Received May 19, 2018, accepted June 6, 2018, date of publication June 19, 2018, date of current version August 15, 2018.

Digital Object Identifier 10.1109/ACCESS.2018.2846045

Multi-Criteria Handover Using ModifiedWeighted TOPSIS Methods forHeterogeneous NetworksMOHANAD ALHABO , (Graduate Student Member, IEEE), andLI ZHANG, (Senior Member, IEEE)School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Corresponding author: Mohanad Alhabo (elmdj@leeds.ac.uk)

This work was supported by the School of Electronic and Electrical Engineering, Institute of Communication and Power Networks,University of Leeds.

ABSTRACT Ultra-dense small cell deployment in future 5G networks is a promising solution to the everincreasing demand of capacity and coverage. However, this deployment can lead to severe interference andhigh number of handovers, which in turn cause increased signaling overhead. In order to ensure servicecontinuity for mobile users, minimize the number of unnecessary handovers and reduce the signalingoverhead in heterogeneous networks, it is important to model adequately the handover decision problem.In this paper, we model the handover decision based on the multiple attribute decision making method,namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The base stations areconsidered as alternatives, and the handover metrics are considered as attributes to selecting the properbase station for handover. In this paper, we propose two modified TOPSIS methods for the purpose ofhandover management in the heterogeneous network. The first method incorporates the entropy weightingtechnique for handover metrics weighting. The second proposed method uses a standard deviation weightingtechnique to score the importance of each handover metric. Simulation results reveal that the proposedmethods outperformed the existing methods by reducing the number of frequent handovers and radio linkfailures, in addition to enhancing the achieved mean user throughput.

INDEX TERMS Heterogeneous networks, handover, small cells, interference, MADM, weight, TOPSIS.

I. INTRODUCTIONThe rapid growth of the number of smart mobile devicesconnected to the wireless network has led to the high datatraffic demand. The capacity demand of the cellular networkis estimated to be 1000 times higher by year 2020 [1]. Thealready deployed traditional macrocell (MC) base stationsare incapable of coping with this demand because it is verycostly to deploy MCs any time anywhere. The concept ofsmall cells (SCs), which are economic small base stationswith lower transmit power and radius coverage comparedto the MCs, has been introduced to deal with the highcapacity demand. The networks consisting of both MCs andSCs are known as heterogeneous networks (HetNets) [2].The SCs have a great benefits in enhancing the networkperformance especially for the users at MC edges. Despitetheir huge benefits, the dense deployment of SCs has ledto the problems of interference, and frequent unnecessaryhandovers. The number of handovers is very high in denseHetNets compared to the homogeneous MC-only networks.This can also lead to high probability of radio link failure.

As a consequence, the quality of service (QoS) deliveredto the end user is degraded [3]. Therefore, it is necessaryto solve these problems when dense SCs are deployed tomaximize their benefits. There have been many researchesdealing with the problem of handover (HO) in the literature.Xu et al. [4] used received signal strength (RSS) and path lossas metric for HO. Window function has been applied to theRSS of both the SC and MC to compensate for the uneventransmit power of both cells. However, the large variation ofthe path loss may lead to high number of ping-pong HOs.Singoria et al. [5] propose a call admission control to reducethe unnecessary HO in SC networks. User velocity, RSS andthe time required to maintain the minimum RSS for servicecontinuity are used as HO metrics. Only low speed user areallowed to perform HO to SC. While medium speed usersare only permitted to HO to SC when their traffic type isreal time traffic such as ongoing phone conversation. In [6],we proposed a method to minimize the number of target SCsand reduce the unnecessary HOs in HetNet. A SC target list isformed by using the distance between the user and the SC in

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M. Alhabo, L. Zhang: Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

addition to the user’s angle of movement. High speed usersare prevented from performing the HO to SCs. The resultsshow a good performance in terms of SC list minimization,unnecessary HO reduction, and network throughput improve-ment. Alhabo et al. [7] proposed a method to reduce both ofthe unnecessary HO and HO failure. A predicted time of stay(ToS) is used to remove SC, which could lead to unnecessaryHO or HO failure, from the target HO SC list. The user ishanded over to the SC, which provides the sufficient signal tointerference plus noise ratio (SINR) and has enough capacityto deliver services. Time threshold and the SINR are also usedto find a compromise between the unnecessary HO and HOfailure. Results reveal that both of the unnecessary HO andHO failure have been minimized. An inbound HO methodfor throughput enhancement and load balancing is proposedin [8]. The impact of interference and estimated ToS is used toperform offloading from MC to SC. An inbound HO marginbased on serving cell load and interference level is derivedso as to accomplish the traffic offloading. Results show thatthis method has reduced the unnecessary HO and outageprobability in addition to enhancing the achieved throughputfor both the user and the network.

The multiple attribute decision making (MADM) dealswith the selection of the best alternatives which are char-acterised based on multiple attributes. Basically, all of theMADM methods have the following characteristics:

Alternatives: sometimes called options or candidates. Allof the alternatives are ranked based on certain criteria and thebest one is nominated as candidate.

Attributes: also named metrics or criteria. Multipleattributes are taken into account when selecting the alterna-tive.

Decision matrix: the MADM problem is formulated asa matrix whose rows represent the alternatives and columnsrepresent the attributes of each alternative.

Weighting of attributes: every attribute must be weightedto measure the importance of them.

Normalization: because different attributes have differentunit of measurement, hence, the normalization is applied sothat the attributes have same scale.

The HO decision can be taken by considering differentmetrics [9]. Therefore, the MADM techniques can be a goodsolution to model and solve the HO decision problem. In thiswork, the HO decision takes into considerations the time ofstay in the target cell, user angle of movement and the SINRfor the target cell.

The selection of the attributes (HO metrics) is a crucialfactor for making the HO decision, especially in ultra-denseSCs environment. The advantages of the handover decisioncriteria can be explained as follows:

Signal to interference plus noise ratio (SINR): the smallcells are usually deployed in an unplanned manner wherethey share the spectrum with macrocell causing a severeinterference and eventually results in poor Quality of Service.The achievable data rate of a mobile device is a function ofSINR. Therefore, the best performance cannot be achieved if

only the received signal strength (RSS) is used as handovercriterion. Therefore, in this paper, we take the interference inthe network into consideration by using SINR as a selectioncriterion.

Time of Stay (ToS): the short association of the user tothe base station can be considered as an unnecessary han-dover. Therefore, the predicted time of stay which is anindication of the time that a user may stay in the coveragearea of the target base station will help making a decisionwith reduced chance to handover to a base station and stay fora short time. This will eventually improve the service experi-enced by the end user and reduce the signalling overhead.

The user angle of movement with respect to the target cell(θ ): the user can have some neighbour small cells that offergood communication channel in terms of SINR but thesesmall cells may locate in an opposite direction of the user’smovement. Therefore, it is not recommended to perform thehandover to such small cells because this may cause unneces-sary handover and lead to high signalling overhead. For thisreason, we use the user angle of movement with respect to thesmall cell as one of the decision criteria to reduce the numberof target base stations.

Giving fixed weights for the attributes is inefficient strat-egy because this may lead to improper cell selection and canresult in either unnecessary HO or HO failure which eventu-ally will reduce the throughput and increase the signallingoverhead. Therefore, we deploy two weighting techniquesthat compute the attribute weight based on the actual valuesof these attributes and for all alternatives. The three criteriawhich take into account the most influential factors are usedin TOPSIS base station selection and weighted using twotechniques, the entropy and standard deviation. When themoving speed or angles change, the weighting techniqueassign different weights for handover criteria. In other words,if any of the metrics has no significant influence on thehandover decision making, then the weighting techniqueswill assign a poor weight for this metric and vice versa. In thisway, the best target base station can always be selected in thepresence of mobility.

In general, the selection of the best alternative among theavailable ones is widely adopted in wireless sensor networksresearch field through the use of TOPSIS method. However,in the field of heterogeneous networks (specifically for ultra-dense small cells), the TOPSIS method is rarely investigated.Moreover, the few works available are dealing with basestation selection for static users [10] and do not considerthe handover due to the user mobility which is a big chal-lenge in future 5G networks. To the best of our knowledge,the exploitation of entropy and standard deviation weight-ing techniques (for handover metrics weighting), which areconsidered as an objective weighting techniques that assignvery small weights to the attributes with small influence ondecision making, in TOPSIS method is also not considered inthe literature.

In this work, a modified weighted TOPSIS methods areproposed. We deployed the entropy weighting technique for

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attributes weighting. We also adopt the standard deviationweighting method to weight the importance of HO metrics ofeachMC and SC in the heterogeneous network. The HOmet-ric with the higher deviation variation, compared to the meanvalue, will obtain larger weight value. In other words, this HOmetric will have a higher impact in HO decision making com-pared to other HOmetrics. To the best of our knowledge, bothof the SD and entropy weighting techniques have not beenapplied on HO problem in ultra-dense SCs heterogeneousnetworks. Using numerical simulations, the proposed meth-ods’ performance is compared against other exiting methodsin terms of the number of HOs, radio link failures and usermean throughput.

We proposed two TOPSIS methods, one of themuses entropy weighting to weight the attributes (namedPE-TOPSIS) and the second one uses the standard devi-ation weighting technique (named PSD-TOPSIS). ThePSD-TOPSIS shows better performance but higher compu-tational complexity. On the other hand, PE-TOPSIS showslower complexity but worse performance. As we know,there are different small cells with different sizes and trans-mit power, and hence, different capabilities. For example,the femtocells have small capabilities in terms of size andtransmit power compared to the picocells.We draws a conclu-sion that when the complexity is not an issue in the applica-tion, then the PSD-TOPSIS method would be a good solutioni.e., it can be used in picocell base stations. On the other hand,the PE-TOPSIS method can be used for femtocells.

Upper-case boldface letters are used to represent matricesand lower-case boldface are used to represent vectors. Themajor contribution of this paper can be summarized as fol-lows:• The well-known MADM technique, TOPSIS, is used tomodel the HO problem. Two methods are proposed andboth of them use the user angle of movement, ToS andSINR to form the HO decision matrix.

• The first method weights the attributes via entropyweighting technique, and hence named as (PE-TOPSIS).

• The second proposedmethod uses the standard deviationweighting technique to assign weights to the attributes(HO metrics) and hence, named as proposed weightedtechnique for order preference by similarity to an idealsolution (PSD-TOPSIS).

• Results revealed that the proposed methods PE-TOPSISand PSD-TOPSIS have outperformed the existing meth-ods in the literature by reducing the number of HOs andradio link failure, in addition to enhancing the achievedmean user throughput.

• Based on the complexity of calculations, we suggestusing the PE-TOPSIS method for low power SCs (e.g.residential femtocells) and the PSD-TOPSIS method forother types of SCs (e.g. picocells).

The rest of the paper is organized as follows. Section IIpresents the related work. The system model is given insection III. The proposed methods’ procedures are illustratedin section IV. Section V gives the proposed weighting tech-

niques. The performance and results analysis are given insection VI. Finally, the conclusion and future work are drawnin section VII.

II. RELATED WORKSMADM techniques are widely adopted recently in makingdecisions for multiple criteria problems. One of the mostwidely used MADM method is the Technique for OrderPreference by Similarity to Ideal Solution (TOPSIS). TOPSISmethod’s principle, in wireless network field, is to selectthe target which is closest to the positive ideal solution andfarthest from the negative ideal solution. Positive ideal solu-tion is based on the best value for the attributes used indecisionmaking.While negative ideal solution is based on theworst attributes values [11]. In the field of network selection,many researches in the literature have been accomplishedby using TOPSIS method to solve the HO decision mak-ing. Bari and Leung [12] proposed a TOPSIS method takinginto account cost, total bandwidth, network utilization, delay,and jitter when building the HO decision matrix. Anotherresearch paper in [13], a TOPSIS method is proposed torank the available networks. Different parameters are usedwhen forming the decision matrix, such as the availablebandwidth, cost, and security level. Chen et al. [14] proposeda TOPSIS based method to reduce the connection failure inheterogeneous networks. The user performs HO to the targetcell in either two ways. First, when the received power isvery low, even before the time to trigger expires so as toavoid radio link failure. Second, when the received signalfrom the serving cell is high enough but the downlink SINRdrops below a predefined threshold. Results show that thismethod reduce the number of HOs, packet loss and increaseuser mean throughput. However, the use of predefined valuefor weighting the HO metrics could show some deficiency inHOdecision due to the large variation in signal power becauseof user mobility specially for fast moving ones in dense SCsscenarios.

III. SYSTEM MODELIn this work, as shown in Fig.1, we consider a two-tierdownlink HetNet scenario consisting of a single MC of 500mradius and Nsc number of SCs with a radius of 100meach. Thus, we have a total number of Nbs base stations inthe network. SCs are deployed randomly following uniformdistribution. Both tiers are deployed with the same carrierfrequency. The minimum distance between MC site and SCsites is set to 75m and the SC to SC site distance is set to40m [2], which ensures an overlapping between SCs. Usersare distributed uniformly in the MC coverage area and theymove in a random direction with a constant speed. In thismobility model, the UE moves in straight line with a constantspeed. It goes to a selected direction [0, 2π ] to the boundary.Upon completing the movement by reaching the boundary,the UE pauses and decides to move to another direction andtravels to complete a second movement. This process is inde-pendently repeated until the simulation is finished. Which

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means that the UE has different angle of movement duringthe simulation. In this case, the UE angle of movement ismeasured with regards to the coordinates of the base stationsat each period of time, so it is not constant. This movementdirection, i.e., angle θ , is used to compute the time of stay andit is different with respect to different base stations.

FIGURE 1. HetNet system model.

For the sake of clarity, we define a list of abbreviations asdepicted in table 1.

TABLE 1. List of abbreviations.

A. CHANNEL MODELA large scale channel is considered using the path loss modeland shadowing effects. The path loss between theMC and theuser is defined as in [15] by

δm→uek = 128.1+ 37.6 log10(dm→uek ), (1)

where dm→uek is the distance between the user and the MCbase station in kilometres. The path loss between the SC and

the user is defined as in [16] by

δsci→uek = 38+ 30 log10(dsci→uek ), (2)

where dsci→uek is the distance between the user and SC i inmetres.

The SINR from SC i and MC received at user k canrespectively be expressed as

γ rsci→uek =Prsci→uek∑Nbs

j=1,j 6=i Prbsj→uek + σ

2, (3)

γ rm→uek =Prm→uek∑Nbs

j=1,j 6=m Prbsj→uek + σ

2, (4)

where Prsci→uek and Prm→uek are respectively the referencesignal received power (RSRP) received from SC i and MC,Prbsj→uek is the RSRP from the interfering MC/SCs, γ rm→uekis the SINR received fromMC at user k , γ rsci→uek is the SINRreceived from SC i at user k , σ 2 is the noise power, and Nbsis the total number of MC and SCs in the network.

B. TIME OF STAY MEASUREMENTAs depicted in Fig.2, the real ToS, ToSrealuek , can be measuredas

ToSrealuek =|−−−−→AinAout |Vk

=2Ri cos(α)

Vk, (5)

where Ain, and Aout are respectively the entry and the exitpoints of the UE to and from base station i, Ri is the basestation radius, and Vk is the velocity of user k .

FIGURE 2. Time of stay measurement.

We can get the following from Fig.2

| A1A0 |sin(180− α)

=Ri

sin(θ ), (6)

where A0, and A1 are respectively the location of base stationi, and the previous location of the UE.

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M. Alhabo, L. Zhang: Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

Equation (6) can be rewritten as

sin(α) =| A1A0 | sin(θ )

Ri(7)

Therefore

cos(α) =

√√√√1−

(| A1A0 | sin(θ )

)2R2i

(8)

The angle between the UE trajectory and the base station i, θ ,can also be calculated as

θ = arccos( −−→

A1A0 ·−−→A1A2

|−−→A1A0 | × |

−−→A1A2 |

), (9)

where A2 is the current location of the UE.Finally, we substitute (8) and (9) in (5) to get the real time

of stay as

ToSrealuek =

2Ri

√√√√√1−

(|−−→A1A0|· sin

(arccos

( −−−→A1A0·

−−−→A1A2

|−−−→A1A0|×|

−−−→A1A2|

)))2

R2i

Vk.

(10)

IV. PROPOSED WEIGHTED TECHNIQUES FOR ORDERPREFERENCE BY SIMILARITY TO AN IDEAL SOLUTIONThe proposed methods adopt one of the well known MADMtechniques, Technique for Order Preference by Similarity toan Ideal Solution (TOPSIS), to select the proper target cellfor HO by ranking the available neighbouring candidate cells.The attributes (i.e. HO metrics) used to rank the target cellsare: the time of stay (ToSrealuek ), the user angle of movement (θ )and the SINR of the target cell.

The HO decision is based on choosing a proper alternative(i.e. base station) among the available set of alternatives. Theproposed methods grant that the selected HO target cell issuboptimal solution i.e. near the positive ideal solution andfar from the negative ideal solution. Henceforth the basestation(s) will be called alternative(s) and the HO decisionmetric(s) will be called attribute(s). The user has a set of Nbstarget alternativesm = {1, 2, · · · ,Nbs}with a set of attributesn = {1, 2, 3} and attributes weighting vector w. We canpresent our proposed methods’ procedures as follows:Procedure 1: The decision matrix, D, is formed by map-

ping the alternatives against the attributes as shown

D =

a11 a12 · · · a1na21 a22 · · · a2na31 a32 · · · a3n...

......

...

am1 am2 · · · amn

, (11)

where the each row represents one alternative, and thecolumns represent their correspondent attributes, n =

1, · · · , 3, m = 1, 2, · · · ,Nbs, aij represents the value of thejth attribute (HO metric) for the ith alternative (base station).In this paper, ai1 = θ , ai2 = ToS, and ai3 = SINR.

Procedure 2: The decision matrix is then normalized usinga Square root normalization method as described in (12)

anormij =aij√∑mi=1 a

2ij

, anormij ∈ [0, 1], (12)

where anormij is the jth normalized attribute of the ith alterna-tive. Which means that each element in the decision matrixD is divided by its correspondent column squared-elementssum. Thus, we can write the normalized decision matrix, Dn,as

Dn=

a11√∑mi=1 a

2i1

a12√∑mi=1 a

2i2

a13√∑mi=1 a

2i3

a21√∑mi=1 a

2i1

a22√∑mi=1 a

2i2

a23√∑mi=1 a

2i3

a31√∑mi=1 a

2i1

a32√∑mi=1 a

2i2

a33√∑mi=1 a

2i3

......

...am1√∑mi=1 a

2i1

am2√∑mi=1 a

2i2

am3√∑mi=1 a

2i3

. (13)

Procedure 3: The normalized matrix is weighted in thisstep so as to take into account the importance of eachattribute. The detailed weighting calculations are presentedin sections V-B and V-A. Thus, the weighted normalizeddecision matrix can be expressed as

Dn,w=

anorm11 · w1 anorm12 · w2 anorm13 · w3

anorm21 · w1 anorm22 · w2 anorm23 · w3

anorm31 · w1 anorm32 · w2 anorm33 · w3...

......

anormm1 · w1 anormm2 · w2 anormm3 · w3

=

d11 d12 d13d21 d22 d23d31 d32 d33...

......

dm1 dm2 dm3

(14)

subject to∑j∈n

wj = 1, (15)

where dij is the jth weighted normalized attribute of the ith

alternative i.e., d11 = anorm11 · w1, d12 = anorm12 · w2 and so on.Procedure 4: The weighted normalized decision matrix

is used to find the ideal positive solution (best alternativewhich has the best attribute values, denoted as a+) and theideal negative solution (worst alternative which has the worstattribute values, denoted as a−) by

a+ ={(maxi∈m

Dn,wij | j ∈ j+), (mini∈m

Dn,wij | j ∈ j−)}

=

{d+1 , d

+

2 , d+

3

}, (16)

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M. Alhabo, L. Zhang: Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

a− ={(mini∈m

Dn,wij | j ∈ j+), (maxi∈m

Dn,wij | j ∈ j−)}

=

{d−1 , d

−

2 , d−

3

}, (17)

where j+ is the set with the attributes having positiveimpact (i.e., the higher value the better) such as SINR andToS, and j− is the set with the attributes having negativeimpact (i.e., the lower value the better) such as θ . The bestalternative value for the attributes θ , ToS and SINR arerespectively min(θ ), max(ToS) and max(SINR). On the otherhand, the worst alternative for the attributes are respectivelymax(θ ), min(ToS) and min(SINR). Hence, θ is considered asa cost attribute and both ToS and SINR are considered asbenefit attributes.Procedure 5: Compute the Euclidean distance between

each alternative and both the positive and negative idealsolutions as shown below

dist+ =

√√√√ n∑j=1

(Dn,wij − d+

j )2, ∀i = 1, · · · ,m (18)

dist− =

√√√√ n∑j=1

(Dn,wij − d−

j )2, ∀i = 1, · · · ,m (19)

Procedure 6: In this step, the ranking network vector,r, is obtained so as to measure the relative closeness of eachcandidate alternative to the ideal solution, as shown

r =dist−

dist+ + dist−, ∀i = 1, · · · ,m. (20)

According to [17], it has been shown that in some situationsthe above equation in (20) cannot ensure that the optimalalternative is having the shortest distance from the positiveideal solution and the farthest distance from the negative idealsolution at the same time. Therefore, the formula in (20)can be replaced by the revised closeness as in (21), whichcomputes the extent to which the optimal alternative closesto the positive ideal solution and far from the negative idealsolution, that is

r =dist−

max(dist−)−

dist+

min(dist+), ∀i = 1, · · · ,m. (21)

Indeed, ∀i = 1, · · · ,m, r(i) ≤ 0, bigger r means the betteralternative. When an existing alternative satisfies both ofthe conditions

(max(dist−) = dist−

)and

(min(dist+) =

dist+), this means that this alternative is the best one which

is close to the positive ideal solution and far away from thenegative ideal solution.Procedure 7: The resulted vector from the previous step

is then ranked in descending order and the best alternative(with the highest rank) from r vector is selected as a target(i.e., the HO target base station)

HOtarget = argmax r(i). (22)

V. ATTRIBUTE WEIGHTING MEASUREMENTSAttributes weighting represents a very significant role inHO decision making. Thus, the way of determining theweights is a crucial factor for the proposedmethods. Differenttechniques have been proposed to deal with the weights.We present two weighting techniques in this section, namelythe entropy and standard deviation weighting techniques.We also validate and compare the differences between the twotechniques using a numerical example in the subsection V-C.

A. ENTROPY ATTRIBUTES WEIGHTINGThe entropy weighting technique measures the uncertainty inthe data by using the probability theory. This means that if thedata distribution is broad then the uncertainty is higher. On theother hand, if the data distribution is sharply peaked thenthe uncertainty is lower. The entropy weighting techniqueprecisely calculates the amount of decision information thateach attribute has in the decision matrix [18]. The entropytechnique is a type of objective weighting techniques whichmeasures the attribute weight based on the relative differencebetween them. The resulted weight of the attribute is thennormalized to obtain the entropy weight of that attribute [19].The jth entropy coefficients divergence degree, denoted ej,can be measured using the normalized decision matrix

ej = 1− cj, (23)

where cj =[

1ln(n)

n∑i=1

anormij ln(anormij )], (24)

and the term 1ln(n) is a constant which ensures that value of

coefficient cj ∈ [0,1] i.e., 0 ≤ cj ≤ 1.The entropy coefficient divergence degree ej represents the

inherent contrast intensity of the attributes (i.e., HO met-rics). The more divergent the values of anormij for attribute j,the higher its corresponding entropy coefficient divergencedegree ej, and the more important the attribute j for HOdecision. In other words, this means that if the alternativeshave similar performance ratings for a certain attribute, thenthis attribute has less influence in HO decision making.On the other hand, if an attribute j for all alternatives in thedecision matrix is identical, then this attribute is not usefulin HO decision making because it has absolutely no usefulinformation for the decision maker [20]. For example, for agiven attribute j, when all elements anormij are the same, thenthe coefficient cj ≈ 1 which means that ej ≈ 0 and hence,the weight of this attribute becomes zero as well. This meansthat this attribute has no effect on the HO decision.

Finally, the entropy weighting of the jth attribute isexpressed as

wej =ej∑nj=1 ej

, (25)

where wej is the final weight of the jth attribute using theentropy weighting technique. The entropy weighting tech-nique is not affected by the range of different attributes values

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M. Alhabo, L. Zhang: Multi-Criteria Handover Using Modified Weighted TOPSIS Methods for Heterogeneous Networks

because it uses the normalized attributes (i.e., anormij ) forweight calculation [21].

B. STANDARD DEVIATION ATTRIBUTES WEIGHTINGThe proposed method also deploys the standard deviation(SD) weighting technique [22] so as to rate the importance ofthe attributes for each cell in the network. The SD weightingtechnique measures the weights of each attribute in terms ofthe standard deviation.

The SD weighting technique gives a small weight for anattribute if the value of this attribute is identical for all avail-able alternatives. For example, if an attribute has an equalvalues on all available alternatives, then it has no significantimpact on HO decision making and hence, its weight is null.In other words, attributes with small standard deviation aregiven smaller weights and vice versa.

The weighting vector w represents the importance of theattribute (HOmetrics). Thus, w1, w2, and w3 are respectivelythe weights of θ , ToS, and SINR. The weights can be calcu-lated using SD technique as

wsdj =σj∑3k=1 σk

, (26)

σj =

√√√√ 1m

m∑i=1

(anormij − µj)2, (27)

µj =1m

m∑i=1

anormij , (28)

where σj and µj are respectively the standard deviation andthe mean value of the jth normalized attribute.

C. NUMERICAL EXAMPLETo validate and compare the differences between the weight-ing techniques, we examine a numerical example, whosedecision matrix is given as

D =

θ ToS SINR

A1 80 100 −109A2 45 20 −106A3 20 50 −81A4 5 90 −45

where Ai is the ith alternative ∀i = 1, · · · , 4.First, the decision matrix is normalized by Square root

normalization method as

Dn=

θ ToS SINR

A1 0.8504 0.6901 0.6149A2 0.4783 0.1380 0.5937A3 0.2126 0.3450 0.4537A4 0.0531 0.6211 0.2521

Then, we can obtain the weighting vector for the entropy andSD techniques respectively as

we=[0.0189 0.0144 0.9667

],

wsd=[0.4522 0.3310 0.2168

],

It is clear that the entropy and SD techniques evaluate thethree attributes with different ranking, i.e., w3 > w1 > w2for entropy and w1 > w2 > w3 for SD, where w1, w2 and w3are respectively the weights of θ , ToS and SINR.The entropy technique gives very high weight for the

SINR, about 97%, and fewer weights for θ and ToS,about 1.8% and 1.4% respectively. Unlike the entropy tech-nique, the SD technique assigns more moderate and accurateweights for the attributes 45%, 33% and 21% for θ , ToS andSINR respectively.

The entropy technique nearly gives thewholeweight to oneattribute (i.e., SINR) which is undesirable because the ToSand θ attributes are also significant factors for HO decision.The user may receive high SINR from a certain cell but itsToS is very short and its moving direction is away from thecell (i.e., θ is very large) and hence, assigning a higher weightfor only SINR is considered as a drawback of this techniquewhich will result in an increase in the number of unnecessaryHOs and leads to throughput reduciton. These problems havebeen avoided by the SD technique by distributing the weightsmore positively among attributes.

Thus, we now have two proposed methods. The firstmethod utilizes the entropy weighting technique to find theweighting vector w and is named as PE-TOPSIS. The sec-ond one uses the SD weighting technique for measuringthe weighting vector w and is named as PSD-TOPSIS. Theprocedures of the proposed methods PE-TOPSIS and PSD-TOPSIS are illustrated in Fig.3. The procedures begin byfirst obtaining the cells that have a downlink RSRP greaterthan or equal to the threshold (RSRPth). This step is essentialto reduce the number of alternatives in the decision matrixand hence, reducing the computational complexity. For eachof the obtained cells, the parameters θ , ToS, and SINR aremeasured to build the decision matrix. Then, the normaliza-tion of the decisionmatrix is applied. After that, theweightingvector w is calculated using the entropy weighting techniquefor PE-TOPSIS method and standard deviation weightingtechnique for PSD-TOPSIS method. The resulted cells fromthe previous steps are combined in vector r. Finally, the HOtarget is the cell with highest order in vector r.

VI. PERFORMANCE AND RESULTS ANALYSISThe performance of the PE-TOPSIS and PSD-TOPSIS meth-ods is evaluated in terms of number of handovers, radiolink failure and user mean throughput and compared againstother three methods, the conventional method, the networkcontrolled HO method (NCH) in [23] and the method in [14]denoted as TOPSIS, which uses a predefined weighting vec-tor with fixed values. Simulations parameters are listed intable 2 [24].

According to [25], the density of the number of nodes (hereSCs) in a given coverage area can be obtained by using thedefinition of the density metric, Dsc, as

Dsc =| Nsc | πR2sc

πR2m, (29)

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FIGURE 3. Procedures of PE-TOPSIS and PSD-TOPSIS.

TABLE 2. Simulation parameters.

where Rsc and Rm are respectively the SC andMC radius. Thedenominator represents the area of the umbrella base stationi.e., the MC coverage area. Thus, if the SC density metricDscis equal to 1, this means that the deployment of the SCs coversthe whole area of the MC coverage area. While a higher than1 value means that the SCs are covering the whole area ofMCand an overlapping is ensured among the SCs. We set up thenumber of SCs to 50, which means that Dsc ≈ 2 and hence,the dense SCs scenario is achieved.

FIGURE 4. Number of handovers.

First, we only compare the PE-TOPSIS with the conven-tional, NCH and TOPSIS methods.

A. NUMBER OF HANDOVERSFig.4 depicts the total number of HOs per second. Two dif-ferent scenarios are shown, when the density of the users are1 and 5 per one MC. For all methods, the lower the densityof the users the lower the number of HOs for all velocities.It is clear that the conventional andNCHmethods have highernumber of HOs compared to TOPSIS and PE-TOPSIS. Thisis because that both methods do not predict the target cell forHOs and they respectively perform the HO when the down-link received power from the neighbour cell is offset greaterthan that of the serving cell for TTT period of time and if theSINR is below the SINR threshold for NCH method. On theother hand, the TOPSIS and PE-TOPSIS have less numberof HOs compared to the other two methods. The PE-TOPSIShas also outperformed the TOPSIS method by reducing thenumber of HOs due to the modified entropy weighting calcu-lations which leads to proper assigning of importance to theHO metrics θ , ToS and SINR. Unlike the TOPSIS methodwhich assigns a fixed weights for the HO metrics. Unlikethe high speed users, the low speed users will not cause ashort time of stay phenomena, therefore, the number of HOsis lower for low speed users which clarify the advantageof incorporating the ToS criterion. Additionally, the anglecriterion omits the base stations that are not in the user’smovement direction resulting in a fewer number of targetbase stations, and hence, reduce the number of unnecessaryhandovers compared to the competitive methods.

The percentage of each type of HO compared to the totalnumber of HOs is presented in Fig.5. These percentage havebeen taken for three types of user velocity, low at 20km/h,medium at 60km/h and high at 100km/h. For the case ofoutbound HO (i.e., SC to MC HO), both the conventionaland NCH methods have the higher percentages of HO andthese percentages grow as the user velocity increases. On theother hand, TOPSIS and PE-TOPSIS methods have lower

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percentages of HOs with the PE-TOPSIS having less percent-age than that of the TOPSIS. It is obvious from Fig.5 that thePE-TOPSIS has eliminated the outbound HO for low speeduser, hence, low speed users are preferred to stay connectedto SC rather than performing HO to MC. For the case ofinbound HO (i.e., MC to SC HO), all of the four methodshave an instantaneous increase in the HO percentage with theincrease in user velocity with the PE-TOPSIS method havinga slight drop at high speed due to the HO target predictionwhich reduces the unnecessary HO to SC for high speedusers. For the case of inter-SC HO (i.e., SC to SC HO),all methods also show an instantaneous increase in the HOpercentage as the velocity increases due to the high densityof SCs. It is clear that PE-TOPSIS has lower HO percentagethan that of the TOPSIS at high speed because the properHO target prediction of PE-TOPSIS let the high speed usersoccasionally perform HO to SC (when the SINR of MC is notsufficient) so as to reduce the radio link failure which maylead to HO failure.

FIGURE 5. The percentage of handover frequency.

B. RADIO LINK FAILUREA radio link failure is declared if the HO is initiated to thetarget cell from vector r but the SINR of that cell drops belowthe threshold γth for a period of time window T310, which is1 second, as defined in [26]. The radio link failure is depictedin Fig.6. The higher the speed the higher the radio link failurefor all methods. The conventional method yields higher fail-ure due to the frequent HOs as the velocity increases, hence,the HOwill be initiated but interrupted before completion dueto the sudden drop in the target cell received power at the userside. The NCH method has lower failure compared to theconventional method because it performs the HO when theSINR of the serving cell drops below a predefined threshold.Both the TOPSIS and PE-TOPSIS methods have the lowestradio link failure with the PE-TOPSIS outperforming spe-cially at high speeds due to the early HO to the correctlypredicted HO target cell. The low radio link failure in thePE-TOPSIS method emphasizes the accuracy of weightingassignment to the HO metrics which leads to an accurate

cell selection compared to the other methods. Additionally,the low link failure in PE-TOPSIS method comes from thepositive influence of utilizing the angle criterion where theusers will avoid initiating the HO to the base station thatare located away from it is movement direction, and hence,the failure will be reduced.

FIGURE 6. Radio link failure.

FIGURE 7. User mean throughput.

C. USER MEAN THROUGHPUTFig.7 shows the user mean throughput for the four methods.All methods have dropped in the mean user throughput as thevelocity increase. The conventional and NCH methods havethe lowest throughput compared to the other two methodsbecause of their higher number of unnecessary HOs whichresults in producing a lower throughput for the user (since thehigh speed users will result in radio link failure which leads topoor throughput gain). The TOPSIS and PE-TOPSISmethodsproduce higher throughput because they perform the HOupon the proper target predictionwith the PE-TOPSIS outper-forming the TOPSIS method. Higher throughout especiallyfor low speed users reflects the receiving of high SINR at the

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FIGURE 8. Number of handovers.

FIGURE 9. Radio link failure.

user side. Therefore, the incorporation of SINR criterion hasthe advantage of improving the throughput at all velocities.

D. COMPARING PE-TOPSIS AND PSD-TOPSISIn this subsection we compare the performance ofPE-TOPSIS with that of the PSD-TOPSIS methods in termsof the number of HOs, radio link failure, user mean through-put and complexity of calculations.

Fig.8 shows that the number of HOs has been reducedin PSD-TOPSIS method compared to PE-TOPSIS. For allvelocities, the PSD-TOPSIS method produces less numberof HOs. The SD weighting technique provides more stableweights to the attributes which in turn leads to an efficientalternative selection among the available options.

The radio link failure is depicted in Fig.9. ThePSD-TOPSIS method reduces the radio link failure, whichmay cause HO failure. The level of increase in the link failureincreases with the increase in user velocity according to thecommon sense because the fast moving users may leave thecoverage area of the cell before completing the HO process,hence the failure increases.

FIGURE 10. User mean throughput.

In Fig.10, the mean user throughput is illustrated.As expected the PSD-TOPSIS method produces higherachieved throughput for the user.

For the sake of clarity, we did not compare the proposedPSD-TOPSIS method with the conventional, NCH or TOP-SIS because those methods have already been outperformedby our proposed method PE-TOPSIS.

To further conclude the impact of the weighting techniqueson the proposed methods, we compare the performance in aform of tables. Tables 3, 4 and 5 give the numerical results ofthe PE-TOPSIS and PSD-TOPSISmethods when the velocityis 20km/h, 40km/h and 80km/h respectively.

TABLE 3. Performance analysis at 20 km/h.

TABLE 4. Performance analysis at 40 km/h.

TABLE 5. Performance analysis at 80 km/h.

We can see from the tables that the PSD-TOPSIS methodhas outperformed PE-TOPSIS at all velocities. For instance,when the velocity is 20km/h, the number of HOs is reduced by9%. Furthermore, the radio link failure is reduced by 30.7%in the same case. At a velocity of 80km/h, the number ofHOs is reduced by approximately 5.2% for PSD-TOPSIScompared to PE-TOPSIS. Furthermore, the radio link failure

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is minimized by 8% in the same case and the user meanthroughput is enhanced by 78.8%.

When using the entropy weighting technique the overallperformance is getting worse (but still better than that of theTOPSIS, NCH and the conventional methods) compared tothat when using SD weighting technique. This proves theadvantage of the SD over the entropy weighting techniquein distributing the weights between the attributes and hence,gives a better performance in terms of reducing the number ofHOs and radio link failures in addition to enhancing the usermean throughput.

FIGURE 11. Complexity analysis.

To further analyze the benefits of the proposed methods,PE-TOPSIS and PSD-TOPSIS, we evaluate the complexity ofboth methods. Fig.11 depicts the computational complexityof the proposed methods. This is done by evaluating thetwo methods in terms of the total number of floating pointoperations (flops) with different sizes of the decision matrix(i.e., different densities of SCs). We used the Matlab functiondefined in [27] which scans and parses each line of thesimulation code and counts the number of flops. As can benoticed from Fig.11, the computational complexity increaseslinearly with the increase in the size of the decision matrix forboth methods. The PSD-TOPSIS method has slightly highercomplexity operations compared to the PE-TOPSIS. In fact,as the size of the decision matrix increases the differencebetween the two methods in terms of complexity increases.We conclude that, when the complexity is not an issue in theapplication, then the PSD-TOPSIS method would be a goodsolution. Otherwise, the PE-TOPSIS method is an alternativeat the expense of less accuracy on attributes weight assign-ment, and hence, higher HO and link failure levels in additionto less achieved throughput.

Furthermore, higher complexity means higher energy con-sumption. Therefore, deploying PE-TOPSIS or PSD-TOPSISalso depends on the capability of the SCs. For example,when residential SCs are deployed (e.g. femtocells), then thePE-TOPSIS is more preferred due to the limited calculationcapabilities of the femtocell. On the other hand, when other

SC types are used (e.g. picocell), then the PSD-TOPSIS couldbe the best option.

VII. CONCLUSION AND FUTURE WORKIn this paper, modified weighted MADM TOPSIS methodshave been presented. The proposed methods exploit the TOP-SIS principle of ranking the HO candidate cells based ontheir attributes and the weights of each attribute. The finalHO destination cell is selected when it is close to positiveideal solution and far from the negative ideal solution. In thefirst method, PE-TOPSIS, we deploy the entropy weightingtechnique to weight the attributes. This method shows agood performance in reducing the number of HOs and radiolink failures and enhancing the achieved user throughputcompared to the NCH, TOPSIS and conventional methods.The second proposed method, PSD-TOPSIS, deploys thestandard deviation weighting technique to scale the impor-tance of each attribute for all HO candidate cells. As theresults show, our proposed PSD-TOPSIS method reachedlow number of HOs and low radio link failure, while highermean user throughput is achieved compared to the existingmethods. This method shows even better results in enhancingthe network performance by reducing the number of HOsand radio link failure, in addition to increasing the mean userthroughput owing to the accurate weight distribution betweenthe attributes. Furthermore, we compare the performance ofPE-TOPSIS and PSD-TOPSIS in terms of complexity andsuggest to choose the method based on the size and capabilityof calculations of the SCs. For smaller size SCs, the PE-TOPSIS is more suitable, otherwise, the PSD-TOPSIS is analternative solution.

As a future work, we intend to investigate the influenceof different normalization techniques on the network perfor-mance, in addition to studying the phenomena of what socalled network ranking abnormality in MADM methods.

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MOHANAD ALHABO (GS’16) received thebachelor’s degree in computer and informationengineering from the Faculty of Electronics Engi-neering, University ofMosul,Mosul, Iraq, in 2007,and the master’s degree in computer networkadministration and management engineering fromthe University of Portsmouth, Portsmouth, U.K.,in 2009. He is currently pursuing the Ph.D. degreewith the School of Electronic and Electrical Engi-neering, University of Leeds, Leeds, U.K. He was

a Network Engineer for more than four years. His research interests includemobility management, handover, and interference issues in dense small cellsheterogeneous networks.

LI ZHANG (S’01–M’05–SM’12) received thePh.D. degree in communications from the Univer-sity of York, York, U.K., in 2003. Since 2004, shehas been with the School of Electronic and Electri-cal Engineering, University of Leeds, Leeds, U.K.,where she became a Senior Lecturer in 2011. Hermain research interests include communicationsand signal processing. She has been a member ofthe U.K.’s prestigious Engineering and PhysicalSciences Research Council Peer Review College

since 2006. She received the Nuffield Award given to newly appointedlecturers in 2005 and the IEEE COMMUNICATION LETTERS Exemplary ReviewerAward in 2010. She is serving as an active reviewer for a number of journalsand as a technical program committee member of a number of conferences.

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