research article cell outage detection and compensation in two-tier heterogeneous...

Research ArticleCell Outage Detection and Compensation in Two-TierHeterogeneous Networks

Wenqian Xue Hengzhi Zhang Yong Li Dong Liang and Mugen Peng

Wireless Signal Processing and Network Lab Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts amp Telecommunications Beijing 100876 China

Correspondence should be addressed to Mugen Peng pmgbupteducn

Received 14 February 2014 Accepted 18 April 2014 Published 13 May 2014

Academic Editor Xiang Zhang

Copyright copy 2014 Wenqian Xue et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Heterogeneous networks (HetNets) can increase network capacity through complementing themacro-base-stationwith low-powernodes in response to the ongoing exponential growth in data traffic demandWhile unprecedented challenges exist in the planningoptimization andmaintenance in HetNets especially activities such as cell outage detection andmitigation are labor-intensive andcostly One potential solution to address these issues is to introduce the extensively attracted self-organizing network (SON) Thispaper is mainly devoted to cell outage detection and compensation methods in two-tier HetNets where macrocell and picocells arecoexisted A 119870-nearest neighbor (KNN) classification algorithm is employed to detect the cell outage automatically Consider thebreakdownpicocell can reload its degraded service to the overlappedmacrocell via vertical handover only the breakdownmacrocellexecutes the performance compensation Power adjustment on each resource block is carried out via Lagrange optimizing algorithmto compensate the breakdown cell Through intensive numerical experiments with the help of our proposal the outage cells canbe successfully detected and performance gain for the outage macrocell can reach 914 with 120572 = 13

1 Introduction

The proliferation in traffic consumption stimulated by anew generation of wireless devices urges network operatorsto achieve dramatic capacity enhancement To meet thisoverwhelming requirement cost-effectively a paradigmshift in cellular network deployment is occurring towardsheterogeneity creating what is referred to as heterogeneousnetworks (HetNets) through increasing node density withlow-power nodes (LPNs) such as pico- femto- and relaynodes [1] These newly introduced LPNs usually providesmall coverage with transmit power ranging from 250mW to2W making a distinct difference from high-power macron-ode which has a transmit power of about 40W In additionthey are expected to be densely deployed in a targetedmanner[2] Femtocells are intended for indoor use with a restrictedassociation and picocells are popular for hotspot coverageThe relay nodes can be deployed where wired backhaul is notavailable Such dynamic network topologies and the intenseinteractions among heterogeneous LPNs and the attachingmacrocell would impose great complexity for network

operation and maintenance Traditional troubleshooting onfault management is a manual process The growing networksizes and increased complexity make it unrealistic for ahuman to analyze such a large amount of informationHenceto minimize human involvement and maximize maintainingefficiency intelligent network aiming at automating most ofnetwork procedures could be potentially agreeable [3]

Self-organizing network (SON) is one of themost promis-ing paradigm proposed for future networks by the nextgeneration mobile networks (NGMN) Alliance and the thirdgeneration partnership project (3GPP) in 2008 [3] Theprinciple objective for SON is to achieve operational andcapital expenditure reduction substantially by means of self-configuring self-optimizing and self-healing functionalities[4] which can minimize human involvement and improveservice quality with lower investment Such advantages inSON make it attractive and beneficial to enable operators tostreamline operational activities and ameliorate the overallmaintaining efficiency into HetNets Recently considerableresearch works on self-configuration and self-optimizationhave been conducted A set of use cases including automatic

Hindawi Publishing CorporationInternational Journal of Antennas and PropagationVolume 2014 Article ID 624858 9 pageshttpdxdoiorg1011552014624858

2 International Journal of Antennas and Propagation

physical cell identifier assignment mobility managementand energy saving have been addressed in HetNets [4]Whilethe study of self-healing technique in the context of HetNetsstill remains an open issue

Self-healing technique is supposed to handle outage cellswhere a cell outage refers to the total loss of radio servicesin its coverage area that resulted from hardwaresoftwarefailures or other functional faults [5] It can be dividedinto two parts cell outage detection (COD) and cell outagecompensation (COC) The former attempts to detect andlocate potential faults and the latter is responsible foralleviating performance degradation Instead of employinghighly experienced staff the automated detection entity nowdepends on data mining or mathematical statistics [6 7]which can transform raw large database into meaningfulinformation and then identify possible faults As for perfor-mance compensation optimization theory is exploited on thebasis of radio parameters like the antenna tilt and the celltransmit power in surrounding cells [8] It is worth nothingthat the capacitycoverage offered to the outage area shouldbe retained as large as possible while that in neighboring cellscannot be affected significantly at the same time [9]

To solve the self-healing problemmost if not all conven-tional research works on cell outage detection and cell out-age compensation are conducted separately Most proposedmethods are designed for homogeneous networks where onlymacrocells are considered Regardless of the communicationoverhead incurred by dense deployments of LPNs somecentralized statistics analysis like [6] may be applicable forHetNets But small coverage of LPNs leads to sparse userstatistics available sometime and the approach in [6] fallsshort since it considers only event-triggered measurementsIn the worst case the gathered measurements may be causedby momentary severe shadow fading Therefore self-healingschemes in homogeneous networks usually cannot be applieddirectly into HetNets In such context this paper is devotedto focusing on the design of self-healing mechanism forHetNets enduring the aforementioned challenges There isstill a few literature resources devoted to this realm Todevelop autonomous femtocell outage detection cooperativeoutage detection architecture in femtomacro cellular net-works is brought forward in [10] which designs a triggerdecision through investigating intracell correlations of RSRP(reference signal received power) statistics in space domainand detects outage femtocells through extracting correlationsof intercell RSRP statistics in both space and time domainsAs for mitigation measures [11] proposes a cooperativeresource allocation algorithm based on subchannel andpower resources via cooperation among femtocells

In this paper we consider a systematic self-healingscheme in a two-tier macropico network which consists ofdetection and compensation stages In the detection stageinspired by [12] 119870-nearest neighbor (KNN) is adoptedto detect anomaly macrocell and picocells when collectedperiodicalmeasurements start to become similar to the previ-ously known radio link failure (RLF) samples119891-measurmentis then employed for further assessment with respect todetection accuracy In the compensation stage only theoutage macrocell is considered since users in the outage

picocell can handover to an available macrocell Firstlythe unoccupied spectrum resources once belonging to theoutage macrocell are reallocated to users by compensationpicocells that lie in the outage area Power adjustment on eachresource block (RB) is carried out via Lagrange optimizingalgorithm Meanwhile compensation gains in the sense ofaverage throughput per user and per cell are investigated

The remainder of this paper is organized as followsSection 2 presents the system model for achievable self-healing mechanism Section 3 includes a detailed descriptionfor the outage detection and the corresponding performancecompensation algorithms Further the simulation results todemonstrate the efficiency of our proposed methods areprovided in Section 4 Finally the conclusion of this paper isgiven in Section 5

2 System Model

The self-healing is a functionality aiming to minimize thenetwork performance deterioration when failures occur ina network element (NE) through immediately autonomouscell outage detection and compensation actions Firstly per-formance parameters from NEs in both access and core net-works are collectedThe outage detection entity then exploitsthem to perform problem identification and localizationduring current monitoring period When any type of faultdescribed above is detected the outage compensation entityis timely activated to execute feasible recovery procedures soas to restore the degraded service

To evaluate the self-healing algorithms into a two-tierLTE-Advanced system picocells are deployed to eliminatecoverage holes in hot spots The simulation environment iscomprised of 19 regular hexagonal macrocells and 76 overlaidpicocells Each macrocell is filled with four picocells Theouter tier 12 macrocells and 48 picocells are merely usedto generate interference without any user equipments (UEs)deployed The 7 center macrocells and 28 picocells are themain cells of interest with UEs randomly distributed workingtogether for healing purpose The system scenario is shownin Figure 1 where macrocell0 and picocell23 are configuredas faulty cells Upon the detection of configured failures thenormal four picocells deployed at the outage area are to betreated as compensation cells to alleviate the degradationrespecting coverage and quality

21 Macropicocell Outage Detection Framework Cell outagedetection process in the two-tier macropico network hasrecently been studied in [12] As shown in Figure 1 it consistsof two main phases model-learning phase and problem-detecting phase To construct a robust learning model ref-erence simulation is implemented during which RLF eventis assumed to be triggered slightly faster than usual so thatnot only periodicalmeasurements but also someRLF samplescan be gatheredThemodel is created by labeling the trainingdata as periodical and RLF-like categories In the anomalydetection phase hardware failure is simulated by transmitpower outage In Figure 1 macrocell0 and picocell23 turninto faulty cells by lowering the power at some time during

International Journal of Antennas and Propagation 3

OAM

23

19

22 0 20

21

Performance evaluation

Self-healingprocess

Model-learning

COD

Resources-reallocating

Power-optimizing

COC

Operatorpolicy

Collected measurements

Problem-detecting

Figure 1 System model for self-healing mechanism in heterogeneous macropico networks

normal operation Consequently periodical measurementsand more RLF-triggered data are collected as testing datafor further deep analysis The data part is made up of fournumerical features serving and maximum neighbor RSRPserving and maximum neighbor signal to interference plusnoise ratio (SINR) [12] Moreover additional informationincluding position and serving cell global identification(CGI) is obtained to demonstrate the detection performance

22 Macropicocell Outage Compensation Framework Asmentioned above compensation for outage picocell usersis not considered in this paper When an outage macrocellis detected picocells overlaid in the macrocell rather thanin surrounding macrocells will be triggered to act as com-pensation cells They are mainly responsible for resourcesreallocation and power optimization activities as illustratedin Figure 1 Due to the occurrence of numerous RLF eventsthe affected macrousers that have lost connections to theprevious serving cell try to launch connection reestablish-ments with picocell19ndashpicocell22 The chosen picocells thenallocate spectrum resources to newly added users using RBsonce a part of the faulty macrocell resources The powerfor each RB is initialized by average allocation In orderto maximize the throughput for users including previouslyserved picousers and newly added users each compensationpicocell sequentially executes power optimization based onthe specified compensation timeslot Here compensationtimeslot is defined as a timeslot during which no data orcontrol information of neighboringmacrocells is transmittedTherefore partial interference cancelation and power opti-mization are achievable To confirm the feasibility of thisscheme compensation gains measured on average through-put per user and per cell are displayed

Further some necessary assumptions aremade to cater tosimulation simplicity Firstly spectrum resources applied formacrocells and picocells are orthogonal so that users amongdifferent type of cells will get no interference Secondly thecell outage is emerged by reducing the transmit power of a cell

to a certain extent so as to cause performance degradationThirdly during the interruption UEs located in outage areasare also able to receive weak signals which is critical foranomaly detection [12]

3 Algorithms Description

In this section a detailed description about the algorithmin performance compensation is presented The algorithmfor outage detection is given briefly more details can begot from [12] In the detection stage we first construct atraining database and then process the testing database as aclassification problem Finally evaluation criterion regardingclassification accuracy is provided Since the problem tomaximize the throughput for users in compensation picocellsis a constrained nonconvex problem finding an optimalsolution isNPhardThus the compensation stage is proposedto involve two steps namely RBs reallocation and power perRB optimization

31 Cell Outage Detection Stage For outage detectionthe algorithm to analyze collected measurements is oftenachieved via knowledge-based approaches Clustering in [13]has been applied while the number of clusters is usuallyhard to decide any improved clustering algorithms maytake a relatively longer time So we consider the applica-tion of classification Classification is an area of machinelearning that takes raw data and classifies it as belongingto a particular class [14] 119870-nearest neighbor (KNN) is asupervised learning algorithm which involves two stepstraining model construction and testing data labeling Herethe training data collected in reference simulation are labeledas periodical and RLF-like in which RLF-like samples areregarded as anomalies Once the configured outages happenthe testing data are gathered and classified by examining thebest possible match against the training data

Assume the training data set is denoted as 119879 =

1199051 1199052 119905

119894 119905

119860 where 119905

119894 a four-dimensional data vector


expressed by RSRP119904RSRP

119899 SINR

119904 SINR

119899 means the 119894th

collected training data 119860 denotes the total number of thetraining data The data set is labeled as 119871 = 119897

1 1198972 where

1198971represents the periodical class and 119897

2is the RLF-like class

When there occur cell outages in the simulation scenariothe testing data will be collected and is defined as 119883 =

1199091 1199092 119909

119895 119909

119861 where 119909

119895is the 119895th collected testing

data similar to 119905119894 119895 = 1 2 119861 Before classification the

training data and testing data should be normalized first toeliminate errors caused by nonuniform measurement unitsTo determine the label for each unknown testing data 119909

119895

according to KNN a set 119863 of nearest neighbors from thetraining database is pivotal for accurate labeling Onemethodis achieved by calculating the Enclidean distance from thetesting data 119909

119895to all points in training database For testing

data 119909119895and training data 119905

119894119863(119909119895 119905119894) = radicsum

4

119889=1(119909119889

119895minus 119905119889

119894)2 For

the first 119870-element set 1198631198951 119863119895

2 119863

119895

119870 its corresponding

label set is 1198971

1015840 1198972

1015840 119897119870

1015840 Then the label 119897

119895for testing data

119909119895can be decided as follows

119897119895= argmax119897120581

(

sum119870

119896=11 119897119896

1015840= 119897120581

sum2

120581=1sum119870

119896=11 119897119896

1015840= 119897120581

) (1)

where the indicator function 1sdot is equal to 0 if sdot is false and1 otherwise119870 is an adjustable integer parameter Different119870may lead to different classification results

In order to validate the KNN performance on cell outagedetection efficiency 119891-measurement is learned on the basisof each serving cell where performance statistics collectedin a cell including macrocell and picocell is taken as acluster Based on the defined precision and recall [12] 119891-measurement is determined in the following form

119891measurement (119888 1198972) =2 times precision (119888 119897

2) times recall (119888 119897

2)

precision (119888 1198972) + recall (119888 119897

2) (2)

For cluster 119888 and RLF-like label 1198972 precision(119888 119897

2) =

1198991198881198972

119899119888and recall(119888 119897

2) = 119899

1198881198972

1198991198972

where 119899119888denotes the total

number of data in cluster 119888 1198991198972

denotes the number of RLF-like data in all clusters and 119899

1198881198972

is the number of RLF-like datain cluster 119888 Then 119891-measurement is further expressed by

119891measurement (119888 1198972) =21198991198881198972

119899119888+ 1198991198972

(3)

If 119891measuremnt(119888 1198972) in the problematic simulation is muchlarger than that in reference simulation the cell 119888 is very likelyat an outage status since it does not fit well with the normalobservation

32 Performance Compensation Stage Upon the detectionof the anomaly macrocell and picocell users in the picocellwill smoothly handover to the overlapped macrocell whileusers in the macrocell will make an attempt to re-establisha connection with compensation picocells because theyexperience more severe peformance degradations It is basedon the principle that each user chooses the picocell that

1st timeslot 2nd timeslot 3rd timeslot

Traditional timeslot

Timeslot with compensation timeslot introduced

Figure 2 Illustration of differences between traditional timeslot andtimeslot with compensation timeslot introduced

can offer the strongest signal power as its new serving cellTo provide satisfied serving quality compensation picocellsallocate unoccupied RBs through a priority system to thenewly added users Besides these RBs are orthogonal to onesin picocells as assumed in Section 2The priority sequence isdetermined by a Manhattan distance from an outage user toits new serving picocell Assume the distance set is denotedby D

1D2 D

119894 D

119873 where 119894 refers to an outage user

and119873means the total number of the users The user 119894 whoseD119894is larger will get a better priority to choose a vacant RB So

the distance values should be arranged in descending orderDescend

1Descend

2 Descend

119873 for picocells to cope

with RBs allocation Once the RB is selected it will be takenout of the remaining available RBs in case of interference

The next step is to perform power adjustment Eachcompensation picocell makes it separately For a picocell thepower per RB is initialized by average allocation Lagrangeoptimizing algorithm is then exploited to maximize thethroughput for users in compensation picocells so as toprovide the best possible compensation gains for outageusers while at the same time not much affecting previouslyserved picousers However the outage users are interferedmore seriously by users in neighboring macrocells thanones in picocells because the employed spectrum resourceswere once owned by the outage macrocell According to thealgorithm severely interfered users tend to be allocated lowtransmit power which does not conform with our expecta-tionThe only solution to cope with such problem is to reduceintercell interference generated by neighboring macrocellsWith the introduction of compensation timeslot duringwhich neighboring macrocells are in a sleep mode outageusers get smaller interference and thus receive more powerA parameter 120572 is defined as a proportion of compensationtimeslot to the total transmission time Take 120572 = 13for example as shown in Figure 2 Assume the informationtransmission requires 3 timeslots the first one is regarded asa compensation timeslot In this context the outage users canget interference reduced by about 13

The intercell interference among macrocells can bedynamically alleviated through adjustment of the parameter120572 Hence the power allocated to outage users would beimproved based on the optimization algorithm However


120572 should be moderate instead of the larger the better Atradeoff in the sense of serving quality among outage userspreviously served picocell users and users in surroundingmacrocells should be taken into account

Assume the set of compensation picocells is denoted byCP According to our simulation scenario there are fourpicocells to be exploited for compensation For picocell CP

119898

the throughput of a user 119894 with RB 119899 occupied in its coveredarea can be written as

T[119899]

119898119894(119901[119899]

119898119894) = 119882 log

2(1 + SINR[119899]

119898119894(119901[119899]

119898119894)) (4)

where the SINR for previously served picousers is

SINR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 + sum119895isinneighbor of picocell 119898 119901

[119899]

119895119894119866119895119894

(5)

For outage users it is

SINR[119899]119898119894(119901[119899]

119898119894)

=

119901[119899]

119898119894119866119898119894

1205902 + sum119895isinneighbor of the outage macrocell 119901

[119899]

119895119894119866119895119894

(6)

119901[119899]

119898119894is the allocated power for user 119894 who uses RB 119899 and is

served by picocell CP119898 119866119898119894

represents the path loss fromuser 119894 to its new serving cell CP

119898 During the compensation

timeslot the SINR for outage users turns into SNRThat is tosay userswill no longer suffer from severe interference causedby surrounding macrocells The SNR is expressed as

SNR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 (7)

Given the above equations the objective to maximizethe throughput for users in compensation picocells can beachieved by solving the following optimizing problem

maximize 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+(1minus120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1+SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

subject to 119875119898max = sum

119894isin119880119872119898119899isinRB

119872

119901[119899]

119898119894+ sum

119894isin119880119875119898119899isinRB

119875

119901[119899]

119898119894

(8)

where RB119872

and RB119875denote a set of RBs offered to outage

users and previously served picousers respectively 119880119872119898

indicates the set of outage users now served by picocell CP119898

and 119880119875119898

means the set of original picousers in CP119898

To find an optimal solution Lagrange optimizationscheme is adopted It is achieved by the well known Lagrangefunction [15]

Λ (119901 120582) = 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+ (1 minus 120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ 120582(119875119898max minus

N

sum

119894=1

119901[119899]

119898119894)

(9)

where 120582 is a nonnegative Lagrange multiplier and N isthe number of users in compensation picocells Also theequation can be simplified by taking the derivative withrespect to 119901

120572119866119898119894I[119899]119898119894+ 1198661198981198941205902+ 119901[119899]

119898119894(119866119898119894)2

(1205902 + 119901[119899]

119898119894119866119898119894) (I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

119866119898119894

(I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

(10)

The unknown 120582 can be obtained by a bisection algorithmAs a supplement the interference of previously served

picousers in CP119898mainly comes from other compensation

picocells which leads to the situation that the interferencechanges with RBrsquos power alteration So it should be updatedafter each iteration

4 Simulation Results

For the simulation a system simulation tool is employed incompliancewith 3GPP specification [16] Based on the systemmodel depicted in Section 2 macrocell0 and picocell23 areconfigured as faulty cells and picocell19ndashpicocell22 are takenas compensation cells of the outage macrocell0 The detailedsimulation parameters are listed in Table 1 The simulationbegins at a proper operational state with shadow fadingadded Normal periodical performance metrics and a smallamount of RLF-triggered data are reported to construct atraining model At some point in simulation transmit powerof macrocell0 and picocell23 is set to decrease to 40dBmto simulate hardware failures The performance in outagecells then experiences a dramatic breakdown numerous RLFevents happen and most of periodical data collected inoutage cells at thismoment start to show indication of outageUpon the discovery of the outage macrocell picocell19ndashpicocell22 will allocate unoccupied RBs and optimize thepower perRB for their new serving users so that the degradedperformance gets restored

41 Cell Outage Detection Results The training databasethat defines the characteristics of two distinct categories is


Table 1 Simulation parameters configuration

Simulation parameters ValueMacrocell Picocell

Cellular layout 19 cells 4 picoscellUser numbers 20 userscell 10 userspicoBandwidth 5MHz 5MHzPass loss model 119871 = 1281 + 376log

10119877 119871 = 1407 + 367log

10119877

Preference eNodeB transmit power 46 dBm 30 dBmProblematic eNodeB transmit power 6 dBm minus10 dBmShadow-fading standard deviation 8 dB 10 dBShadow-fading correlation 05siteUser placement Randomly distributed119876out threshold minus80 dB119876in threshold minus60 dB119879310 timer 100ms

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60minus20 minus10 0 10 20 30 40 50 60

Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data

Normal periodical data

(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)

minus60 minus40 minus20 0 20 40 60

Serving SINR (dB)

RLF-labeled data


30

(b)

Figure 3 Classification results with KNN classifier in different simulations (a) results in the reference simulation and (b) results in theproblematic simulation

constructed from 136 RLF-triggered data points and 1260periodical data points KNN is adopted to undertake thetesting data labeling task As a comparison not only test-ing data but also reference periodical data are labeled InFigure 3(a) we can see that a few normal periodical data arelabeled as RLF-like which is caused by shadow fadingWhilein Figure 3(b) two distinct clusters are shown and moretesting periodical data perform similarly as RLF-triggeredones since there exist transmit power outages

After the implementation of KNN classifier RLF-labeleddata are utilized to make a relationship with the additionalcollected information such as position information andserving cell global identification (CGI) We take CGI forvalidating of the classification results It can be observedfrom Figure 4(a) that there indeed occur a few radio link

failures in normal operational phase especially in ID 1925 37 and 45 with around 111 of all training samplesdetected as RLF-like It should be pointed out that each cellincludingmacrocell and picocell is numbered sequentially forbrevity Figure 4(b) depicts that about 1130macrodata and 180picodata are labeled as RLF-like which is consistent with thepreconfigured simulation configuration

Afterwards 119891-measurement is applied to further verifythe performance of KNN classifier We can see from Figure 5that in reference simulation119891-measurement values in ID 1925 37 and 45 are relatively larger but they are all less than005 while in the outage situation 119891-measurement reaches091 in ID 0 and 024 in ID 23 which refer to the faultymacrocell0 and picocell23 respectively Since the number ofusers in a macrocell is larger than that in a picocell the limit


5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number

Macrocell ID Picocell ID

(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number

Microcell ID Picocell ID

(b)

Figure 4 The distribution of RLF-labeled data based on CGI information (a) the distribution in the reference simulation and (b) thedistribution in the problematic simulation

1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue

f-measurement value in reference simulationf-measurement value in problematic simulation

Figure 5 The distribution of 119891-measurement values for RLF-labeled data in both reference and problematic simulations

of the 119891-measurement for macrocell is bigger than that forthe picocell Then it can be concluded that macrocell0 andpicocell23 are experiencing performance degradations

42 Cell Outage Compensation Results As described inSection 32 an adaptive parameter 120572 relating compensationtimeslot is introduced to mitigate the interference caused bysurrounding macrocells As a result outage users now servedby picocells are able to receive more power through Lagrangeoptimizing Figure 6 shows the average user throughput withrespect to compensation timeslot for various interference

reduction strategies Two system states preoutage and post-compensation are considered Further it should be notedthat the 119883 axis represents the proportion of compensationtimeslot to the total transmission timeslots

Figure 6(a) depicts the circumstances in the outage areaIt can be seen that before outage the average throughputof users in previous serving picocells and the macrocellkeeps a straight line which indicates the introduction ofcompensation timeslot dose not work on the normal systemstate Since the resources applied by macrocells and picocellsare orthogonal compensation timeslot proposed in the senseof neighbor macrocells actually has little impact on previ-ously served picousers Thus the average user throughputfor picocells keeps almost unchanged before outage andafter compensation For outage users the performance getscompensated with satisfying average user throughput butis worse than that of previously served picousers becausethe interference of outage users is still relatively larger com-pared with that of picousers Also the average outage userthroughput is gradually improved as the proportion of com-pensation timeslot increases As for the performance impacton surrounding macrocells Figure 6(b) plots the averageneighbor macrouser throughput regarding different valuesof compensation timeslot Before outage the throughput formacrocell1ndashmacrocell6 has nothing to do with the proposedcompensationmechanism Once the outage occurs and at thetime at which it is compensated the average user throughputgets increased with the reduced proportion of compensationtimeslot When the proportion reaches 110 the throughputis approaching that before outage

From Figure 6 it can be concluded that the average userthroughput offered to outage users is improved at the costof decreased serving qualities of surrounding macrousersSo a balance to obtain satisfying service for both outageusers and neighbor macrousers should be made Fortunatelythe tradeoff can be achieved through adaptively adjusting


18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10

1x compensation timeslot

Preoutage for macrouserPostcompensation for macrouser

Preoutage for picouserPostcompensation for picouser

(a)

125

12

115

11

105

1

095

09

085

Preoutage for surrounding macrouserPostcompensation for surrounding macrouser

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)

Figure 6 Average user throughput with respect to various proportions of compensation timeslot (a) users in the outage area and (b) usersin the surrounding macroarea

25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4

Preoutage Postoutage before Post

120572 = 13

Post

120572 = 110 compensation compensation compensation

(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post

120572 = 110compensation compensation compensation

(b)

Figure 7 Average cell throughput with respect to different system states (a) users in the outage area and (b) users in the surroundingmacroarea

the proportion of compensation timeslot The performancegain can be presented in terms of average cell throughput forboth the outage area and surrounding macrocells Figure 7displays such changes in the form of bars under threenetwork states preoutage postoutage before compensationand postcompensationMoreover two different compensatedresults are represented by right two bars when the proportionof compensation timeslot is 13 and 110 respectively

Figure 7(a) shows the change of average cell throughputin the outage area We can see that the degraded servingqualities for outage users get enhanced while they are worsethan that before outage That is readily understandable since

the affected performance cannot be entirely restored withoutprovision of external resources When the proportion ofcompensation timeslot is 110 the average cell throughputafter compensation is 880 of the one before outage while itreaches 914 with 13 compensation timeslot It means thatthe larger the proportion of compensation timeslot is thebetter compensated performance will be obtained for outagearea However the impact on surrounding macrousersshould also be taken into account From Figure 7(b) it canbe known that the average surrounding cell throughput getsincreased during postoutage before compensation stagefor the reason that the faulty macrocell0 does not generate


any interference When the outage is compensated thethroughput is reduced to 729 with 13 compensationtimeslot and 983 with 110 compensation timeslotrespectively Consider the performance loss for neighbormacrocells a tradeoff should be made with a favorable 120572

5 Conclusion

In this paper we present a self-healing process in amacropicoheterogeneous network through the employment of keyperformance indicators Using collected periodical and RLF-triggered data119870-nearest neighbor (KNN) classifier has beenimplemented successfully to detect the outage macrocell andpicocell Due to the fact that users in the anomaly picocellcan restore its degraded service by smoothly handover onlythe outage macrocell is considered regarding performancecompensation Four picocells located in the outage macrocellare used as compensation cells They allocate RBs that oncebelong to the outage macrocell to affected users and employLagrange function to optimize the power per RB To reducethe intercell interference a new concept ldquocompensationtimeslotrdquo is introduced Finally verification for KNN classi-fier on the basis of119891-measurement and that for compensationmechanism in terms of compensation gains are illustrated

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This work was supported in part by the National NaturalScience Foundation of China (Grant no 61361166005) theState Major Science and Technology Special Projects (Grantno 2013ZX03001001) Beijing Natural Science Foundation(Grant no 4131003) and the Specialized Research Fund forthe Doctoral Program of Higher Education (SRFDP) (Grantno 20120005140002)

References

[1] M Peng Y Liu D Wei W Wang and H Chen ldquoHierarchicalcooperative relay based heterogeneous networksrdquo IEEEWirelessCommunications vol 18 no 3 pp 48ndash56 2011

[2] B Han WWang Y Li and M Peng ldquoInvestigation of interfer-ence margin for the co-existence of macrocell and femtocell inOFDMA systemsrdquo IEEE System Journal vol 7 no 1 pp 59ndash672013

[3] R Barco P Lazaro and PMunoz ldquoA unified framework for self-healing in wireless networksrdquo IEEE Communications Magazinevol 50 no 12 pp 134ndash142 2012

[4] M Peng D Liang Y Wei et al ldquoSelf-configuration and self-optimization in LTE-advanced heterogeneous networksrdquo IEEECommunications Magazine vol 51 no 5 pp 36ndash45 2013

[5] M Peng Z Ding Y Zhou and Y Li ldquoAdvanced self-organizingtechnologies over distributed wireless networksrdquo InternationalJournal of Distributed Sensor Networks vol 2012 Article ID821982 2 pages 2012

[6] F Chernogorov J Turkka T Ristaniemi and A AverbuchldquoDetection of sleeping cells in LTE networks using diffusionmapsrdquo in Proceedings of the IEEE 73rd Vehicular TechnologyConference (VTC rsquo11) pp 1ndash5 May 2011

[7] Q Liao M Wiczanowski and S Stanczak ldquoToward cell outagedetection with composite hypothesis testingrdquo in Proceedings ofthe IEEE International Conference onCommunications (ICC rsquo12)pp 4883ndash4887 June 2012

[8] M Amirijoo L Jorguseski R Litjens and L C SchmelzldquoCell outage compensation in LTE networks algorithms andperformance assessmentrdquo in Proceedings of the IEEE 73rdVehicular Technology Conference (VTC rsquo11) pp 1ndash5 May 2011

[9] M Amirijoo L Jorguseski T Kurner et al ldquoCell outagemanagement in LTE networksrdquo in Proceedings of the 6thInternational Symposium on Wireless Communication Systems(ISWCS rsquo09) pp 600ndash604 September 2009

[10] W Wang J Zhang and Q Zhang ldquoCooperative cell outagedetection in self-organizing femtocell networksrdquo in Proceedingsof the 32nd IEEE International Conference on Computer Com-munications (INFOCOM rsquo13) pp 782ndash790 April 2013

[11] K Lee H Lee and D Cho ldquoCollaborative resource allocationfor self-healing in self-organizing networksrdquo in Proceedings ofthe IEEE International Conference on Communications (ICC rsquo11)pp 1ndash5 June 2011

[12] WXueM Peng YMa et al ldquoClassification-based approach forcell outage detection in self-healing heterogeneous networksrdquoin Proceedings of the IEEE Wireless Communications and Net-working Conference (WCNC rsquo14) 2014

[13] Y Ma M Peng W Xue et al ldquoA dynamic affinity propagationclustering algorithm for cell outage detection in self-healingnetworksrdquo in Proceedings of the IEEE Wireless Communicationsand Networking Conference (WCNC rsquo13) pp 2266ndash2270 April2013

[14] M Islam Q Wu M Ahmadi and M A Sid-Ahmed ldquoInvesti-gating the performance of Naive- Bayes classifiers and K- near-est neighbor classifiersrdquo in Proceedings of the 2nd InternationalConference on Convergent Information Technology (ICCIT rsquo07)pp 1541ndash1546 November 2007

[15] B Han M Peng Z Zhao and W Wang ldquoA multidimensionalresource allocation optimization algorithm for the networkcoding-based multiple-access relay channels in OFDM sys-temsrdquo IEEE Transactions on Vehicular Technology vol 62 no8 pp 4069ndash4078 2013

[16] 3GPP TR 36814 V900 ldquoEvolved Universal Terrestrial RadioAccess (E-UTRA) mobility enhancements in heterogeneousnetworkrdquo February 2012

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Active and Passive Electronic Components

Control Scienceand Engineering

Journal of



RotatingMachinery


Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics


Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



physical cell identifier assignment mobility managementand energy saving have been addressed in HetNets [4]Whilethe study of self-healing technique in the context of HetNetsstill remains an open issue

Self-healing technique is supposed to handle outage cellswhere a cell outage refers to the total loss of radio servicesin its coverage area that resulted from hardwaresoftwarefailures or other functional faults [5] It can be dividedinto two parts cell outage detection (COD) and cell outagecompensation (COC) The former attempts to detect andlocate potential faults and the latter is responsible foralleviating performance degradation Instead of employinghighly experienced staff the automated detection entity nowdepends on data mining or mathematical statistics [6 7]which can transform raw large database into meaningfulinformation and then identify possible faults As for perfor-mance compensation optimization theory is exploited on thebasis of radio parameters like the antenna tilt and the celltransmit power in surrounding cells [8] It is worth nothingthat the capacitycoverage offered to the outage area shouldbe retained as large as possible while that in neighboring cellscannot be affected significantly at the same time [9]

To solve the self-healing problemmost if not all conven-tional research works on cell outage detection and cell out-age compensation are conducted separately Most proposedmethods are designed for homogeneous networks where onlymacrocells are considered Regardless of the communicationoverhead incurred by dense deployments of LPNs somecentralized statistics analysis like [6] may be applicable forHetNets But small coverage of LPNs leads to sparse userstatistics available sometime and the approach in [6] fallsshort since it considers only event-triggered measurementsIn the worst case the gathered measurements may be causedby momentary severe shadow fading Therefore self-healingschemes in homogeneous networks usually cannot be applieddirectly into HetNets In such context this paper is devotedto focusing on the design of self-healing mechanism forHetNets enduring the aforementioned challenges There isstill a few literature resources devoted to this realm Todevelop autonomous femtocell outage detection cooperativeoutage detection architecture in femtomacro cellular net-works is brought forward in [10] which designs a triggerdecision through investigating intracell correlations of RSRP(reference signal received power) statistics in space domainand detects outage femtocells through extracting correlationsof intercell RSRP statistics in both space and time domainsAs for mitigation measures [11] proposes a cooperativeresource allocation algorithm based on subchannel andpower resources via cooperation among femtocells

In this paper we consider a systematic self-healingscheme in a two-tier macropico network which consists ofdetection and compensation stages In the detection stageinspired by [12] 119870-nearest neighbor (KNN) is adoptedto detect anomaly macrocell and picocells when collectedperiodicalmeasurements start to become similar to the previ-ously known radio link failure (RLF) samples119891-measurmentis then employed for further assessment with respect todetection accuracy In the compensation stage only theoutage macrocell is considered since users in the outage

picocell can handover to an available macrocell Firstlythe unoccupied spectrum resources once belonging to theoutage macrocell are reallocated to users by compensationpicocells that lie in the outage area Power adjustment on eachresource block (RB) is carried out via Lagrange optimizingalgorithm Meanwhile compensation gains in the sense ofaverage throughput per user and per cell are investigated

The remainder of this paper is organized as followsSection 2 presents the system model for achievable self-healing mechanism Section 3 includes a detailed descriptionfor the outage detection and the corresponding performancecompensation algorithms Further the simulation results todemonstrate the efficiency of our proposed methods areprovided in Section 4 Finally the conclusion of this paper isgiven in Section 5

2 System Model

The self-healing is a functionality aiming to minimize thenetwork performance deterioration when failures occur ina network element (NE) through immediately autonomouscell outage detection and compensation actions Firstly per-formance parameters from NEs in both access and core net-works are collectedThe outage detection entity then exploitsthem to perform problem identification and localizationduring current monitoring period When any type of faultdescribed above is detected the outage compensation entityis timely activated to execute feasible recovery procedures soas to restore the degraded service

To evaluate the self-healing algorithms into a two-tierLTE-Advanced system picocells are deployed to eliminatecoverage holes in hot spots The simulation environment iscomprised of 19 regular hexagonal macrocells and 76 overlaidpicocells Each macrocell is filled with four picocells Theouter tier 12 macrocells and 48 picocells are merely usedto generate interference without any user equipments (UEs)deployed The 7 center macrocells and 28 picocells are themain cells of interest with UEs randomly distributed workingtogether for healing purpose The system scenario is shownin Figure 1 where macrocell0 and picocell23 are configuredas faulty cells Upon the detection of configured failures thenormal four picocells deployed at the outage area are to betreated as compensation cells to alleviate the degradationrespecting coverage and quality

21 Macropicocell Outage Detection Framework Cell outagedetection process in the two-tier macropico network hasrecently been studied in [12] As shown in Figure 1 it consistsof two main phases model-learning phase and problem-detecting phase To construct a robust learning model ref-erence simulation is implemented during which RLF eventis assumed to be triggered slightly faster than usual so thatnot only periodicalmeasurements but also someRLF samplescan be gatheredThemodel is created by labeling the trainingdata as periodical and RLF-like categories In the anomalydetection phase hardware failure is simulated by transmitpower outage In Figure 1 macrocell0 and picocell23 turninto faulty cells by lowering the power at some time during


OAM

23

19

22 0 20

21


Self-healingprocess

Model-learning

COD


Power-optimizing

COC

Operatorpolicy


Problem-detecting










1199051 1199052 119905

119894 119905

119860 where 119905




119899 SINR

119904 SINR



1 1198972 where




1199091 1199092 119909

119895 119909

119861 where 119909




119895




119894119863(119909119895 119905119894) = radicsum

4

119889=1(119909119889

119895minus 119905119889

119894)2 For


2 119863

119895



1015840 1198972

1015840 119897119870




119897119895= argmax119897120581

(

sum119870

119896=11 119897119896

1015840= 119897120581

sum2

120581=1sum119870

119896=11 119897119896

1015840= 119897120581

) (1)




2) times recall (119888 119897

2)


2) (2)


2) =

1198991198881198972

119899119888and recall(119888 119897

2) = 119899

1198881198972

1198991198972




1198881198972


119891measurement (119888 1198972) =21198991198881198972

119899119888+ 1198991198972

(3)








1D2 D

119894 D




1Descend

2 Descend








119898


T[119899]

119898119894(119901[119899]

119898119894) = 119882 log

2(1 + SINR[119899]

119898119894(119901[119899]

119898119894)) (4)


SINR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(5)


SINR[119899]119898119894(119901[119899]

119898119894)

=

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(6)

119901[119899]






SNR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 (7)


maximize 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+(1minus120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1+SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))


119894isin119880119872119898119899isinRB

119872

119901[119899]

119898119894+ sum

119894isin119880119875119898119899isinRB

119875

119901[119899]

119898119894

(8)

where RB119872




and 119880119875119898



Λ (119901 120582) = 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+ (1 minus 120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ 120582(119875119898max minus

N

sum

119894=1

119901[119899]

119898119894)

(9)


120572119866119898119894I[119899]119898119894+ 1198661198981198941205902+ 119901[119899]

119898119894(119866119898119894)2

(1205902 + 119901[119899]

119898119894119866119898119894) (I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

119866119898119894

(I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

(10)











10119877 119871 = 1407 + 367log

10119877


20

10

0

minus10

minus20

minus30

minus40

minus50


Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data


(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)


Serving SINR (dB)

RLF-labeled data


30

(b)







5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










OAM

23

19

22 0 20

21


Self-healingprocess

Model-learning

COD


Power-optimizing

COC

Operatorpolicy


Problem-detecting










1199051 1199052 119905

119894 119905

119860 where 119905




119899 SINR

119904 SINR



1 1198972 where




1199091 1199092 119909

119895 119909

119861 where 119909




119895




119894119863(119909119895 119905119894) = radicsum

4

119889=1(119909119889

119895minus 119905119889

119894)2 For


2 119863

119895



1015840 1198972

1015840 119897119870




119897119895= argmax119897120581

(

sum119870

119896=11 119897119896

1015840= 119897120581

sum2

120581=1sum119870

119896=11 119897119896

1015840= 119897120581

) (1)




2) times recall (119888 119897

2)


2) (2)


2) =

1198991198881198972

119899119888and recall(119888 119897

2) = 119899

1198881198972

1198991198972




1198881198972


119891measurement (119888 1198972) =21198991198881198972

119899119888+ 1198991198972

(3)








1D2 D

119894 D




1Descend

2 Descend








119898


T[119899]

119898119894(119901[119899]

119898119894) = 119882 log

2(1 + SINR[119899]

119898119894(119901[119899]

119898119894)) (4)


SINR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(5)


SINR[119899]119898119894(119901[119899]

119898119894)

=

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(6)

119901[119899]






SNR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 (7)


maximize 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+(1minus120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1+SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))


119894isin119880119872119898119899isinRB

119872

119901[119899]

119898119894+ sum

119894isin119880119875119898119899isinRB

119875

119901[119899]

119898119894

(8)

where RB119872




and 119880119875119898



Λ (119901 120582) = 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+ (1 minus 120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ 120582(119875119898max minus

N

sum

119894=1

119901[119899]

119898119894)

(9)


120572119866119898119894I[119899]119898119894+ 1198661198981198941205902+ 119901[119899]

119898119894(119866119898119894)2

(1205902 + 119901[119899]

119898119894119866119898119894) (I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

119866119898119894

(I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

(10)











10119877 119871 = 1407 + 367log

10119877


20

10

0

minus10

minus20

minus30

minus40

minus50


Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data


(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)


Serving SINR (dB)

RLF-labeled data


30

(b)







5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











119899 SINR

119904 SINR



1 1198972 where




1199091 1199092 119909

119895 119909

119861 where 119909




119895




119894119863(119909119895 119905119894) = radicsum

4

119889=1(119909119889

119895minus 119905119889

119894)2 For


2 119863

119895



1015840 1198972

1015840 119897119870




119897119895= argmax119897120581

(

sum119870

119896=11 119897119896

1015840= 119897120581

sum2

120581=1sum119870

119896=11 119897119896

1015840= 119897120581

) (1)




2) times recall (119888 119897

2)


2) (2)


2) =

1198991198881198972

119899119888and recall(119888 119897

2) = 119899

1198881198972

1198991198972




1198881198972


119891measurement (119888 1198972) =21198991198881198972

119899119888+ 1198991198972

(3)








1D2 D

119894 D




1Descend

2 Descend








119898


T[119899]

119898119894(119901[119899]

119898119894) = 119882 log

2(1 + SINR[119899]

119898119894(119901[119899]

119898119894)) (4)


SINR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(5)


SINR[119899]119898119894(119901[119899]

119898119894)

=

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(6)

119901[119899]






SNR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 (7)


maximize 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+(1minus120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1+SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))


119894isin119880119872119898119899isinRB

119872

119901[119899]

119898119894+ sum

119894isin119880119875119898119899isinRB

119875

119901[119899]

119898119894

(8)

where RB119872




and 119880119875119898



Λ (119901 120582) = 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+ (1 minus 120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ 120582(119875119898max minus

N

sum

119894=1

119901[119899]

119898119894)

(9)


120572119866119898119894I[119899]119898119894+ 1198661198981198941205902+ 119901[119899]

119898119894(119866119898119894)2

(1205902 + 119901[119899]

119898119894119866119898119894) (I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

119866119898119894

(I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

(10)











10119877 119871 = 1407 + 367log

10119877


20

10

0

minus10

minus20

minus30

minus40

minus50


Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data


(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)


Serving SINR (dB)

RLF-labeled data


30

(b)







5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation












119898


T[119899]

119898119894(119901[119899]

119898119894) = 119882 log

2(1 + SINR[119899]

119898119894(119901[119899]

119898119894)) (4)


SINR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(5)


SINR[119899]119898119894(119901[119899]

119898119894)

=

119901[119899]

119898119894119866119898119894


[119899]

119895119894119866119895119894

(6)

119901[119899]






SNR[119899]119898119894(119901[119899]

119898119894) =

119901[119899]

119898119894119866119898119894

1205902 (7)


maximize 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+(1minus120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1+SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))


119894isin119880119872119898119899isinRB

119872

119901[119899]

119898119894+ sum

119894isin119880119875119898119899isinRB

119875

119901[119899]

119898119894

(8)

where RB119872




and 119880119875119898



Λ (119901 120582) = 120572 sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SNR[119899]

119898119894(119901[119899]

119898119894))

+ (1 minus 120572) sum

119894isin119880119872119898119899isinRB

119872

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ sum

119894isin119880119875119898119899isinRB

119875

119882 log2(1 + SINR[119899]

119898119894(119901[119899]

119898119894))

+ 120582(119875119898max minus

N

sum

119894=1

119901[119899]

119898119894)

(9)


120572119866119898119894I[119899]119898119894+ 1198661198981198941205902+ 119901[119899]

119898119894(119866119898119894)2

(1205902 + 119901[119899]

119898119894119866119898119894) (I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

119866119898119894

(I[119899]119898119894+ 1205902 + 119901

[119899]

119898119894119866119898119894) (ln 2)

= 120582

(10)











10119877 119871 = 1407 + 367log

10119877


20

10

0

minus10

minus20

minus30

minus40

minus50


Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data


(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)


Serving SINR (dB)

RLF-labeled data


30

(b)







5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













10119877 119871 = 1407 + 367log

10119877


20

10

0

minus10

minus20

minus30

minus40

minus50


Nei

ghbo

ring

SIN

R (d

B)

Serving SINR (dB)

RLF-labeled data


(a)

20

10

0

minus10

minus20

minus30

minus40

minus50

minus60

Nei

ghbo

ring

SIN

R (d

B)


Serving SINR (dB)

RLF-labeled data


30

(b)







5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










5

45

4

35

3

25

2

15

1

05

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

minus10 0 10 20 30 40 50

Cell ID number


(a)

-10 0 10 20 30 40 50 60 70 80 90

1200

1000

800

600

400

200

0

The n

umbe

r of R

LF-la

bele

d m

etric

s

Cell ID number


(b)


1

09

08

07

06

05

04

03

02

01

0minus10 0 10 20 30 40 50

Cell ID number


f-m

easu

rmen

t val

ue









18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










18

16

14

12

1

08

06

04

02

Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10




(a)

125

12

115

11

105

1

095

09

085


Aver

age u

ser t

hrou

ghpu

t (M

bps

user

)

3 4 5 6 7 8 9 10


(b)


25

20

15

10

5

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(a)

20

18

16

14

12

10

8

6

4

2

0

Aver

age c

ell t

hrou

ghpu

t (M

bps)

1 2 3 4


120572 = 13

Post


(b)







5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











5 Conclusion




Acknowledgments


References



















RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article cell outage detection and compensation in two-tier heterogeneous...

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