model, is reached and is continued using Active Learning
The destination of study.
The purpose of continuous learning is: improving prior model, classifies to different network optimization problems.It can after classification
To use different optimisation strategies for different optimization problems.Wherein prior model is in the nature characteristic information and problem category
Corresponding relationship, as shown in the figure:
When there is new network problem, the appearance of new characteristic information is also meaned that.Assuming that prior model such as Fig. 2 institute
Show, when new network problem occurs, characteristic information is characterized information 1, characteristic information 3, characteristic information 4 and new feature
Information 9, then the network problem will be classified as classification 1, at the same time, prior model can be also updated, characteristic information 9
Problem category 1 can be pointed to.The prior model of Fig. 2 will be updated to as shown in Figure 3.
After classifying to network optimization problem, start the self-optimizing of network, as shown in figure 4, detailed process is as follows:
Step 6, after the classification of known network problem, the optimisation strategy for belonging to such optimization problem is obtained；
Step 7, such all optimisation strategy is executed；
Step 8, effect of optimization corresponding with performed optimisation strategy is obtained；
Step 9, each effect of optimization is evaluated according to preset Indexes of Evaluation Effect；
Step 10, all evaluations are compared, optimisation strategy described in optimal Network Optimization Strategy is selected to distinguish
To cover the optimisation strategy of class problem and the optimisation strategy of signal quality class problem；
Wherein, covering class problem optimisation strategy includes adjustment antenna feeder, adjustment pilot power, adjustment system ovelay range, inspection
Whether normal look into neighbor stations RxLev；
The optimisation strategy of signal quality class includes PCI optimization, adjustment antenna feeder, the leading covering of enhancing, adjustment pilot power.
The Indexes of Evaluation Effect includes covering class index, call setup class index, calling holding class index and time delay class
Index
The covering class index includes RSRP, RSSI, RSRQ, SINR.
The call setup class index includes that RRC connection is created as that power, RRC connection is created as power, E-RAB is created as
Power, wireless percent of call completed.
It includes RRC connection abnormal cutting off rate, E-RAB cutting off rate that the calling, which keeps class index,.
The time delay class index include UE converted from Idle state to Active state time delay, Attach time delay, user plane time delay,
X2 service switchover interrupts time delay in system, S1 service switchover interrupts time delay, different system service switchover interrupts time delay in system.
It is excellent to train a continuous renewal for the method that network self-organization method provided by the invention uses continuous learning first
The prior model of change later classifies to non-classified network optimization problem, and then selection belongs to such optimisation strategy pair
Network optimizes, and is finally evaluated using preset evaluation index effect, selects optimum optimization strategy.
Specific embodiment is as follows:
Example 1, when occurring unknown problem in network, and when the problem is new network problem, processing step is as follows:
Step 1: extraction characteristic information first；
Step 2: being classified later using prior model to the problem；
Step 3: and then be updated using the characteristic information extracted to prior model；
Step 4: assuming that the problem is classified as downlink signal quality problem (network problem classification 1), for such problem,
There are three types of optimisation strategies；
Step 5: executing such three kinds of all optimisation strategy, obtain corresponding effect of optimization；
Step 6: three kinds of effect of optimization being evaluated according to preset Indexes of Evaluation Effect；
Step 7: three kinds of evaluations being compared, optimal Network Optimization Strategy is selected；
Involved database is as shown in Figure 5 in this example:
Example 2, when going wrong in network, and when the problem has been classified, processing step is as follows:
Step 1: extraction characteristic information first；
Step 2: being classified later using prior model to the problem；
Step 3: assuming that the problem is classified as downlink signal quality problem (network problem classification 1), for such problem,
There are three types of optimisation strategies；
Step 4: executing such three kinds of all optimisation strategy, obtain corresponding effect of optimization；
Step 5: three kinds of effect of optimization being evaluated according to preset Indexes of Evaluation Effect；
Step 6: three kinds of evaluations being compared, optimal Network Optimization Strategy is selected.
Key point in continuous learning is: preparing a series of network optimization problems classified and uses deep learning side
Method or model extract characteristic information therein and are trained a prior model, and it is complete that the two operations do not need user
At and after network operator trains a prior model, which can be constantly updated and perfect by continuous learning.
Key when executing optimisation strategy is: executing, but holds until meeting preset evaluation index not in accordance with sequence
All optimisation strategies under the row category select the strategy with optimal evaluation effect to be applied in network.
Claims (8)
1. a kind of network self-organization method based on continuous learning, it is characterised in that: include continuous learning process and the network optimization
Process；The continuous learning process specifically includes the following steps；
Step 1, prepare the network optimization problem classified；
Step 2, the characteristic information for the network optimization problem that extraction step 1 prepares, and the characteristic information extracted is trained to one
A prior model；
Step 3, classified using prior model to subsequent network problem；
Step 4, if there is new network optimization problem, the spy of new network optimization problem is extracted using unsupervised learning
Reference breath；
Step 5, the characteristic information obtained according to step 4 is by Active Learning come the prior model of renolation step 2 training；
The network optimization process specifically includes the following steps:
Step 6, after the classification of known network optimization problem, the optimisation strategy for belonging to such network optimization problem is obtained；
Step 7, the optimisation strategy of such network optimization problem is executed；
Step 8, effect of optimization corresponding with the optimisation strategy executed is obtained；
Step 9, it is evaluated according to each effect of optimization of preset Indexes of Evaluation Effect to acquisition；
Step 10, all evaluations are compared, select optimal Network Optimization Strategy.
2. a kind of network self-organization method based on continuous learning according to claim 1, it is characterised in that: in step 2
In, the characteristic information of the network optimization problem of step 1 preparation is extracted using deep learning method.
3. a kind of network self-organization method based on continuous learning according to claim 1, it is characterised in that: in step 6
In, the optimisation strategy is respectively the optimisation strategy of the optimisation strategy and signal quality class problem that cover class problem；
Wherein, covering class problem optimisation strategy includes adjustment antenna feeder, adjustment pilot power, adjustment system ovelay range, checks phase
Whether neighboring station RxLev is normal；
The optimisation strategy of signal quality class includes PCI optimization, adjustment antenna feeder, the leading covering of enhancing, adjustment pilot power.
4. a kind of network self-organization method based on continuous learning according to claim 1, it is characterised in that: in step 9
In, the Indexes of Evaluation Effect includes covering class index, call setup class index, calling holding class index and time delay class index.
5. a kind of network self-organization method based on continuous learning according to claim 4, it is characterised in that: the covering
Class index includes RSRP, RSSI, RSRQ, SINR.
6. a kind of network self-organization method based on continuous learning according to claim 4, it is characterised in that: the calling
Establishing class index includes that RRC connection is created as power, E-RAB is created as power, wireless percent of call completed.
7. a kind of network self-organization method based on continuous learning according to claim 4, it is characterised in that: the calling
Keeping class index includes RRC connection abnormal cutting off rate, E-RAB cutting off rate.
8. a kind of network self-organization method based on continuous learning according to claim 4, it is characterised in that: the time delay
Class index includes that UE converts time delay, Attach time delay, user plane time delay, X2 service switchover in system from Idle state to Active state
S1 service switchover interrupts time delay in interruption time delay, system, different system service switchover interrupts time delay.
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| CN107517474B (en) * | 2017-10-20 | 2021-11-12 | 北京神州泰岳软件股份有限公司 | Network analysis optimization method and device |
| CN109474497A (en) * | 2018-12-19 | 2019-03-15 | 四川艾贝斯科技发展有限公司 | A kind of reliable network maintenance terminal deep learning algorithm |
| CN111385822B (en) * | 2018-12-29 | 2021-11-09 | 华为技术有限公司 | Configuration method and controller |
| CN112188505B (en) * | 2019-07-02 | 2024-05-10 | 中兴通讯股份有限公司 | Network optimization method and device |
| CN112512059A (en) * | 2020-05-24 | 2021-03-16 | 中兴通讯股份有限公司 | Network optimization method, server, network side equipment, system and storage medium |
| CN113705067B (en) * | 2021-10-29 | 2022-02-18 | 中国电力科学研究院有限公司 | Microgrid optimization operation strategy generation method, system, equipment and storage medium |
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