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Anomaly Prevision in Radio Access Networks UsingFunctional Data Analysis

Yosra Ben Slimen, Sylvain Allio, Julien Jacques

To cite this version:Yosra Ben Slimen, Sylvain Allio, Julien Jacques. Anomaly Prevision in Radio Access Networks UsingFunctional Data Analysis. IEEE GlobeCom 2017, Dec 2017, Singapour, Singapore. �hal-01613475�

Anomaly Prevision in Radio Access NetworksUsing Functional Data Analysis

Yosra BEN SLIMENOrange Labs, Belfort, France

Universite Lyon 2, ERIC, Lyon, FranceEmail: yosra.benslimen@orange.com

Sylvain ALLIOOrange Labs, Belfort, France

Email: sylvain.allio@orange.com

Julien JACQUESUniversite de Lyon, Universite Lyon 2,

ERIC EA3083, Lyon, FranceEmail: julien.jacques@univ-lyon2.fr

Abstract—In order to help the network maintainers with thedaily diagnosis and optimization tasks, a supervised model formobile anomalies prevention is proposed. The objective is todetect future malfunctions of a set of cells, by only observingkey performance indicators that are considered as functionaldata. Thus, by alerting the engineers as well as self-organizingnetworks, mobile operators can be saved from a certain perfor-mance degradation. The model has proven its efficiency with anapplication on real data that aims to detect capacity degradation,accessibility and call drops anomalies for LTE networks.

Index Terms—Anomaly prevention, Multivariate functionaldata prevision, Functional data analysis, LTE network, Trou-bleshooting, Optimization

I. INTRODUCTION

The mobile telecommunication industry has and is stillundergoing interesting changes resulted by the introductionof new technologies and services. The operators and manu-facturers of mobile equipments are undertaking huge effortsto adapt the cellular networks to the new technologies, whileaiming to maintain the level of service of the current networks.Consequently, the operation of the radio network is becom-ing increasingly complex in an environment where the faultmanagement (troubleshooting) and network optimization arestill a manual process. They are accomplished by experts indiagnosis. These latter are personnel dedicated to daily analyzethe main performance indicators and other information, inorder to detect problems in different cells and to solve themafter diagnosing the causes. Ensuring that the malfunctionsin the cells are rapidly solved is important because if a cellis temporarily non-operational, probably neighbouring cellswill also be affected and a degradation in performance of acluster of cells will be resulted. However, the growing size ofcellular networks, together with their increasing complexity,make it very difficult for a human to analyze such a largeamount of information. For this reason, Self-Organizing Net-works (SON, [1]) are being proposed in order to automatenetwork procedures which significantly reduces the costs. TheSON covers three different functions: self-configuring, self-optimization and self-healing.

In order to automate these functions, some solutions canbe found in the literature. Some studies propose to define therelationship between symptoms and causes through supervisedmodels. In [2] and [3], this relationship is expressed bydependency graphs in order to automate the troubleshooting

of mobile networks through Bayesian networks. In [4] and[5], the relationship is defined by compiling expert knowledgeabout symptoms and their causes, into causal graphs. Forinterference optimization purposes, this relationship is definedby a liner regression model such as [6] or a genetic algorithmsuch as [7]. The challenge with these methods is that historicalrecords of previously solved problems are needed. However,labeled cases i.e. those associating identified faults with theirsymptoms are hard to get from live networks. This is dueto three main reasons: (1) the absence of a normalizedtroubleshooting procedure; (2) the difficulty in determiningthe real fault that degraded the performance since it is frequentthat the cause remains unknown even if the problem is solved(by a reset for example); (3) since the evolution of networksis so fast, the knowledge rapidly becomes obsolete and theexpertise thus gathered from experts is generic.

To cope with this problem, solutions based on unsupervisedtechniques have been proposed. Several works have demon-strated the utility of using self-organizing maps. For instance,in [8], the objective is to automate the fault detection phase ofthe troubleshooting. In [9] and [10], the objective is to analyzemultidimensional 3G network performance data in order toaid in the manual fault diagnosis. In [11], the work has beendevoted to cope with uncertainty in the data by using fuzzylogic theory. A co-clustering model is proposed in [12] inorder to provide a simplified representation of the observeddata for an easier analysis. However, experts are still neededto interpret the obtained clusters. The interpretation is not aneasy task especially when the number of clusters are high orwhen data into one cluster are not homogeneous enough tobe easily understandable. Moreover, these algorithms are ableto detect and to correct the problem after the damage is doneand the network performance has been already degraded.

With the presented work, the objective is to anticipatethe anomalies since it is more interesting to predict themalfunctions of mobile networks when they didn’t happenyet. With this anomaly prevision, diagnosis experts could havethe chance to correct the problem before it occurs and thusto save the quality of services of mobile operators. The dataused by the proposed model are Key Performance Indicators(KPIs) which are measurements responsible for evaluatingthe network’s performance. They are defined by mathematicalformulas derived from different counters and computed peri-

odically from the network with different temporal granularities(weekly, daily, hourly or less). For instance, Figure 1 illustratesa sample of p = 30 KPIs for n = 20 daily observations. From

Fig. 1. An example of a functional data set composed of 20 observations(rows) and 30 KPIs (columns)

a statistical perspective, these KPIs are considered as func-tional data [13] which have become a commonly encounteredtype of data. With the advance of modern technology, moreand more data are being recorded continuously during a timeinterval (or intermittently at numerous discrete time points).They become frequent, not only in the telecommunicationfield, but in numerous other domains like medicine, economicsand chemometrics (see [13] for an overview). Functionaldata is the observation (sample path) of a stochastic processX = {X(t), t ∈ T}, where T can be for instance a timeinterval, or any other continuous subset.

The novelty of our work is that it allows to anticipate theanomalies in radio access networks which may be helpfulfor mobile operators to maintain a top quality of services.Besides,it takes into consideration the functional aspect of theKPIs and, up to our knowledge, the proposed model is thefirst prevision model for multivariate functional data.

The paper is organized as follows. Section II introducesthe malfunction prevision model for multivariate functionaldata i.e. for the curves of multiple KPIs per observation. Thebehavior of the model is studied on real data extracted withinan internal tool of Orange France in Section III. Three usecases are considered: problems related to call drops, capacitydegradation and accessibility degradation in LTE networks.

II. ANOMALY PREVISION MODEL

The proposed method offers a supervised technique thataims to prevent anomalies in mobile networks. It is assumedthat a labeled dataset is provided. The dataset is relatedto a specific use case (the target anomaly to detect) with

the corresponding KPIs. It contains data related to normalbehaviour of the network as well as data related to previousfailures. The model is window-based: the size of the window,the step of the window as well as the prediction horizon areparameters to be set. Each observation corresponds to the setof the different KPIs curves, for one cell and for one window.The labeling is assured by observing if an anomaly will occurin the prediction horizon.

Statistically speaking, the data X under study are a sampleof n observations. Each observation Xi is described by a setof p curves and a label. The curves are the functional featuresthat correspond to the daily evolution of p KPIs. The statisticalmodel underlying data, represented by multivariate curves, isa stochastic process with continuous time:

X = {(Xi(t), labeli)}t∈[0,T ],1≤i≤n

with Xi(t) = (Xi1(t), . . . , Xip(t))′ ∈ Rp, p ≥ 1

and labeli=1 if there is a problem in the prediction horizongiven Xi(t), 0 otherwise.

The approach is composed of three steps. Since the collectedKPIs have discrete values, the first step of the proposed modelis to retrieve the functional nature of KPIs. By considering thatthe numbers of the observed KPIs and cells could be huge andthat the observation duration could be long, a dimensionalityreduction seems inevitable. Therefore, a Functional Princi-pal Components Analysis (FPCA, [13]) should be applied.Once the labeled dataset is resumed in terms of principalcomponents, a classification algorithm can be applied. Thisclassification allows to predict future malfunctions given theobserved KPIs for a specific cell and for a fixed window.

A. From discrete data to functional data

The main difficulty when dealing with functional dataconsists in the fact that these latter belong to an infinite-dimensional space, whereas in practice, KPIs are observed atdiscrete time points and with some noise. Thus, in order toreflect the functional nature of the KPIs, a smoothing may beconsidered. Smoothing methods consider that the true curvebelongs to a finite-dimensional space spanned by some basisof functions such as trigonometric functions, B-splines orwavelets (see [13] for a detailed study). Smoothing assumesthat each observed curve xij (1 ≤ i ≤ n, 1 ≤ j ≤ p)can be expressed as a linear combination of basis functions{ϕjℓ}ℓ=1,...,Mj :

xij(t) =

Mj∑ℓ=1

aijℓϕjℓ(t), t ∈ [0, T ], (1)

where {aijℓ}ℓ=1,...,Mj are the basis expansion coefficients.These coefficients can be estimated by least square smoothingfor instance [13]. In this work, due to the nature of the KPIsunder study, the same B-spline basis {ϕℓ}ℓ=1,...,M is used forall the functional features. The choice of the basis as well asthe number of basis functions strongly depends to the natureof data. Hence, they can be set empirically.

B. Principal components analysis for functional data

From the set of functional data, it is interesting to havean optimal representation of curves into a functional space ofreduced dimension. The main tool to answer this request isthe principal components analysis for functional data (FPCA,[13]). It consists in computing the principal components Ch

and principal factors fh of the Karhunen-Loeve expansion:

X(t) = µ(t) +∑h≥1

Chfh(t), t ∈ [0, T ]. (2)

When curves are assumed to be decomposed into a finite basisof function (1), FPCA consists in a usual PCA of the basisexpansion coefficients using a metric. This latter is definedby the inner products between the basis functions. In theory,the number of principal components are infinite. However,in practice, the curves are observed at discrete time pointsand they are approximated on a finite basis of functions. Forthis reason, the maximum number of components one cancompute is equal to the number M of basis functions usedfor approximation.

In this work, in order to project all the data onto thesame FPCA space, functional principal components analysisis applied to the whole data set of curves x. Moreover, inorder to reduce the dimensionality of the problem, only thefirst m ≤ M principal components are considered. This latteris fixed empirically so that the principal components expressa given part of the total variance. As a result, each curve isdefined by a vector of its principal components of size m.

C. Classification and anomaly prevention

Once the labeled dataset is transformed in terms of principalcomponents, a training data with numeric values and binaryclasses is ready for a training phase. The training is assured byany suitable classification algorithm that deals with numericvariables and binary classes. Examples of the algorithms thatcan be used are neural networks, support vector machines anddecision trees [14]. The classification allows to create a trainedmodel that is able to discern a pattern describing the normalbehaviour of the network and a pattern describing suspiciousbehaviours.

This trained model will be used in order to predict if theirwill be a malfunction in the future for new observations.Given the smoothing basis {ϕjℓ}ℓ=1,...,M , the FPCA basisand the learned classification model, prediction is as follows:(1) for each new observation, a smoothing is first applied forevery KPI using {ϕjℓ}ℓ=1,...,M ; (2) the observation in terms offunctional features is then projected on the same FPCA basisused in the training phase; (3) given the obtained principalcomponents, the learned model will then predict if there willbe any anomaly in the prediction horizon.

III. EXPERIMENTAL STUDY

The aim of this section is to apply the anomaly previsionmodel on Long Term Evolution (LTE) cells to evaluate itsefficiency through real KPI measurements. In order to presentwhere our application is located in terms of LTE system,

Figure 2 illustrates the architecture of the Evolved PacketSystem (EPS) bearer. The EPS is composed of the radioaccess part E-UTRAN (Evolved Universal Terrestrial RadioAccess Network), the core part EPC (Evolved Packet Core)and the Packet Data Network (PDN). Our experiment focuseson anomaly prevention concerning the radio access part ofLTE networks. In this part, KPI measurements are performedthrough the Enhanced Radio Access Bearer (E-RAB) service.The role of the E-RAB is to transport packets of EPS bearerbetween the user equipment (UE) and the EPC and it isgenerated from a combination of radio bearer and S1 bearer.LTE KPIs are mainly classified into five classes [15], [16]:

Fig. 2. EPS bearer architecture

• Accessibility: it regroups measurements that allow opera-tors to gather information related to the mobile services’accessibility for the subscriber.

• Retainability: it measures how many times a service wasinterrupted or dropped during use. Thus, it prevents thesubscriber from using the service and the operator cannotcharge for it.

• Mobility: it measures how many times a service wasinterrupted or dropped during a subscriber’s handover ormobility from one cell to another.

• Integrity: it measures the high or low quality of a servicewhile the subscriber is using it (latency and throughput).

• Availability: it measures a service’s availability for thesubscriber.

Three of the five classes of LTE KPIs are addressed in thispaper. The first use case aims to detect retainability anomaliesby analyzing call drop problems. The second use case aimsto detect accessibility anomalies by analyzing radio and S1bearers setup success rate. The third use case aims to detectintegrity anomalies by analyzing capacity degradation throughdelay and throughput. Two families of experimentation aredistinguished. The first family, denoted by Family1, considersthat only one KPI i.e. one uni-variate functional feature iscapable of predicting the anomaly. The second family, denotedby Family2, considers that determining an anomaly needs theanalysis of a set of KPIs i.e. multivariate functional features. Inthe following, a description of the data and the experimentationover Family1 and Family2 are presented.

A. Network description

The analysis of the proposed model has been conductedin a real LTE network in an urban area with a population ofnearly one million. It corresponds to Lyon, a big city in France.Figure 3 illustrates the geographical area used for the dataextraction. For the Family1, the data are generated within

Fig. 3. The geographical area used for the data extraction

191 cells with different parameters and they are situated atdifferent locations and thus, they reflect different environmentconditions. Table I summarizes the main parameters of theLTE network. As for Family2, 704 cells are used. The reasonbehind the choice of a bigger study zone is that the labels ofnormal and problematic observations are unbalanced whichmay mislead the model in its training phase. The dataset isfirst balanced by ignoring some observations. Therefore, the191 cells used for Family1 provides insufficient data forFamily2 and a bigger number of cells is needed. The period

TABLE IPARAMETERS OF THE REAL LTE NETWORK

Network layout Urban environmentTechnology Alcatel LTE FDDSystem frequency band 800 MHz 1800 MHz 2600 MHzMax transmit power 46 dBm 44.8 dBm 46 dBmSystem bandwidth 10 MHz 20 MHzNumber of cells 82 (43%) 9 (5%) 100 (52%)for Family1Number of cells 296 (42%) 38 (5%) 370 (53%)for Family2Days under observation 14

of observation covers two weeks from December 5, 2016to December 18, 2016. This period contains some ordinaryworkdays, weekends, the beginning of holidays and a specialevent of festival of lights that has been held in Lyon fromDecember 8 to December 10. Therefore, normal behavior ofthe network can be found in this period as well as problematicobservations. These latter correspond to anomalies related tocapacity degradation, call drops and accessibility problems dueto the huge number of users in the cells.

For all the following experiments, the size of the windowis fixed to one day. The step of the window is equal tothe prediction horizon so that no replications nor holes arepossible. The KPIs are extracted with a granularity of 15minutes (therefore, each daily KPI contains 96 values). 80%of the labeled dataset is used for the training phase and theremaining 20% is used for the test phase.

B. Experiments of the anomaly prevision model in case ofuni-variate functional feature

The objective of this experiment is to test the efficiencyof the model at predicting future problems by using only oneKPI. The test is held over ten KPIs. Three among them belongto Retainability class and more specifically they correspondto dropped calls indicators. Two KPIs belong to Accessibilityclass and they correspond to radio and S1 bearers setup successrate. The last five KPIs belong to Integrity class and they areindicators of throughput, delay and traffic volume (see TableII).

The data contains missing values. They are easily treated byapplying a smoothing for each curve, since it allows to gain thefunctional behavior of the daily KPIs which is an advantagewhen dealing with functional data. The used smoothing basisis B-splines where the number of basis functions is empiricallyset to M = 20. A FPCA for univariate data is then applied.The number of principal components m is chosen so thatat least 80% of the information is covered. The horizonprediction varies from 3 hours to one day. The classificationis performed with a neural network having 2 layers and 100iterations. The evaluation is held in terms of the followingperformance indicators [17]:

• Confusion matrix (M): it is a matrix that contains in-formation about actual and predicted classification. Twogroups are considered: ”group0” for normal behaviourin the prediction horizon and ”group1” for a futuremalfunction in the network. A confusion matrix M issuch that Mk,z is equal to the number of observationsknown to be in group k but predicted to be in group z.

• F-measure (F): it is the weighted harmonic mean of theprecision and the recall of the test. Recall calculates theproportion of malfunctions predicted by the model amongthe real malfunctions that should have been detected.Precision calculates to which point we can trust the modelif a malfunction is predicted. The F-measure is high whenboth precision and recall are high.

• Correct-classification rate (C): it calculates the number ofcorrect predictions among all the predicted observations.

The results are presented in Table II. We notice that for allthe tests, the correct classification rate is promosing, varyingfrom 70% to 97%. Moreover, the false alarms generated bythe model are moderate as shown by the F-measure that variesfrom 66% to 98%. With the confusion matrices, we can verifythat the model does not have a tendency to predict one classover another. Consequently, the model has proven its efficiencyin anomaly prevention related to retainability, accessibility and

TABLE IIEXPERIMENTAL RESULTS FOR PERFORMANCE DEGRADATION PREVENTION USING UNI-VARIATE FUNCTIONAL FEATURE

Problem KPI 24 hours 12 hours 6 hours 3 hoursM F C M F C M F C M F C

Ret

aina

bilit

y DROP CALL RATE0 1

0 44 31 13 18

0.83 0.840 1

0 186 501 46 169

0.78 0.790 1

0 705 2781 257 899

0.77 0.750 1

0 1161 3501 311 1094

0.77 0.77

UECTXDROP RATE0 1

0 57 101 16 52

0.8 0.810 1

0 227 431 36 221

0.85 0.850 1

0 708 1241 138 641

0.83 0.840 1

0 1966 3741 324 1613

0.82 0.84

ERAB DROP RLF RATE0 1

0 84 351 36 101

0.74 0.720 1

0 373 1431 116 438

0.78 0.760 1

0 576 1951 180 545

0.74 0.750 1

0 693 2151 141 677

0.79 0.79

Acc

essi

bilit

y S1 CNX ESTAB SR0 1

0 69 391 1 38

0.66 0.730 1

0 104 561 2 66

0.7 0.750 1

0 178 221 7 137

0.90 0.920 1

0 286 321 5 264

0.93 0.94

LTE CSSR0 1

0 56 201 6 41

0.76 0.790 1

0 86 361 1 63

0.77 0.80 1

0 167 181 0 123

0.93 0.940 1

0 287 211 10 248

0.94 0.95

TAUXCONGDL CAUSEPDCCH0 1

0 96 161 0 97

0.92 0.920 1

0 179 111 4 192

0.96 0.960 1

0 354 151 8 343

0.97 0.970 1

0 626 181 15 648

0.98 0.97

AVGUSERMAC UL THROUGHPUT0 1

0 34 61 13 37

0.8 0.790 1

0 177 481 12 141

0.82 0.840 1

0 535 1321 71 490

0.83 0.830 1

0 1318 2391 222 1396

0.86 0.85

Inte

grity

UE RRC CONNECTED AVG PERCELL0 1

0 142 301 15 131

0.85 0.860 1

0 363 551 31 327

0.88 0.890 1

0 994 951 105 945

0.9 0.910 1

0 1701 1511 138 1672

0.92 0.92

LTE RADIO DL .DELAY0 1

0 45 101 2 24

0.7 0.770 1

0 195 831 9 105

0.88 0.890 1

0 728 3091 62 489

0.72 0.770 1

0 2147 5851 203 1298

0.77 0.81

LTE DL TRAFFIC VOLUME0 1

0 63 81 10 38

0.81 0.850 1

0 196 221 30 173

0.87 0.880 1

0 662 1441 102 628

0.84 0.840 1

0 1901 4601 269 1603

0.81 0.83

integrity degradations when only one uni-variate functionalfeature is used for the prediction.

C. Experiments of the anomaly prevision model in case ofmultivariate functional features

In this experiment, the objective is to test the efficiency ofthe model at predicting future problems related to the three usecases by using a set of KPIs. The data is extracted from 704cells. For each curve, a smoothing with B-spline basis is usedwith M = 20. A FPCA for multivariate data is then appliedfor the dataset of smoothed curves. The horizon predictionvaries from 3 hours to one week and the evaluation is held bythe same performance metrics that are used for the uni-variatecase. Since the neural network, in this experiment, takes along time before it converges, a decision tree is used instead.The resulted confusion matrices are presented in Table III.Figure 4 presents the correct classification rates and the F-measure results of the prevention model applied to the threeuse cases when multivariate KPIs are used for the prediction.We notice that the algorithm has the potential to prevent fromfuture problems since the correct classification rate is greaterthan 70% even for a long prediction horizon, equal to oneweek. The model does not have a tendency to predict a specificclass nor to generate false alarms as proven by the confusionmatrices and by the F-measures. The performance of the modeldecreases when the prediction horizon increases which isexpected. The data does not allow to have a prediction horizonbigger than one week since the extraction covers two weeks.The proposed model supposes that the network configuration is

fixed. However, network behaviour is complex and it exhibitsbehavioural dynamics on multiple seasonal timescales (specialevents, fault conditions, and the non-stationarity over time astraffic volumes generally grows...), so for the proposed modelto maintain accuracy, different models related to differentgeographical areas should be trained separately. The trainingshould be programmed periodically with updated training data.

IV. CONCLUSION

The paper presents an anomaly prevision model that aimsto detect future anomalies in mobile networks by observ-ing key performance indicators. The KPIs are consideredas functional data. Through a smoothing step, a functionalprincipal components analysis and a classification phase, themodel is able to prevent from a performance degradation. Itis useful for engineers as well as SONs for troubleshootingand optimization purposes. The model is window-based andit has proven its efficiency through a real data applicationon LTE networks. The objective of this application is todetect future malfunctions related to degradation in cellscapacity, accessibility and call drops. One advantage of theproposed approach is that it allows to take into considerationthe temporal dynamic of the KPIs evolutions which explainsthe good prediction results obtained in the experimentation.Another advantage is that it is capable of dealing with missingdata. Moreover, up to our knowledge, it is the first previsionmodel for multivariate functional data since no reference canbe found related to this topic.

TABLE IIICONFUSION MATRICES FOR PERFORMANCE DEGRADATION PREVENTION USING MULTIVARIATE FUNCTIONAL FEATURES

Problem KPIs 3 hours 6 hours 12 hours 24 hours 48 hours 72 hours 1 week

Retainability UECTXDROP RATEERAB DROP RLF RATE

0 10 1248 2751 279 1291

0 10 1084 2011 228 1077

0 10 873 1391 183 886

0 10 590 1021 154 624

0 10 420 1161 72 378

0 10 209 461 76 326

0 10 61 201 15 69

Accessibility S1 CNX ESTAB SRRRC cnx estab SR

0 10 1085 761 60 1046

0 10 639 1061 67 587

0 10 466 581 56 442

0 10 291 581 69 300

0 10 204 481 31 211

0 10 125 441 48 163

0 10 94 511 13 73

IntegrityTAUXCONGDL CAUSEPDCCHUE RRC CONNECTED AVG PERCELLAVGUSERMAC UL THROUGHPUT

0 10 367 181 60 387

0 10 291 281 36 316

0 10 255 351 32 230

0 10 189 321 14 163

0 10 130 231 9 98

0 10 75 151 23 79

0 10 50 151 5 53

Fig. 4. Experimental results in terms of F-measure and correct classification rate (CCR) for performance degradation prevention regarding Retainability (left),Accessibility (middle) and Integrity (right) anomalies using multivariate functional features

As future work, an application of the model on other usecases could be interesting. Moreover, the data extracted withintwo weeks was not enough to determine the maximum sizeof the prediction horizon that the model could reach. For thisreason, an application on the different use cases with large ex-tractions should be considered. Another interesting perspectiveis to consider mixed data. Actually, besides the KPIs, engineersdeal with other source of information such as alarms and thenetwork parameters. Hence, an anomaly prevision model formixed data will be proposed which gives more flexibility tothe model. Finally, in order to automatically generate up-to-date models, a dynamic version will be considered in whichadding new information will not lead to re-learn the modelbut to sum-up old trainings and to generate a new model inan incremental way.

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