BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS CELLULAR NETWORKS

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1 Joural of Egieerig Scieces, Assiut Uiversity, Vol. 35, No., pp.3-43, Jauary 7 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS CELLULAR NETWORKS I. I. Ibrahim ; A. S. Ali Faculty of Egieerig, Helwa Uiversity, Cairo, Egypt iiibrahim@sofome.et ; ah_salah@sofome.et A. F. Ghaim Telecom Egypt, Wireless et. laig Dept, Cairo, Egypt ahmed.fay@telecomegypt.com.eg (Received August, 6 Accepted December 4, 6) This paper presets a advaced badwid reservatio techique to improve Quality of Service (QoS) by reducig e forced call termiatio durig hadoff. Oe solutio to esure cotiuity of o-goig calls ad reduce e forced call termiatio durig hadoff is to predict Mobile Statio (MS) trajectory ad do resource reservatio i advace. So, we propose a ew scheme at uses a accurate mobility predictio algorim to predict MS ext locatio ad uses is data i badwid reservatio scheme to improve QoS. The performace of e algorim is evaluated via simulatios. The simulatio results show at our algorim reduces hadoff call drop rate wiout over reservig e resources hece it improves badwid utilizatio ad reduces ew call blockig rate. I. INTRODUCTION Mobility i wireless etworks allows more flexible model of commuicatio, but it also makes problems for etwork desigers to decide e etwork capacity, hadoff, badwid reservatio ad QoS. Oe of e most importat QoS issues is how to cotrol droppig ruig calls. To reduce e call drop probability e etwork saves some chaels for hadoff calls (guarded chael). New calls will be blocked if e umber of idle chaels is equal to or less a e umber of guarded chaels, while hadoff calls ca be served util all e chaels are occupied. May algorims proposed to give priority for e existig call over e ew call usig guard chael. Geerally ere are two strategies: fixed guard chael ad dyamic guard chael. I fixed guard chael [, ] fixed portio of chaels is reserved for hadoff calls. This will reduces hadoff calls droppig probability substatially at e expese of slight icreases i ew call blockig probability by givig badwid access priority to hadoff calls over ew calls i call admissio cotrol. Dyamic guard chael overcomes e shortage of fixed guard chael by dyamically chagig e reserved badwid for hadoff depedig o required badwid for expected hadoff [3 -]. 3

2 3 I. I. Ibrahim ; A. S. Ali & A. F. Ghaim I [3,, ], Shadow Cluster (SC) algorim is proposed. It assumes at every active mobile exerts a ifluece upo e cells i e viciity of its curret locatio ad alog its directio of travel. The algorim estimates e required reserved badwid i e cells uder SC. redictive Schemes for Hadoff rioritizatio [7], propose at based o MS positio ad orietatio e Base Trasceiver Statio (BTS) estimate MS pa. Whe e MS is wii a certai distace (reshold distace) from e ext cell it seds chael reservatio to ext cell. This meod was furer pursued i [8], but istead of usig reshold distace it uses reshold time. I dyamic chael reservatio algorim [5], e guard chael is dyamically chaged accordig to e requested probability which is determied by e mobility patters of e MS ad traffic load. I [6], based o e MS s speed, directio ad road iformatio, e BTS estimates e probability at e MS will eter eighbor cell. The BTS e calculate e reserved badwid. But e algorim does t determie e time whe e MS hadoff to ad from e cell. Also it assumes at e MS is movig i costat speed durig call holdig time ad all MSs move i roads which are iside e road map ad ca t move outside e road map. May badwid reservatio algorims have bee proposed to improve QoS. It cosiders oly e hadoff to e cell ad dose ot cosiders hadoff from e cell [6, ]. I is paper, we propose a ew scheme at uses a accurate mobility predictio algorim to predict MS ext locatio i mai road as well as small street ad ope area ad e use is data to predict e expected pa of e MS ad e expected time for e MS to hadoff to ad from e cell. We e develop a adaptive badwid reservatio scheme which cosiders bo icomig ad out goig call. The reservatio algorim adaptively reserves badwid at each BTS accordig to predictio algorim to reduce hadoff drop rate ad improve badwid utilizatio ad QoS. The remaider of is paper is orgaized as follows. Sectio II describes e badwid reservatio techique which we have developed. Sectio III presets e simulatio results. Fially, i sectio IV we preset e coclusio. II. DYNAMIC BANDWIDTH RESERVATION ALGORITHM I is sectio we preset a Dyamic Badwid Reservatio Algorim (DBR) which uses mobility predictio to give priority for hadoff call over ew call ad improves QoS. The algorim reserves some badwid pool for hadoff calls. This reserved pool is dyamically adjusted accordig to e required badwid i e BTS ad its eighbors. The preseted algorim firstly predicts e MS movemet usig road map iformatio i mai road ad shadow cluster meod i small streets. For each call e algorim estimates e arrival ad departure time. Fially e badwid reservatio algorim estimates e required reserved badwid i each cell. A. Assumptio Ad Mobility redictio Algorim I our proposed techique we use e traffic flow eory [3-5] which is cocered about safe ad efficiet car ad user movemet. Traffic flow eory suggests at street system i urba area ca be divided ito: small street which provides lad access wi

3 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS.. 33 small speed, ad highway which provides high speed, ad collector road which coect small street wi high way. Sice it's ot possible to feed e BTS wi a very large umber of small streets ad itersectios, so we assume at e BTS will be equipped wi road map which cotais mai road data (highways ad collectors road). I e proposed badwid reservatio algorim preseted i is paper we use our mobility predictio algorim [7]. The operatio of our mobility predictio algorim is divided ito: Mai roads ad small streets. I mai road, e probability at MS i cell i to visit cell j is determied as follows, i, j r r i, j / r () Where r is e probability at MS takes a specific route 'r', i,j/r is e probability at e MS i cell 'i' reach cell 'j' give at MS takes route 'r' ad r are e all routes from cell i to cell j. I small streets, e probability at MS i cell i to visit cell j is determied as follows, i,j k N Where k is e umber of poits o e elliptic sector which are iside cell j, N is e umber of all poits o e elliptic sector. Mobility predictio algorim will be used to determie e probability of certai MS to hadoff to certai BTS wii ext algorim updatig time (T upd ). At e same time it will be used to calculate e arrival ad departure time of e MS to ad from e cell. B. Estimatio Of Arrival Ad Departure Time. I order to get desirable call blockig probability wiout over reservatio of badwid, BTS should kow e exact time for MS to hadoff to ad from e BTS ad e required badwid. Sice e BTS dose ot kow e exact pa of e MS ad also e time for e MS to hadoff to ad from it, mobility predictio algorim is used to predict arrival ad departure time of e MS. But if e MS hadoff to e BTS occurs before its expected arrival time or hadoff from e BTS occurs later a its expected departure time is will icrease hadoff droppig rate. Therefore to overcome is problem i badwid reservatio algorim, we assume outgoig hadoff occurs at latest departure time (which is e upper boud for departure time) ad icomig hadoff occurs at earliest arrival time (which is e lower boud for departure time) as described below. I mai roads, we ca calculate e probability at e MS will sped less time a T ld (e latest departure time of e MS) before it leaves curret cell as follows, i, j / r Tdelay( i) di / vi Tld (3) i i ()

4 34 I. I. Ibrahim ; A. S. Ali & A. F. Ghaim Tdelay ( i) (Tld d / v i, j / r i i) i i (4) let T ld di / vi i = (5) / i, j / r e z dz =.5 + e y dy i, j /r (6).5.5 erf / (7) To get T ld, we will use a probability reshold value such at i, j /r, e.5.5erf / (8) T ld T ld d i / v i i erf ( (.5)) d / i v i erf ( (.5)) i So we ca get T ld as, T ld d / i v i erf ( (.5)) i Similarly, we ca calculate e probability at e MS will sped greater time a T ea (earliest arrival time of e MS) util it eters ext cell as follows, (9) () () i, j / r Tdelay( i) di / vi Tea () i i Tdelay ( i) ea i, j / r i i) i i ( T d / v (3) let T ea di / vi i =

5 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS.. 35 / e z dz.5.5 erf / =.5- i, j/r e y dy i, j/r (4) (5) To get T ea, We will use a probability reshold value such at i, j /r /.5.5erf, e (6) erf (.5 ) T ld d v i i / i (7) Tea d / i v i erf ( (.5 )) i So we ca get T ea as, Tea d / i v i erf ( (.5 )) i (8) (9) O e oer had, i local streets, latest departure time ad earliest arrival time of MS ca be calculated as follows. Actually e hadoff happes i most cases ear e eoretical cell boudary (assume cell radius R) wi error (imagiary borders) for e cell i additio to e existig border. The ier oe of em has radius of R- R, ad will be used to calculate e time whe e MS hadoff to e eighbor cell (T ea ). The outer has radius of R+ R ad will be used to calculate e time whe e MS hadoff from e curret cell (T ld). Earliest arrival time ca be calculated as, R as show i fig.. We will defie two borders T ea = (d av / v av ) () Where v av is e average speed of e MS ad d av is e average distace at MS passed to eterext cell. For example i fig.. The average distace for e MS to etercell is e average distace betwee MS ad ier border of cell 3. I is case d av is, d av = (d + d +d 3 )/3. Cell 3 will calculate T ea ad seds it to cell. Cell will cosider icomig hadoff will occur at time T ea. Also latest departure time ca be calculated as, T ld =(D av / v av ) ()

6 36 I. I. Ibrahim ; A. S. Ali & A. F. Ghaim Where D av is e average distace at MS passed util it leaves curret cell. For example i fig.. The average distace for e MS to leave cell 3 is e average distace betwee MS ad outer border of cell 3. I is case D av is, D av = (D + D + D 3 )/3 Cell 3 will cosider outgoig hadoff will occurs at time T ld. Fig.. Calculatio of T ea, T ld i small street For clarity, we Assume at we have referece cell cell i ad its eighbor cell cell j e referece cell should do e followig procedure to cosider icomig hadoff ad outgoig hadoff. - Referece cell calculates T ld for MSs curretly coected to it ad will leave to eighbor cell j ad e probability of visitig e eighbor cell. Referece cell will cosider is hadoff as outgoig hadoff at time T ld. - Referece cell calculates T ea for MSs curretly coected to it ad will etereighbor cell j ad e probability of visitig e eighbor cell, ad e it seds ese data to eighbor cell. Cell j will cosider is hadoff as icomig hadoff at time T ea. 3- Referece cell receives from eighbor cell (cell j) T ea for MSs curretly coected to eighbor cell (cell j) ad will hadoff to e referece cell ad e probability of

7 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS.. 37 visitig e referece cell. Referece cell will cosider is hadoff as icomig hadoff at time T ea. 4- Referece cell will do e previous steps for all its eighbor cells. 5- All cells will do as what referece cell does. C- Badwid Reservatio To give priority for e hadoff call over e ew call, e BTS reserves some badwid for hadoff call. This reserved badwid is shared for hadoff MSs eve if it did ot do badwid reservatio i advace. This reserved badwid called reservatio pool R p. To calculate e required reserved badwid, each cell cosiders icomig hadoff as icreasig of required reserved badwid ad outgoig hadoff as decreasig of required reserved badwid. The total required reserved badwid chages at each hadoff. The total required reserved badwid durig ext T up is e summatio of ese hadoffs. As show i fig. We cosider icomig hadoff as up arrow ad outgoig hadoff as dow arrow. The leg of e arrow is equal to e required badwid for is hadoff multiplied by e probability of is hadoff. We ca see at e maximum required reserved badwid uit (R p ) durig ext algorim updatig time (assume T upd = sec) is 5 uit so R p =5 badwid uit. The reserved badwid i cell j by eighbor cell l (R plj ) ca be writte as, R plj = [ B t T B U t T i ij U ( eai ) k kl ( ldk ) ] () Where probability of MS i hadoff to cell j at time T ea, are all MS i cell j at will hadoff to eighbor cell l i time T ld < T upd, kl is e probability of MS k i cell j to hadoff to cell l at time T ld, B is e badwid required by e MS (We assume at every MS eed oe badwid uit to make a call) ad U(t) is e uit step fuctio. i are all MS i cell l ad will hadoff to cell j at time T ea < T upd, ij is e The total reserved badwid i cell j, R pj, is, R pj = Maximum peak of [ Where q are all eighbors of cell j. k R q pqj ] (3) As metio above, to give priority for hadoff call over ew call, each BTS should reserve some badwid, R p, at ca oly be utilized by hadoff call. A ew call is accepted i cell j oly if e free remaiig badwid after acceptig e ew call is greater a or equal to R p i.e., C(j) ( C u (j) +B N ) R pj (4) Where C(j) is e total badwid i cell j, C u (j) is e used badwid i cell j, B N is e required badwid for e ew call ad R pj is e reserved pool i cell j.

8 38 I. I. Ibrahim ; A. S. Ali & A. F. Ghaim Hadoff call is accepted i cell j if ere is sufficiet badwid for it i.e., C(j) ( C u (j) +B h ) (5) where B h is e required badwid for e hadoff call. Fially we ca summarize e goal of our algorim as, we give priority for hadoff call over ew call by reservig some badwid, R p, at ca oly be utilized by hadoff call. This badwid is dyamically chaged based o mobility predictio algorim used. As a result we ca get low hadoff drop rate wiout over reservig e resources. This will result i better badwid utilizatio ad low ew call drop rate. Fig.. The reserved badwid Rp. III. SIMULATION RESULTS The performace measuremet has bee made by comparig our proposed scheme (DBR) wi Road Map [6] ad elliptic curve [] i terms of ew calls block rate, hadoff calls drop rate ad badwid utilizatio. Mobility Model I is paper we use Gauss Markov mobility model to simulate user s mobility. The Gauss Markov model represets a wide rage of mobility patters, icludig, as its two extreme cases, e radom-walk ad e fluid-flow models [6]. Therefore e future locatio of MS ca be calculated based o previous MS s locatio usig Gauss Markov model. Recursive realizatio of Gauss Markov model ca be writte as [6], vx(t)=α. vx(t-)+(- α)µx+ σ x vy(t)=α. vy(t-)+(- α)µy+ σ y Nx (6) Ny (7) Where vx is e MS s speed compoet i x directio ad µx its mea ad σ x its stadard deviatio, vy is e MS s speed compoet i y directio ad µ y its mea

9 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS.. 39 ad σ y its stadard deviatio, N is e ormally distributed radom variable wi zero mea ad uit variace ad α is e memory level. We assume at statioary user remais statioary durig call holdig time, ad movig user ca be divided ito two groups: pedestria ad low speed users i small street ad high speed users i mai roads. We modulate e MS s motio usig Gauss Markov model. If e MS s locatio i mai road, e MS i is case will follow e road ad ca tur oly at e itersectios wi predefied tur probability, ad e MS s speed depeds o e MS s average speed ad road average speed. Memory level i is case is high (α ear ) which mea at e MS s motio ears fluid-flow model. If e MS is t i mai road (iside local street or ope area) We modulate e MS s motio usig Gauss Markov model wi α ear, which mea at e MS s motio ears radom-walk. We assume at e MS ca start call at ay locatio i e cellular etwork which cosists of idetical hexago cells. The MS is coected to e earest BTS. The simulatio area is circular if e MS go out of left side it will reeter from right side. Our predictio algorim updates e MS s speed every T upd. Ulike previous algorim [] which assumes at e MS moves i costat speed durig call holdig time. We assume at cell size is.5 km, umber of cells m=9, all cells are e same size, e MS s mea speed i mai road is i e rag of 6 to km/h, i local street i e rag of to 7 km/h, = /6, probability of meetig a red traffic light red =.4, mea delay time of red traffic light red = 35 sec,, = =.7, ( ca be used to dyamically chage e reservatio pool), variace delay time of red traffic light =5 sec ad algorim updatig time T upd = sec. red Traffic Model It was assumed at e arrival rate for ew calls is a oisso distributio wi a mea of λ. The call holdig time is expoetially distributed wi mea / μ. The umber of chaels i each cell is a costat C. The ormalized offered traffic load of e system is defied to be, L= m c C Where m is e umber of cells i e system ad e load is measured i Erlag. erformace ad result We preset simulatio results for e ew call blockig probability ( cb ) which mea at ew call block due to e lack of badwid i curret cell, hadoff droppig probability ( hd ) which me at e call drop durig hadoff from oe cell to eighbor cell due to lack of badwid i eighbor cell. I additio we will represet e results for badwid utilizatio which mea e usage of e total badwid i each cell. This is calculated as e ratio of all used badwid to all e badwid for at cell. The remaiig part of e badwid is composed of reserved ad available badwid at is ot curretly used. It is said to be worse performace at e badwid utilizatio is low while e system is heavily loaded because e utilizatio should (8)

10 4 I. I. Ibrahim ; A. S. Ali & A. F. Ghaim grow wi system load i a optimal coditio. Fially we will compare our algorim wi Road Map (RM) algorim ad elliptic curve algorim. First we assume at all MS at move i e etwork are i e road at equipped i e road map. From fig. 3, we fid at, our algorim (DBR) performs better a RM i terms of cb. This is because our algorim cosider bo icomig call ad outgoig call so it reserve less resources which result i more badwid available for ew call. Also we fid at RM gives better result a elliptic curve at's because elliptic curve did't cosider e delay time at road itersectio so it reserves more resource. From fig. 4, we fid at e ree algorims give low hadoff drop rate (less a.5). I fig.5, we fid at our algorim gives better badwid utilizatio a RM ad elliptic curve. That is because our algorim ca correctly estimate e required reserved badwid. Also we fid at RM give better badwid utilizatio a elliptic curve. This is because elliptic curve reserve more resources a required. Sice road map oly cotais e mai road data. So RM algorim will be less efficiet if MSs move i small street or ope area. This will icrease hd. For example if 8% of e MSs move i small street e result will be as follow. I fig. 6, we fid at e proposed algorim give better result i terms of ew call blockig rate a RM. Also RM gives better result a elliptic curve sice elliptic curve reserve more resources a required. From fig. 7, we fid at elliptic curve algorim gives a little bit better results i terms of hadoff drop rate a our algorim, while our algorim gives better results a RM. Sice our algorim use a accurate mobility predictio algorim for MS motio i mai road as well as small street, it ca correctly estimate e required badwid. This lead to reduce hd wiout over reservig e resources so at we ca get better badwid utilizatio as show i fig.8. I is figure we fid at e proposed algorim gives better badwid utilizatio a road map ad road map gives better result a elliptic curve. We ca otice at bo values of cb ad hd obtaied whe e traffic load is.6 or less are very close to each oer for all e algorims examied. Therefore, we may have a low hadoff drop rate eve wiout usig mobility predictio algorim. DBR.4 cb RM.3 elliptic.. load DBR hd.6 RM.5.4 elliptic load Fig. 3. System load versus cb Fig.4. System load versus hd.

11 BANDWIDTH RESERVATION ALGORITHM FOR WIRELESS BW utillizatio DBR RM elliptic load.3.. cb DBR RM elliptic load Fig.5. System load versus BW utilizatio Fig.6. System load versus cb (8% out of e road).3 hd.7 DBR.4..8 RM elliptic load BW utilizatio DBR RM ellipti c. load Fig.7. System load versus hd Fig.8. System load versus BW utilizatio (8% out of e road) (8% out of e road) IV. CONCLUSION I is paper, we propose a dyamic badwid reservatio algorim (DBR) for wireless etworks. The DBR uses a accurate mobility predictio scheme at is suitable for MS motio predictio i mai road as will as small street. Amog e may possible applicatios where mobility predictio ca be used, we use it for hadoff prioritizatio. The algorim predicts e expected pa of e MS as well as e expected time for e MS to hadoff to ad from e cell, ese predictio values are used to determie e required badwid at should be reserved i each cell. This reserved badwid is dyamically chagig depedig o MS mobility. The proposed reservatio algorim cosiders bo icomig ad outgoig calls. The goal of our reservatio algorim is to reduce hadoff drop rate wiout over reservig e badwid for hadoff so at we ca get better badwid utilizatio. We compared our reservatio algorim wi similar algorims, Road Map ad Elliptic Curve. Simulatio results show at our algorim reduce hadoff drop rate at e same time it gives better badwid utilizatio ad better probability of ew call block rate.

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