A fuzzy based vertical handover network selection scheme for device-to-device communication

0.45 MB·English

by  Meenakshi Subramani, Vinoth Babu Kumaravelu

IndonesianJournalofElectricalEngineeringandComputerScience
Vol. 17,No. 1,January2020,pp. 324∼330
ISSN:2502-4752,DOI:10.11591/ijeecs.v17i1.pp324-330 (cid:114) 324
A fuzzy based vertical handover network selection scheme
for device-to-device communication
MeenakshiSubramani,VinothBabuKumaravelu
SchoolofElectronicsEngineering,VelloreInstituteofTechnology,Vellore,TamilNadu,India
ArticleInfo ABSTRACT
Articlehistory: Oneofthemostattractiveandchallengingareasintheupcomingnext-generation5G
wirelessnetworkistheverticalhandover(VHO).Recently,manyoftheheterogeneous
ReceivedApr1,2019
wirelesscommunicationtechnologiesareintroducedtosatisfythedemandsofusers
RevisedJun17,2019
inallsituations. Duetothedeploymentofheterogeneousnetworks,theuserscanac-
AcceptedJul7,2019
cess the internet anywhere, anytime through different wireless networks. To obtain
seamlessserviceandservicecontinuity,thedeviceshouldbehandedovertothebest
Keywords:
wirelessnetworks. Here,ahalfhandoverschemeforDevice-to-Device(D2D)com-
Analytichierarchyprocess municationisimplementedfortheselectionofthebestnetwork. Thetargetnetwork
(AHP) selectionforverticalhandovercanbehandledusingmultipleattributedecisionmak-
Device-to-Device(D2D) ing (MADM) methods. An intelligent and fast vertical handover decision is much
communication needed,whichshouldbereliableevenforrandomanduncertainenvironments.Fuzzy
Fuzzylogic logic is proved to be effective in handling imprecise data. Hence, in this work, the
impact of combining fuzzy with the conventional MADM scheme, simple additive
Handoverdecisiondelay
weighting(SAW)isanalyzedandthehybridschemeiscomparedwiththeconventional
Simpleadditiveweighting
MADMschemeslikeSAW,Techniquesfororderpreferencebysimilaritytoidealso-
(SAW)
lution (TOPSIS), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR)
VerticalHandover(VHO)
in terms of handover decision delay. Since, the numbers of handovers executed are
low,thehandoverdecisiondelayperformanceoftheproposedschemeissuperiorthan
theconsideredclassicalMADMschemes.
Copyright(cid:13)c 2020InsituteofAdvancedEngineeeringandScience.
Allrightsreserved.
CorrespondingAuthor:
VinothBabuKumaravelu,
SchoolofElectronicsEngineering,
VelloreInstituteofTechnology,
Vellore,TamilNadu,India.
Email: [email protected]
1. INTRODUCTION
Duetoincreaseddemandsoftheusers,5Gwillovercometheshortagesof4Gcommunication,which
is expected to offer higher data rate, increased voice quality calls, improved spectrum efficiency, reduced la-
tency, etc. to the end users [1]. 5G networks will support the emerging and existing technologies, which
integratesnewersolutionstoobtaintheincreasingdemandforhigherdatarate. Oneoftheimportantfeatures
of 5G is D2D communication [2]. In D2D, two devices in close proximity communicate directly rather than
communicatingthroughtheevolvednodebasestation(eNB)orthecorenetwork.
Duetoitsdirectlinkcommunication,itoffloadsdatatrafficandimprovesspectralefficiency. Italso
reducespowerconsumptionandlatency. In5G,theconceptofsmallcellsisverypopular,wherethecoverage
rangeofaccesspointsarereduced[3]. Duetothemobilitynature,thedevicesundergoingD2Dcommunica-
tion requires frequent handovers [2]. The selection of the target network is to ensure proper communication
and seamless service between the devices, which acts as a basic requirement in many practical applications.
To achieve always best connection (ABC) and to meet the good quality of service (QoS), the device has to
Journalhomepage: http://ijeecs.iaescore.com/index.php/IJEECS
IndonesianJElecEng&CompSci ISSN:2502-4752 (cid:114) 325
switchtodifferentnetworks[4]. TheprocessofswitchingtodifferentnetworksiscalledVHOandtheprocess
of switching between the same networks is called horizontal handover [2]. The interesting and challenging
researchareainthewirelessenvironmentistoselectthebestnetworkfromseveralcandidatenetworks. When
one of the devices undergoing D2D communication moves away from the other device, the link quality be-
tweenthembecomesverypoor. ThisleadstopoorQoSandconnectionbreakdown. Hence,oneofthedevices
is handed over to the other network. After the handover, these devices may continue their communication
through cellular links. This type of handover is termed as half handover. A sample D2D scenario requiring
handoverisshowninFigure1a. D1andD2markedinFigure1arepresentthedevicesundergoingD2Dcom-
munication.TheprocessofhalfhandoverisillustratedinFigure1b.Insomecases,boththedevicesundergoing
D2D communication may move towards the neighboring network. For seamless service continuity, both the
devicesarejointlyhandedovertothebestneighboringnetwork. Thisprocessistermedasjointhandover[2],
whichisillustratedinFigure1c.
(a) (b) (c)
Figure1. (a)AscenarioforthedeviceinD2Dcommunicationrequiringhandover,(b)Afterhalfhandover,
(c)Afterjointhandover
Target network selection is an important task to achieve a seamless connection and QoS in VHO
environment. Thenetworkgatherstheparametersfromeverycandidatenetworkandranksthemtochoosethe
best targetnetwork. Mostof the conventionalhandover schemes make useof received signalstrength (RSS)
to select the target network [4, 5]. This introduces a ping-pong effect, which leads to decreased throughput,
increasedlatencyanddroppingrate. Manyoftherecentapproachesmakeuseofvariousparameterstodecide
thehandoverandthetargetnetwork. Theseincludeasignaltonoiseratio(SNR),achievablebitrate,biterror
rate(BER),outageprobability,cost,security,powerconsumption,availablebandwidth,etc. [4].
Duetoitssimplicityinoperation,MADMalgorithmsarewidelyusedinVHOdecisionmakingand
target network selection. In literature, there exist many compensatory and non-compensatory MADM algo-
rithms. Most of the network selection algorithms proposed are based on the compensatory method. The
compensatoryalgorithmscombinemultiplecriteriatofindthebestnetwork,whereasnon-compensatoryalgo-
rithms combine multiple criteria to find the acceptable network, which satisfies the minimum requirements.
SAW[6],TOPSIS[7],VIKOR[8]algorithmscomeundercompensatorycategory. Thesearepopularforlower
computationalcomplexityandimprovedaccuracyindecisionmaking.
Fuzzy logic models complex systems fairly without any bias, which is not in the case of the AHP
process [9, 10]. Fuzzy logic is capable of processing a large number of inputs and makes a soft decision. It
efficientlyhandlestheimprecisedataandrepresentsitinaninnateform[3]. MostoftheconventionalVHO
decision-making algorithms are based on RSS, which fluctuates based on various parameters like distance,
mobility,speedandshadowingfactor,etc. Theimpreciseinputmaycauseinaccuratedecisionindecidingthe
handover. Thisleadstooverorunder-utilizationofnetworkresources. Fuzzylogicisprovedtobeefficientin
handlingthedatarelatedtoradio,QoSanduserpreferences[4].
2. THEPROPOSEDMETHOD
Inthiswork,fuzzylogiciscombinedwiththeconventionalAHPandSAWmethodstosimultaneously
process a large number of inputs and to effectively handle the imprecise data related to handover decision
makingandtargetnetworkselection. Basedontheinputcriteria,FuzzyAHPisusedtocalculatetheweights
ofeachcriteria. TheseweightsandtheavailablecandidatenetworksaregivenastheinputfortheFuzzySAW
scheme. Thenetworkwiththehighestrankischosenasthetargetnetworktohandover. Theproposedmethod
formulti-criterianetworkselectionblockdiagramisshowninFigure2.
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326 (cid:114) ISSN:2502-4752
Therestoftheworkisarrangedasfollows: Anintroductiontofuzzytheory,FuzzyAHPandFuzzy
SAWareexplainedinsection3. Theresultsarediscussedinsection4andthepaperisconcludedinsection5.
Figure2. BlockdiagramofFuzzyAHP-FuzzySAWMADMnetworkselection
3. RESEARCHMETHOD
3.1. Fuzzytheory
Fuzzysettheorydenotestheambiguousdatainaninnateform[3]. Duetothelowercomputational
complexityandsimplermathematicalimplementation,triangularfuzzynumber(TFN)iswidelypreferredfor
theapplicationsrelatedtowirelesscommunication. Itisalsoprovedthatthecomplexitiesrelatedtohandling
impreciseanduncertaintyinformationusedfornetworkselectionisminimizedwithTFN.Hence,inthiswork,
TFNisutilizedtoestablishambiguitymetrics. TFNisoneoftheimportantclassificationoffuzzynumberwith
itsmembershipfunctiondefinedby(x,y,z),threecrispnumbersas(x≤y ≤z),wherexisthelowerlimit,
y is the modal value and z is the upper limit. When x = y = z, then the fuzzy number will become a real
number. AfuzzysetG(cid:101)inauniverseofdiscourseAisrepresentedbyamembershipfunctionµ (a),whichis
G(cid:101)
relatedtoeachelementainAandtherealnumberwithaninterval[0,1]. Theµ (a)functionvalueisreferred
G(cid:101)
as the membership grade of a in G(cid:101). The fuzzy number G(cid:101) on R to be TFN, when the membership function,
µ (a):R→[0,1]. TFNcanbedefinedas
G(cid:101) 
(a−x) ,x≤a≤y
 (y−x)
µ (a)= (z−a) ,y ≤a≤z (1)
G(cid:101) (z−y)
 0 ,otherwise
3.2. FuzzyAHP
AHPismainlyusedfordecision-makinginmulti-criteriaproblems,whichisinitiatedwithameasure-
ment of ratio scales [11]. AHP use comparison and pre-set option selection, which depends on the pairwise
comparisonsbetweentheoptionsandcriteria.
Here, a qualitative judgment is used for pairwise comparison. Fuzzy AHP is very easy and simple
to analyze in making decisions. Fuzzy AHP causes the significance of fuzzy, which occurs in the same row
withtwocombinationitems. ThefuzzyextensionisneededbecausethebasicAHPfailedtoaddressthemain
issue of handling the high degree of imprecise in subjective personal judgments and its preferences. Fuzzy
AHP is applicable to tackle the problem at hand by considering the structure of multi-criteria and vague-
ness in a real-time environment, which improves the consistency in the network selection. Fuzzy AHP uses
a9-pointfundamentalscale,whichisexpressedintermsofTFNtoindicatetherelativeperformancebetween
thepairwisedecisionfactors. TheTFNvaluesarerepresentedinTable1.
Table1. 9-pointfundamentalfuzzyscaleusingTFN[12]
Fuzzyscale
intensityofimportance Linguisticscaledescription TFN ReciprocalofTFN
(cid:101)1 Equalimportance (1,1,1) (1,1,1)
(cid:101)2 Moderatelyequalimportant (1,2,3) (1/3,1/2,1)
(cid:101)3 Moderatelyimportant (1,3,5) (1/5,1/3,1)
(cid:101)4 Moderatelystronglyimportant (2,4,6) (1/6,1/4,1/2)
(cid:101)5 Stronglyimportant (3,5,7) (1/7,1/5,1/3)
(cid:101)6 Stronglyveryimportant (4,6,8) (1/8,1/6,1/4)
(cid:101)7 Verystronglyimportant (5,7,9) (1/9,1/7,1/5)
(cid:101)8 Verystronglyextremelyimportant (6,8,9) (1/9,1/8,1/6)
(cid:101)9 Extremelyimportant (7,9,9) (1/9,1/9,1/7)
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TheFuzzyAHPhasthefollowingsteps:
Step1: Basedontheobjectives,selectthesuitablecriteria.
Step2: ConstructapairwisecomparisonmatrixR(cid:101)basedoneachcriteria. Theelementsr(cid:102)ij inR(cid:101)representsthe
pairwisecomparisonofcriteriaiwithj.
 
r r r ...r
(cid:102)11 (cid:102)12 (cid:102)13 (cid:102)1n
 r(cid:102)21 r(cid:102)22 r(cid:102)23···r2n 
R(cid:101)=[r(cid:102)ij]n×n = .. .. .. ..  (2)
 . . . . 
r r r ...r
(cid:102)n1 (cid:102)n2 (cid:102)n3 (cid:103)nn
wheren denotesthenumberofcriteriadecisioncomparedand i = j = 1,2,....n.r representstherelative
(cid:102)ij
strengthoftwoelementsbasedonTFN.
(cid:18) (cid:19)
1 1 1
r =[r ]−1 =(x ,y ,z )−1 = , , (3)
(cid:102)ji (cid:102)ij ij ij ij z y x
ij ij ij
Step3: TheFuzzyAHPcomparisonmatrixisrepresentedwithTFNas
 
(1,1,1) (x ,y ,z ) ··· (x ,y ,z )
12 12 12 1n 1n 1n
 (x21,y21,z21) (1,1,1) ··· (x2n,y2n,z2n) 
R(cid:101)=[r(cid:102)ij]n×n = .. .. ..  (4)
 . . . 
(x ,y ,z ) (x ,y ,z ) (1,1,1)
n1 n1 n1 n2 n2 n2
Step4: Thefuzzygeometricmeanandfuzzyweightforeachcriteriaiscalculatedas[13,14]
l(cid:101)i =(r(cid:102)i1⊗r(cid:102)i2⊗.....⊗r(cid:102)in)1/n (5)
(cid:16) (cid:17)−1
w(cid:102)i =l(cid:101)i⊗ l(cid:101)1⊕l2⊕···⊕ln (6)
The sign ⊗ indicates fuzzy multiplication and sign ⊕ indicates fuzzy addition. r is the fuzzy comparison
(cid:102)in
valueofithcriteriatonthcriteria,l(cid:101)i isthegeometricmeanoffuzzycomparisonvalueofithcriteriatoeach
criteria,w isthefuzzyweightofithcriteria.
(cid:102)i
Step 5: In order to check the inconsistency in the pairwise comparison matrix, consistency index (CI) is
introducedtoobtaintheconsistencyratio(CR).CIisgivenas
λ −n
CI = max (7)
n−1
where
λ = R(cid:101).w(cid:101) (8)
max w
(cid:101)
whereλmaxisthelargestEigenvalueofthecomparisonmatrixR(cid:101),nrepresentsthenumberofcriteriaandw(cid:101)is
theweightvaluecalculatedtoobtainEigenvector. BasedontheCI,thevalueofCRiscalculatedas
CI
CR= (9)
RI
where RI is the random consistency index as shown in Table 2. Various authors measured RI values for a
different number of criteria. This is tabulated in [15]. In [15], the authors estimated RI for each number of
criteria using 100,000 matrices. They have generated random matrices with uniform distribution. Then, CIs
arecalculatedforeachmatrix.TheRIforeachnumberofcriteriaisobtainedbytakingmeanoftheseCIvalues.
It is proved that the RI value calculated by [16] is efficient than the other methods. Hence, in this work, we
haveusedthesametable,whichwaslistedin[16]. IftheestimatedCRvalueislessthan0.1,thenthepairwise
constructionisacceptable,otherwise,thematrixhastoberevised[17].
Table2. Randomconsistencyvalue[16]
Criteria 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49
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3.3. FuzzySAW
Becauseofitssimplicity, FuzzySAWiswidelyusedintheMADMalgorithms[18,4]. FuzzySAW
requiresthenormalizingprocedureforthedecisionmatrixintoascale,whichiscomparedwitheverynetwork
rating. The main idea of Fuzzy SAW method is to detect the weighted sum of performance rating of every
networkoneachcriteria. Thestepsareasfollows:
Step1: WeightcalculationObtaintheweightvalueforeachcriteriafromFuzzyAHPmethod.
Step 2: Fuzzy decision matrix Formulate the decision matrix F(cid:101) and select the suitable linguistic variables
withrespecttodifferentnetworksandcriteria. Aq×nfuzzydecisionmatrixisformulatedwiththeratingsof
eachnetworkwitheachcriteria. TheentriesareTFNinsteadofcrispvalues.
C C C ···C
1 2 3 n
 
Nw1 b(cid:102)11 b(cid:102)12 b(cid:102)13··· b(cid:102)1n
Nw2  b(cid:102)21 b(cid:102)22 b(cid:102)23··· b(cid:102)2n 
F(cid:101) = Nw3  b(cid:102)31 b(cid:102)32 b(cid:102)33··· b(cid:102)3n  (10)
...  ... ... ... ··· ... 
Nw
q b(cid:102)q1 b(cid:102)q2 b(cid:102)q3··· b(cid:102)qn
whereNw ,Nw ,Nw ,...........Nw arethepossiblenetworks,C ,C ,C ,...........,C arethecriteria. The
1 2 3 q 1 2 3 n
element b(cid:102)ij is the fuzzy decision matrix, which indicates the performance rating of each network Nwi with
respecttocriteriaC ,wherei=1,2,3,......qandj =1,2,3,.....nrespectively.
j
Step3: FuzzynormalizationweightvalueThefuzzydecisionmatrixF(cid:101) dependsonthelinguisticvariables
and the corresponding TFN. Each entry of F(cid:101) is TFN, which corresponds to 3 values, i.e, b(cid:103)ijx,b(cid:103)ijy,b(cid:103)ijz..The
normalizedfuzzydecisionmatrixg isgivenby
(cid:102)ij
(cid:32) (cid:33)
g = b(cid:103)ijx,b(cid:103)ijy,b(cid:103)ijz (11)
(cid:102)ij + + +
b(cid:101)j b(cid:101)j b(cid:101)j
Forbenefitcriteria,themaximumof3rdvalueofTFNinthefuzzydecisionmatrixF(cid:101)isidentifiedusing
+
b(cid:101)j =maxb(cid:103)ijz,wherej ∈B (12)
i
where B is the set of benefit criteria, which is always expected to be maximum. All the elements of F(cid:101) are
+
normalizedusingb(cid:101)j .. Alternatively,costcriteriacanalsobeused. Thenormalizedfuzzydecisionmatrixg(cid:102)ij
forcostcriteriaisgivenby
(cid:32) (cid:33)
c − c − c −
g = (cid:101)j , (cid:101)j , (cid:101)j (13)
(cid:102)ij
b(cid:103)ijz b(cid:103)ijy b(cid:103)ijx
Forcostcriteria,theminimumofthe1stvalueofTFNinthefuzzydecisionmatrixF(cid:101)isidentifiedusing
c(cid:101)j− =minb(cid:103)ijx,wherej ∈C (14)
i
where C is the set of cost criteria, which is always expected to be minimum. All the elements of F(cid:101) are
normalizedusingc −.
(cid:101)j
Forbenefitcriteria,thehighlyacceptablevalueisconsideredasthebestvalue. Forexample,RSSand
datarate. Forcostcriteria,theloweracceptablevalueisconsideredasthebestvalue.
(cid:16) (cid:17)
Step4: WeightnormalizationvalueInthisstep,aweightnormalizeddecisionmatrix M(cid:103)ij isformulated
by multiplying each element g of normalized fuzzy decision matrix with the weights w obtained through
(cid:102)ij (cid:101)
FuzzyAHPinstep4ofsection3as
 
w g w g w g ··· w g
(cid:102)1(cid:102)11 (cid:102)2(cid:102)12 (cid:102)3(cid:102)13 (cid:102)n(cid:102)1n
 w(cid:102)1g(cid:102)21 w(cid:102)2g(cid:102)22 w(cid:102)3g(cid:102)23··· w(cid:102)ng(cid:102)2n 
M(cid:103)ij = .. .. .. .. ..  (15)
 . . . . ··· . 
w g w g w g ··· w g
(cid:102)1(cid:102)q1 (cid:102)2(cid:102)q2 (cid:102)3(cid:102)q3 (cid:102)n(cid:102)qn
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IndonesianJElecEng&CompSci ISSN:2502-4752 (cid:114) 329
Step5: Networkranking
Fuzzy SAW algorithm evaluates all the networks and makes a decision to perform handover.
Theoverallrankofithnetworkisevaluatedusing
n
1 (cid:88)
D = g .w (16)
SAWi n (cid:102)ij (cid:102)j
j=1
4. RESULTSANDDISCUSSION
MATLAB 2017a tool is used for simulation. The considered simulation parameters are listed
in Table 3. Figure 3 shows the handover decision delay versus a number of inputs. The simulations are
carried out for streaming applications. During the vertical handover decision, the handover decision delay is
consideredasoneoftheimportantparameters. ThedelayinthehandoverdecisionleadstoQoSdegradation.
If the handover decision is done too early, it leads to unnecessary handover. The goal is to measure
thehandoverdecisiondelayfortheincreasednumberofinputs. Inmanyreal-worldproblems,decision-makers
may not be sure about their preferences. This leads to uncertainty. The inclusion of fuzzy logic effectively
handlesvariousdecision-makingproblems. InFuzzyAHP,thepairwisecomparisonmatrixisformulatedwith
the help of TFN. This avoids ambiguities in finding the weights. These weights are used in fuzzy SAW for
ranking and selecting the target networks. The decision makers also use the fuzzy logic in formulating the
decisionmatrixwhichcomparesalternativeswithcriteria. Since,theuncertaintiesarehandledeffectivelyand
fast,thehandoverdecisiontimeduringtheselectionofthetargetnetworkisverylowfortheFuzzyAHP-Fuzzy
SAWscheme.
Table3. Simulationparameters
Parameters Values
Networks Wi-Fi/WiMAX/LTE-A
Cellradius(km) Wi-Fi:0.25;WiMAX:10;LTE-A:3
TransmitPower(dBm) Wi-Fi:13;WiMAX:47;LTE-A:46
Bandwidth(MHz) Wi-Fi:20;WiMAX:40;LTE-A:100
PathlossmodelforWi-Fi PL(dB)Wi−Fi=34.48+32.79log d(m)[2]
10
PathlossmodelforWiMAX PL(dB)WiMAX =130.62+37.6log d(km)[2]
10
PathlossmodelforLTE-A PL(dB)LTE−A=103.8+20.9log d(km)[2]
10
Figure3. HandoverdecisiondelayversusnumberofinputscomparisonofvariousMADMschemes
For three criteria, Fuzzy AHP-Fuzzy SAW offers 29.03 %, 43.59 %, 55.55 %, 20 %, reduction in
handoverdecisiondelayovertheconventionalSAW[4,6],TOPSIS[4,7],VIKOR[4,8]andFuzzySAW[4]
schemes. Foranynumberofincreasedinputs,FuzzyAHP-FuzzySAWhandoverdecisiontimewillbelesser.
Hence, Fuzzy AHP-Fuzzy SAW selects the best target network to perform an efficient, fast and seamless
handover.
5. CONCLUSION
SinceD2Dcommunicationmostlyhappensforasmallerduration,thehandoverdecisiondelayshould
bemuchsmallerforaseamlessconnection.MostoftheconventionalMADMschemesarenotfastandreliable.
Theyalsofailwhenhandlingtheimprecisedata. Hence,inthiswork,theconceptoffuzzyiscombinedwith
Afuzzybasedverticalhandovernetworkselection... (MeenakshiSubramani)
330 (cid:114) ISSN:2502-4752
theconventionalSAWandthehybridschemeFuzzyAHP-FuzzySAWisprovedtoofferimprovedhandover
decisiondelayperformanceoverSAW,TOPSIS,VIKOR,FuzzySAWschemes. Duetolowerhandoverdeci-
sion delay, the packet drop ratio and service interruption are greatly reduced for the proposed scheme. This
increases the average data rate experienced by each device. This also minimizes the latency of the proposed
scheme. This simple work can be extended for other criteria, networks and MADM algorithms. As a future
study,theseschemescanalsobecombinedandtestedwithneuro-fuzzybasedalgorithms.
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