Paper ID# 900723.PDF

LOW LATENCY SCANNING ALGORITHM USING RECEIVED SIGNAL STRENGTHFOR FLEXIBLE NETWORK MOBILITY

lung-Min Moon* and Dong-Ho Cho!School of Electrical Engineering and Computer Science

Korea Advanced Institute of Science and Technology (KAIST)Email: jmmoon@comis.kaist.ac.kr*.dhcho@ee.kaist.ac.krt

Abstract-To support highly flexible network mobility in mo­bile communication systems, various types of movable networkentities, especially mobile base stations (M-BSs), will appear innear future. In this environment, a scanning process to identifythe availability of neighbor M-BSs for handoff should be newlydesigned, because a conventional method does not reflect themotion of base stations. Therefore, we propose an efficientscanning algorithm based on the concept of sample vectors andcomparison metrics for estimating the relative motion of a servingM-BS and a user. By using the proposed algorithm, the user canavoid unnecessary scanning trials for the neighbor M-BSs thatare moving away from the user and simulation results show asubstantial performance gain with respect to the interruptiontime caused by the scanning process.

I. INTRODUCTION

In future mobile communication networks, a significantincrease in capacity and a high flexibility in mobility, whichare ranged from personal area to global access domain, areexpected to be realized for maximizing user satisfaction.According to the degree of mobility that could be supportedby the networks, two different types of access systems are nowbeing developed [1]. The first is a nomadic/local area accesssystem that will support users who are static or moving veryslowly with the data rates of up to approximately 1 Gbps. Thedevelopment of femtocells to improve both indoor coverageand capacity can be viewed as one of the examples in thiscategory. The second is a mobile/wide area access system thatwill support users moving at high speed on highways or fasttrains (60 kmlh to 250 kmlh, or more) with the data ratesof up to approximately 100 Mbps. Especially in the secondtype of access system, not only fixed base stations and relaystations but also movable network entities, which are generallycalled moving networks, will be introduced. To make seamlessinterworking among the above mentioned technologies, suit­able mobility management and handoff mechanisms should beprovided. In this context, much research has been conductedin the field of dynamic location registration, efficient scanningof neighbor base stations and overall handoff protocol design.

In this paper, we are dealing with the environment wheremovable network entities travelling with ground vehicles oraerial vehicles are introduced. Here, it is assumed that directcommunications between these movable network entities andmobile users can be performed through wireless links, so wedenote these movable network entities as mobile base stations

978-1-4244-5239-2/09/$26.00 ©2009 IEEE

(M-BSs). The concept of the M-BS is presented in severalsystems such as the IEEE 802.16j multihop relay systems andmilitary tactical networks [2][3]. In the IEEE 802.16j multihoprelay systems, the development of mobile relay stations (M­RSs) that are travelling with transportation vehicles is one ofthe key research items. Thus, the main scenario of applicationis to support mobile users who are located inside the M-RSby relaying traffic and signaling between the base station andthe mobile users through the M-RS. On the other hand, inthe military tactical networks, reliability in communicationsmust be provided. So, more than one wireless link are neededto overcome harsh environmental conditions. Thus, directmessage exchanges between the M-BS, which may belong totank or airborne networks, and mobile users, or soldiers, arebasically required while still maintaining several relay links.Especially if we consider the situation where mobile users aredirectly served by the M-BS, the scanning process to identifythe availability of neighbor M-BSs for handoff [4] should benewly designed, because a conventional scanning process doesnot reflect the motion of base stations. Therefore, an efficientway of scanning neighbor M-BSs when the highly flexiblemobility is supported will be discussed in this paper.

Many related works have been studied to enhance theperformance of scanning and handoff process. The primaryobjective in this filed of research is to reduce the time, orpower consumption, required to detect a target base stationfor handoff and to decide optimal handoff initiation timing.In this circumstance, T. Chung et al. [6][7] proposed networkdiscovery algorithms based on the motion detection of a user.In their works, the user's motion was classified into three statesby measuring received signal strength (RSS): approaching,stationary and leaving states. Then, the network discoverywas performed only in the leaving state for power saving. S.Woon et al. [8] and S. Yoo et al. [5] proposed an effectivelink triggering method to prepare handoff in advance. In theirworks, the time to initiate handoff was determined based onthe variation of RSS from the serving base station and theuser velocity, so packet loss rate during the handoff waslargely reduced. Even though the efficiency of these workswas already verified by numerical and simulation results, theintroduction of movable network entities, or M-BSs, and therelative motion between the M-BS and the user were notconsidered in the scanning and handoff process.

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Therefore , we propose an efficient scanning algorithm thatcan be utilized when the user connected to the serving M­BS tries to detect a suitable target M-BS for handoff. Themain idea of the proposed algorithm is to generate samplevectors for estimating the relative motion of the serving M­BS and the user. Then, it is possible to set the priority forscanning neighbor M-BSs according to a proposed comparisonmetric that is based on the sample vectors and the variation inRSS. Through this procedure, the user can avoid unnecessaryscanning trials for the neighbor M-BSs that are moving awayfrom the user and simulation results show that there is asubstantial performance gain in terms of the interruption timecaused by the scanning process.

The rest of the paper is organized as follows. In Section II,overall description of the proposed algorithm and its analyticalmodel are presented . Next, performance evaluation throughsimulation works is discussed in Section IV and conclusionsare made in Section V.

II. Low LATENCY SCANNING ALGORITHM FOR M-BSFig. I. Operation procedure of proposed algorithm

where V s is a sample value of speed and Bi is a sample valueof z-th direction that is set to be equal to 27fi/ N, so total N

where Vm and 1; represent the speed and the direction of theserving M-BS, respectively. In a similar way, the z-th samplevector Vs (i) ,k that is generated for estimating the motion of anMS can be written as follows:

and the provided motion vector of the serving BS, it is possibleto expect the distance between the serving M-BS and the MSfor each sample vector. Then, the corresponding changes inthe value of the RSS from the serving BS can be derived.These results are used to determine the sample vector that mostproperly describes the motion of the MS by comparing theestimated value of the RSS with the actually measured valueof the RSS from the serving BS. Note that the comparison isperformed by using the proposed comparison metric, whichwill be discussed in Section II-C.

By using the comparison metric for each sample vectorand the positioning information of neighbor M-BSs, which issupported by the serving M-BS, we can set scanning priorityfor neighbor M-BSs. Therefore , the reduction of scanninglatency can be achieved by giving a lower priority to theneighbor M-BSs that are moving away from the MS, or haveless probability to be a target M-BS for handoff.

C. Analytical Model of Proposed Algorithm

In this subsection, we present an analytical model of a pro­posed algorithm, mainly focusing on how to set the scanningpriority for neighbor M-BSs. Let Vm,k denote the motionvector of a serving M-BS at time k. By using the standardbasis vectors, el and e2, in two dimensional space, Vm,k canbe written as follows:

(2)

(1)

Here, we explain a general aspect of the scanning processto clarify the problem to be discussed in this paper. Then, thedetail of a proposed algorithm and its analytical model aredescribed .

A. Brief Summary of Scanning Process

The aim of scanning is to identify and monitor the status ofneighbor base statons (BSs) for initial network entry or handoff[4]. To achieve this purpose, a mobile station (MS) periodicallymeasures certain parameters , such as RSS, based on infor­mation about neighbor BSs provided by a serving BS. Then,a target BS for initial network entry or handoff is decidedby using scanning results . Note that communications betweenthe serving BS and the MS are temporarily unavailable duringscanning process , because a radio interface of the MS is tunedto other frequency bands of neighbor BSs. Thus, we shouldminimize this gap of communications, or interruption time, bydesigning an efficient scanning process .

B. Overall Description of Proposed Algorithm

For the purpose of developing an efficient scanning algo­rithm in the consideration of M-BSs, we assume that severalM-BSs and MSs are freely moving in two-dimensional space.In addition, a serving M-BS has a capability to inform theMSs of its motion vector, or the speed and direction, withthe assistance of positioning systems. However, in the case ofthe MSs, there is no particular way to understand their motionvectors exactly, so a proper estimation method should be used.

As shown in the block diagram of Fig. 1, the first stage ofa proposed algorithm is to generate sample vectors in the MSside. Here, the generation of the sample vectors means to makea set of motion vectors that may include the estimated vectorthat properly describes the motion of the MS. Thus, the speedis decided by randomly choosing in a certain interval and thedirections are decided by equally dividing all directions in thespace. Based on both the generated sample vectors of the MS

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120Z1 z2 z3 Value of comparison metric

LL~ 0.05c:ag 0.04.2.~ 0.03

"-c~ 0.02:c'"E! 0.01a.

0.06,----- - - - - - - - - - - - - - - -----,

Fig. 2. Example of probability density functions for each comparison metric

ddk] = IlV m ,k - Vs(i) ,k ll

= b/(vm cos ¢ - V s cosBi )2+ (vm sin ¢ - V s sinBi )2 (3)

Thus, the corresponding RSS change, which is measured bythe MS, can be obtained based on path loss and log-normalshadowing model in the MS side. Let Ptx and Pdk] denotetransmit power and the estimated value of the RSS from theserving M-BS, respectively. Then, Pdk] can be expressed asfollows:

sample vectors are made. Then, the expected distance ddk]between the serving M-BS and the MS at time k, which isestimated by using Vs(i) ,k, can be expressed as follows:

independent random variables whose probability distributionis represented in (7). Thus, the probability distribution of y~

can be written as follows:

Pdk] = Ptx - 0: - ,B log10 (ddkJ) - 'l/JdB,s (4)

where 0: and ,B are decided by the path loss model and 'l/JdB,srepresents the shadow fading with a standard deviation (Is.Also, the actually measured value of the RSS from the servingBS, denoted by Pm [k], can be expressed as follows:

Pm[k] = Ptx - 0: - ,B log10 (dm [kJ) - 'l/JdB, m (5)1 K { ( ddj ] )}2

where x= 2 + 2 L ,B loglO ~[.](Is (Im j=l mJ

(9)

(8)

where U and Pu denote the number of scanning trials anda corresponding probability, respectively. According to theprobability distribution of the comparison metric in (9) andthe illustration in Fig. 2, Pu can be calculated as follows:

In (II), F( z) represents the cumulative distribution functionof comparison metric Z for the desired sample vector andeach Zi represents the intersection point of the distributionsfor the desired sample vector and the other sample vectors,as shown in Fig. 2. If the value of the comparison metric forthe desired sample vector lies within the interval Z E [0, zj ),

(10)

Pr{Z < zt} = F( zt} , for u = 1

Pr{zu- l < Z < zu}F(zu) - F(zu-t} , for 2 ::::; u ::::; U - 1

Pr{zu -l ::::; Z} = 1 - F(ZU-l) , for u = U (II)Pu =

In (9), K specifies the degree of freedom, or the observationtime, and >. is the parameter related to the mean. Note thatfor the noncentral chi-square random variable, the mean andvariance are K + >. and 2(K + 2>'), respectively [10].

With the assumption that each neighbor M-BS is positionedin the direction corresponding to each sample vector, scanningpriority can be set as an increasing order of the comparisonmetric. Since we know the probability distribution of the com­parison metric in (9), the number of scanning trials required todetect a suitable target M-BS for handoff can be calculated inadvance. For example, Fig. 2 shows the probability distributionof each comparison metric when U = 4 sample vectors aregenerated . If the marked distribution represents the distributionformed by desired sample vector, which leads to select acorrect target M-BS, the number of scanning trials denotedby N; can be obtained as follows:

U

N s = LUPuu=l

v

Y~

where dm [k] denotes the actual distance between the servingBS and the MS and 'l/JdB, m represents the shadow fading witha standard deviation (Im whose value may be different from (Isto reflect the difference between the estimated and measuredvalues of the RSS.

To generate a reasonable comparison metric, let Xi,k be theRSS difference between the measured and estimated values attime k when the sample vector Vs(i ),k is used. Then, Xi,k canbe expressed as follows:

Xi,k = Pdk] - Pm[k]

,B loglO(ddkJldm [kJ) - 'l/JdB,s + 'l/JdB ,m (6)

As a result, we suggest a comparison metric for each samplevector that is formed by averaging the square of Xi,k duringobservation time K. Thus, if the comparison metric for thei-th sample vector has the smallest value, it indicates that thei-th sample vector most properly describes or approximatesthe actual motion of the MS in a probabilistic sense. Thecomparison metric Yi for the i-th sample vector can be writtenas follows:

K 2 2 K ( )2. = ~~ x 2 . = (Is + (Im ~ Xi,jY, K L..; ',J K L..; vi 2 2

j =l j = l (Is + (Im

Here, we can find that Xi,k is a normally distributed ran­dom variable, because Pd k] and Pm[k] are independent andmodelled by the log-normal shadowing. Thus, its probabilitydistribution can be written as follows:

By exammmg the known probability distribution of Xi,kand the form of the comparison metric in (8), we can findthat y~ is a random variable having the noncentral chi-squaredistribution [10], because it is the sum of the square of the K

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III. P ERFORMANCE EVALUATION

TABLE IPARAMETERS FOR PERFORMANCE EVALUATION

(13)

0.5

o

oLocation (km)

-0.5

M .

Tin ,c = 10 + 40 + 20 L ~ (ms)i=l

-1

. / .\ MS with /

\ simple mobility /

\" .....~ ..

\ /\ /

O-:~ :~-/lr-i*----(): with simple mobility/ / . \" . . . As etof ..:

... .. .. . '/ . . . \ . :" ' "sample vectors. I \ : .

II MS with random \ \ :o waypoint mobility 0

Fig. 3. Simulation environment for performance evaluation

-1

0.5

-0.5

§ 0

§...J

where M denotes the total number of neighbor M-BSs. On theother hand, when a proposed method is used, the derivation ofthe scanning priority for the neighbor M-BSs can be possibleby using comparison metric. Thus, unnecessary scanning trialsfor the neighbor M-BSs that are moving away from the MSmay be effectively avoided. Therefore, the interruption timecould be reduced. Then, we now examine performance gainwith respect to the interruption time for several differentparameters.

Fig. 4 shows the interruption time over different values ofthe observation time K for the cases that the conventionalmethod and the proposed method are used. Both the simplemobility and the random waypoint mobility are applied totest more realistic situation. Basically, we can notice that theproposed method provides a smaller amount of the interruptiontime compared to the conventional method. In particular, theproposed method has the performance gain of about 30.6 %for the simple mobility and 15.2 % for the random waypointmobility when K = 10. The reason for this result is that themotion of the MS and the M-BS is considered when the scan­ning priority is determined. As the observation time becomeslonger, the interruption time is continuously decreased whenthe simple mobility is applied. It is because the variance ofthe comparison metric has a smaller value for larger K, somore accurate estimation of the relative motion between theMS and the M-BS can be conducted. However, if the randomwaypoint mobility is applied, the interruption time keeps thesame value when K > 10. In this case, the MS randomlychanges its speed and direction at an arbitrary time interval.Consequently, the effective observation time for detecting themotion of the MS is randomized. Thus, we can find that theinterruption time is eventually saturated.

(12)Tin = 10 + 40 + 20Ns (ms)

it has the largest probability to be the minimum among allthe comparison metrics. Thus, the M-BS corresponding to thedesired sample vector gets the first priority for scanning. Ina similar way, in the case of Z E [Zl , zz), the M-BS getsthe second priority for scanning, and so on . Consequently,we can expect the number of scanning trials by performingthe weighted summation of the scanning priority u for thecorrect target M-BS and the corresponding probability Pu , asdescribed in (10).

The simulation environment for performance evaluation isillustrated in Fig. 3. We consider a serving M-BS and sixneighbor M-BSs that are moving in a certain direction. In thissituation, an MS connected to the serving M-BS tries to detecta suitable target M-BS for handoff. To further investigate theeffect of the motion of the MS, both the simple mobility, whichhas the fixed speed and direction, and the random waypointmobility [II], which changes the speed and direction at arandom time interval are applied. Some related parameters forthe simulation are listed in Table I.

Here, we adapt the interruption time caused by performing ascanning process as a main performance indicator. The numberof scanning trials that we have analyzed in the previoussection can be mapped into interruption time Tin accordingto following equation [12] :

where 10 ms is the interruption time caused when the MSchanges its frequency band from the serving M-BS to thetarget M-BS and 40 ms is the time required to exchangeinformation for scanning between the serving M-BS and theMS . In addition, 20 ms is the scanning latency generatedto measure downlink signal from a neighbor M-BS and N s

denotes the number of scanning trials.When a conventional method is used, neighbor M-BSs are

scanned in an arbitrary order, because there is no such mecha­nism to assign the scanning priority. So, each neighbor M-BShas the same probability to be scanned and the interruptiontime can be calculated as follows :

Path loss model (dB) 130 .19 + 37 .6 log 10 (d)Standard deviation CTm = 8 (default) and I ~ 12 (variable)of shadow fading (dB) CTs = a (for estimation)

Num. of sample vectors N =6Observation interval (s) T =1Observation time (s) K = 15T (default) and T ~ 30T (variable)

Speed of MS (km/h) V s = 60 (default) and 10 ~ 120 (variable)

Speed of M-BS (km/h) V m = 60 (default) and 10 ~ 120 (variable)

Random waypoint Speed E [0 , 2v s ] Angle E [0, 27f]mobility of MS Time interval E [0 , 0.5K]Tx power of M-BS (dBm) Pi« = 46Threshold of RSS (dBm) P th = - 80 (for initiation of scanning)

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___ Conventional--e--- Proposed : simple mobility--+-- Proposed : random waypoint mobility

5 10 15 20Observation time (K)

25 30

c: ..2 100 .15.E~.!:

___ Conventional--e--- Proposed: simple mobility--+-- Proposed: random waypoint mobility

20 30 40 50 60 70 80 90 100 110 120Estimated speed of MS (vs) (krnlh)

Fig. 4. Interruption time vs. observation time Fig. 6. Interruption time vs. estimated speed of MS (VB)

c:~ 100

E~.!:

___ Conventional

--e--- Proposed : simple mobility--+-- Proposed : random waypoint mobility

2 3 4 5 6 7 8 9 10 11 12Standard deviation of shadow fading (am)

c:.2 100 .15.Es.!:

20 30 40 50 60 70 80 90 100 110 120Speed of M-BS (vm) (krnlh)

Fig. 5. Interruption time vs. standard deviation of shadow fading

To investigate the robustness of the proposed method, theinterruption time over the standard deviation of shadowingam is represented in Fig. 5. Although the channel variationdue to shadow fading is high, the proposed method still hasperformance gain, For example, we can see a performance gainby about 32.0 % for the simple mobility and 15.1 % for therandom waypoint mobility when am = 10 dB. Particularly,there is little change in view of the interruption time whenthe random waypoint mobility is applied. It means that theproposed method is more dependent on the observation timethan the channel variation as the motion of the MS tends tobe random.

Next, the effect of the estimation error between the actualspeed of the MS whose value is set to 60 kmlh in thissimulation and the estimated speed V s of the MS is examinedin Fig. 6. We vary the estimated speed V s from 10 kmlh to 120kmlh. The minimum interruption time is obviously achievedwhen the estimated speed is close to the actual speed. Inaddition, the performance gain is shown as 14.7 % for thesimple mobility and 8.8 % for the random waypoint mobility

Fig. 7. Interruption time vs. speed of M-BS (v m )

in the case of V s = 90 kmlh. These values of the gain are abouta half of the gain when there is no estimation error, or V s = 60kmlh. However, the proposed method still outperforms theconventional method even though the estimation error exists.

Finally, we look into the interruption time over the speedof the M-BS. As shown in Fig. 7, the interruption time isdecreased until V m = 50 kmlh and keeps similar values whenVm > 50 kmlh. This phenomenon is observed because the pro­posed method is designed based on the periodic measurementof the RSS. Note that each value of the RSS is more clearlydistinguished when there is enough distance between twomeasurement locations. So, the comparison metric gives morereliable information for a certain level of the moving speedof the M-BS. In addition, the proposed method tends to havethe same gain as the conventional method when the relativemotion between the MS and the M-BS is small, becauseeach comparison metric has a similar value. Thus, the smallamount of the gain is observed in the case of V m = 10 kmlh.Therefore, we can see that the proposed method is suitable inthe environment where the relative motion between several

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movable network entities and users should be taken intoaccount to develop proper scanning and handoff mechanisms.

IV. CONCLUSIONS

In this paper, we have developed an efficient scanning al­gorithm in the consideration of an M-BS, which is introducedin order to support highly flexible network mobility. Throughan RSS measurement and the suitable design of comparisonmetric for estimating the relative motion of a serving M-BSand an MS, a substantial amount of reduction in interruptiontime, which is generated during scanning process, could beachieved. Thus, we are expecting that the proposed algorithmgives desirable effects on the performance of the scanningprocess with respect to the accurate and seamless detectionof a target M-BS for handoff and the save of the powerconsumption required in this process. Especially, the useful­ness of the proposed algorithm is substantially recognizedin the military tactical networks where a central node formanaging communications is carried on fast ground vehiclesor unmanned aerial vehicles.

ACKNOWLEDGMENT

This work was supported by the IT R&D program ofMKE/IITA. [2008-F-002-02, Development of original tech­nology for next-generation Tactical Defence CommunicationNetwork]

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