Research Article An Adaptive Measurement Report Period and ...downloads.hindawi.com/journals/mpe/2015/360835.pdf · An Adaptive Measurement Report Period and Handoff Threshold Scheme

Research ArticleAn Adaptive Measurement Report Period and HandoffThreshold Scheme Based on SINR Variation in LTE-A Networks

Jenhui Chen and Ching-Yang Sheng

Department of Computer Science and Information Engineering School of Electrical and Computer Engineering College of EngineeringChang Gung University Kweishan Taoyuan 33302 Taiwan

Correspondence should be addressed to Jenhui Chen jhchenmailcguedutw

Received 26 September 2014 Accepted 25 March 2015

Academic Editor Yuming Qin

Copyright copy 2015 J Chen and C-Y Sheng This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

This paper deals with the problem of triggering handoff procedure at an appropriate point of time to reduce the ping-pong effectproblem in the long-term evolution advanced (LTE-A) network In themeantime we also have studied a dynamic handoff thresholdscheme named adaptive measurement report period and handoff threshold (AMPHT) based on the user equipmentrsquos (UErsquos)reference signal received quality (RSRQ) variation and themoving velocity of UE AMPHT reduces the probability of unnecessarilypremature handoff decisionmaking and also avoids the problem of handoff failure due to too late handoff decisionmakingwhen themoving velocity of UE is high AMPHT is achieved by two critical parameters (1) a dynamic RSRQ threshold for handoff making(2) a dynamic interval of time for the UErsquos RSRQ reporting The performance of AMPHT is validated by comparing numericalexperiments (MATLAB tool) with simulation results (the ns-3 LENA module) Our experiments show that AMPHT reduces thepremature handoff probability by 34 at most in a lowmoving velocity and reduces the handoff failure probability by 25 in a highmoving velocity Additionally AMPHTcan reduce a large number of unnecessary handoff overheads and can be easily implementedbecause it uses the original control messages of 3GPP E-UTRA

1 Introduction

To determine a more accurate handoff triggering point oftime is a critical point to greatly reduce the failure probabilityof handoff [1 2] Too early handoff triggering point of time(ie too slow moving velocity of the user equipment (UE))will cause ping-pong effect problems in handoff processes[3 4] Similarly too late handoff triggering point of time (ietoo fast moving velocity of the UE) will also lead to handofffailure [3 4] How to find an exact handoff triggering pointof time to increase the success probability of handoff and todecrease handoff overheads is a critical issue

In the 3GPP Evolved Universal Terrestrial Radio Access(E-UTRA) standard [5] a decision of handoff makingdepends on the UErsquos current reference signal received quality(RSRQ) which is the received signal strength in dB orreference signal received power (RSRP) which is the receivedsignal strength in dBm The RSRQ measurement providesadditional information when RSRP is not sufficient to make areliable handover or cell reselection decision The serving

eNB (SeNB) periodically sends the measurement controlmessage (MCM) to ask the UE to report its latest RSRQ valueby replying the measurement report message (MRM) to theSeNB When the RSRQ value is lower than a predefinedthreshold the handoff procedure will be initiated Howeverthe reported UErsquos RSRQ value is highly related to the circum-stance that the UE stays and is also related to the movingvelocity of UE [6] Moreover an appropriate handoff trigger-ing point of time is hard to determine (ie for a better handoffquality as well as lower handoff overheads) because in3GPP E-UTRA the value of RSRQ for triggering handoffprocedures and the value of RSRQ report period are fixed

Researchers were devoted to developing the queueingmodel for investigating the performance of handoff proce-dures in the last decades [7ndash10] They improved the handoffperformance by decreasing the number of steps of handoffprocedures based on the global positioning system (GPS) formonitoring the position of the UE and the UErsquos correspond-ing movement intension [11 12] However the GPS maysuffer a serious interference problem under an extremely bad

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2015 Article ID 360835 10 pageshttpdxdoiorg1011552015360835

2 Mathematical Problems in Engineering

weather situation [11 12] To overcome the drawback manyhandoff schemes without the GPS application were studied[13ndash16] Nevertheless these schemes cannot determine anaccurate handoff triggering point of time and prevent theping-pong effect as well [17 18] Some studies consideredthe signal power into their proposed methods [6 19ndash24] butthey neglected the impact of the moving velocity of UE onsignal strength variation AlthoughGuo et al [25] consideredthe mobility management they did not consider using thesignal power to assist handoff decision making Fu et al [26]proposed a mathematical model to estimate a proper delaytime and set a delay timer for handoff triggering to reducesignaling overheads but theirmethod cannot reflect the effectof moving velocity of UE on handoff success probability

The aforementioned handoff drawbacks and problemsmotivate us to investigate a better handoff scheme taking avariable RSRQ-based handoff threshold and a variable reportperiod related to RSRQvariationWe observed that the RSRQvariation is highly related to the moving velocity of UE andthe environments the UE staysThat is the RSRQ variation ofUE is highly related to its moving velocity and diverseenvironments Based on these we propose a signal-aware andsignal-variation-aware (velocity-aware) handoff schemecalled adaptive measurement report period and handoffthreshold (AMPHT) to accurately trigger the handoffpoint of time AMPHT not only increases handoff successprobability but also reduces the handoff ping-pong effectproblem Moreover AMPHT is compatible with 3GPP E-UTRA standard because it uses the original control messagesof 3GPP E-UTRA [5]

The rest of this paper is organized as follows Section 2introduces the details of handoff procedure of 3GPP E-UTRAstandard and related works Section 3 presents the systemmodel of proposed AMPHT mechanism The details ofAMPHT are presented in Section 4 We show the numericalexperiments and simulation results on different performancemetrics in Section 5 Finally the conclusion is given inSection 6

2 Background

In this section we simply introduce the handoff mechanismof 3GPP E-UTRA [5] and related system control messagesThe handoffmechanismof 3GPPE-UTRAmakes the handoffdecision based on the measurement of UErsquos RSRQ valueAccording to 3GPP TS 36133 [27] and TS 36213 [28] therelation between the signal-to-interference plus noise ratio(SINR) and RSRQ (in dB) can be given by

SINR = 1

1 (119873scRSRQ) minus 119909 (1)

where 119873sc = 12 represents the number of subcarriers perresource block and 0 lt 119909 le 1 is the instantaneous serving cellsubcarrier activity factor In this paper we follow the 3GPP E-UTRA [5] to use themeasure of RSRQ because RSRQ reflectsthe signal quality over the resource block The RSRQ value isgotten by periodical UErsquos MRM reporting The detailedmechanism is given below

Table 1 Event list predefined in 3GPP E-UTRA standard

Event Triggering situationA1 RSRQ of serving cell becomes better than 120595ℎ1A2 RSRQ of serving cell becomes worse than 120595

ℎ1

A3 RSRQ of neighbor cell becomes offset better than SeNBA4 RSRQ of neighbor cell becomes better than 120595ℎ1A5 RSRQ of PCell becomes worse than 120595ℎ1 and RSRQ of

neighbor cell becomes better than 120595ℎ2A6 RSRQ of Neighbor cell becomes offset better than TeNB

(i) Checking UErsquos RSRQ periodically The SeNB sendsMCM periodically to require UE to report its latestRSRQ value denoted as 120595 via the MRM back to theSeNB The MCM and MRM are the radio resourcecontrol (RRC) connection reconfiguration messagesdefined in 3GPP E-UTRA [5] In the 3GPP E-UTRAstandard there are 6 different events as shown inTable 1 for handling the reported RSRQ from the UEThe terms PCell and TeNB represent the primary celland the target eNB respectively The SeNB passivelyhandles the reported RSRQ from the UE and coordi-nates the handoff procedure if it is needed

(ii) Fixed RSRQ thresholds 120595ℎ1 and 120595ℎ2 In the 3GPP E-UTRA standard there are two fixed RSRQ thresholdvalues denoted as 120595ℎ1 (a higher value) and 120595ℎ2 (thelow bound for remaining connected) for judging thenecessity of handoff Usually event A2 is the caseof starting the handoff procedure All handoff algo-rithms depend on these two threshold values to judgetheir handoff approaches

Although the 3GPP E-UTRA provides the guideline ofhandling the time point of handoff triggering the two RSRQthreshold values are fixed These fixed threshold values arenot feasible for implementing a dynamic handoff schemewhich may reflect the condition of the variation of RSRQ val-ues (ie caused bymoving velocity) and the environment theUEs stayWe investigate the relation between two consecutive120595 values denoted by Δ120595 and the difference of UErsquos movingvelocities and the relation betweenΔ120595 and report periods Inour designΔ120595 plays an important role of achieving AMPHTWhen Δ120595 is high the time interval of two consecutive timepoints of MCM transmission has to be set shorter to preventtoo late handoff triggering Inversely the time interval can beset longer to greatly reduce theMCMoverheads By obtainingΔ120595 the SeNB can predict the time of 120595 lower than 120595ℎ1 andadjusts the next time point ofMCM transmission byAMPHTto prevent the next 120595 being lower than 120595ℎ2 Thus AMPHTcan find a more accurate handoff triggering point of timeaccording to Δ120595

3 System Model

The system is modeled as cellular networks where UEs roamamong cells over wireless fading channels Each cell 119894 isdominated by one eNB which resides at the center of the

Mathematical Problems in Engineering 3

Distance

Cell A Cell B

Cell Alocation

PeriodicMRM

Event-triggeredMRM

Move slow PHP Move fast Cell B location

Ping-pong effect

may handoff failure

120595

tn

tn+1

Low 120595 and120595h2

120595h1

DF = VF middot TH

DN = VN middot TH

DS = VS middot TH

Figure 1 Diagram of distance versus 120595 for the 3GPP E-UTRAhandoff model

cell The UE processes handoff procedures when it movesacross the boundary of the cell 119894 to another cell (one of theneighboring cells of the serving cell)

Figure 1 depicts a process where an UE is under the ser-vice of cell A andmoving toward cell B In this way the signal120595 that the UE has received from cell A is decreasing andthat fromcell B is increasing simultaneouslyThehandoffpro-cedure is set as when 120595 reaches 120595ℎ1 the handoff is triggeredBelow we show three cases to illustrate the relation betweenthe moving velocity of the UE and the network turn-aroundtime of handoff procedure 119879119867 in distance

Case 1 (moderate velocity) If the moving velocity of UE ismoderate denoted by 119881119873 the handoff procedure of UE willbe finished at a perfect handoff point (PHP) In this casethe moving distance of UE denoted by 119863119873 is 119863119873 = 119881119873 sdot119879119867 The UE completes handoff procedure perfectly and staysconnected This case is the situation given in 3GPP E-UTRAthat the handoff procedure finishes at the PHP

Case 2 (high velocity) If the moving velocity of UE is fastdenoted by 119881119865 therefore the moving distance denoted by119863119865 will be 119863119865 = 119881119865 sdot 119879119867 In this situation due to the longermoving distance the UE may be out of the coverage of cell Abefore handoff procedure finishes or gets service interruptionwhen 120595 is lower than 120595ℎ2 As a result the handoff procedurefails

Case 3 (low velocity) If moving velocity of UE is slowdenoted by119881119878 it may suffer a premature handoff problem Inthis case the moving distance is119863119878 = 119881119878 sdot 119879119867 Since the mov-ing distance is shorter the UE may get a lower RSRQ fromcell B when it finishes the handoff procedure Afterward inorder to obtain better RSRQ the UE performs handoff andswitches back to cell AThese unnecessary handoffs circulateuntil theUE reaches the PHP As a result the ping-pong effectoccurs

In Cases 2 and 3 we clarify the necessity of takingmovingvelocity ofUE into account Except for themoving velocity anUE also requires triggering handoff when entering buildingsor being blocked by obstacles Although the UE can activelyinitiate handoff procedure by MRM handoff failure andservice interruptions may still happen in the time intervalbetween two consecutive MCMs because of a sudden signalweaknessObviously an effective prediction of theUErsquos hand-off triggering point of time can achieve a better and seamlesshandoff As to how to make it the following two technicalissues have to be solved first

(i) The SeNB has to keep monitoring 120595 of the UE beforeit is below 120595ℎ1 However under the consideration ofmoving velocity the requirement for a fixed reportperiod will cause handoff failure at a high velocityof UE or more overheads In AMPHT the idea ofdynamically adjusting the period of MCM is used tosolve this problem effectivelyThis idea reduces a largenumber of unnecessary overheads Its performance isstudied in Section 5

(ii) The handoff scheme must be able to recognize bothΔ120595 and its moving direction to avoid ping-pongeffect or handoff failures To achieve an effectivehandoff scheme Δ120595 is one of the key parameters topredict the UErsquos triggering time point of handoff

As shown in Figure 1 when an UE is under the serviceof cell A and moving to the coverage area of cell B before 120595reaching 120595ℎ1 the SeNB transmits an MCM to the UE andrequests it to reply an MRM to the SeNB periodically Wedenote the time points of the UEs replyingMRM to the SeNBby 119905119899 119899 = 1 2 infin and the time interval of two consecu-tive MRMs by 119879119899 119899 = 1 2 infin where 119879119899 = 119905119899 minus 119905119899minus1 Let120595119899 denote the reported 120595 by the UE at 119905119899 and let Δ120595119899 denotethe SINR difference between 119905119899 and 119905119899minus1 Let the coverage ofserving cell be denoted by119860 119894 and let the next cell that UE willenter be denoted by 119860 119894+1 Let 120596119894 denote the overlapping timethat the UE can receive signals from both cell 119894 and cell 119894 + 1let 119911119894 be the time of the UE staying in the nonoverlapped areaof cell 119894 namely the UE can obtain signals from cell 119894 onlySuppose 120596119894 and 119911119894 are exponentially distributed with bothrates 120578 and 120577 respectively Then the corresponding expectedvalues can be expressed as 119864[120596119894] = 1120578 and 119864[119911119894] = 1120577 Theexpected association time that an UE is served by the SeNBin cell 119894 can be expressed as

119864 [119910119894] = 119864 [119911119894] + 119864 [120596119894] =

1

V=

1

120577

+

1

120578

=

120578 + 120577

120578120577

(2)

where V is the average moving rate of the UE In [29] Linand Pang had shown that the UErsquos association time 119910119894 with itsSeNB in cell 119894 has an exponential distributionwith the densityfunction

119886 (119910119894) = V119890minusV119910119894 (3)


Start

Step 1

Yes

No

Step 2

No

Yes

Step 3

Yes

No

Connection safetyexamination

Yes

No

Step 4

Send MCM to trigger the handoff after

EndΔ120595n lt 0

a duration Tt =120595h1 minus 120595n

Δ120595n

Tn

Δ120595n+1

Tn+1ge

120595n+1 minus 120595h2

TH

120595n gt 120595h1

Estimate 120595h1 and 120595n+1

(1)Δ120595n

Tn=

120595h2 minus 120595h1

TH

120595h1 = 120595h2 minusTHTn

Δ120595n

120595n+1 = 120595n +Tn+1Tn

120595n

Initially Tn = Tp(1) The SeNB after a period Tn sendsthe MCM to request the UE to reportits 120595n via the MRM at time point tn or(2) The UE actively sends the MRM tothe SeNB once an event occurs at timepoint tn

Obtain 120595n+1Δ120595n+1 and Tn+1

by 120595n 120595nminus1 tnand tnminus1

(1) Let Δ120595n = 120595n minus 120595nminus1

(2) Because Δ120595n prop 1Tn+1

|Δ120595n| middot Tn+1 = |Δ120595nminus1| middot Tn

Tn+1 = min(Tp |Δ120595nminus1|

|Δ120595n|Tn)

ElseTn+1 = Tp

If Δ120595n ne 0 then

(2)Δ120595n

Tn=

Δ120595n+1

Tn+1

120595n+1 lt 120595h1

997904rArr

997904rArr

997904rArr

997904rArr

Figure 2 Flowchart of AMPHT

4 Adaptive Measurement Report Period andHandoff Threshold Scheme

In this section we present the proposed scheme AMPHT ina step-by-step mannerThe flowchart of AMPHT is shown inFigure 2

Step 1 (monitoring 120595119899 and event) Initially 119879119899 is set as adefault value 119879119901 to periodically obtain 120595 from a specific UEBased on the reported 120595119899 at 119905119899 Δ120595 of the UE can be gottenaccordingly The SeNB can obtain 120595119899 of the UE at time 119905119899 intwo ways

(1) The SeNB gets the latest 120595119899 from the UE at time point119905119899 by transmitting the MCM to the UE after a period119879119899 This action is periodically triggered by the SeNBin order to get the latest radio quality of the UE

(2) The SeNB passively receives the MRM sent by the UEwhen the event A2 occurs In this case 120595119899 is enclosedin the MRM

In the 3GPP E-UTRA standard the SeNB can obtain theTrackingAreaCode which is a set of predefined codes to rep-resent each area in a cell by the MRM from the UE AMPHTuses the TrackingAreaCode to determine whether the UE


approaches the boundary of the serving cell Once the SeNBobtains the rough moving direction and Δ120595 it can furthercheck the event for handoff Except for the polling from theSeNB an UE may actively send an MRM to the SeNB if theUE satisfies the conditions of event A3 A4 A5 or A6 asshown in Table 1 The SeNB checks the event reported fromthe UE for further handoff computing If 120595119899 gt 120595ℎ1 AMPHTgoes to Step 2 to calculate the next time point of MCMOtherwise if 120595119899 le 120595ℎ1 the SeNB estimates 120595119899+1 Δ120595119899+1 and119879119899+1 used in the Connection Safety Examination stage forchecking the connection safety to prevent connection inter-ruptionThe way of obtaining 119879119899+1 can be found in Step 2 (6)and 120595119899+1 and Δ120595119899+1 can be found in Step 3 (9) respectively

Step 2 (119879119899+1 estimation) Based on 120595119899 and 120595119899minus1 Δ120595119899 can beobtained by

Δ120595119899 = 120595119899 minus 120595119899minus1 (4)

If |Δ120595119899| as compared to |Δ120595119899minus1| becomes larger 119879119899+1 has tobe decreased to prevent excessive reporting of 120595 Inversely if|Δ120595119899| as compared to |Δ120595119899minus1| becomes smaller 119879119899+1 has to beincreased to decrease MCM overheads The aforementioneddescriptions show thatΔ120595119899 is inversely proportional to119879119899 wehave

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899+1 =1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

119879119899 (5)

where |119883| represents the absolute value function of119883 Basedon (5) we can obtain the next report period 119879119899+1 by

119879119899+1 =

1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899 (6)

Since the value of119879119899+1may be greater than119879119901 we set119879119901 as theupper bound of the report period Then the next reportingpoint of time can be easily calculated by

119905119899+1 =

119905119899 + 119879119901 if Δ120595119899 = 0

119905119899 +min(1198791199011003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899) if Δ120595119899 = 0(7)

Here we illustrate three cases for AMPHT design

Case 1 When Δ120595119899 asymp Δ120595119899minus1 the moving velocity of UE isalmost unchanged In this case we set 119879119901 as the next reportperiod

Case 2 When Δ120595119899 gt Δ120595119899minus1 the moving velocity of UEbecomes higher In this case 119879119899+1 has to be set shorter toreflect the velocity change

Case 3 When Δ120595119899 lt Δ120595119899minus1 the moving velocity of UEbecomes lower In this case119879119899+1 has to be set longer to reflectthe velocity change

After the estimation of 119905119899+1 the SeNB checks if Δ120595119899 lt 0If it is negative120595119899+1may becomeweaker then AMPHT goesto Step 3 Otherwise AMPHT goes back to Step 1

Step 3 (120595ℎ1 and Δ120595119899+1 estimation) When |Δ120595119899| is small thevalue of 120595ℎ1 has to be lowered to prevent premature handoffSimilarly when |Δ120595119899| is large the value of 120595ℎ1 has to beraised to avoid handoff failure due to handoff procedure notbeing completed Since the low bound threshold 120595ℎ2 and therequired handoff time119879119867 are fixed values we use the propor-tion of Δ120595119899 and 119879119899 to calculate the new 120595ℎ1 we have

Δ120595119899

119879119899

=

120595ℎ2 minus 120595ℎ1

119879119867

997904rArr 120595ℎ1 = 120595ℎ2 minus

119879119867

119879119899

Δ120595119899 (8)

We note that the value of Δ120595119899 is always negative in Step 3because we have a decision block before entering Step 3 asshown in Figure 2 After calculating120595ℎ1120595119899+1 can be obtainedby

Δ120595119899

119879119899

=

Δ120595119899+1

119879119899+1

997904rArr 120595119899+1 = 120595119899 +

119879119899+1

119879119899

Δ120595119899 (9)

After obtaining 120595119899+1 and 120595ℎ1 the SeNB checks whether120595119899+1 is smaller than 120595ℎ1 If the obtained value 120595119899+1 lt 120595ℎ1 itmeans that the UErsquos RSRQ 120595119899+1 will be lower than 120595ℎ1 at 119905119899+1AMPHT proceeds with the connection safety examination toprevent handoff failure Otherwise AMPHT goes back toStep 1

Connection Safety Examination To prevent the situationof handoff failure during 119905119899+1 and 119905119899+2 (ie the UErsquos 120595becomes lower than 120595ℎ2 before 119905119899+2) AMPHT examines theconnection safety based on the inequality

Δ120595119899+1

119879119899+1

ge

120595119899+1 minus 120595ℎ2

119879119867

(10)

If (10) holds it means the connection is unsafe AMPHT goesto Step 4 to trigger the handoff after the duration

119879119905 =

120595ℎ1 minus 120595119899

Δ120595119899

119879119899 (11)

Otherwise the moving velocity of UE is slow and has enoughtime to do handoff before its 120595 becomes lower than 120595ℎ2

Step 4 (handoff triggering point of time estimation) To avoidthe handoff failure AMPHT uses (11) to calculate the nexthandoff triggering point of time at 119905119899+1 + 119879119905

Termination AMPHT terminates here

5 Numerical Experiments andSimulation Results

In this section we present the performance of AMPHTThe performance of AMPHT is validated by comparing thenumerical experiments (MATLAB tool) with the simulationresults (ns-3 LENA simulator) Related system parametersare given as follows The serving cell is surrounded with 6neighboring cells and the radius of each cell is 1000m Thedistance between two adjacent eNBs is 1800m which means


Table 2 Parameters of system scenario

Parameter ValueNumber of eNB 7Power of eNB 46 dBmRadius of eNB 1000mDistance between adjacent eNB 1800mNoise figure 5 dBNetwork response time of handoff procedure 500msPredefined handoff measurement period 200ms

there exists an overlapping area between any two adjacentcells We assume an UE is always in radio resource control-(RRC-) connected mode that is AMPHT is always on whenthe UE is RRC-connected Each numerical experiment andsimulation result are obtained by averaging 10000 samplesIn each sample UEs are randomly distributed within thecoverage of serving cell The UE moves randomly in a fixedvelocity until triggering handoff procedure and moving intothe neighboring cell In our analysis the network responsetime of handoff procedure is 500ms The predefined handoffmeasuring period is 200ms All parameters are listed andshown in Table 2 The path loss model we use is the ITUM1225 [30] it can be expressed as

119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)

where 119889 is the distance in meters and 119891 is the frequency ofthe system which is fixed at 2000MHz to conform with the3GPP E-UTRA standard

To simplify the numerical experiments we set noisefigure as 5 dB to represent additive white Gaussian noise(AWGN) and other possible interference in analysis scenarioWe assume our analysis scenario is a free space and 120595 is onlyaffected by path loss Based on this assumption and accordingto the path loss model (12) we set the initial 120595ℎ1 PHP and120595ℎ2 to geographical distance from SeNB In our numericalexperiments compared to reality we adopt a more strictcondition that the initial 120595ℎ1 PHP and 120595ℎ2 are 990m 995mand 1000m (boundary of serving cell) respectively Thedefinition of the premature handoff probability is the proba-bility that 120595 of UE is between 120595ℎ1 and PHP when the handoffprocedure is completed The definition of the handoff failureprobability is the probability that 120595 of UE exceeds 120595ℎ2 andcauses service interruptions when the handoff procedure iscompleted If 120595 is located between PHP and 120595ℎ2 we considerthis situation as an acceptable handoff

After these parameters of system scenarios and thesituations of handoff have been determined Our numericalexperiments use four levels of moving behavior each velocitylevel of which is shown in Table 3 We randomly generate themoving velocity of UE 10000 times in each level of velocity toachieve this numerical experiment

Also we have compared the proposedAMPHTalgorithmand the original 3GPP E-UTRA handoff algorithm by sim-ulation Comparing with numerical experiments to obtainthe results that are closer to the reality we use ns-3 LENA

Table 3 Analysis parameters of numerical experiments

Parameter ValueWalk velocity 2ndash5 kmhBicycle velocity 10ndash20 kmhVehicular velocity 80ndash120 kmhHigh speed railway (HSR) velocity 250ndash300 kmhAnalysis times 10000 times

simulator to simulate the metropolitan area By using ns-3LENA simulator UErsquos RSRQ value is affected by buildingsand obstacles in the simulated area Except for the accuracyof handoff triggering we further compare the event-triggeredhandoff probability and the MRM transmission times bysimulation resultsThe comparison of numerical experimentsand simulation results are shown below

51 Premature Handoff Probability The numerical exper-iments of premature handoff probability are shown inFigure 3(a) The handoff procedure in 3GPP E-UTRA alwaysperforms premature handoff when the moving velocity ofUE is low such as during walking and riding a bicycle Theresults of numerical experiments prove our opinion that thelowmoving velocity of UE causes short moving distance aftertriggering the handoff procedure Too slow moving velocitycauses the UE to finish the procedure too early AMPHT iscapable adjusting the period of MCM and 120595ℎ1 by Δ120595 Such asimple adjustment can reduce the probability of prematureoccurrence by 347 and 364 for walking and riding abicycle respectively Meanwhile the premature handoffprobability is not increased in high and medium movingvelocities of UE

Next we examine the simulation results of prematurehandoffprobability that is shown in Figure 3(b) In this figurewe can observe that both standard handoff procedure andAMPHT have higher premature handoff probability in lowmoving velocity This trend verifies our opinion once againWhen the moving velocity of UE is 10 kmhr the prematurehandoff probability of standard handoff procedure is 9783On the other hand the premature handoff probability ofAMPHT is 6378 which is improved over 30 The pre-mature handoff situation in AMPHT passes off whenmovingvelocity reaches 90 kmhr which is more adaptive to movingvelocity than standard handoff procedure

Based on the analysis of premature handoff probabilityAMPHT is more effective than standard handoff procedureto reduce the premature handoff probability The prematurehandoff takes an important part in the cause of ping-pongeffect

52 Handoff Failure Probability The numerical experimentsof handoff failure probability are shown in Figure 4(a) Thehandoff procedure in 3GPP E-UTRA always fails when themoving velocity of UE is high as HSR Also the 3GPP E-UTRA handoff procedure exists the probability of 332 toperform handoff failure in mediummoving velocity of UE ofvehicle The results of numerical analysis also prove our


Walk Bicycle Vehicle HSR0

10

20

30

40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(b) ns-3 LENA simulator

Figure 3 Effect of moving velocity on premature handoff probability by MATLAB tool and ns-3 LENA simulator


10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT


Figure 4 Effect of moving velocity on handoff failure probability by MATLAB tool and ns-3 LENA simulator

opinion that the high moving velocity of UE causes longmoving distance after triggering the handoff procedure Toofast moving velocity of UE leads to the UE finishing itshandoff procedure too late The simple adjustment proposedbyAMPHT improves 336 for highmoving velocity ofUE asHSR AMPHT eliminates the handoff failure probability forthe moving velocity of UE in vehicle level Meanwhile theprobability of handoff failure in low moving velocity is notincreased

Next the simulation results of the handoff failure prob-ability are shown in Figure 4(b) We can observe the sametrend with numerical experiments of both methods where

higher moving velocity of UE leads to higher handoff fail-ure probability When the moving velocity of UE reaches130 kmhr the handoff failure probability of standard handoffprocedure reaches 100 On the other hand the handofffailure probability of AMPHT is only 3947 which isimproved over 60Themain reason for this improvement isour proposed connection safety examination AMPHTprechecks the UErsquos RSRQ value next time to reduce theservice interruptions

Based on the analysis of handoff failure probability weshow that the AMPHT is more effective than standardhandoff procedure to reduce handoff failure probability The


0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT

Figure 5 Effect of moving velocity on event-triggered handoffprobability by ns-3 LENA simulator

handoff failure takes an important part in the cause of serviceinterruptions

53 Event-Triggered Handoff Probability Since our proposedAMPHT can adjust 120595ℎ1 we can mitigate the emergencysituation that UE is forced to trigger the handoff procedureby event A2 Here we simulate the event-triggered handoffprobability by ns-3 LENA simulator and the simulationresults are shown in Figure 5

Whenmoving velocity of UE is low and the RSRQ value isclose to handoff triggering point 120595ℎ1 the UE still has enoughtime to wait for MCM because RSRQ variation is small Butalong with the moving velocity of UE getting higher the tol-erance of MCM waiting time is getting shorter According tothe simulation results the event-triggered handoff procedureof 3GPP E-UTRA exceeds 95 when the moving velocity ofUE is higher than 130 kmhr On the other hand the event-triggered handoff procedure of AMPHTmerely exceeds 70when the moving velocity of UE is higher than 140 kmhrThis situation proves that the AMPHT effectively reduces theemergency situation of UE triggered handoff procedure Thehigher probability of 120595 suddenly being lower than 120595ℎ1 is thehigher probability of handoff failure is

54 MRM Transmission Times Since our proposed AMPHTcan adjust the period of UErsquos RSRQ checking by RSRQvariation we can reduce more unnecessary MCM andMRMtransmission times to decrease the handoff overheads Herewe compare the MRM transmission times between 3GPPE-UTRA handoff procedure and AMPHT by ns-3 LENAsimulatorThe simulation results ofMRM transmission timesare shown in Figure 6

0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT

Figure 6 Effect of moving velocity on MRM transmission timesobtained by ns-3 LENA simulator

When the moving velocity of UE is relatively high theresidential time of UE in a cell will be shorter As a result thetimes of MRM transmission will also decrease When themoving velocity of UE is 10 kmhr standard handoff proce-dure transmits MRM 3528 times to check the UErsquos RSRQvalue periodically On the other hand the adjustable periodfeature of AMPHT can reduce the MRM transmission timesto 1453 It is worth noting that most of MRM are used forreplying MCM to SeNB In this way AMPHT reduces about4000 times to transmit unnecessary message in the movingvelocity of UE of 10 kmhr

6 Conclusion

In this paper we have investigated an adaptive measurementreport period and handoff threshold (AMPHT) handofftriggering scheme for SeNB AMPHT periodically monitorsthe UErsquos RSRQ when UE is moving AMPHT also calculatesthe variance of RSRQ to adjust the period of transmittingMCM in advance Numerical experiments and simulationresults have shown that the premature handoff probabilityand the handoff failure probability can be decreased Here wesort out some situations that AMPHT are recommended toapply

(i) To achieve AMPHT the SeNB has to maintain everyUErsquos RSRQ value at least three times to calculate thenextMCMtransmission periodTheUErsquos RSRQvaluemaintenance and next MRM transmission periodprediction will increase handoff overheads of SeNBTo reduce SeNB handoff overheads we stronglyrecommend that AMPHT should be appliedwhen the


moving velocity of UE is high since service inter-ruptions are more unacceptable compared with ping-pong effect

(ii) By monitoring the RSRQ variation of UE periodi-cally and estimating the moving velocity of UE tochange the interval time to time the SeNB is able todetermine an accurate handoff triggering point oftime smartly for reducing theMCM andMRM trans-mission times To reduce the message exchange timesbetween SeNB and UE we strongly recommend thatAMPHT should be applied when the moving velocityof UE is low since low movement or static causesamounts of messages to monitor UErsquos RSRQ forstandard handoff procedure

Based on the discussion listed above system operatorshould apply the AMPHT by different situation Finally con-sidering the current service application of UEs AMPHT canbe investigated further for supporting real-time QoS or QoEamong eNB for moving UE

Conflict of Interests

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

Acknowledgment

This work was supported in part by the Ministry of Scienceand Technology Taiwan under Contract MOST-103-2221-E-182-042

References

[1] Y Lee B Shin J Lim and D Hong ldquoEffects of time-to-triggerparameter on handover performance in SON-based LTE sys-temsrdquo in Proceedings of the IEEE 6th Asia-Pacific Conference onCommunications (APCC rsquo10) pp 492ndash496 IEEE AucklandNew Zealand November 2010

[2] P Munoz R Barco and I de la Bandera ldquoOn the potential ofhandover parameter optimization for self-organizing net-worksrdquo IEEE Transactions on Vehicular Technology vol 62 no5 pp 1895ndash1905 2013

[3] K Kitagawa T Komine T Yamamoto and S Konishi ldquoPerfor-mance evaluation of handover in LTE-advanced systems withpico cell Range Expansionrdquo in Proceedings of the IEEE 23rdInternational Symposium on Personal Indoor and Mobile RadioCommunications (PIMRC rsquo12) pp 1071ndash1076 September 2012

[4] S S Mwanje and A Mitschele-Thiel ldquoMinimizing handoverperformance degradation due to LTE self organized mobilityload balancingrdquo in Proceedings of the IEEE 77th VehicularTechnology Conference (VTC rsquo13) pp 1ndash5 Dresden GermanyJune 2013

[5] 3GPP ldquoEvolved universal terrestrial radio access (E-UTRA) andevolved universal terrestrial radio access network (E-UTRAN)overall description stage 2rdquo ETSI TS 36300 V1140 Release 112013

[6] J Chen C C Yang and S T Sheu ldquoDownlink femtocellinterference mitigation and achievable data rate maximizationusing FBS association and transmit power control schemesrdquo

IEEE Transactions on Vehicular Technology vol 63 no 6 pp2807ndash2818 2014

[7] C-J Chang T-T Su and Y-Y Chiang ldquoAnalysis of a cutoffpriority cellular radio system with finite queueing and reneg-ingdroppingrdquo IEEEACM Transactions on Networking vol 2no 2 pp 166ndash175 1994

[8] V Erceg D G Michelson S S Ghassemzadeh et al ldquoModelfor the multipath delay profile of fixed wireless channelsrdquo IEEEJournal on Selected Areas in Communications vol 17 no 3 pp399ndash410 1999

[9] R A Guerin ldquoQueueing-blocking system with two arrivalstreams and guard channelsrdquo IEEE Transactions on Communi-cations vol 36 no 2 pp 153ndash163 1988

[10] A E Xhafa and O K Tonguz ldquoDynamic priority queueing ofhandover calls in wireless networks an analytical frameworkrdquoIEEE Journal on Selected Areas in Communications vol 22 no5 pp 904ndash916 2004

[11] S Hong M H Lee H-H Chun S-H Kwon and J LSpeyer ldquoExperimental study on the estimation of lever arm inGPSINSrdquo IEEE Transactions on Vehicular Technology vol 55no 2 pp 431ndash448 2006

[12] S-S Hwang and J J Shynk ldquoBlindGPS receiver with amodifieddespreader for interference suppressionrdquo IEEE Transactions onAerospace and Electronic Systems vol 42 no 2 pp 503ndash5132006

[13] I F Akyildiz and W Wang ldquoThe predictive user mobility pro-file framework for wireless multimedia networksrdquo IEEEACMTransactions on Networking vol 12 no 6 pp 1021ndash1035 2004

[14] J Chen and W-K Tan ldquoPredictive dynamic channel allocationscheme for improving power saving and mobility in BWAnetworksrdquo Mobile Networks and Applications vol 12 no 1 pp15ndash30 2007

[15] P N Pathirana A V Savkin and S Jha ldquoLocation estimationand trajectory prediction for cellular networks withmobile basestationsrdquo IEEETransactions onVehicular Technology vol 53 no6 pp 1903ndash1913 2004

[16] Y-L Chen and S-L Tsao ldquoA low-latency scanning withassociation mechanism for real-time communication in mobileWiMAXrdquo IEEE Transactions on Wireless Communications vol11 no 10 pp 3550ndash3560 2012

[17] L Barolli F Xhafa A Durresi and A Kovama ldquoA fuzzy-basedhandover system for avoiding ping-pong effect in wirelesscellular networksrdquo in Proceedings of the 37th International Con-ference on Parallel ProcessingWorkshops (ICPP rsquo08) pp 135ndash142Portland Ore USA September 2008

[18] Y Shen T Luo and M Z Win ldquoNeighboring cell search forLTE systemsrdquo IEEE Transactions on Wireless Communicationsvol 11 no 3 pp 908ndash919 2012

[19] H Tang P Hong and K Xue ldquoHeNB-aided virtual-handoverfor range expansion in LTE femtocell networksrdquo Journal ofCommunications and Networks vol 15 no 3 pp 312ndash320 2013

[20] N W Sung N-T Pham T Huynh and W-J Hwang ldquoPre-dictive association control for frequent handover avoidance infemtocell networksrdquo IEEE Communications Letters vol 17 no5 pp 924ndash927 2013

[21] T Guo A U Quddus and R Tafazolli ldquoSeamless handover forLTE macro-femto networks based on reactive data bicastingrdquoIEEECommunications Letters vol 16 no 11 pp 1788ndash1791 2012

[22] A Rath and S Panwar ldquoFast handover in cellular networks withfemtocellsrdquo in Proceedings of the IEEE International Conferenceon Communications (ICC rsquo12) pp 2752ndash2757 Ottawa CanadaJune 2012


[23] A Roy J Shin and N Saxena ldquoMulti-objective handover inLTE macrofemto-cell networksrdquo Journal of Communicationsand Networks vol 14 no 5 pp 578ndash587 2012

[24] Y Yu and D Gu ldquoThe cost efficient location management inthe LTE picocellmacrocell networkrdquo IEEE CommunicationsLetters vol 17 no 5 pp 904ndash907 2013

[25] T Guo A Quddus N Wang and R Tafazolli ldquoLocal mobilitymanagement for networked femtocells based on X2 trafficforwardingrdquo IEEETransactions onVehicular Technology vol 62no 1 pp 326ndash340 2013

[26] H-L Fu P Lin and Y-B Lin ldquoReducing signaling overheadfor femtocellmacrocell networksrdquo IEEE Transactions onMobileComputing vol 12 no 8 pp 1587ndash1597 2013

[27] 3GPP ldquoEvolved Universal Terrestrial Radio Access (E-UTRA)and Evolved Universal Terrestrial Radio Access Network (E-UTRAN) Requirements for Support of Radio Resource Man-agementrdquo ETSI TS 36133 V1260 Release 12 January 2015

[28] 3GPP LTE Evolved Universal Terrestrial Radio Access (E-UTRA) Physical Layer Procedures ETSI TS 36213 V1240Release 12 3GPP 2015

[29] Y-B Lin and A-C Pang ldquoComparing soft and hard handoffsrdquoIEEE Transactions on Vehicular Technology vol 49 no 3 pp792ndash798 2000

[30] ITU-R Rec M1225 Guidelines for Evaluation of Radio Trans-mission Technologies for IMT-2000 ITU-R 1997

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

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Applied MathematicsJournal of


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Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


weather situation [11 12] To overcome the drawback manyhandoff schemes without the GPS application were studied[13ndash16] Nevertheless these schemes cannot determine anaccurate handoff triggering point of time and prevent theping-pong effect as well [17 18] Some studies consideredthe signal power into their proposed methods [6 19ndash24] butthey neglected the impact of the moving velocity of UE onsignal strength variation AlthoughGuo et al [25] consideredthe mobility management they did not consider using thesignal power to assist handoff decision making Fu et al [26]proposed a mathematical model to estimate a proper delaytime and set a delay timer for handoff triggering to reducesignaling overheads but theirmethod cannot reflect the effectof moving velocity of UE on handoff success probability

The aforementioned handoff drawbacks and problemsmotivate us to investigate a better handoff scheme taking avariable RSRQ-based handoff threshold and a variable reportperiod related to RSRQvariationWe observed that the RSRQvariation is highly related to the moving velocity of UE andthe environments the UE staysThat is the RSRQ variation ofUE is highly related to its moving velocity and diverseenvironments Based on these we propose a signal-aware andsignal-variation-aware (velocity-aware) handoff schemecalled adaptive measurement report period and handoffthreshold (AMPHT) to accurately trigger the handoffpoint of time AMPHT not only increases handoff successprobability but also reduces the handoff ping-pong effectproblem Moreover AMPHT is compatible with 3GPP E-UTRA standard because it uses the original control messagesof 3GPP E-UTRA [5]

The rest of this paper is organized as follows Section 2introduces the details of handoff procedure of 3GPP E-UTRAstandard and related works Section 3 presents the systemmodel of proposed AMPHT mechanism The details ofAMPHT are presented in Section 4 We show the numericalexperiments and simulation results on different performancemetrics in Section 5 Finally the conclusion is given inSection 6

2 Background

In this section we simply introduce the handoff mechanismof 3GPP E-UTRA [5] and related system control messagesThe handoffmechanismof 3GPPE-UTRAmakes the handoffdecision based on the measurement of UErsquos RSRQ valueAccording to 3GPP TS 36133 [27] and TS 36213 [28] therelation between the signal-to-interference plus noise ratio(SINR) and RSRQ (in dB) can be given by

SINR = 1

1 (119873scRSRQ) minus 119909 (1)

where 119873sc = 12 represents the number of subcarriers perresource block and 0 lt 119909 le 1 is the instantaneous serving cellsubcarrier activity factor In this paper we follow the 3GPP E-UTRA [5] to use themeasure of RSRQ because RSRQ reflectsthe signal quality over the resource block The RSRQ value isgotten by periodical UErsquos MRM reporting The detailedmechanism is given below

Table 1 Event list predefined in 3GPP E-UTRA standard

Event Triggering situationA1 RSRQ of serving cell becomes better than 120595ℎ1A2 RSRQ of serving cell becomes worse than 120595

ℎ1

A3 RSRQ of neighbor cell becomes offset better than SeNBA4 RSRQ of neighbor cell becomes better than 120595ℎ1A5 RSRQ of PCell becomes worse than 120595ℎ1 and RSRQ of

neighbor cell becomes better than 120595ℎ2A6 RSRQ of Neighbor cell becomes offset better than TeNB

(i) Checking UErsquos RSRQ periodically The SeNB sendsMCM periodically to require UE to report its latestRSRQ value denoted as 120595 via the MRM back to theSeNB The MCM and MRM are the radio resourcecontrol (RRC) connection reconfiguration messagesdefined in 3GPP E-UTRA [5] In the 3GPP E-UTRAstandard there are 6 different events as shown inTable 1 for handling the reported RSRQ from the UEThe terms PCell and TeNB represent the primary celland the target eNB respectively The SeNB passivelyhandles the reported RSRQ from the UE and coordi-nates the handoff procedure if it is needed

(ii) Fixed RSRQ thresholds 120595ℎ1 and 120595ℎ2 In the 3GPP E-UTRA standard there are two fixed RSRQ thresholdvalues denoted as 120595ℎ1 (a higher value) and 120595ℎ2 (thelow bound for remaining connected) for judging thenecessity of handoff Usually event A2 is the caseof starting the handoff procedure All handoff algo-rithms depend on these two threshold values to judgetheir handoff approaches

Although the 3GPP E-UTRA provides the guideline ofhandling the time point of handoff triggering the two RSRQthreshold values are fixed These fixed threshold values arenot feasible for implementing a dynamic handoff schemewhich may reflect the condition of the variation of RSRQ val-ues (ie caused bymoving velocity) and the environment theUEs stayWe investigate the relation between two consecutive120595 values denoted by Δ120595 and the difference of UErsquos movingvelocities and the relation betweenΔ120595 and report periods Inour designΔ120595 plays an important role of achieving AMPHTWhen Δ120595 is high the time interval of two consecutive timepoints of MCM transmission has to be set shorter to preventtoo late handoff triggering Inversely the time interval can beset longer to greatly reduce theMCMoverheads By obtainingΔ120595 the SeNB can predict the time of 120595 lower than 120595ℎ1 andadjusts the next time point ofMCM transmission byAMPHTto prevent the next 120595 being lower than 120595ℎ2 Thus AMPHTcan find a more accurate handoff triggering point of timeaccording to Δ120595

3 System Model

The system is modeled as cellular networks where UEs roamamong cells over wireless fading channels Each cell 119894 isdominated by one eNB which resides at the center of the


Distance

Cell A Cell B

Cell Alocation

PeriodicMRM

Event-triggeredMRM


Ping-pong effect

may handoff failure

120595

tn

tn+1

Low 120595 and120595h2

120595h1

DF = VF middot TH

DN = VN middot TH

DS = VS middot TH











119864 [119910119894] = 119864 [119911119894] + 119864 [120596119894] =

1

V=

1

120577

+

1

120578

=

120578 + 120577

120578120577

(2)


119886 (119910119894) = V119890minusV119910119894 (3)


Start

Step 1

Yes

No

Step 2

No

Yes

Step 3

Yes

No


Yes

No

Step 4


EndΔ120595n lt 0


Δ120595n

Tn

Δ120595n+1

Tn+1ge

120595n+1 minus 120595h2

TH

120595n gt 120595h1


(1)Δ120595n

Tn=

120595h2 minus 120595h1

TH

120595h1 = 120595h2 minusTHTn

Δ120595n

120595n+1 = 120595n +Tn+1Tn

120595n








|Δ120595n|Tn)

ElseTn+1 = Tp


(2)Δ120595n

Tn=

Δ120595n+1

Tn+1

120595n+1 lt 120595h1

997904rArr

997904rArr

997904rArr

997904rArr











Δ120595119899 = 120595119899 minus 120595119899minus1 (4)


1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899+1 =1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

119879119899 (5)


119879119899+1 =

1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899 (6)


119905119899+1 =

119905119899 + 119879119901 if Δ120595119899 = 0

119905119899 +min(1198791199011003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899) if Δ120595119899 = 0(7)







Δ120595119899

119879119899

=

120595ℎ2 minus 120595ℎ1

119879119867

997904rArr 120595ℎ1 = 120595ℎ2 minus

119879119867

119879119899

Δ120595119899 (8)


Δ120595119899

119879119899

=

Δ120595119899+1

119879119899+1

997904rArr 120595119899+1 = 120595119899 +

119879119899+1

119879119899

Δ120595119899 (9)



Δ120595119899+1

119879119899+1

ge

120595119899+1 minus 120595ℎ2

119879119867

(10)


119879119905 =

120595ℎ1 minus 120595119899

Δ120595119899

119879119899 (11)










119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)














10

20

30

40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Distance

Cell A Cell B

Cell Alocation

PeriodicMRM

Event-triggeredMRM


Ping-pong effect

may handoff failure

120595

tn

tn+1

Low 120595 and120595h2

120595h1

DF = VF middot TH

DN = VN middot TH

DS = VS middot TH











119864 [119910119894] = 119864 [119911119894] + 119864 [120596119894] =

1

V=

1

120577

+

1

120578

=

120578 + 120577

120578120577

(2)


119886 (119910119894) = V119890minusV119910119894 (3)


Start

Step 1

Yes

No

Step 2

No

Yes

Step 3

Yes

No


Yes

No

Step 4


EndΔ120595n lt 0


Δ120595n

Tn

Δ120595n+1

Tn+1ge

120595n+1 minus 120595h2

TH

120595n gt 120595h1


(1)Δ120595n

Tn=

120595h2 minus 120595h1

TH

120595h1 = 120595h2 minusTHTn

Δ120595n

120595n+1 = 120595n +Tn+1Tn

120595n








|Δ120595n|Tn)

ElseTn+1 = Tp


(2)Δ120595n

Tn=

Δ120595n+1

Tn+1

120595n+1 lt 120595h1

997904rArr

997904rArr

997904rArr

997904rArr











Δ120595119899 = 120595119899 minus 120595119899minus1 (4)


1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899+1 =1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

119879119899 (5)


119879119899+1 =

1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899 (6)


119905119899+1 =

119905119899 + 119879119901 if Δ120595119899 = 0

119905119899 +min(1198791199011003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899) if Δ120595119899 = 0(7)







Δ120595119899

119879119899

=

120595ℎ2 minus 120595ℎ1

119879119867

997904rArr 120595ℎ1 = 120595ℎ2 minus

119879119867

119879119899

Δ120595119899 (8)


Δ120595119899

119879119899

=

Δ120595119899+1

119879119899+1

997904rArr 120595119899+1 = 120595119899 +

119879119899+1

119879119899

Δ120595119899 (9)



Δ120595119899+1

119879119899+1

ge

120595119899+1 minus 120595ℎ2

119879119867

(10)


119879119905 =

120595ℎ1 minus 120595119899

Δ120595119899

119879119899 (11)










119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)














10

20

30

40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Start

Step 1

Yes

No

Step 2

No

Yes

Step 3

Yes

No


Yes

No

Step 4


EndΔ120595n lt 0


Δ120595n

Tn

Δ120595n+1

Tn+1ge

120595n+1 minus 120595h2

TH

120595n gt 120595h1


(1)Δ120595n

Tn=

120595h2 minus 120595h1

TH

120595h1 = 120595h2 minusTHTn

Δ120595n

120595n+1 = 120595n +Tn+1Tn

120595n








|Δ120595n|Tn)

ElseTn+1 = Tp


(2)Δ120595n

Tn=

Δ120595n+1

Tn+1

120595n+1 lt 120595h1

997904rArr

997904rArr

997904rArr

997904rArr











Δ120595119899 = 120595119899 minus 120595119899minus1 (4)


1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899+1 =1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

119879119899 (5)


119879119899+1 =

1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899 (6)


119905119899+1 =

119905119899 + 119879119901 if Δ120595119899 = 0

119905119899 +min(1198791199011003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899) if Δ120595119899 = 0(7)







Δ120595119899

119879119899

=

120595ℎ2 minus 120595ℎ1

119879119867

997904rArr 120595ℎ1 = 120595ℎ2 minus

119879119867

119879119899

Δ120595119899 (8)


Δ120595119899

119879119899

=

Δ120595119899+1

119879119899+1

997904rArr 120595119899+1 = 120595119899 +

119879119899+1

119879119899

Δ120595119899 (9)



Δ120595119899+1

119879119899+1

ge

120595119899+1 minus 120595ℎ2

119879119867

(10)


119879119905 =

120595ℎ1 minus 120595119899

Δ120595119899

119879119899 (11)










119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)














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Velocity

Prem

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abili

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)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

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Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

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60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












Δ120595119899 = 120595119899 minus 120595119899minus1 (4)


1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899+1 =1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

119879119899 (5)


119879119899+1 =

1003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899 (6)


119905119899+1 =

119905119899 + 119879119901 if Δ120595119899 = 0

119905119899 +min(1198791199011003816100381610038161003816

Δ120595119899minus1

1003816100381610038161003816

1003816100381610038161003816

Δ120595119899

1003816100381610038161003816

119879119899) if Δ120595119899 = 0(7)







Δ120595119899

119879119899

=

120595ℎ2 minus 120595ℎ1

119879119867

997904rArr 120595ℎ1 = 120595ℎ2 minus

119879119867

119879119899

Δ120595119899 (8)


Δ120595119899

119879119899

=

Δ120595119899+1

119879119899+1

997904rArr 120595119899+1 = 120595119899 +

119879119899+1

119879119899

Δ120595119899 (9)



Δ120595119899+1

119879119899+1

ge

120595119899+1 minus 120595ℎ2

119879119867

(10)


119879119905 =

120595ℎ1 minus 120595119899

Δ120595119899

119879119899 (11)










119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)














10

20

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40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra













119875119871 = 40log10 (119889

1000

) + 30log10(119891) + 49 (12)














10

20

30

40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











10

20

30

40

50

60

70

80

90

100

Velocity

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Prem

atur

e han

doff

prob

abili

ty (

)

3GPP E-UTRAAMPHT




10

20

30

40

50

60

70

80

90

100

Velocity

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT

(a) MATLAB tool

0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Han

doff

failu

re p

roba

bilit

y (

)

3GPP E-UTRAAMPHT








0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










0 20 40 60 80 100 120 140 1600

10

20

30

40

50

60

70

80

90

100

Velocity (kmh)

Even

t-trig

gere

d ha

ndoff

pro

babi

lity

()

3GPP E-UTRAAMPHT






0 20 40 60 80 100 120 140 1600

400

800

1200

1600

2000

2400

2800

3200

3600

Velocity (kmh)

Aver

age M

RM tr

ansm

its ti

mes

3GPP E-UTRAAMPHT



6 Conclusion









Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra















Acknowledgment


References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra

























Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









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