CN110139315A - A kind of wireless network fault detection method based on self-teaching - Google Patents
A kind of wireless network fault detection method based on self-teaching Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0677—Localisation of faults
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L43/06—Generation of reports
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract
The invention discloses a kind of wireless network fault detection method based on self-teaching, specific as follows: acquisition network key performance indicator KPI is recorded and is saved as data acquisition system；Uneven processing is carried out using SMOTEENN method to the data acquisition system, the equilibrium data collection after being balanced；By sparse self-encoding encoder from no label auxiliary data focusing study base vector；It is the form of each base vector linear combination by the equilibrium data set representations after balance, and obtains the normally disaggregated model with fault category through the training of support vector machines method on the equilibrium data collection under new expression herein；Classified using well-established disaggregated model to the KPI data record that network generates in real time, and then achievees the purpose that fault detection.The present invention is more accurate and network failure is effectively detected；And the form of self-teaching facilitates migration, can obtain the model of fault detection quickly under new network environment, improve the fault detection efficiency of previous methods.
Description
Technical field
The present invention relates to the network technique field in wireless communication, especially a kind of wireless network event based on self-teaching
Hinder detection method.
Background technique
It is one of the critical function of wireless self-organization network from healing, accurately and efficiently carrying out fault detection is to realize certainly
Cure the important step of function.Changed in Current wireless communication network using macro base station shown in FIG. 1-Home eNodeB heterogeneous structure
The in-door covering of kind macro base station provides capacity gain.Compared to traditional homogeneous network, the Home eNodeB of user's autonomous deployment may
Since operating mistake is easier to that the allocation problems such as the improper, channel confliction of transimission power setting occur；In addition, the quantity of Home eNodeB is remote
More than macro base station, distributed structure/architecture is but also family is easier to break down.Therefore it is badly in need of a kind of self-organizing for heterogeneous network
Network fault detecting method.
The method of the current fault detection for homogeneous network is not often suitable for heterogeneous network, and reason essentially consists in together
Structure Network Fault Detection is mostly based on mode classification, and the accuracy rate of classification depends on network history data quantity to a certain extent
Size.And the available data of Home eNodeB are often limited in heterogeneous network.There are two the reason of leading to this, is on the one hand portion
Administration's Home eNodeB indoors usually services that number of users is less, and it is few that obtainable data compare homogeneous network；On the other hand it is
Home eNodeB deployment is determined that the operations such as switching, redeploy regularly causes network topology structure dynamically to change by user,
The historical data of base station long-term accumulation cannot will directly be used to analyze the current state in the base station, i.e. network history data exists
The low problem of timeliness.Femtocell user data rareness, historical data in heterogeneous network exist in the prior art in summary
Easily out-of-date problem.
Summary of the invention
It is provided the technical problem to be solved by the present invention is to overcome the deficiencies in the prior art a kind of based on self-teaching
Wireless network fault detection method, the fault detection method based on self-teaching that uses can be used in Home eNodeB in the present invention
User data is few, generate the easy mistake of data in the case of, it is more accurate and network failure is effectively detected；And the form of self-teaching
Facilitate migration, the model of fault detection can be obtained quickly under new network environment, improve the fault detection of previous methods
Efficiency.
The present invention uses following technical scheme to solve above-mentioned technical problem:
A kind of wireless network fault detection method based on self-teaching proposed according to the present invention, comprising the following steps:
Step 1: acquisition network key performance indicator KPI is recorded and is saved as data acquisition system；
It combines synthesis minority class oversampling technique SMOTE Step 2: being used to the data acquisition system and screens arest neighbors ENN's
SMOTEENN method carries out uneven processing, the equilibrium data collection after being balanced；
Step 3: by sparse self-encoding encoder from no label auxiliary data focusing study base vector；
Step 4: being the form of each base vector linear combination by the equilibrium data set representations after balance, and new expression herein
Under equilibrium data collection on through support vector machines method training obtain the normally disaggregated model with fault category；
Step 5: being classified using well-established disaggregated model to the KPI data record that network generates in real time, in turn
Achieve the purpose that fault detection.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Normally refer to non-faulting.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Step 1 is specific as follows:
In heterogeneous network, user periodically reports all kinds of KPI information in the form of measurement report；
From the KPI information for obtaining reporting of user in certain time in the heterogeneous network for needing to carry out fault detection, and according to
The reporting of user moment is to the normal KPI information corresponding with malfunction label of its serving BS；KPI information and correspondence markings are protected
Save as data acquisition systemForm, m be T in element total number, i.e., altogether acquisition
M item record；I-th of element in TInRepresent the n dimension KPI information of certain moment reporting of user, subscript
L indicates label data,I-th to report has label KPI information, RnFor n-dimensional vector space, i=1,2 ..., m；y(i)It is correspondingLabel have normal condition and two kinds of malfunction to report moment user service base station state in which.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Y when normal condition(i)=+1, y when malfunction(i)=-1.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Normal condition refers to non-faulting state.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Step 2 is specific as follows:
2.1, to every record that label in T is failureAccording to the KPI information in recordIt is chosen from T
Comprising KPI information withApart from the nearest similar KPI record of k item, failure refers to y(i)=-1 separately takes a new data set T'=T；
2.2, r item is extracted from the KPI information of the similar record of k item with putting back to, r is taken as 1 to the integer value between k, often
When extracting one this record include KPI withLine on randomly select and a little generate new failure classes KPI xl_new,
Subscript l_new, which indicates new, label data；Enable xl_newCorresponding label be -1；
2.3,2.1,2.2 processes are repeated, are recorded in every failure classesIt is upper to generate the new failure classes record of r itemIt is incorporated in data set T', i.e. update T' is For the v articles new KPI of generation
Information；Indicate the v articles new record generated；∪ expression takes its front and back union of sets collection；
2.4, it to every record in T', chooses and it is recorded apart from nearest k' item；
2.5, judge whether the label to occupy the majority in k' item record is consistent with the label of record to be judged, protects if consistent
It stays this to record, this record is rejected if inconsistent；
2.6, final equilibrium data collection is thus obtainedWhat se was puts down
The number of element in weighing apparatus data set；Q-th of element in T'Iny'(q)It is correspondingLabel, y'(q)
Normal condition, y' are indicated when=+ 1(q)=-1 indicates malfunction；Q-th of element is concentrated for equilibrium data
In include KPI information, q=1,2 ..., se.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
It is as follows without label auxiliary data collection acquisition modes in step 3:
One is obtained from existing a large amount of heterogeneous network user's history measurement report to assist containing g sample without label
Data setG is sample total number in A, p-th of unlabeled exemplarsP=1,2 ...,
G,Each dimension is the KPI information in user's measurement report, and subscript u indicates no label data.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Step 3 is specific as follows:
By formula (1) to p-th of unlabeled exemplars in no label auxiliary data collection AIt is operated, p=1,
2 ..., g obtains the potential character representation without label auxiliary data collection；
Auxiliary data is concentrated p-th of unlabeled exemplars by first item in formulaIt is expressed as the form of b weighted linear combination,
Wherein b={ b1,b2,…,bsThe basal orientation duration set that is, s is the total number of base vector, bj∈Rn, j=1,2 ..., s, bj
It is to indicateJ-th of base vector；A={ a(1),a(2),...,a(g)It is activation vector set, p-th of element a in a(p)∈
Rs, RsFor s dimensional vector space, a(p)J-th of componentFor bjIt is correspondingActivation amount；Section 2 is L1 penalty term, β in formula
For degree of rarefication control parameter, value determines that the relative importance of first item and Section 2, s.t. indicate to obey pact thereafter
Beam condition.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Step 4 is specific as follows:
To in equilibrium data collection T'It, will wherein by formula (2)It is expressed as learning to arrive in step 3
Basal orientation duration set b in element combinations mode；
Wherein, h(q)∈RsFor each element pair in basal orientation duration set bActivation vector, h(q)J-th of componentFor
bjIt is correspondingActivation amount；α is punishment term coefficient；What solution obtainedAsNew table at basal orientation duration set b
Show；In T'InIt is operated through this and obtains its new expression on b, thus equilibrium data collectionReplacement is expressed asForm,For the balance number under new representation
According to collection；
It willAs the training data of svm classifier method, solved using method of Lagrange multipliers
Formula (3) obtains disaggregated model (ω, d), and ω, d respectively correspond the normal vector and displacement item of categorised decision plane；
Subscript tran in constraint condition indicates the transposition of vector.
Scheme is advanced optimized as a kind of wireless network fault detection method based on self-teaching of the present invention,
Step 5 is specific as follows:
Using obtained disaggregated model (ω, d), KPI information x is tieed up to the n of network user's real-time reportrt∈RnThrough formula
(4) the label y of the user reported is determinedrt, subscript rt expression is in real time；
The invention adopts the above technical scheme compared with prior art, has following technical effect that
(1) this method is not limited to carry out network state analysis using the limited user data of Home eNodeB, and by certainly
My learning method obtains the promotion in fault detection performance in the form of auxiliary data collection；In addition, it is contemplated that in a network
Fault rate is lower, and the data that base station fault is reacted in the Home eNodeB and user data that can be collected into are less, normal
Data are more, i.e. the unbalanced problem of Home eNodeB data set, also introduce in the present invention in conjunction with SMOTE (Synthetic
Minority Oversampling Technique synthesizes minority class oversampling technique) and ENN (Edited Nearest
Neighbor, screen arest neighbors) method of sampling SMOTEENN unbalanced dataset processing method, obtain equilibrium data collection energy
It is enough effectively prevented from minority class fault sample to be ignored in disaggregated model, causes what fault detection accuracy rate was greatly lowered to ask
Topic；
(2) present invention is embodied in three aspects using the main advantage that the method for self-teaching carries out fault detection: first is that
It only needs a small amount of Home eNodeB to have the sample of label (whether illustrate failure) in method flow, meets real network condition, save
It has saved to data set tagged plenty of time and human cost；Second is that auxiliary data collection may be from arbitrarily with Home eNodeB number
According to the available data with general character, these data are often largely present among network and are not efficiently used；In the present invention
Method fully consider and apply these data so that data user rate is improved in network；Third is that timeliness is strong,
In new network state environment, it is not necessary to which time-consuming collects mass data and establishes model, improves the efficiency of fault detection；
(3) in the present invention fault detection method based on self-teaching that uses can it is few in family's base station user data,
It is more accurate and network failure is effectively detected in the case of generating the easy mistake of data；And the form of self-teaching facilitates migration,
The model that can obtain fault detection quickly under new network environment, improves the fault detection efficiency of previous methods.
Detailed description of the invention
Fig. 1 is two layers of macro-home base station network scene.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer, below in conjunction with the accompanying drawings and the specific embodiments
The present invention will be described in detail.
Technical solution of the present invention mainly includes two stages: modelling phase and fault detection stage, process is such as
Shown in table 1.Modelling phase mainly includes five steps, is acquiring and saving network KPI (Key Performance
Indicator, Key Performance Indicator) it is recorded as after data acquisition system form, the side SMOTEENN is used to the data acquisition system first
Method carries out uneven processing；Later by sparse self-encoding encoder from one group of auxiliary data focusing study " base vector ", these " basal orientations
It include the feature of image watermarking in amount "；It is again the form of each " base vector " linear combination by the KPI data set representations after balance
And it is instructed on the equilibrium data collection under new expression through SVM (Supporting Vector Machine, support vector machines) method herein
Get the disaggregated model normally with fault category.
Self-organizing network fault detection method of the table 1 based on self-teaching
The fault detection stage divides the KPI data record that network generates in real time using a stage well-established model
Class, and then achieve the purpose that fault detection.
Step 1: it in heterogeneous network pickup area, is obtained by way of the measurement report that user periodically reports
All kinds of KPI information.The Reference Signal Received Power RSRP that user measures is collected hereins(Reference Signal
Receiving Power), serving cell Reference Signal Received Quality RSRQs(Reference Signal Receiving
Quality), maximum adjacent area Reference Signal Received Power (RSRPn), maximum adjacent area Reference Signal Received Quality (RSRQn) etc. four classes
Information, wherein subscript s and n respectively indicates serving cell serving and adjacent area neighbor, is used as the KPI of fault detection
Information.
The above-mentioned four classes KPI information of the reporting of user in 5 second time is obtained, and according to the status indication of user service base station
Corresponding K PI information.
It saves KPI and respective markers is data acquisition systemForm, on
Mark m expression acquires m item record.(i=1,2 ..., m) a element i-th in TInSubscript l is the initial of labeled, indicates label data；y(i)It is corresponding
Its label reports moment user's state in which, there is normal (y(i)=+1) and failure (y(i)=-1) two kinds of state.In addition,
From existing a large amount of heterogeneous network reporting of user without taking one to be used as auxiliary data collection in label measurement reportSubscript g is sample total number in A.Wherein a sample of pth (p=1,2 ..., g)Under
Mark u is the initial of unlabeled, indicates no label data.
Step 2: step 1 obtain data set T in label be positive normal element be much more than label be failure element,
Data acquisition system T is unbalanced dataset.So SMOTEENN method is used to pre-process to obtain equilibrium data data set T
Collect T'.Detailed process is as follows:
(1) to every record that label in T is failureIn KPI informationIt is chosen from T with its distance most
The similar KPI information of close k item, k=6 in this example.Enable T'=T.
(2) have extraction 3 put back to from this 6 similar KPI information, it is every when extracting one in itself and xlLine on
Machine, which is chosen, a little generates new failure classes KPI, and enabling its corresponding label is -1.
(3) (1), (2) process are repeated, is recorded in every failure classesOn produce 3 new failure classes recordsIt is incorporated in T', that is, updates
(4) to every record in T', choose and it take k'=3 in nearest k'(this example) item record.
(5) judge whether the label to occupy the majority in k' item record and the record label are consistent.Retain this note if consistent
This record is rejected in record if inconsistent.
(6) final equilibrium data collection is thus obtainedWhat se was puts down
The number of element in weighing apparatus data set.
A element of q in T' (q=1,2 ..., se)InSubscript l is the lead-in of labeled
Mother indicates label data；y'(q)Its corresponding label, there is normal condition y'(q)=+1, malfunction y'(q)=-1 two kind.
Step 3: by formula (1) to p-th yuan of prime element in auxiliary data collection AP=1,2 ..., g is grasped
Make the basal orientation duration set b of our available no label auxiliary data collection.
Step 4: to each element in equilibrium data collection T'(q=1,2 ..., se), by formula (2),
It will whereinBe expressed as the mode of element combinations in the above-mentioned basal orientation duration set b learnt, obtain its it is corresponding activate to
Amount
Each element in T'InCan obtain its new expression on b through this operation, thus put down
Weigh data setIt can replace and be expressed asForm.
Step 5: willAs the training data of svm classifier method, according to bright using glug
Day multiplier method solution formula (3) obtains disaggregated model (ω, d).
Two, the fault detection stage
The disaggregated model (ω, d) obtained using the modelling phase, to the four-dimensional KPI information of network user's real-time report
xrt∈R4, subscript rt expression is real-time, and the label y for the user that the moment reports is determined through formula (4)rt。
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by those familiar with the art, all answers
It is included within the scope of protection of the present invention.
Claims (10)
1. a kind of wireless network fault detection method based on self-teaching, which comprises the following steps:
Step 1: acquisition network key performance indicator KPI is recorded and is saved as data acquisition system；
It combines synthesis minority class oversampling technique SMOTE Step 2: being used to the data acquisition system and screens arest neighbors ENN's
SMOTEENN method carries out uneven processing, the equilibrium data collection after being balanced；
Step 3: by sparse self-encoding encoder from no label auxiliary data focusing study base vector；
Step 4: being the form of each base vector linear combination by the equilibrium data set representations after balance, and herein under new expression
The normally disaggregated model with fault category is obtained through the training of support vector machines method on equilibrium data collection；
Step 5: being classified using well-established disaggregated model to the KPI data record that network generates in real time, and then reach
The purpose of fault detection.
2. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that just
Often refer to non-faulting.
3. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that step
Rapid one is specific as follows:
In heterogeneous network, user periodically reports all kinds of KPI information in the form of measurement report；
From the KPI information for obtaining reporting of user in certain time in the heterogeneous network for needing to carry out fault detection, and according to user
It reports constantly to the normal KPI information corresponding with malfunction label of its serving BS；KPI information and correspondence markings are saved as
Data acquisition systemForm, m be T in element total number, i.e., acquire m altogether
Item record；I-th of element in TInRepresent the n dimension KPI information of certain moment reporting of user, subscript l table
Label data is shown with,I-th to report has label KPI information, RnFor n-dimensional vector space, i=1,2 ..., m；y(i)It is right
It answersLabel have normal condition and two kinds of malfunction to report moment user service base station state in which.
4. a kind of wireless network fault detection method based on self-teaching according to claim 3, which is characterized in that just
Y when normal state(i)=+1, y when malfunction(i)=-1.
5. a kind of wireless network fault detection method based on self-teaching according to claim 3, which is characterized in that just
Normal state refers to non-faulting state.
6. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that step
Rapid two is specific as follows:
2.1, to every record that label in T is failureAccording to the KPI information in recordIt is chosen from T and includes
KPI information withApart from the nearest similar KPI record of k item, failure refers to y(i)=-1 separately takes a new data set T'=T；
2.2, r item is extracted from the KPI information of the similar record of k item with putting back to, r is taken as 1 to the integer value between k, every extraction
At one this record include KPI withLine on randomly select and a little generate new failure classes KPI xl_new, subscript
L_new, which indicates new, label data；Enable xl_newCorresponding label be -1；
2.3,2.1,2.2 processes are repeated, are recorded in every failure classesIt is upper to generate the new failure classes record of r itemIt is incorporated in data set T', i.e. update T' is For the v articles new KPI of generation
Information；Indicate the v articles new record generated；∪ expression takes its front and back union of sets collection；
2.4, it to every record in T', chooses and it is recorded apart from nearest k' item；
2.5, judge whether the label to occupy the majority in k' item record is consistent with the label of record to be judged, retaining if consistent should
Item record rejects this record if inconsistent；
2.6, final equilibrium data collection is thus obtainedThe balance number that se is
According to the number for concentrating element；Q-th of element in T'Iny'(q)It is correspondingLabel, y'(q)=+1
When indicate normal condition, y'(q)=-1 indicates malfunction；Q-th of element is concentrated for equilibrium dataMiddle packet
The KPI information contained, q=1,2 ..., se.
7. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that step
It is as follows without label auxiliary data collection acquisition modes in rapid three:
Obtained from existing a large amount of heterogeneous network user's history measurement report one containing g sample without label auxiliary data
CollectionG is sample total number in A, p-th of unlabeled exemplars
Each dimension is the KPI information in user's measurement report, and subscript u indicates no label data.
8. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that step
Rapid three is specific as follows:
By formula (1) to p-th of unlabeled exemplars in no label auxiliary data collection AIt is operated,It obtains
Take the potential character representation of no label auxiliary data collection；
Auxiliary data is concentrated p-th of unlabeled exemplars by first item in formulaIt is expressed as the form of b weighted linear combination, wherein b
={ b1,b2,…,bsThe basal orientation duration set that is, s is the total number of base vector, bj∈ Rn, j=1,2 ..., s, bjIt is table
ShowJ-th of base vector；A={ a(1),a(2),…,a(g)It is activation vector set, p-th of element a in a(p)∈Rs, RsFor s
Dimensional vector space, a(p)J-th of componentFor bjIt is correspondingActivation amount；Section 2 is L1 penalty term in formula, and β is sparse
Control parameter is spent, value determines that the relative importance of first item and Section 2, s.t. indicate to obey constraint condition thereafter.
9. a kind of wireless network fault detection method based on self-teaching according to claim 1, which is characterized in that step
Rapid four is specific as follows:
To in equilibrium data collection T'It, will wherein by formula (2)It is expressed as the base learnt in step 3
The mode of element combinations in vector set b；
Wherein, h(q)∈RsFor each element pair in basal orientation duration set bActivation vector, h(q)J-th of componentFor bjIt is right
It answersActivation amount；α is punishment term coefficient；What solution obtainedAsNew expression at basal orientation duration set b；
In T'InIt is operated through this and obtains its new expression on b, thus equilibrium data collectionReplacement is expressed asForm,For the balance number under new representation
According to collection；
It willAs the training data of svm classifier method, method of Lagrange multipliers solution formula is utilized
(3) it obtains disaggregated model (ω, d), ω, d respectively correspond the normal vector and displacement item of categorised decision plane；
Subscript tran in constraint condition indicates the transposition of vector.
10. a kind of wireless network fault detection method based on self-teaching according to claim 9, which is characterized in that
Step 5 is specific as follows:
Using obtained disaggregated model (ω, d), KPI information x is tieed up to the n of network user's real-time reportrt∈RnIt is true through formula (4)
The label y of the fixed user reportedrt, subscript rt expression is in real time；
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Cited By (3)
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| CN110972174A (en) * | 2019-12-02 | 2020-04-07 | 东南大学 | Wireless network interruption detection method based on sparse self-encoder |
| CN112906727A (en) * | 2019-12-04 | 2021-06-04 | 中国电信股份有限公司 | Method and system for real-time online detection of virtual machine state |
| WO2022236807A1 (en) * | 2021-05-14 | 2022-11-17 | Qualcomm Incorporated | Model status monitoring, reporting, and fallback in machine learning applications |
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