Computer Networks 55 (2011) 3759–3773

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Computer Networks

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Improvement of handover prediction in mobile WiMAXby using two thresholds

Zdenek Becvar ⇑, Pavel Mach, Boris SimakCzech Technical University in Prague, Faculty of Electrical Engineering, Department of Telecommunication Engineering, Technicka 2, 16627 Prague, Czech Republic

a r t i c l e i n f o

Article history:Available online 8 May 2011

Keywords:Channel characteristicsHandoverMobilityTwo thresholds predictionWiMAX

1389-1286/$ - see front matter � 2011 Elsevier B.Vdoi:10.1016/j.comnet.2011.03.020

⇑ Corresponding author. Tel.: +420 2 2435 5994; faE-mail addresses: zdenek.becvar@fel.cvut.cz (Z

fel.cvut.cz (P. Mach), simak@fel.cvut.cz (B. Simak).

a b s t r a c t

One of the most important challenges in mobile wireless networks is to provide full mobil-ity together with minimum degradation of quality of service. This can be ensured by hand-over prediction. Handover prediction means a determination of the next station that willserve a mobile station. This paper proposes a prediction technique based on monitoringthe signal quality between the mobile station and all base stations in its neighborhood.The proposed technique utilizes two different thresholds for selection of the most likelytarget base station. Further, the potential improvement of the prediction efficiency viatechniques originally proposed for minimizing the number of redundant handovers is ana-lyzed. The efficiency of the proposed prediction technique is evaluated and compared withother prediction techniques based on channel characteristics in scenarios according to IEEE802.16m. The proposed technique achieves a prediction hit rate of up to 93%.

� 2011 Elsevier B.V. All rights reserved.

1. Introduction

Mobile wireless networks should enable full mobilityfor all users simultaneously while guaranteeing the re-quested Quality of Service (QoS). The QoS is significantlyinfluenced by the mobility of users. As users move, theBase Station (BS) to which they are connected (denotedas serving BS) has to be updated accordingly. This processis known as a handover. To perform a handover properly,a Mobile Station (MS) continuously scans its neighborhoodand monitors the channel parameters, for example the sig-nal strength or packet delay, of all available BSs. If some ofthe monitored signal parameters of the serving BS drop be-low a predefined level or below the level of a neighboringBS, the MS should perform a handover to provide the re-quired QoS.

The mobile WiMAX defines three types of handover:hard handover, Macro Diversity Handover (MDHO), andFast Base Station Switching (FBSS). Hard handover is man-

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x: +420 2 2333 9810.. Becvar), machp2@

datory in WiMAX systems and the other two types ofhandover are optional. During the hard handover process,the MS closes all connections with the serving BS. Subse-quently, it initiates establishment of new connections toa new BS, which is denoted as the target BS. After all con-nections with the serving BS are closed the MS is discon-nected from the network until new connections to thetarget BS are set up. This short time break, known as hand-over interruption, handover delay, or handover latency [1],should be minimized since it decreases QoS [2]. Handoverinterruption occurs if a hard handover is utilized, that is,when the MS always communicates with just one BS. How-ever, the MS can be simultaneously connected to morethan one BS in the case of MDHO or FBSS. The list of BSs in-volved in such a communication is usually called the diver-sity set in WiMAX [3]. To ensure optimum networkperformance, the size of the diversity set (the number ofBSs in the diversity set) needs to be optimized accordingto the network conditions and signal quality [4]. The hand-over interruption as well as the optimization of soft hand-over can be solved by prediction of the target BS.

This paper proposes a new prediction technique that al-lows high prediction efficiency while neither modification

3760 Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773

of management signaling nor additional demands on usersand network are required. The assessment of the predic-tion efficiency is in line with the recommendation on eval-uation of networks according to IEEE 802.16m standard[1].

The rest of the paper is structured as follows. The sec-ond section provides an overview of the state of the art re-lated to handover prediction. Section 3 introduces theprinciple of handover in WiMAX and techniques for elimi-nation of redundant handovers. The subsequent sectiondescribes the proposed prediction technique. The fifth sec-tion defines simulation scenarios and parameters consid-ered for assessment of prediction efficiency. The resultsof simulations are presented in Section 6. This section alsoanalyzes possible efficiency improvement of the proposedtechnique and the impact of the proposal on the elimina-tion of redundant handovers. The last section gives ourconclusions and future work plans.

2. Related works

The advantage of handover prediction is twofold: min-imization of interruption during hard handover andachievement of the optimal diversity set size in the caseof soft handover. If a proper and efficient handover pre-diction is performed, the number of redundant handovers(sometimes termed ‘‘unnecessary handovers’’ in the liter-ature) can be reduced. Redundant handovers are causedby the so-called ping-pong effect, when the MS is contin-uously being switched between two neighboring BSs sinceit is moving along the edge of cells’ boundaries [5]. An-other purpose of the utilization of handover predictionis to optimize an admission control as presented in [6]and [7]. The utilization of handover prediction for reser-vation of resources for admission control is also presentedin [8]. The paper proposes two admission control schemesto optimize the utilization of dedicated bandwidth. Pre-diction for resource allocation is investigated in [9 and[10]. In [9], the authors investigate two approaches tohandover prediction: ‘‘cell’’ and ‘‘user’’. The cell approachpredicts the number of users in the cell whereas the userapproach utilizes mobility prediction to determine infor-mation on the next handover. The paper summarizesthe advantages of both approaches and their suitabilityfor utilization in different scenarios. An extension of theprevious paper is presented in [10]. The authors proposea new resource allocation mechanism showing that theuser approach performs better at providing a reductionin handover failures. On the other hand, the cell approachtogether with the proposed resource allocation mecha-nism improves cell blocking probability. A modificationof the handover procedure for mobile WiMAX exploitingthe prediction of target BS and thus significantly reducinghandover interruption is proposed in [11]. The papershows that the prediction can reduce the downlinkhandover interruption by up to 90% compared to aconventional IEEE 802.16e handover.

The prediction of target BS can be based on severalapproaches utilizing: handover history, user’s movementtrajectory, and radio channel characteristics.

The first approach stores information related to the pre-vious handovers of all MSs in the network. This method re-quires all updates of the serving BS of all MSs in thenetwork to be monitored. This means that identificationof the serving and target BSs must be stored in the memoryif a MS performs the handover. The prediction is based onthe ratio of handovers among individual pairs of BSs per-formed in the past. An analysis of the efficiency of hand-over prediction utilizing handover history is presented in[12]. This paper further investigates the impact of thenumber of neighboring BSs on the prediction efficiency.According to the results, the maximum efficiency is onlyapproximately 45% for three neighboring BSs in Manhat-tan-like street deployment.

The second approach to handover prediction is to deter-mine the next positions of the MS based on its movementin the past as addressed, for example, in [13]. Three generalassumptions must be fulfilled for highly efficient predic-tion of the MS’s movement [14]: (i) exact knowledge ofcurrent and previous positions of the MS, (ii) knowledgeof the user’s profile, and (iii) knowledge of the profile ofthe area in which the prediction is performed. Knowledgeof the exact position implies a utilization of localizationequipment such as GPS (Global Positioning System) [15].The second assumption requires the acquisition of userinformation such as areas of interest, favorite places, timeschedule, etc. Consequently, each user has to fill in thisinformation and keep it updated. To meet the last assump-tion, it is necessary to have a proper geographic map ofareas in which the prediction is being executed. This infor-mation can be acquired by a network provider and alsomust be kept up to date. Fulfillment of all the above men-tioned assumptions imposes very high demands on theuser, mobile equipment, and network. Therefore, the pre-diction is convenient for neither users nor operators. In[16], the authors analyze the effectiveness of the predictionto reduce power consumption in ad-hoc networks. Thisgoal is achieved by delaying the communication until aMS becomes closer to the target BS. The prediction is alsobased on the MS’s movement history with all its draw-backs. Prediction of the user’s position is further exploitedfor example in [13] and [17]. Both papers present advancedalgorithms for prediction of the user’s location. However,the papers do not eliminate general weaknesses of thiskind of prediction and assume there is knowledge of theexact position of the user.

The last prediction approach, based on the channel (ornetwork) characteristics, exploits information which isusually exchanged among MSs and core network (repre-sented by BSs) during normal operation (e.g., for handoverpurposes). Hence, no additional requirements are impliedfor either the MSs or the BSs. The efficiency of techniquesusing the channel characteristics is significantly higherthan the efficiency of techniques based purely on the hand-over history (see [18] and [12]). On the other hand, thesetechniques are usually outperformed by position basedprediction. Prediction utilizing channel characteristicsis investigated in [19]. The authors generally describe thenew network evaluation method as a criterion forhandover decision with user preference and the usualestimation standards. Handover prediction is performed

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3761

according to a weighted combination of several networkparameters such as bit rate, latency, or power consump-tion. In [18], the authors evaluate several filtering methodsfor handover prediction. The authors compare the effi-ciency of handover prediction for Grey [20], Kalman [21],Fourier [22], and Particle [23] filtering of RSSI (ReceivedSignal Strength Indication) values. The prediction is basedon the mutual relation of RSSIs of the target and servingBSs. The prediction is performed if the difference betweenthe two RSSI levels falls into a predefined interval. The re-sults show the best performance (roughly 80% successfulhandover prediction) for Grey filtering. Grey filtering isalso analyzed in [24]. The paper evaluates and proves thepositive impact of Grey prediction on the reduction ofredundant handovers. Chan presents QoS adaptive predic-tion combined with the second type of prediction in [25].The maximum prediction efficiency of the proposed tech-nique is roughly 75%. The results are achieved for regularmovement trajectories followed by employees in theirworkplaces.

In [26], the authors compare several handover predic-tion techniques like the handover history, mobility pattern,movement extrapolation, or distance. The paper showsthat the best performance (highest ratio of correct predic-tions) can be achieved by prediction based on a mobilitypattern or movement extrapolation for a road mobilitymodel or a random waypoint mobility model [26] respec-tively. The prediction efficiency is approximately 60% inboth cases.

The goal of this paper is to design a prediction tech-nique which can achieve efficiency as high as that obtainedby techniques based on knowledge of the user’s positionwhile eliminating their drawbacks – additional demandson users, mobile equipments, and network. Hence, predic-tion using channel characteristics is considered as the basisof the proposed technique. The improvement of the predic-tion efficiency is achieved by utilization of two mutuallyindependent thresholds for making a decision on the selec-tion of the predicted target BS. Furthermore, the rise in pre-diction efficiency obtained by techniques originally usedfor reduction of the number of redundant handovers(avoiding the ping-pong effect) is also investigated in thispaper. Three techniques are considered: Hysteresis Margin(HM) [3], [27], windowing (also known as signal averaging)[27], and Handover Delay Timer (HDT) [28]. All assump-tions for the proposed prediction are based only on param-eters and metrics that can be obtained during conventionalactivities of networks according to the IEEE 802.16e andIEEE 802.16j standards [29].

3. Handover in mobile WiMAX

3.1. Principle of handover

The handover procedure can be split into several stagesaccording to [3]: network topology advertisement, scan-ning of the MS’s neighborhood, cell reselection, handoverdecision and initiation, and network re-entry. The firsttwo stages are performed before the handover process be-gins. During both, the MS searches its neighborhood and

collects information on neighboring BSs with the aim offinding a suitable target BS. Based on the results of thescanning process the possible target BS is selected in theframe of the cell reselection phase. If all the conditionsfor handover are met, the handover decision and initiationphase is performed. Then, the MS starts synchronizationwith the downlink of the target BS. As soon as the synchro-nization is finished, the MS initiates the network re-entryconsisting of three substages: ranging, re-authorization,and re-registration. After successful accomplishment ofall three substages, the MS can start with normal operation,that is, it can exchange data with the new serving BS.

The above described procedure explains the principle ofhard handover. In the case of MDHO or FBSS, the principleis similar; however the MS is connected to more BSs simul-taneously. When the MDHO or FBSS is supported, a list ofBSs which are involved in the handover procedure (i.e.,the diversity set) is maintained by the MS and BSs. Thisset is updated via MAC (Medium Access Control) manage-ment messages (see [3] and/or [29] for more information).The diversity set is defined for each MS in the network. Inthe case of MDHO, the MS continuously monitors all BSs inthe diversity set and selects an anchor BS. The MS is syn-chronized, authorized, and registered to the anchor BS. Fur-thermore, the MS performs ranging and monitors adownlink channel of the anchor BS for control information.The MS communicates simultaneously (including user traf-fic) with the anchor BS and with all active BSs in the diver-sity set. Unlike the MDHO, the MS communicates only withthe anchor BS for all types of uplink and downlink trafficincluding management messages while FBSS is utilized.The anchor BS can be changed on a frame to frame basisdepending on a BS selection scheme. This means that theMS can receive individual frames from different BSs outof all BSs in the diversity set.

3.2. Elimination of redundant handovers

In general, the hard handover is of low complexity andeasy to implement in mobile networks. On the other hand,it results in more significant degradation of QoS (see e.g.,[2]). Moreover, any type of handover is interconnectedwith the generation of additional management overhead.To avoid both negative phenomena, the elimination ofso-called redundant handovers has to be ensured. Theredundant handover represents the case when handoveris executed but not finished before the time when the nexthandover decision takes place. Also, handovers repeatedfrequently between two adjacent cells in a short timeinterval (i.e., ping-pong effect) should be considered asredundant handovers since the MS cannot take advantageof the connection to the new BS. Redundant handoversare usually caused either by fading effects or by move-ments of users along the edges of cells.

Several techniques can be utilized for minimization ofthe number of redundant handovers. Standard IEEE802.16e defines HM and Time-To-Trigger (TTT) [3]. Othercommonly used techniques are, for example, windowingor HDT extending conventional TTT. All methods are basedon delaying the handover for a predefined time interval.The utilization of these methods for prediction purposes

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does not increase the management overhead since theparameters are already incorporated in MAC managementmessages in WiMAX. Thus, these parameters are distrib-uted within the network regardless of whether the predic-tion is used or not.

In the case of HM, the handover decision and initiationare based on a comparison of one or several signal param-eters of the serving and target BSs. The handover is initi-ated if the signal parameter of the target BS exceeds thesignal parameter of the serving BS plus HM, as defined bythe next equation:

STari > SSer

i þ HM; ð1Þ

where SSert and STar

t represent the signal quality parametersof the serving and target BSs respectively.

If windowing is applied, the handover decision is madeif the average value of the observed signal parameter fromthe target BS drops below the average level of the sameparameter at the serving BS:

PWSi¼1STar

i

WS>

PWSi¼1SSer

i

WS; ð2Þ

where WS corresponds to the window size, that is, thenumber of samples over which the average value iscalculated.

Implementation of the HDT is based on the insertion ofa short delay between the time when the handover condi-tions are first met and the time when handover initiation isexecuted. This method is based on TTT. In the case of TTTthe signal is continuously monitored for each frame duringa short interval (up to 255 ms in WiMAX), whereas the HDTevaluates only several signal samples measured duringlonger periodic intervals. The handover is performed if:

SSert < STer

t jt 2 ðtHO; tHO þ HDTÞ; ð3Þ

where HDT represents the duration of the handover delaytimer and tHO is the time instant when the handover condi-tions are fulfilled.

Fig. 1. Definition of handover threshold (a) based on

4. Proposal on Two Thresholds Prediction

The principle of the proposed technique is explained inFig. 1. It exploits reports on signal (channel) quality contin-uously obtained by the network in the frame of the MS’sscanning procedure. Our improvement is based on the def-inition of two independent thresholds as depicted inFig. 1a. One threshold is related to the signal level receivedby the MS from the serving BS while the second thresholdis related to the signal level measured by the MS from thepotential target BS. Hence the prediction is entitled TwoThresholds Prediction (TTP).

Fig. 1a depicts RSSI evolution between the MS and sev-eral BSs during the MS’s movement along a straight line(red dashed trajectory in Fig. 1b). The speed of the MS is15 m/s and the observation time is 100 s; that is, the dis-tance covered by the MS within one observation cycle is1500 m. Each curve in Fig. 1a represents the set of RSSIs re-ceived by the MS from all BSs obtained within severalmovements of the MS along the red line. Minor fluctuationof RSSI is caused by variations in channel parametersamong all runs of the MS (for more details on simulationparameters see Section 5).

The following two thresholds are defined: HO_ThrSerX,Y

and HO_ThrTarX,Y. The first represents a typical RSSI levelof the serving BSx at the moment of the initiation of theMS’s handover to the target BSy (see Fig. 1a, where theserving BSx is BS4 and the target BSy is BS2). The secondthreshold corresponds to a typical RSSI level of the pre-dicted target BSy at the moment of initiation of the MS’shandover from the serving BSx. In practice, both thresholdlevels are usually very close in most cases; neverthelessthey are not equal. In principle, the level of HO_ThrTarX,Y

is slightly higher than HO_ThrSerX,Y since the handover deci-sion is generally made if the target BS can provide higherconnection quality than the serving BS in WiMAX. More-over, both thresholds are also unequal due to non-station-ary signal levels. The monitoring and evaluation of thesignal evolution from all neighboring BSs is performed inthe following manner. If the RSSI level from the servingBS decreases and draws near to HO_ThrSerX,Y, the probabil-ity of a handover from BSx to BSy increases (in Fig. 1a

movement of MS along the same direction (b).

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3763

HO_ThrSerX,Y is HO_ThrSer4,2). If the RSSI from one of theneighboring BSs increases and draws near to HO_ThrTarX,Y,the probability of a handover from BSx to BSy also rises(in Fig. 1a HO_ThrTarX,Y is HO_ThrTar4,2). If the RSSI of theserving BS drops below HO_ThrSerX,Y and simultaneouslythe RSSI of a neighboring BS exceeds HO_ThrThrX,Y, the pre-diction result is the expectation of a handover of the MSfrom BSx to BSy. This means that BSy is labeled as the ‘‘pre-dicted target BS’’.

Both thresholds for each pair of BSx and BSy (thresholdsHO_ThrSerX,Y and thresholds HO_ThrThrX,Y) are averaged outin order to derive one specific value related to the servingBS threshold as well as one value related to the target BSthreshold. The mean values of the typical thresholds forthe handover are calculated as an average of several previ-ous signal levels leading to the handover initiation. Deter-mination of the sufficient number of RSSI samples is theobject of investigation addressed further in this paper.The mean thresholds can be described by the followingequations:

AvgHO ThrSerX;Y ¼1

HOBSX;BSY

XHOBSX;BSY

i¼1

RSSIHOiMS;BSX ; ð4Þ

AvgHO ThrTarX;Y ¼1

HOBSX;BSY

XHOBSX;BSY

i¼1

RSSIHOiMS;BSY ; ð5Þ

where HOBSX,BSY represents the number of handoversthat occur between the current serving BS and the poten-tial target BS during the observed time interval; RSSIHOi

MS;BSX

and RSSIHOiMS;BSY are RSSIs received at the MS from BSx and

BSy respectively at the time instant of the handover deci-sion; and index i specifies the individual handover event.

It is not appropriate to perform the target BS predictiononly when the typical thresholds are reached since the pre-diction would be made too late for exploitation of the pre-diction results in advance of the handover execution.Therefore, the target BS should be selected when the RSSIsbetween the MS and the serving and target BSs are withinintervals of HOZone defined by the following formulas:

HO ThrSerX;Y þ HOZone > RSSIMS;BSX ; ð6Þ

HO ThrTarX;Y � HOZone < RSSIMS;BSY ; ð7Þ

where HOZone represents the interval when the BSy ismarked as the predicted target BS (see Fig. 1a) andRSSIMS,BSX and RSSIMS,BSY correspond to the signal level cur-rently received by the MS from the serving and target BSsrespectively.

The utilization of two thresholds instead of conventionalprediction with one threshold enables higher efficiency tobe achieved since it can reduce the ratio of incorrect targetBS prediction. In the case of conventional prediction (de-fined by (8)), the prediction efficiency can be influencedby signal level fluctuation, for example due to shadowing,fast fading, and so on. If only one signal level (RSSIMS,BSX orRSSIMS,BSY) is affected by fast fading or shadowing, (8) canbe fulfilled even if the user does not change location.

RSSIMS;BSX � RSSIMS;BSY < HOOTZone; ð8Þ

where HOOTZone represents the interval between the two sig-

nal levels in which the BS is determined as predicted targetBS in competitive proposals.

If two thresholds are considered, the fluctuation of onlyone signal level (e.g. RSSIMS,BSX) does not necessarily lead toselection of BSy as the predicted target BS since the condi-tion related to the second RSSI (e.g. RSSIMS,BSY) is still notfulfilled.

All neighboring BSs of BSx (including BSy) are includedin the so-called Neighbor Set of BSx denoted in this paperas NSx. Considering the NS, the probability of handoverfrom the BSx to the BSy (labeled as Px,y) can be formulatedby the following equation:

Px;y ¼ax;y; BSy 2 NSx;

0; BSy R NSx;

�ð9Þ

where ax,y represents the exact probability value of thehandover from BSx to BSy. It is in the range 0 6 ax,y 6 1.The value of ax,y depends not only on the number of BSsin the NS but also on the layout of the area where the pre-diction is analyzed and monitored (e.g., the layout ofstreets, deployment of buildings and transmitters, etc.).

Px,y depends on the probability of fulfillment of (6) and(7). As both conditions are independent, Px,y can be rewrit-ten as:

Px;y ¼ PðAvgHO ThrSerX;Y þ HOZone > RSSIMS;BSXÞ� PðAvgHO ThrTarX;Y þ HOZone < RSSIMS;BSYÞ � n

¼ PðBSSerÞ � PðBSTarÞ � n: ð10Þ

The probability of the case when the MS will not performthe handover to the predicted BSy ðPx;�yÞ despite fulfillmentof conditions (6) and (7) by BSy is expressed by the n func-tion. According to (6) and (7), Px,y is a function of parameterHOZone and actual values of RSSIMS,BSX and RSSIMS,BSY. If ax,y

represents the value of probability Px,y, the probability ofthe handover from BSx to all BSs excluding BSy ðPx;�yÞ is1 � ax,y. For example, this probability corresponds to thecase when the MS randomly turns away from the antici-pated direction. Then the probability of handover fromBSx to BSy could be generally described by a function for-mulated in the following way:

Px;y ¼ f ðRSSIMS;BSX ;RSSIMS;BSY ;HOZone; nÞ: ð11Þ

The function n is directly proportional to the probabilitythat the MS will not change direction significantly enoughto perform the handover to a different BS in the next timeinterval s. This probability is labeled as PSD

MS. Thus, n can beformulated as:

n � PSDMSðsÞ; ð12Þ

sis a function of speed and distance as expressed by thenext formula:

s ¼ DistMS;cell

vMSð13Þ

where vMS is the average velocity of the MS and DistMS,cell isthe distance of the MS from the place where the handovershould be executed (cell edge). The distance is measuredalong the MS’s trajectory. Then Eq. (11) can be rewrittenas follows:

Table 1Simulation parameters and scenario definition for channel characteristics.

Parameter Value

Number of BSs 15Number of MSs 48BS transmitting power [dBm] 46BS height [m] 32MS height [m] 2MS speed [m/s] 15Frequency band [GHz] 2.5Frame duration [ms] 10Scanning reporting period [s] 1Simulation duration [s] 10800HOZone[dB] 0–16Hysteresis margin [dB] 4, 8, 12, 16, or 20HDT [s] 1/2/3/4/5Window size [samples] 5, 8, 10, 15, or 20Path loss model Urban Macrocell [1]Channel variation r = 0, 0.3, 0.6, or 0.8Shadowing Standard deviation 8 dB [1]Mobility model PRWMMSize of simulated area [m] 2330 � 2100 (4.89 km2)

3764 Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773

Px;y ¼ f ðRSSIMS;BSX ;RSSIMS;BSY ;HOZone;vMS;DistMS;cell; PSDMSÞ

¼ PðBSSerÞ � PðBSTarÞ � PSDMS

DistMS;cell

mMS

� �:

ð14Þ

The previous formula can be expressed in another way asthe probability of successful prediction of the target BSyif the MS is moving out of the coverage area of the servingBSx. Considering the above mentioned facts, the probabil-ity of successful prediction increases with either a decreasein DistMS,cell or a decrease in the difference between the cur-rent values of RSSIMS,BSx and AvgHO_ThrSerX,Y as well asRSSIMS,BSy and AvgHO_ThrTarX,Y. On the other hand, a dropin PSD

MS (caused by lowering mMS) can reduce the probabilityof successful handover prediction. The impact of the lastparameter in (14), HOZone, is investigated later in thispaper.

Since the MS can arrive in an area where more than onepotential target BS fulfills the conditions for the prediction,some mechanisms for the selection of the single mostlikely target BS should be defined. This mechanism is basedon the calculation of the minimum difference betweenboth thresholds AvgHO_ThrSerX,Y and AvgHO_ThrTarX,Y andthe current RSSIMS,BSX and RSSIMS,BSY respectively. This isdone for all possible target BSs with RSSI values in therange defined by (6) and (7). These stations are listed inListOfTargetBS. The selection of the target BS that will be la-beled as the predicted target BS is done according to the re-sults of the next equation:

DiffBSX;BSY ¼ jAvgHO ThrSerX;Y � RSSIMS;BSX jþ jAvgHO ThrTarX;Y � RSSIMS;BSY j: ð15Þ

The differences between RSSI levels and thresholds for eachpotential target BS are compared afterwards and the BSwith the minimum DiffBSX,BSY is selected as the predictedtarget BS (see next formula).

PredictedTargetBS ¼ fYgjListOfTargetBSY

¼minðListOfTargetBSÞ: ð16Þ

As the prediction of more than one target BS can be profit-able, all BSs fulfilling conditions (6) and (7) can be denotedas predicted target BSs as well if required, for example, ifthe most likely target BS cannot accept the MS due tooverloading.

Other signal parameters such as CINR (Carrier to Inter-ference and Noise Ration), SNR (Signal to Noise Ratio), de-lay, or other parameters expressing the quality of channelbetween a MS and BSs can be used for the prediction in-stead of RSSI. The only limitation on the parameter utilizedfor the prediction is that it has to correspond to the metricused in scanning reports sent by the MS to the serving BS(message MOB_SNC-REP; see more details in [3]). If theparameter is not listed among the parameters convention-ally used in mobile WiMAX, two messages related toscanning must be modified: the scanning response (MOB_SNC-RSP) and scanning report (MOB_SNC-REP). Themodification of both messages is very simple since onlyinclusion of a new parameter in the field ‘‘Report metric’’is necessary. There are four reserved bits in the field

‘‘Report metric’’, and therefore one of those bits can beused to identify the new metric. This method retains thebackward compatibility with former WiMAX standards.

The above described TTP assumes that the scanning re-sults of the MSs’ neighborhood are stored, which can bedone in either the MSs or the BSs. Therefore, the predictioncan be made by either the MSs or the BSs. Nevertheless,several aspects should be considered before the entityresponsible for prediction is selected. Firstly, a lot of datahave to be kept in the station’s memory. Furthermore, ifthe MS is responsible for the prediction, all informationon all handovers in the network has to be delivered to thisMS. This significantly increases management overhead. TheMS can only exploit information on handovers performedby itself, which dramatically prolongs the time requiredto gather enough information to ensure high efficiency ofthe prediction. If the BSs are responsible for the prediction,no additional information has to be exchanged among MSsand BSs since BSs receive all information via scanning re-ports provided by MSs. Thus, BSs can easily determineappropriate thresholds. Then, the prediction of a targetBS is performed by BSs based on evaluated thresholdsand actual reports provided by MSs.

All of the above mentioned aspects indicate that predic-tion by the BSs is distinctly more efficient. Hence, predic-tion performed by BSs is considered in the rest of the paper.

5. Simulation scenario

The prediction of the target BS is evaluated for the casewhen the reporting of RSSI is assumed. The simulationparameters are summarized in Table 1. The simulationsare performed in a developed MATLAB simulator.

All BSs are deployed in a symmetric manner (see Fig. 2)at the same height and transmit at the same power levelaccording to the recommendations on evaluation of anIEEE 802.16 m network [1]. At the beginning of the simula-tion, the positions of all 48 MSs are randomly generated. AProbabilistic Random Waypoint Mobility Model (PRWMM)

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3765

[30] is considered for the MSs’ movement since it providesa higher level of movement randomness than other mobil-ity models. Thus the obtained results correspond to theworst case scenario.

Signal strengths among all MSs and BSs are calculatedusing the urban macrocell path loss model defined in [1].Channel variation is represented by low signal level fluctu-ation [31]. The path loss with channel variation is equal to

PLCVi¼ PLi þ ðCVrandi

� SiÞ; ð17Þ

where i indicates a step of the simulation, PL is the macro-cell path loss defined in [1], CVrand is a random level of fluc-tuation, the exact value of fluctuation is randomly selectedaccording to lognormal distribution with l = 0 and r = 0,0.3, 0.6, or 0.8 (depending on the specific scenario) [31],and S is the sign (positive or negative) of CVrand. Si is ex-pressed by the following formula:

PðSi ¼ 1Þ ¼a; Si�1 � 1;1� a; Si�1 ¼ 1;

�

PðSi ¼ �1Þ ¼1� a; Si�1 ¼ �1;a; Si�1 ¼ 1:

� ð18Þ

The signal parameters are evaluated in each scanningreporting period (i.e., 1 s).

The performance of the proposed prediction scheme isevaluated by means of three parameters: the ratio of suc-cessfully predicted handovers HR (Hit Ratio), the ratio ofnot predicted handovers (NPR), and the ratio of wrong pre-diction (WPR). The handover prediction is assumed to besuccessful if the MS executes the handover to the predictedtarget BS. The number of successfully predicted handovers(SPHO) is used for the calculation of the prediction hit ratioaccording to the following formula:

HR ¼ SPHO

NHO0 6 HR 6 1; ð19Þ

where NHO represents the total number of all handovers inthe network. It can be calculated as:

NHO ¼XNBS

BSX¼1

XNBS

BSY¼1

HOBSX;BSY BSX – BSY; ð20Þ

where NBS is an overall number of BSs in the network.

Fig. 2. Deployment of BSs in the simulation.

Not predicted handover occurs if the handover is car-ried out despite the fact that no target BS has been pre-dicted since the time when the MS performed theprevious handover. This situation takes place especiallyat the beginning of the simulation as not enough data havebeen collected to perform successful prediction of the tar-get BS (typical thresholds cannot be set up precisely aseither there is still no information on the previous hand-overs at all or the information gathered is insufficient).The ratio of not predicted handovers (NPR) is calculatedin the following way:

NPR ¼ NPHO

NHO0 6 NPR 6 1; ð21Þ

where NPHO is the total number of not predictedhandovers.

An error in prediction (wrong prediction) occurs if thepredicted target BS differs from the real target BS of theMS. The ratio of wrong predictions (WPR) can be expressedby the following equation:

WPR ¼WPHO

NHO0 6WPR 6 1; ð22Þ

where WPHO is the total number of incorrectly predictedhandovers.

6. Evaluation of proposed technique efficiency

Several sets of simulations are performed consideringdifferent types of scenarios. The first one analyzes the im-pact of the HOZone parameter on the prediction efficiency ifno other technique for efficiency improvement is consid-ered (i.e. HM, HDT, or windowing). Next, three sets of sim-ulations investigate the impact of all the individualtechniques on prediction efficiency. The last one deter-mines the optimum setting of all three techniques in coop-erating mode to obtain maximum prediction efficiency.The results acquired by simulations are separated into fivesubsections according to the investigated techniques.

6.1. Impact of HOZone on prediction efficiency

The results of the simulation are shown in Figs. 3 and 4.These figures present the dependence of the predictionefficiency on the simulation time. Both figures containthe results of HR, NPR, and WPR.

It is evident that if the WPR decreases together with theNPR, the overall ratio of successful prediction (HR) in-creases proportionally since the relation among thoseparameters is: HR = 1 � NPR �WPR. Figs. 3 and 4 enabledetermination of the minimum time interval for collectionof RSSI information to ensure sufficiently high predictionefficiency. This is slightly over 1000 s (at that time, roughly2000 handovers have already been performed within thesimulation). This corresponds to roughly 10 handovers be-tween each pair of neighboring BSs).

The individual figures differ in diverse parameter set-tings of the channel model. While Fig. 3 assumes that thehandover prediction is performed only according to RSSIevolution, Fig. 4 also takes into account another factor,

Fig. 3. Results of handover prediction based on the RSSI evolution, without channel variation.

Fig. 4. Results of handover prediction based on the RSSI evolution, with channel variation (r = 0.8).

3766 Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773

i.e., channel variation with r = 0.8 [1], [31] and shadowingwith a standard deviation of 8 dB [1]. Windowing, HDT,and HM are disabled in Figs. 3 and 4.

The results of the prediction hit ratio acquired during 3 h(10800 s) of data monitoring are summarized in Fig. 5. Thisfigure also considers other levels of channel variation, thatis, r = 0.3 and r = 0.6. The maximum HR (approximately71%) is achieved if the channel variation is not considered.The higher channel variation degrades maximum HR to 45,41, or 37% for r = 0.3, 0.6, or 0.8 respectively. Additionally,Fig. 5 demonstrates significant dependence of the predic-tion and its effectiveness on the HOZone. The predictionefficiency is under 10% if HOZone is equal to 0 dB. The opti-mal value of HOZone observed in Fig. 5 is 4 dB for all levelsof channel variation. At this HOZone level, the predictionmechanism shows the highest ratio of successfully pre-dicted target BSs (71%) and low NPR (14%) as well as WPR(15%) if no channel variation is introduced. Fig. 6 indicatesthat the channel variation increases the NPR to slightly over30% for all levels of r at HOZone = 4 dB. The impact ofdifferent channel variation levels on the NPR is negligible.

The WPR also rises to 25, 28, or 31% for r = 0.3, 0.6, or 0.8respectively at HOZone = 4 dB (see Fig. 7).

In the other simulations focused on a comparison of theimpact of windowing, MH, and HDT, the channel variationwith r = 0.8 will be taken into account (denoted as ‘‘CVon’’). The results of the scenario without consideration ofthe channel variation are also depicted in the following fig-ures to enable comparison of results (denoted as ‘‘CV off’’).The detailed behaviors of the prediction ratios over thesimulation time, as depicted in Figs. 3 and 4, are not pre-sented in the following subsections. The overall resultsare illustrated in the form of separate results for HR, NPR,and WPR so that they are more transparent.

6.2. Impact of windowing on prediction efficiency

In order to suppress the negative impact of the channelvariation on the prediction and to increase its efficiency,the ‘‘windowing’’ technique can be used. Fig. 8 demon-strates a distinguishable increase in the HR by windowingeven if channel variation (r = 0.8) is assumed. The Window

Fig. 5. Target BS prediction hit ratio over HOZone.

Fig. 6. Ratio of not predicted handovers over HOZone.

Fig. 7. Ratio of wrongly predicted target BSs over HOZone.

Fig. 8. Target BS prediction hit ratio over HOZone for a set of WSs.

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3767

Size (WS) is set to 5, 8, 10, 15, and 20 samples. The utiliza-tion of the windowing technique with WS = 5 increases HRfrom 37% to 65% (28% increase in HR) in comparison to the

case with no windowing. In the rest of the paper, if the gainis for example 10%, it means an increase of, for example,50% to 60%.

An additional gain of 6% is introduced when the WS isfurther increased to eight samples. In this case, the impactof the channel variation is eliminated and the HR for thechannel variation with r = 0.8 and WS = 8 is equal to thescenario with no channel variation and no windowing. Inboth cases, maximum HR is 71%. Another gain of 2% and5% is observed by increasing WS to 10 and 15 samplesrespectively. However, the results show only a marginal in-crease in the HR for WS = 20. Therefore, a further increasein WS is useless since it brings no additional improvementof the prediction efficiency.

Analogical conclusions can be derived from Figs. 9 and10. Fig. 9 shows merely a slight decrease in the NPR forWS above 15 samples. Similarly, Fig. 10 presents only amarginal decrease in the WPR for values where WS risesabove 15 samples. The NPR decreases while the level ofHOZone rises since a larger HOZone allows the prediction tobe performed earlier. However, earlier prediction increasesthe WPR as well. This is due to the higher probability of theMS turning away from the anticipated direction when pre-diction takes place earlier (see Eq. (14)).

6.3. Impact of hysteresis margin on prediction efficiency

Another way to suppress the negative impact of channelvariation is to use HM. The results of this investigation arepresented in Figs. 11–13 with HM = 4, 8, 12, 16, and 20 dB.The scenario with HM = 4 dB improves the HR by 41% (atHOZone = 4 dB) in comparison to the scenario where HM isnot considered. Note that the HR for HM = 4 dB at HOZo-

ne = 4 dB and the channel variation with r = 0.8 is higherin comparison to the scenario with no channel variation(by roughly 7%). Additional gain is reached by increasingHM up to 12 dB at HOZone = 4 dB. The prediction efficiencyreaches approximately 90% at this HM level. If HM is setto either 16 dB or 20 dB no significant gain in the HR is ac-quired at HOZone = 4 dB in comparison to HM = 12 dB. TheHR at HM above 12 dB reaches its maximum at a higher

Fig. 9. Ratio of not predicted handovers over HOZone for a set of WSs.

Fig. 10. Ratio of wrongly predicted target BSs over HOZone for a set of WSs.

Fig. 11. Target BS prediction hit ratio over HOZone for a set of HMs.

Fig. 12. Ratio of not predicted handovers over HOZone for a set of HMs.

Fig. 13. Ratio of wrongly predicted target BSs over HOZone for a set of HMs.

3768 Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773

HOZone. The maximum for HM = 16 dB and HM = 20 dB isachieved at HOZone = 6 dB (91%) and HOZone = 8 dB (92%)respectively.

In addition, Figs. 12 and 13 show that an increase in theHM above 12 dB leads neither to significant improvementin the reduction of the NPR handovers nor to considerableminimization of the WPR. The NPR is even slightly higherfor HM = 16 dB and HM = 20 dB than for HM = 12 dB.

Another reason for the utilization of a lower level of HMis its impact on the network throughput. A higher value ofHM can decrease the network throughput since the MScommunicates with the BS, which does not provide thebest signal quality. The lower signal quality can result inthe utilization of a more robust modulation and codingscheme (MCS) for communication between the MS andthe BS (see [3]).

6.4. Impact of handover delay timer on prediction efficiency

The third means of mitigating the negative impact ofthe channel variation on prediction efficiency is the appli-cation of HDT. The results of simulation are depicted inFigs. 14–16 with a channel model including channel varia-tion with r = 0.8 and utilization of HDT = 1, 2, 3, 4, and 5 s.As Fig. 14 indicates, HDT cannot fully eliminate the nega-tive impact of the channel variation. If the HDT is set to

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3769

1 s, the HR is improved by only 2% at HOZone = 4 dB in com-parison to the scenario without HDT. A further increase inHDT duration (up to HDT = 3 s) brings rises in the HR of 2%per 1 s of HDT duration. With additional prolongation ofHDT (above 3 s) the improvement in the HR is negligible(less than 1% per 1 s of HDT duration).

The HDT increases the NPR up to roughly 15% (depend-ing on HOZone) in comparison to the scenario with no HDT,HM, or windowing. The duration of HDT has only a minorimpact on the NPR (see Fig. 15). On the other hand, theWPR is improved by approximately 15% (see Fig. 16) byimplementation of HDT. However, the WPR is influencedonly marginally by the duration of HDT.

Fig. 16. Ratio of wrongly predicted target BSs over HOZone for a set ofHDTs.

6.5. Maximization of handover prediction efficiency

This section presents the results when all three meth-ods are used together in order to improve overall predic-tion efficiency. A very high number of combinations of alltechniques can be specified. Therefore, only the combina-tions which potentially offer the best results (according

Fig. 14. Target BS prediction hit ratio over HOZone for a set of HDTs.

Fig. 15. Ratio of not predicted handovers over HOZonefor a set of HDTs.

to results taken from previous subsections) are assumed.If the system performs similarly for two different values,the lower one is selected as optimal since a lower negativeimpact on the throughput can be assumed [32]. To find anoptimal setting of all parameters, the values of individualmethods showing the best performance of the predictionare summarized in Table 2.

The complete list of all investigated scenarios is intro-duced in Table 3. Scenarios are defined with respect tothe impact of each particular technique on their mutualcooperation. Values of HM higher than 12 dB are not con-sidered in the following scenarios as these values notice-ably decrease the MS’s throughput (see [32]). Note thatthe minimal value for WS is 1 sample, that for HM is0 dB, and that for HM is 0 s.

In total, 12 scenarios are defined for the investigation ofmaximum prediction efficiency. All results are distributedin two figures due to the higher clarity of plotted curves(the first one contains the results of scenarios A-F; the sec-ond one presents scenarios G-L).

As can be observed from Fig. 17, the highest HR can beachieved by scenario C (HR is 93% at HOZone = 6 dB). Thisscenario corresponds to the case when the optimumparameters of HM and WS are set up (see Table 2) whileHDT is disabled.

Furthermore, an increase in optimal HOZone with HDT isnoticeable from Fig. 17 (compare, e.g., scenarios A, B, andC). However, the maximum HR decreases with increasesin the duration of HDT (compare scenarios A, B, and C orD, E, and F). Another conclusion can be obtained by com-paring scenario C with F, B with E, A with D, or G with H.These scenarios show higher improvement in the HR with

Table 2Best performing parameters of particular techniques.

WS [samples] HM [dB] HDT [seconds]

15 at HOZone = 4 dB 12 at HOZone = 4 dB 1 at HOZone = 10 dB10 at HOZone = 4 dB 16 at HOZone = 6 dB 2 at HOZone = 4 dB20 at HOZone = 4 dB 20 at HOZone = 8 dB 3 at HOZone = 4 dB

Table 3List of all simulation scenarios.

Scenario WS [samples] HM [dB] HDT [seconds]

A 15 12 3B 15 12 1C 15 12 0D 1 12 3E 1 12 1F 1 12 0G 10 12 1H 2 12 1I 2 1 1J 15 1 1K 8 4 0L 3 2 0

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WS only as long as the HDT is turned off. Otherwise, it isbetter to utilize a lower WS. Scenarios I and J in Fig. 17demonstrate that utilization of low HM together with en-abled HDT leads to a HR under 50%. Moreover, HR is nearlyconstant for HOZone higher than 4 dB. Additionally, scenar-ios K and L achieve very high HR (89% and 82%) in spite offact that both utilize very low values of HM and WS simul-taneously with HDT equal to zero. Both scenarios are ableto outperform the conventional filtering techniques (suchas Grey, Fourier, Kalman, or Particle) presented in [18].

All combinations of techniques improve prediction effi-ciency for all HOZone (compare all scenarios with the greendashed-dotted line) and only very low values of HM andWS allow the impact of channel variation to be eliminatedfully.

Scenarios K and F are also evaluated for disabled chan-nel variation. The results show insignificant impact of thechannel variation on the HR. The improvement in the HRis only approximately 3% for scenario F and 0.5 % for sce-nario K at HOZone = 4 dB. Consequently, the level of channelvariation does not influence the prediction efficiency as itis eliminated by HM and windowing. Hence, a combinationof those techniques can be considered more effective incomparison to the filtering techniques investigated in[18] since it provides higher prediction efficiency. More-

Fig. 17. Target BS prediction hit ratio over HOZone for a set of combina

over, these techniques are already implemented and lar-gely used in mobile networks. Thereupon, neitherhardware modification of equipment nor implementationof new filtering procedures in the software of MSs is re-quired for the implementation of the proposed TTPprediction.

Fig. 18 illustrates the NPR over HOZone for all scenarios.As can be observed, NPR always decreases with rises in HO-Zone. All scenarios with disabled HDT show only a negligibleNPR value for HOZone higher than 4 dB (it is nearly 0%). Thescenarios with enabled HDT indicate gradual decreases inNPR over HOZone. NPR becomes higher while the durationof HDT increases. The impact of the channel variation onthe NPR is insignificant in scenarios F and K.

Exactly opposite behavior in comparison to Fig. 18 canbe observed in Fig. 19. This figure represents the depen-dence of WPR on HOZone. All scenarios perform significantlybetter than the scenario with all techniques turned off. TheWPR is between 4% and 17% at HOZone = 4 dB for all scenar-ios. All scenarios with a higher value of HM (HM = 12 dB)show WPR under 9%. Therefore, lower values of HM leadto higher WPR. Fig. 19 also shows decreases in WPR withincreases in HM.

Turning off the channel variation slightly decreases theWPR in scenario F (up to 3% at HOZone = 4 dB). However, thechannel variation does not influence the WPR in scenario K.

A comparison of the results of the proposed techniquewith other competitive proposals regarding target BS pre-diction is presented in Table 4. The proposed techniqueTTP, which uses two thresholds, shows approximately10% higher efficiency compared to the filtering techniquesdefined in [18] which utilize only one threshold. The re-sults clearly show that only the trajectory prediction tech-nique proposed by Samaan in [14] reaches comparableefficiency. However the trajectory prediction according to[14] requires a huge amount of additional informationfrom the network as well as a lot of inputs related to theuser’s neighborhood and behavior. Therefore, this kind ofprediction is not convenient or practical for utilization inreal networks. In contrast, the TTP requires no additional

tions of HDT, HM, and WS: (a) Scenarios A-F, (b) Scenarios G-L.

Fig. 18. Ratio of not predicted handovers over HOZone for a set of combinations of HDT, HM, and WS: (a) Scenarios A-F, (b) Scenarios G-L.

Fig. 19. Ratio of wrongly predicted target BSs over HOZone for a set of combinations of HDT, HM, and WS: (a) Scenarios A-F, (b) Scenarios G-L.

Table 4Comparison of efficiency of proposed TTP with other prediction techniques.

Prediction technique Maximum predictionefficiency

Mobility extrapolation; Kwon [26] 65%Mobility pattern prediction; Chan [25] 75%Grey/Fourier/Kalman/Particle filtering;

Bellavista [18]78–81%

Trajectory prediction; Samaan [14] 93%Proposed technique – TTP 93%

Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773 3771

information on either users’ behavior or neighborhoodknowledge.

Further, the efficiency of elimination of redundanthandovers by TTP (for scenarios K and F) and filtering tech-niques investigated in [18] is presented in Fig. 20 to provethat our proposal is able to cope sufficiently with this prob-lem. Two scenarios (K and F) are selected since they bothachieve very high efficiency of handover prediction. Sce-nario K could be regarded as the scenario with the lowest

efficiency of elimination of redundant handovers amongthe scenarios reaching high prediction efficiency due toits low HM and WS. Therefore, scenario K can be consideredas one of the worst case scenarios from the point of view ofelimination of redundant handovers. On the other hand,scenario F promises higher elimination of redundant hand-overs since HM is set to a higher level. Comparison of TTPwith other proposals is not included since not enoughinformation is mentioned in these papers for simulationsto be performed. All handovers that lead to the ping-pongeffect are considered as redundant handovers. The valueson the x axis represent the time interval after the handoverinitiation when another handover initiation is determinedto be useless and leads to a redundant handover. For exam-ple, 5 s on the x axis means that if the handover is initiateduntil 5 s after the previous handover of the same MS, thefirst handover is considered redundant. The y axis ex-presses the ratio of the amount of redundant handoversto the count of redundant handovers when no techniquefor their elimination is used.

Fig. 20. Efficiency of elimination of redundant handovers.

3772 Z. Becvar et al. / Computer Networks 55 (2011) 3759–3773

As can be observed in Fig. 20, the performance of TTPscenario K is close to the performance of the filtering meth-ods evaluated in [18] from the point of view of efficiency ofredundant handover elimination. In contrast, TTP scenario Feliminates nearly all redundant handovers and thus outper-forms filtering methods. However, both techniques (TTP aswell as the filtering one) show very high efficiency sincemore than 98.6% of redundant handovers are eliminatedeven if the interval for redundant handovers lasts 10 s.

7. Conclusions and future work

The paper proposes a new technique for handover pre-diction based on channel characteristics. The proposed TTPtechnique differs from other prediction techniques in thedefinition of two thresholds derived from the signal levelsamong the MS and the neighboring BSs at the time of thehandover initiation. One threshold is related to the servingBS and the second is related to the potential target BS.

As the results indicate, the prediction hit ratio can bepositively influenced by use of HM, HDT, and windowingtechniques. The maximum prediction hit ratio reaches93% if individual techniques are combined, and it dependson the level of HOZone. The best prediction performance isusually achieved for HOZone equal to 4 dB. The proposedtechnique enables significantly higher prediction efficiencyto be obtained in comparison to signal filtering methods.The proposed solution outperforms a similar techniquewith only one threshold by roughly 10%.

The proposed solution requires exchange of no addi-tional information except information sent conventionallyamong MSs and BSs during the normal operation of a Wi-MAX network. Additionally, no inputs from users or specialknowledge or capabilities of networks are required. Henceno modifications to the conventional WiMAX MAC layer orhardware are required.

Future work will tackle issues of utilization of the pre-diction results for the purpose of reducing interruptionsin handovers. Further, the same principle of prediction willbe analyzed and evaluated in a scenario for verticalhandovers.

Acknowledgement

This work has been performed in the framework of theFP7 project ROCKET IST-215282 STP, which is funded bythe European Community. The Authors would like toacknowledge the contributions of their colleagues fromROCKET Consortium (http://www.ict-rocket.eu).

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Zdenek Becvar received MSc in telecommu-nication at the Czech Technical University inPrague, Faculty of Electrical Engineering in2005 and Ph.D. degree in 2010. In 2008, hebecame a researcher at the Department ofTelecommunication Engineering. His currentresearch interests include MAC procedures inwireless networks (LTE, LTE-A, WiMAX) withfocus on radio resource management andmobility support. He participated in severalICT FP6 and FP7 projects. Furthermore, he wasactively involved in research activities of

Vodafone and Sitronics R&D centres at CTU in Prague and in severalnational research projects.

Pavel Mach received his M.Sc. and Ph.D.degree in Telecommunication engineeringfrom Czech Technical University, Prague,Czech Republic in 2006 and 2010 respectively.During his study he joined research groups atSintronics and R&D centers focusing on wire-less mobile technologies. He has been activelyinvolved in several national and internationalprojects. He participated in EU FP projectsFIREWORKS, ROCKET and he currently par-ticipates in EU FP7 project FREEDOM. Hisresearch interests include MAN/LAN networks

based on relay architectures. He is dealing with aspects relating to radioresource management in emerging wireless technologies and focuses oncross layer optimization processes.

Boris Šimák is currently professor and thedean of Faculty of Electrical Engineering,Czech Technical University in Prague. He isactively involved in research of digital signalprocessing. He is the technical director of R&Dcentre for mobile communication at CTU inPrague and the member of executive board ofSitronics Centre at CTU in Prague. He partici-pated on several national and internationalprojects.