1

Handoff Management and Admission Control UsingVirtual Partitioning with Preemption in 3G

Cellular/802.16e InterworkingEnrique Stevens-Navarro, Member, IEEE, Vahid Shah-Mansouri, Student Member, IEEE, and

Vincent W.S. Wong, Senior Member, IEEE

Abstract— The integration of third generation (3G) cellularnetworks and IEEE 802.16e networks is an important interwork-ing case within the forthcoming fourth generation (4G) hetero-geneous wireless networks. In this paper, we extend the virtualpartitioning with preemption technique for admission controlin cellular/802.16e interworking and evaluate its performance.First, we describe the mobility scenario between 3G cellularnetworks and IEEE 802.16e networks in terms of the horizontal(intra-system) and vertical (inter-system) handoffs that can occurand derive the corresponding handoff rate equations. We thenpropose admission control algorithms for connection requeststhat consider the class of service (i.e., real-time or non-real-time)and the type of user (i.e., new or handoff). For the handoffrequests, different priority is assigned to each type of handoff.A joint connection and packet-level optimization approach isused for quality of service (QoS) provisioning. The accuracyof the analytical model is validated via simulations. Numericalresults show significant performance improvement. The blockingprobabilities for new connection requests can be reduced by 70%when the joint QoS optimization approach is used.

Index Terms— Cellular/802.16e interworking, handoff manage-ment, admission control, heterogeneous wireless networks.

I. INTRODUCTION

A broad range of wireless access technologies is beingdeployed worldwide. Examples include the third generation(3G) cellular systems such as Universal Mobile Telecommu-nications System (UMTS) and CDMA2000, the wireless localarea networks (WLANs), and the broadband wireless systemssuch as Worldwide Interoperability for Microwave Access(WiMAX). The integration of all these networks is usuallycalled the fourth generation (4G) heterogeneous wireless net-works [1]. The IEEE 802.16e standard (also called mobileWiMAX) [2] includes the support for mobile terminals. Thebenefits such as complementary coverage, lower deploymentcost, and quality of service (QoS) support make IEEE 802.16e

Manuscript received July 16, 2008; revised January 19, 2009, May 1, 2009and July 17, 2009; accepted August 13, 2009. This work was supported by BellCanada, the Natural Sciences and Engineering Research Council (NSERC)of Canada, and the Programa de Mejoramiento del Profesorado (PROMEP)from Mexico. The review of this paper was coordinated by Prof. Yi-Bing Lin.Copyright (c) 2009 IEEE. Personal use of this material is permitted. However,permission to use this material for any other purposes must be obtained fromthe IEEE by sending a request to pubs-permissions@ieee.org.

E. Stevens-Navarro is with Faculty of Science, Universidad Autonomade San Luis Potosi (UASLP), San Luis Potosi, Mexico, e-mail: es-tevens@galia.fc.uaslp.mx. V. Shah-Mansouri and V. Wong are with theDepartment of Electrical and Computer Engineering, University of BritishColumbia, Vancouver, BC V6T 1Z4, Canada, e-mail: {vahids, vin-centw}@ece.ubc.ca.

an important candidate to complement the existing 3G cellularnetworks. It is envisioned that WiMAX will be deployed indifferent phases. It is also crucial to fill the WiMAX coveragegaps with the existing 3G cellular systems in order to providea ubiquitous and seamless user experience. Thus, within the4G heterogeneous wireless networks, the interworking of 3Gcellular systems with mobile WiMAX is a specific case thathas recently gained a lot of attention from the research andstandardization communities [3], [4]. Such integration canfacilitate the service providers to reuse some of the existingbackend systems. It can simplify the network management,customer acquisition, and converge the billing aspects. SinceIEEE 802.16e is connection-oriented, it can also support IP(Internet Protocol) multimedia services.

Currently, standardization on the interworking architecturebetween 3G cellular networks and IEEE 802.16e networks isbeing conducted within the Network Working Group (NWG)of the WiMAX Forum [5]. The NWG is developing speci-fications for the architecture, protocols and services for themobile 802.16e systems. A loosely coupled architecture forcellular/802.16e interworking with QoS support is proposed in[6]. An interworking architecture at the protocol and signalinglevel is proposed in [7] within the context of the 3G cellularIP multimedia services. An architecture for integrating mobileWiMAX within the 3G wireless network is proposed in [8].

In cellular/802.16e interworking, due to mobility, users areable to switch connections among networks. This processis called handoff. A handoff is defined as horizontal if itoccurs between two adjacent cells of the same system. On theother hand, it is defined as vertical if it occurs between twocells of different systems. Since there are now two types ofhandoffs, each one with different execution procedures (e.g.,signaling overhead, context and authentication transfer), thetraditional approach of giving priority to horizontal handoffusers over the new users needs to be extended. In fact, as theinterworking architecture is fully deployed and the dual-modeterminals become available, the number of vertical handoffswill increase significantly. Thus, novel handoff managementtechniques are required and the appropriate mobility scenariosshould be investigated.

The support for multi-class services (e.g., multimedia ses-sions) and the joint design at the connection and packet-level are important design objectives for admission controland QoS provisioning in 4G heterogeneous wireless networks[1]. Admission control is required to limit the number of

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connections that can be accepted into the network while QoSprovisioning guarantees that the resources are used efficientlyand satisfy the application requirements. Admission controlalgorithms for multi-service fixed broadband wireless networkshave been proposed in [9]–[11]. Admission control algorithmsfor IEEE 802.16e have recently been proposed in [12]–[14].The previous works in [9]–[14] only consider IEEE 802.16systems and do not consider the interworking of IEEE 802.16esystems with 3G cellular networks.

The virtual partitioning (VP) is an efficient, fair and robustresource sharing technique. VP with the support for multi-class services is proposed in [15] for wireline networks. Itworks under the idea of pre-allocating a nominal capacityfor each service. In [16], VP is considered for admissioncontrol in cellular networks. Two schemes with preemptionconsidering real-time (RT) and non-RT (NRT) connections arepresented. RT connections receive guaranteed access, whileNRT connections can utilize any unused capacity. Followingthe joint call and packet-level QoS optimization approach in[17], [18], QoS constraints are considered at the call-level interms of the blocking/dropping probabilities and at the packet-level in terms of the packet loss probabilities. However, noneof those previous works in [16]–[18] consider the heteroge-neous wireless access networks and the interworking between802.16e and 3G systems.

In [19], we study the use of VP for admission control in anintegrated cellular/WLAN system. By following the concept ofpolicy functions introduced in [20] for admission control, thecorresponding policy functions for VP are derived. However,only RT connections are considered for admission control andpreemption is not being used. In addition, only the connection-level is considered in the optimization framework. In [21],VP is considered for resource sharing in cellular/WLANsystems. Resource management for multi-class services andload balancing are also investigated. However, mobility issuessuch as horizontal and vertical handoffs are not considered.

In this paper, we propose the use of VP with preemption foradmission control in cellular/802.16e interworking and eval-uate its performance [22]. Our model formulation considersthe joint design at the connection-level and packet-level. Tothis end, we first present the mobility scenario between 3Gcellular networks and IEEE 802.16e networks. The mobilityscenario specifies the horizontal and vertical handoffs thatcan occur in cellular/802.16e interworking. The correspondingset of handoff rate equations is derived. To the best of ourknowledge, this is the first attempt to model this expectedscenario. The contributions of our work are as follows:

1) We propose admission control algorithms for differenttypes of connection requests. For horizontal and verticalhandoff users, different priorities are assigned to eachuser according to the mobility scenario. Preemption rulesare defined for the RT and NRT connections when VPwith preemption is used.

2) We derive the blocking and dropping probabilities atthe connection-level, and the packet loss probability atthe packet-level. We formulate a blocking/dropping costminimization problem following a joint connection andpacket-level QoS optimization approach.

Fig. 1. Integrated 3G cellular/802.16e system.

3) The accuracy of the analytical model is validated viasimulations. The performance of the integrated cellu-lar/802.16e system using VP with preemption is eval-uated. Numerical results show significant performanceimprovement. The blocking probabilities for new con-nection requests can be reduced by 70% when the jointQoS optimization approach is used.

The rest of the paper is organized as follows. Section IIpresents the system model and mobility scenario for cellu-lar/802.16e interworking. Section III describes the use of VPwith preemption, the proposed admission algorithms, and thecost minimization problem. Section IV presents the perfor-mance evaluation results. Conclusions are given in Section V.

II. SYSTEM MODEL

Consider an integrated cellular/802.16e system as shown inFig. 1. A 3G cellular radio access network (RAN) is deployedcovering all the service area. In a highly populated area orregion with high service demand, an IEEE 802.16e accessservice network (ASN) is also deployed to provide additionalcapacity. This will be a common deployment alternative [23].As mentioned in Section I, the NWG is developing an end-to-end network architecture for IEEE 802.16e networks. Itconsiders the loosely coupled interworking architecture [5]in which the access networks are interconnected through theInternet by a gateway. The medium access control (MAC)of the IEEE 802.16e ASN operates in the point-to-multipointmode. Thus, the mobile terminals are served by a centralizedbase station. If the base stations of both systems are co-located[3], then we have an overlapped system.

A. Mobility Scenario

Referring to the integrated cellular/802.16e system in Fig. 1,horizontal handoffs can occur due to mobility of users amongneighboring base stations of the same network (e.g., betweentwo adjacent cells of the 3G RAN). The handoff decisionis based on the received signal strength. On the other hand,vertical handoffs can occur in two cases. First, it can happenwhen a user leaves a cell and the mobile terminal can select

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another network to obtain services. The handoff decision mayincorporate other factors such as monetary cost, types of QoSguarantees, and user’s preferences [24], [25]. In this case, thevertical handoff is optional because although not required, itmay improve the QoS of the connection. A vertical handoff canalso occur when a horizontal handoff cannot be successfullycompleted in the overlapped coverage area. Here, the handoffrequest is vertically transferred to the other network in orderto avoid the connection from being dropped. We consider bothtypes of vertical handoff in our mobility model.

B. Traffic and Mobility Models

We first introduce the notations. Let Mc and Me denote theset of cells in the 3G RAN and the 802.16e ASN, respectively.For each cell i ∈ Mc, let Ac

i denote the set of 3G cellsadjacent to cell i. For each cell k ∈ Me, let Ae

k denote theset of 802.16e cells adjacent to cell k. Let Wc

i denote the set ofoverlapping 802.16e cells to cell i ∈Mc. Let We

k denote theset of overlapping 3G cells to cell k ∈ Me. As an example,Fig. 2 shows an integrated cellular/802.16e system with Mc ={C1, C2, C3, . . . , C11, C12} and Me = {E1, E2, E3}.Thus, we have Ac

C2 = {C1, C3, C4, C5, C6, C7}, AeE2 =

{E1, E3}, WcC2 = {E2}, and We

E2 = {C2}.In the 3G RAN, when a connection is established, a

circuit bearer service is created. It has several QoS attributesthat describe how the packets in that service class shouldbe treated. The 3G partnership project (3GPP) defined fourdifferent classes of QoS for connections [26]: conversational,streaming, interactive, and background. In the 802.16e ASN,when a connection is established, a service flow is createdin the MAC layer with specific QoS parameters. The IEEE802.16e defined five different classes of QoS for serviceflows [2]: unsolicited grant service (UGS), real-time pollingservice (rt-PS), extended rt-PS (Ert-PS), non-real-time pollingservice (nrt-PS), and best effort (BE). For admission control,conversational, streaming, UGS, rt-PS and Ert-PS classes aregrouped into RT connections, while interactive, background,nrt-PS and BE classes are grouped into NRT connections.

Let S denote the set of services available to the mobileusers. The services are classified into two groups: RT servicesas SRT and NRT services as SNRT . Thus, SRT ⊂ S andSNRT ⊂ S . A service s ∈ S requires bc

s basic bandwidth units(BBUs), and be

s BBUs to guarantee its QoS requirements inthe 3G RAN and the 802.16e ASN, respectively. For i ∈Mc,k ∈ Me and s ∈ S , the new connection requests for services arrive at cell i and cell k according to independent Poissonprocesses with rates λc

isand λe

ks, respectively.

The connection duration of service s, ts, is exponentiallydistributed with mean 1/vs. Since the exponential distributionis memoryless, the residual (i.e., remaining) connection timetRs is also exponentially distributed with mean 1/vs. For i ∈Mc and k ∈ Me, we assume that the cell residence times tciand tek are also exponentially distributed with means 1/ηc

i and1/ηe

k, respectively. The channel holding time in cell i is definedas the time that a user continues to use the assigned bc

s BBUsin the 3G RAN (i.e., a circuit bearer service), while in cell k itis the time that a user continues to use the assigned be

s BBUs

Fig. 2. Integrated 3G cellular/802.16e system, 3G RAN with |Mc| = 12and 802.16e ASN with |Me| = 3.

in the 802.16e ASN (i.e., a service flow in the MAC layer).For service s ∈ S , the channel holding time in cell i and cellk are obtained as min(tRs , tci ) and min(tRs , tek), respectively.Since tRs , tci , and tek are exponentially distributed for all s ∈ S ,i ∈Mc, and k ∈Me, the holding times are also exponentiallydistributed with parameters µc

is= vs + ηc

i and µeks

= vs + ηek,

respectively.A user of service s in cell i ∈ Mc may terminate its

connection at the end of its channel holding time and leavethe integrated system with probability qc

is= υs/(υs + ηc

i ).Otherwise, it may move within the system and continue in anadjacent cell with probability 1− qc

is. We have

1− qcis

=∑

j∈Aci

qccijs

+∑

l∈Aek, k∈Wc

i

qceils , (1)

where qccijs

denotes the probability of attempting a horizontalhandoff from cell i ∈ Mc to a neighboring cell j ∈ Ac

i , andqceils

denotes the probability of attempting a vertical handofffrom cell i to a neighboring cell l ∈ Ae

k of overlapped cellk ∈ Wc

i .Similarly, a user of service s in cell k ∈Me may terminate

its connection at the end of its channel holding time and leavethe integrated system with probability qe

ks= υs/(υs + ηe

k).Otherwise, it may move within the system and continue in anadjacent cell with probability 1− qe

ks. We have

1− qeks

=∑

l∈Aek

qeekls +

∑

j∈Aci , i∈We

k

qeckjs

, (2)

where qeekls

denotes the probability of attempting a horizontalhandoff from cell k ∈ Me to a neighboring cell l ∈ Ae

k, andqeckjs

denotes the probability of attempting a vertical handofffrom cell k to a neighboring cell j ∈ Ac

i of overlapped celli ∈ We

k .The arrival rates of service s to cell i and cell k, φc

isand

φeks

, respectively, include all the connection requests from thenew, horizontal, and vertical handoff users. They are given by:

φcis

= λcis

+∑

j∈Aci

hccjis

+ Ψecis

, ∀ i ∈Mc, (3)

φeks

= λeks

+∑

l∈Aek

heelks

+ Ψceks

, ∀ k ∈Me, (4)

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where hccjis

denotes the horizontal handoff rate of service soffered to cell i from its adjacent cell j ∈ Ac

i , and heelks

denotesthe horizontal handoff rate of service s offered to cell k fromits adjacent cell l ∈ Ae

k. On the other hand, the terms Ψecis

and Ψceks

denote the vertical handoff rates offered to cell i andcell k, respectively. From the mobility scenario described, thevertical handoff rates in (3) and (4) are given by:

Ψecis

=∑

k∈Wci

veckis

+∑

l∈Aek, k∈Wc

i

veclis

, ∀ i ∈Mc, (5)

Ψceks

=∑

i∈Wek

vceiks

+∑

j∈Aci , i∈We

k

vcejks

, ∀ k ∈Me, (6)

where veckis

denotes the vertical handoff rate of service soffered to cell i from its overlapped cell k ∈ Wc

i , veclis

denotesthe vertical handoff rate of service s offered to cell i fromneighboring cell l ∈ Ae

k of overlapped cell k ∈ Wci , vce

iks

denotes the vertical handoff rate of service s offered to cell kfrom its overlapped cell i ∈ We

k , and vcejks

denotes the verticalhandoff rate of service s offered to cell k from neighboringcell j ∈ Ac

i of overlapped cell i ∈ Wck. The first term in

both (5) and (6) corresponds to transferred vertical handoffs,while the last term corresponds to optional vertical handoffs.Note that in (5), the transferred vertical handoff requests (i.e.,the first term in the right hand side in (5)) are issued by theoverlapped cell k (not by the mobile users). On the other hand,the optional vertical handoff requests (i.e., the second term inthe right hand side in (5)) are issued by the mobile users wholeave the neighboring cell l. We can state the handoff rateequations as follows:

hccjis

= λcjs

(1−Bc

njs

)qccjis

+∑

x∈Acj

hccxjs

(1−Dc

hhjs

)qccjis

(7)

+∑

y∈Aez, z∈Wc

j

vecyjs

(1−Dc

vhjs

)qccjis

+∑

z∈Wcj

veczj

(1−Dc

vhjs

)qccjis

,

veckjs

= λeks

(1−Be

nks

)qeckjs

+∑

x∈Aek

heexks

(1−De

hhks

)qeckjs

(8)

+∑

y∈Acz, z∈We

k

vceyks

(1−De

vhks

)qeckjs

+∑

z∈Wek

vcezk

(1−De

vhks

)qeckjs

,

heelks

= λels

(1−Be

nls

)qeelks

+∑

x∈Ael

heexls

(1−De

hhls

)qeelks

(9)

+∑

y∈Acz, z∈We

l

vceyls

(1−De

vhls

)qeelks

+∑

z∈Wel

vcezl

(1−De

vhls

)qeelks

,

vcejks

= λcjs

(1−Bc

njs

)qcejks

+∑

x∈Acj

hccxjs

(1−Dc

hhjs

)qcejks

(10)

+∑

y∈Acz, z∈Wc

j

vecyjs

(1−Dc

vhjs

)qcejks

+∑

z∈Wcj

veczj

(1−Dc

vhjs

)qcejks

,

veckis

=∑

x∈Aek

heexks

Dehhks

+∑

y∈Aci

vceyks

Devhks

, (11)

vceiks

=∑

x∈Aci

hccxis

Dchhis

+∑

y∈Aek

vecyis

Dcvhis

, (12)

where Bcnis

and Benks

are the probabilities of blocking con-nection requests from new users of service s in cell i ∈ Mc

and cell k ∈ Me, respectively. Dchhis

and Dehhks

are theprobabilities of dropping connection requests from horizontalhandoff users of service s in cell i ∈ Mc and cell k ∈Me, respectively. Dc

vhisand De

vhksare the probabilities of

dropping connection requests from vertical handoff users ofservice s in cell i ∈Mc and cell k ∈Me, respectively.

III. VIRTUAL PARTITIONING WITH PREEMPTION FORADMISSION CONTROL

Each cell i ∈ Mc has a capacity of Cci BBUs. Recall

from Section II that the transmission rate of service s in the3G RAN (i.e., bc

s) is normalized with respect to the BBUs.Due to scheduling and statistical multiplexing, each cell isupports |S| different services and can admit connections withat most N c

i BBUs, where N ci ≥ Cc

i . The value of N ci is a

design parameter [16], [18]. If N ci has a small value, then the

cell may experience a low packet loss probability and a highconnection blocking probability. On the other hand, if N c

i hasa large value, then the cell may experience a low connectionblocking probability and a high packet loss probability. Thishappens because N c

i restricts the number of connections thatcan be admitted, and the capacity Cc

i restricts the number oftransmitted packets from the admitted connections. Finally, ifN c

i = Cci , then there is no statistical multiplexing gain.

According to the QoS classification mentioned in SectionII, services are grouped into two types: RT and NRT. For ad-mission control in cellular/802.16e interworking, by extendingone of the resource allocation schemes proposed in [16], wecan use VP with preemption for the NRT group. Thus, the RTgroup is offered guaranteed access while the NRT group isoffered best effort access. The basic idea of VP is as follows:In each cell i ∈Mc, a nominal capacity or nominal allocationof N c

RTiand N c

NRTi, is assigned to RT connections and NRT

connections, respectively, such that N cRTi

+N cNRTi

= N ci . The

resource allocation in cell i of the 3G RAN is defined as:∑

s∈Sg

mcis

bcs ≤ N c

gi, ∀ i ∈Mc, g ∈ {RT, NRT}, (13)

where mcis

is the number of service s connections in celli. Given the nominal allocations, VP defines a group asunderloaded if all connections from that group are assignedfewer resources than its nominal allocation, and as overloadedif all connections from that group are assigned more resourcesthan its nominal allocation. In VP, the unused capacity in theunderloaded RT group can be utilized by the NRT connectionsif necessary. The RT connections receive guaranteed accessand better QoS. They can preempt the NRT connections whenthe NRT group is overloaded. VP can be considered as ageneralization of complete sharing and complete partitioningof network resources among different classes. When the arrivalrates from all classes are low, the behavior of VP is similarto complete sharing. On the other hand, when the arrival ratesfrom all classes are high, VP behaves as complete partitioning.

Similarly, each cell k ∈ Me has a capacity of Cek BBUs.

Each cell k supports |S| different services and can admitconnections with at most Ne

k BBUs, where Nek ≥ Ce

k .

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Algorithm 1 - Admission control algorithm for new request withbandwidth requirement bc

s in cell i ∈Mc.1: if s ∈ SRT

2: if∑

s′∈SNRTmc

is′ bcs′ > Nc

NRTi

3: if∑

s′∈S mcis′ b

cs′ + bc

s ≤ Nci − δc

i ,4: then accept5: else if (

∑s′∈S mc

is′ bcs′ ≥ Nc

i −δci and

∑s′∈SRT

mcis′ b

cs′+

bcs ≤ Nc

RTi− δc

i ),6: then accept with preemption7: else reject8: else if

∑s′∈SRT

mcis′ b

cs′ + bc

s ≤ NcRTi

− δci ,

9: then accept10: else reject11: if s ∈ SNRT ,

∑s′∈S mc

is′ bcs′ + bc

s ≤ Nci − δc

i ,12: then accept13: else reject14: end

The parameter Nek of cell k ∈ Me is also partitioned into

NeRTk

and NeNRTk

, with NeRTk

+NeNRTk

= Nek . The resource

allocation in cell k of the 802.16e ASN is defined as:∑

s∈Sg

meks

bes ≤ Ne

gk, ∀ k ∈Me, g ∈ {RT, NRT}, (14)

where meks

is the number of service s connections in cell k.

A. Admission Control for New Requests

Besides the priority of RT connections over NRT connec-tions, for admission control, the mobility of the users alsoneeds to be taken into account. In the case of cellular/802.16einterworking, we consider connection requests from new,horizontal, and vertical handoff users. To this end, we proposeadmission control algorithms for each type of connectionrequest. The admission algorithm for the new requests usingVP with preemption is shown in Algorithm 1.

Let δci denote the number of BBUs that can be reserved

for connection requests from the handoff users of any typein each nominal allocation (i.e., N c

RT and N cNRT ) in cell

i ∈ Mc. Consider the case for a connection request fromservice s ∈ SRT in cell i ∈ Mc (Algorithm 1, step 1).We have N c

RTi− δc

i BBUs that can be used for connectionrequests from either new, horizontal or vertical handoff users.When the NRT group is underloaded, the new request isaccepted if

∑s′∈SRT

mcis′

bcs′ + bc

s ≤ N cRTi

− δci (Algorithm

1, step 8). On the other hand, if the NRT group is overloaded,∑s′∈SNRT

mcis′

bcs′ > N c

NRTi(Algorithm 1, step 2), then two

situations can happen. First, if the total amount of resourcesused by the RT users, the overloaded part of the NRT users(i.e.,

∑s′∈SNRT

mcis′

bcs′ −N c

NRTi), and the new connection is

less than or equal to N cRTi

−δci , then the connection is accepted

(Algorithm 1, steps 3 and 4). Otherwise, preemption is invokedand some connections from the NRT group are removed.

To achieve the preemption, suitable rules need to be defined.These rules specify whether or not the preemption of RTover NRT connections can be applied based on the number ofusers of each service. We modified and extended the originalpreemption rules in [16] to consider connection requestsfrom the vertical handoff users and to include our proposedadmission control algorithms. The preemption rules for the

connection requests from the new users are defined as follows:preemption happens when a connection request from a newuser of service s ∈ SRT arrives under the conditions that theNRT group is overloaded,

∑s′∈S mc

is′bcs′ ≥ N c

i − δci , and∑

s′∈SRTmc

is′bcs′ + bc

s ≤ N cRTi

− δci (Algorithm 1, steps 5 and

6). Recall that new requests can only use N cRTi

− δci BBUs.

Thus, new connection requests are accepted with preemptionuntil we reach the point where N c

RTi− δc

i BBUs are used bythe connections of the RT group.

For connection requests from service s ∈ SNRT in cell i ∈Mc, a new request is accepted if

∑s′∈S mc

is′bcs′ + bc

s ≤ N ci −

δci (Algorithm 1, steps 11 and 12). Note that the parameter

N ci is used instead of the nominal allocation N c

NRTito allow

the connections from the NRT group to use all the availablecapacity in cell i. If preemption of a connection from services ∈ SRT happens over services s′ ∈ SNRT , termination onlyapplies to one service in SNRT which is uniformly selectedat random (i.e., 1/|SNRT |). The number of terminated usersfrom service s′ is given by dbc

s/bcs′e.

B. Admission Control for Handoff Requests

Let αci and βc

i denote the parameters that define the thresh-old values after which connection requests from handoff usersof each type will either be accepted or not in cell i ∈ Mc.Our proposed admission algorithm assigns the values of αc

i

and βci based on the reserved BBUs and sets the priority for

admission control among handoff requests according to themobility scenario described in Section II. Thus, we have

αci =

⌊(∑s∈S

∑j∈Ac

ihcc

jis∑s∈S(φc

is− λc

is)

)δci

⌋, ∀ i ∈Mc, (15)

βci = δc

i − αci , ∀ i ∈Mc. (16)

The value of parameter αci is assigned based on the corre-

sponding horizontal handoff rates. If the number of connectionrequests from horizontal handoff users increases, then moreresources are allocated and hence higher priority is given tothat type of handoff users and vice versa. Both parameters αc

i

and βci have integer values and 0 < αc

i ≤ δci . Note that the

denominator in (15) considers all types of handoff requests.There are three different cases to consider in the proposed

admission algorithm. In case 1 (αci > βc

i ), horizontal handoffswill have the highest priority for admission control and theywill be always accepted as long as there is capacity availablein the cell. In case 2 (αc

i < βci ), vertical handoffs will

have the highest priority for admission control. In case 3(αc

i = βci ), both horizontal and vertical handoffs are equally

important. In all three cases, the connection requests from thenew users will have the lowest priority for admission control.These three cases and the corresponding handoff prioritiesfor service s ∈ SRT in cell i ∈ Mc are shown in Fig. 3.Our proposed admission control algorithms using VP withpreemption for horizontal and vertical handoff requests areshown in Algorithms 2 and 3, respectively.

For connection request from service s ∈ SRT in celli ∈ Mc. In case 1, N c

RTiBBUs in cell i ∈ Mc can be

used as follows: N cRTi

− δci BBUs can be used for connection

requests from either new, horizontal or vertical handoff users,

6

Algorithm 2 - Admission control for horizontal handoff requestwith bandwidth requirement bc

s in cell i ∈Mc.1: if αc

i ≥ βci , then

2: if s ∈ SRT

3: if∑

s′∈SNRTmc

is′ bcs′ > Nc

NRTi

4: if∑

s′∈SRTmc

is′ bcs′ + bc

s + (∑

s′∈SNRTmc

is′ bcs′ −

NcNRTi

) ≤ NcRTi

,5: then accept6: else accept with preemption7: else if

∑s′∈SRT

mcis′ b

cs′ + bc

s+ ≤ NcRTi

,8: then accept9: else reject

10: if s ∈ SNRT ,∑

s′∈S mcis′ b

cs′ + bc

s ≤ Nci ,

11: then accept12: else reject13: else if αc

i < βci , then

14: if s ∈ SRT

15: if∑

s′∈SNRTmc

is′ bcs′ > Nc

NRTi

16: if∑

s′∈SRTmc

is′ bcs′ + bc

s + (∑

s′∈SNRTmc

is′ bcs′ −

NcNRTi

) ≤ NcRTi

− βci ,

17: then accept18: else if (

∑s′∈S mc

is′ bcs′≥Nc

i−βci ,

∑s′∈SRT

mcis′ b

cs′+

bcs ≤ Nc

RTi− βc

i ),19: then accept with preemption.20: else reject21: else if

∑s′∈SRT

mcis′ b

cs′ + bc

s ≤ NcRTi

− βci ,

22: then accept23: else reject24: if s ∈ SNRT ,

∑s′∈S mc

is′ bcs′ + bc

s ≤ Nci − βc

i , then accept25: else reject26: end

βci BBUs can be used for connection requests from either

horizontal or vertical handoff users, and αci BBUs can be used

for requests from horizontal handoff users. When the NRTgroup is underloaded, a horizontal handoff request is acceptedif

∑s′∈SRT

mcis′

bcs′ + bc

s ≤ N cRTi

(Algorithm 2, step 7), and avertical handoff request is accepted if

∑s′∈SRT

mcis′

bcs′+bc

s ≤N c

RTi− αc

i (Algorithm 3, step 9). On the other hand, incase 2, αc

i BBUs can be used for connection requests fromeither horizontal or vertical handoff users, and βc

i BBUs canbe used for requests from vertical handoff users. When theNRT group is underloaded, a horizontal handoff request isaccepted if

∑s′∈SRT

mcis′

bcs′ + bc

s ≤ N cRTi

− βci (Algorithm

2, step 21), and a vertical handoff request is accepted if∑s′∈SRT

mcis′

bcs′ + bc

s ≤ N cRTi

(Algorithm 3, step 22). Incase 3, a horizontal or vertical handoff request is acceptedif

∑s′∈SRT

mcis′

bcs′ + bc

s ≤ N cRTi

(Algorithm 2, step 7) and(Algorithm 3, step 22).

If the NRT group is overloaded, we need to consider thesimilar situations as described for the new requests. If thetotal amount of resources used by the RT users, the overloadedpart of the NRT users (i.e.,

∑s′∈SNRT

mcis′

bcs′ −N c

NRTi), and

the handoff connection is less than or equal to N cNRTi

− βci

or N cNRTi

− αci , then the connection is accepted (Algorithm

2, step 16) or (Algorithm 3, step 4), respectively. Otherwise,preemption is invoked. We define preemption rules for thehandoff requests according to the assigned priorities (seeFig. 3) which are given by each case (e.g., αc

i > βci , etc.).

For the horizontal handoff requests we have rules for αci ≥ βc

i

(Algorithm 2, step 6) and αci < βc

i (Algorithm 2, step 18).

Algorithm 3 - Admission control for vertical handoff request withbandwidth requirement bc

s in cell i ∈Mc.1: if αc

i > βci , then

2: if s ∈ SRT

3: if∑

s′∈SNRTmc

is′ bcs′ > Nc

NRTi

4: if∑

s′∈SRTmc

is′ bcs′ + bc

s + (∑

s′∈SNRTmc

is′ bcs′ −

NcNRTi

) ≤ NNRTi

− αci ,

5: then accept6: else if (

∑s′∈S mc

is′ bcs′≥Nc

i−αci ,

∑s′∈SRT

mcis′ b

cs′+

bcs ≤ Nc

RTi− αc

i ),7: then accept with preemption.8: else reject9: else if

∑s′∈SRT

mcis′ b

cs′ + bc

s ≤ NcRTi

− αci ,

10: then accept11: else reject12: if s ∈ SNRT ,

∑s′∈S mc

is′ bcs′ + bc

s ≤ Nci − αc

i ,13: then accept14: else reject15: else if αc

i ≤ βci , then

16: if s ∈ SRT

17: if∑

s′∈SNRTmc

is′ bcs′ > Nc

NRTi

18: if∑

s′∈SRTmc

is′ bcs′ + bc

s + (∑

s′∈SNRTmc

is′ bcs′ −

NcNRTi

) ≤ NcRTi

,19: then accept20: else if

∑s′∈S mc

is′ bcs′ + bc

s > Nci , then accept with

preemption21: else reject22: else if

∑s′∈SRT

mcis′ b

cs′ + bc

s ≤ NcRTi

, then accept23: else reject24: if s ∈ SNRT ,

∑s′∈S mc

is′ bcs′ + bc

s ≤ Nci , then accept

25: else reject26: end

Fig. 3. Priorities of the different connection requests of service s ∈ SRT incell i ∈Mc. Case 1 (αc

i > βci ), Case 2 (αc

i < βci ), and Case 3 (αc

i = βci ).

For the vertical handoff requests we have rules for αci > βc

i

(Algorithm 3, step 6) and αci ≤ βc

i (Algorithm 3, step 20).

For connection requests from service s ∈ SNRT in celli ∈ Mc, in case 1, a horizontal handoff request is acceptedif

∑s′∈S mc

is′bcs′ + bc

s ≤ N ci (Algorithm 2, step 10), and a

vertical handoff request is accepted if∑

s′∈S mcis′

bcs′ + bc

s ≤N c

i −αci (Algorithm 3, step 12). On the other hand, in case 2, a

horizontal handoff request is accepted if∑

s′∈S mcis′

bcs′+bc

s ≤N c

i −βci (Algorithm 2, step 24), and a vertical handoff request

is accepted if∑

s′∈S mcis′

bcs′ + bc

s ≤ N ci (Algorithm 3, step

24). Finally, in case 3, a horizontal or vertical handoff requestis accepted if

∑s′∈S mc

is′bcs′ + bc

s ≤ N ci (Algorithms 2, step

10) and (Algorithms 3, step 24), respectively. The admissionalgorithms in cell k ∈ Me define similar parameters δe

k, αek,

and βek for the connection requests from the new, horizontal,

and vertical handoff users.

7

C. Connection-level Model

We model the occupancy of cell i ∈ Mc andk ∈ Me as |S|−dimensional Markov chains. Let mc

imcimci =

(mci1

,mci2

, . . . , mci|S|) and me

kmekmek = (me

k1, me

k2, . . . ,me

k|S|) de-note the occupancy vectors in cell i ∈Mc and cell k ∈Me,respectively. They indicate the number of connections of eachservice. Thus, the state space of each cell is restricted to∑

s∈S mcis

bcs ≤ N c

i and∑

s∈S meks

bes ≤ Ne

k . Let φcis

(mcimcimci ) and

φeks

(mekmekmek) denote the total arrival rates of connection requests

of service s ∈ SRT considering the admission algorithms tocell i ∈Mc and cell k ∈Me, respectively. We have

φcis(mc

imcimci )=

λcis+

∑

j∈Aci

hccjis

+Ψecis

, if∑

s′∈SRT

mcis′

bcs′ ≤ N c

RTi− δc

i ,

∑

j∈Aci

hccjis

+Ψecis

, if αci > βc

i and

N cRTi−δc

i<∑

s′∈SRT

mcis′bcs′≤N c

RTi−αc

i ,

∑

j∈Aci

hccjis

+Ψecis

, if αci < βc

i and

N cRTi−δc

i<∑

s′∈SRT

mcis′bcs′≤N c

RTi−βc

i ,

∑

j∈Aci

hccjis

+Ψecis

, if αci = βc

i and

N cRTi−δc

i<∑

s′∈SRT

mcis′bcs′≤ N c

RTi,

∑

j∈Aci

hccjis

, if αci > βc

i and

N cRTi−αc

i<∑

s′∈SRT

mcis′bcs′≤ N c

RTi,

Ψecis

, if αci < βc

i andN c

RTi−βc

i<∑

s′∈SRT

mcis′bcs′≤ N c

RTi,

0, otherwise,(17)

Similar equations can be derived for φeks

(mekmekmek) [22]. For the

total arrival rates of connection requests of service s ∈ SNRT

to cell i ∈ Mc, we simply replace N cRTi

with N ci in (17),

and SRT with SNRT . The departure rates are defined asµc

is(mc

imcimci ) = mc

isµc

isfor

∑s∈S bc

smcis≤ N c

i . Let P ci (mc

imcimci ) and

P ek (me

kmekmek) denote the steady state probabilities of being in states

mcimcimci in cell i. We can state the global-balance equations for the

Markov process in cell i ∈Mc as:

∑

s∈S

[φc

is(mc

imcimci ) + Φc

is(mc

imcimci ) + µc

is(mc

imcimci )

]P c

i (mcimcimci )

=∑

s∈SP c

i (mcimcimci − εsεsεs)φc

is(mc

imcimci − εsεsεs)

+∑

s∈SP c

i (mcimcimci +εsεsεs)µc

is(mc

imcimci + εsεsεs)

+∑

s∈SNRT

∑

s′∈SRT

∑

s′′∈{

1,..,⌈

bs′

bs

⌉}P ci (mc

imcimci + s′′εsεsεs − εs′εs′εs′)

×Φcis

(mcimcimci + s′′εsεsεs − εs′εs′εs′),

where εsεsεs is an |S|-dimensional vector of zeros but with 1 inthe sth position. The term Φc

is(mc

imcimci ) is controlled by the VP

preemption rules and is defined as:

Φcis(mc

imcimci )=

λcis

, if∑

s′∈Smc

is′bcs′ ≥ N c

i− δci and

∑

s′∈SRT

mcis′bcs′+ bc

s ≤ N cRTi− δc

i ,∑

j∈Aci

hccjis

, if∑

s′∈Smc

is′bcs′+ bc

s > N ci , αc

i≥βci ,

∑

j∈Aci

hccjis

, if∑

s′∈Smc

is′bcs′ ≥ N c

i− βci and

∑

s′∈SRT

mcis′bcs′+ bc

s ≤ N cRTi− βc

i , αci<βc

i ,

Ψecis

, if∑

s′∈Smc

is′bcs′+ bc

s > N ci , αc

i≤βci ,

Ψecis

, if∑

s′∈Smc

is′bcs′ ≥ N c

i− αci and

∑

s′∈SRT

mcis′bcs′+ bc

s ≤ N cRTi− αc

i , αci>βc

i .

(18)The terms Φc

is(mc

imcimci ) in the global-balance equations incorpo-

rate the additional state transitions due to preemption of RTconnections over NRT connections. Note that in all cases in(18), the NRT group shall be overloaded.

For notation convenience, we enumerate the services as S ={1, 2, . . . , p, p + 1, . . . , |S|}. The RT services correspond toservice 1 to service p, and the NRT services correspond toservice p + 1 to service |S|. Now, the probability of blockingconnection requests from a new user of service s ∈ SRT incell i ∈Mc following VP with preemption is

Bcnis

=

⌊Nc

RTibc1

⌋

∑

mci1

=

⌊Nc

RTi−δc

i

bc1

⌋. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑

mcip

=

⌊Nc

RTi−δc

i

bcp

⌋ × (19)

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )λ

cis∑

s′∈Sφc

is′(mc

imcimci )

+

⌊Nc

RTi−δc

i

bc1

⌋−1

∑mc

i1=0

. . .

⌊Nc

RTi−δc

i−∑p−1

s′=1mc

is′

bcs′

bcp

⌋−1

∑mc

ip=0

×

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )λ

cis∑

s′∈Sφc

is′(mc

imcimci )

,

where the term λcis

/∑

s′∈S φcis′

(mcimcimci ) is the probability that the

arrival is a new connection request of service s.

For service s ∈ SNRT , the probability of blocking connec-

8

tion requests from a new user in cell i is

Bcnis

=

⌊Nc

RTibc1

⌋

∑mc

i1=0

. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑mc

ip=0

× (20)

⌊Nc

i−∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i −∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )λ

cis∑

s′∈Sφc

is′(mc

imcimci )

.

For service s ∈ SRT , all occupancy vectors mcimcimci need to

satisfy the condition that∑

s′∈SRTmc

is′bcs′ + bc

s > N cRTi

−δci . On the other hand, for service s ∈ SNRT , all occupancy

vectors mcimcimci need to satisfy condition

∑s′∈S mc

is′bcs′ + bc

s >N c

i − δci .

For the probability of dropping connection requests from ahandoff user of service s in cell i ∈ Mc, according to theadmission algorithms, we need to consider three cases:

In case 1 (αci > βc

i ), the probability of dropping connectionrequests from horizontal handoff users of service s is

Dchhis

=

⌊Nc

RTibc1

⌋

∑mc

i1=0

. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑mc

ip=0

× (21)

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

∑

j∈Aci

hccjis

∑

s′∈Sφc

is′(mc

imcimci )

,

where the term∑

j∈Acihcc

jis/

∑s′∈S φc

is′(mc

imcimci ) is the probability

that the arrival is a horizontal handoff request of service s.For service s ∈ SRT , all occupancy vectors mc

imcimci need to

satisfy the condition that∑

s′∈SRTmc

is′bcs′ + bc

s > N cRTi

. Onthe other hand, for service s ∈ SNRT , all occupancy vectorsmc

imcimci need to satisfy the condition that

∑s′∈S mc

is′bcs′+bc

s > N ci .

The probability of dropping connection requests from ver-tical handoff users of service s ∈ SNRT is

Dcvhis

=

⌊Nc

RTibc1

⌋

∑mc

i1=0

. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑mc

ip=0

× (22)

⌊Nc

i−∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )Ψ

ecis∑

s′∈Sφc

is′(mc

imcimci )

,

where the term Ψecis

/∑

s′∈S φcis′

(mcimcimci ) is the probability that

the arrival is a vertical handoff request of service s.For service s ∈ SRT , the probability of dropping connection

requests from vertical handoff users is

Dcvhis

=

⌊Nc

RTibc1

⌋

∑

mci1

=

⌊Nc

RTi−αc

i

bc1

⌋. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑

mcip

=

⌊Nc

RTi−αc

i

bcp

⌋ × (23)

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i −∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )Ψ

ecis∑

s′∈Sφc

is′(mc

imcimci )

+

⌊Nc

RTi−αc

i

bc1

⌋−1

∑mc

i1=0

. . .

⌊Nc

RTi−αc

i−∑p−1

s′=1mc

is′

bcs′

bcp

⌋−1

∑mc

ip=0

×

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i −∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )Ψ

ecis∑

s′∈Sφc

is′(mc

imcimci )

.

For service s ∈ SRT , all occupancy vectors mcimcimci need to satisfy

the condition that∑

s′∈SRTmc

is′bcs′ + bc

s > N cRTi

− αci . On

the other hand, for service s ∈ SNRT , all occupancy vectorsmc

imcimci need to satisfy the condition that

∑s′∈S mc

is′bcs′ + bc

s >N c

i − αci .

In case 2 (αci < βc

i ), the probability of dropping connectionrequests from vertical handoff users of service s is

Dcvhis

=

⌊Nc

RTibc1

⌋

∑mc

i1=0

. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑mc

ip=0

× (24)

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )Ψ

ecis∑

s′∈Sφc

is′(mc

imcimci )

.

For service s ∈ SRT , all occupancy vectors mcimcimci need to

satisfy the condition that∑

s′∈SRTmc

is′bcs′ + bc

s > N cRTi

. Onthe other hand, for service s ∈ SNRT , all occupancy vectorsmc

imcimci need to satisfy the condition that

∑s′∈S mc

is′bcs′ + bc

s >N c

i .The probability of dropping connection requests from hor-

izontal handoff users of service s ∈ SNRT is

Dchhis

=

⌊Nc

RTibc1

⌋

∑mc

i1=0

. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑mc

ip=0

× (25)

⌊Nc

i −∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i −∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

∑

j∈Aci

hccjis

∑

s′∈Sφc

is′(mc

imcimci )

.

9

For service s ∈ SRT , the probability of dropping connectionrequests from horizontal handoff users is

Dchhis

=

⌊Nc

RTibc1

⌋

∑

mci1

=

⌊Nc

RTi−βc

i

bc1

⌋. . .

⌊Nc

RTi−∑p−1

s′=1mc

is′

bcs′

bcp

⌋

∑

mcip

=

⌊Nc

RTi−βc

i

bcp

⌋ × (26)

⌊Nc

i−∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

∑

j∈Aci

hccjis

∑

s′∈Sφc

is′(mc

imcimci )

+

⌊Nc

RTi−βc

i

bc1

⌋−1

∑mc

i1=0

. . .

⌊Nc

RTi−βc

i−∑p−1

s′=1mc

is′

bcs′

bcp

⌋−1

∑mc

ip=0

×

⌊Nc

i−∑p

s′=1mc

is′

bcs′

bcp+1

⌋

∑mc

ip+1=0

. . .

⌊Nc

i−∑|S|−1

s′=1mc

is′

bcs′

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

∑

j∈Aci

hccjis

∑

s′∈Sφc

is′(mc

imcimci )

.

For service s ∈ SRT , all occupancy vectors mcimcimci need to

satisfy the condition that∑

s′∈SRTmc

is′bcs′ + bc

s > N cRTi

−βc

i . On the other hand, for service s ∈ SNRT , all occupancyvectors mc

imcimci need to satisfy the condition that

∑s′∈S mc

is′bcs′ +

bcs > N c

i − βci .

In case 3 (αci = βc

i ), the probability of dropping con-nection requests from horizontal handoff users of service scan be calculated as in (22). The probability of droppingconnection requests from vertical handoff users of services can be calculated as in (23). For service s ∈ SRT , alloccupancy vectors mc

imcimci need to satisfy the condition that∑

s′∈SRTmc

is′bcs′ +bc

s > N cRTi

. On the other hand, for services ∈ SNRT , all occupancy vectors mc

imcimci need to satisfy the

condition that∑

s′∈S mcis′

bcs′ + bc

s > N ci .

Similar admission policy and preemption rules are appliedin the IEEE 802.16e network. Thus, for cell k ∈ Me,the global balance equations, probabilities for blocking newusers and dropping horizontal and vertical handoff users canbe derived similarly with the corresponding IEEE 802.16parameters [22].

Given the network parameters λcis

, λeks

, ηci , ηe

k, µcis

, µeks

,qccijs

, qceils

, qeekls

, qeckjs

, υs, bcs, and be

s for {i, j} ∈ Mc, {k, l} ∈Me, and s ∈ S , we can solve equations in the model atthe connection-level and obtain the blocking and droppingprobabilities Bc

nis, Dc

hhis, Dc

vhis, Be

nks, De

hhks, and De

vhksfor

i ∈ Mc, k ∈ Me, and s ∈ S . Note that to compute thearrival rates, we need to solve the set of fixed-point equationsgiven by the handoff rate equations. We can use the iterativefixed-point algorithm of repeated substitutions [27].

D. Packet-level Model

At the packet-level, for i ∈ Mc and k ∈ Me, theperformance metric of interest is the probability of packet loss.We consider that packet loss can occur when the total number

of required BBUs from the active connections exceeds thecapacity of the cell. Thus, let Lc

isand Le

ksdenote the packet

loss probabilities experienced by connections of service s dueto statistical multiplexing in cell i and cell k, respectively.

To calculate Lcis

and Leks

, we assume that once a connectionis accepted, it behaves as an exponentially distributed ON −OFF traffic source. The ON−OFF traffic model can be usedto model voice, bursty data, and video sources [28], [29]. Asource from service s requires three parameters to represent it:the transmission rate bc

s, the average time that the source is inthe ON state (i.e., the burst length) tONs , and the fractionof time that the source is in the ON state or the activityfactor ρs. To model traffic sources as ON−OFF sources, wefollow the methodology described in [29]. Further details aregiven in Section IV. This model is general enough to capturethe behavior where the performance is mainly determined bythe burst-level of the sources. In this case, we are interestedin the situation when the aggregated traffic from the activeconnections (i.e., in the ON state) exceeds the capacity. Thus,when a connection of service s in cell i ∈ Mc (k ∈ Me)is in the ON state, it generates packets at a rate that requirebcs BBUs (be

s BBUs) to transmit the packets. In any network,the fraction of time that a connection spends in the ON stateis given by ρs = tONs/(tONs + tOFFs), where tOFFs is thetime that the connection is in the OFF state.

Let m̄cis

and m̄eks

denote the number of connections in theON state in cell i ∈Mc and cell k ∈Me, respectively. Then,the probabilities that there are m̄c

isand m̄e

ksconnections of

service s in the ON state given that there are mcis

and meks

connections in each cell are given by:

P (m̄cis=j|mc

is)=

(mc

is

j

)ρj

s(1−ρs)mcis−j , 0 ≤ j ≤ mc

is, (27)

and

P (m̄eks

=j|meks

)=(

meks

j

)ρj

s(1−ρs)meks−j , 0 ≤ j ≤ me

ks. (28)

Packet loss can occur in a cell when the total number ofBBUs required from the connections in the ON state exceedsthe capacity of the cell (i.e.,

∑s∈S m̄c

isbcs > Cc

i ). Recall thatparameters N c

i and Nwk must not be less than the capacities

of the cells. Considering that the RT connections have priorityover the NRT connections, the packet loss probability dueto statistical multiplexing for service s connections in celli ∈Mc is

Lcis

=

⌊Nc

ibc1

⌋

∑mc

i1=0

· · ·

⌊(Nc

i−∑|S|−1

s′=1mc

is′

bcs′ )

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

mci1∑

m̄ci1

=0

· · ·mc

i|S|∑m̄c

i|S|=0

P (m̄ci1 |mc

i1) · · ·P (m̄ci|S| |mc

i|S|)Θis(m̄

cim̄cim̄ci )

PScli

, (29)

10

where Θcis

(m̄cim̄cim̄ci )

=

∑

s′∈SNRT

m̄cis′

bcs′ , if

∑

s′∈SRT

m̄cis′

bcs′ > Cc

i

and s ∈ SNRT ,[

∑

s′∈SNRT

m̄cis′

bcs′−

(Cc

i −∑

s′∈SRTm̄c

is′bcs′

)]+, if

∑

s′∈SRT

m̄cis′

bcs′ ≤ Cc

i

and s ∈ SNRT ,[ ∑

s′∈SRT

m̄cis′

bcs′ − Cc

i

]+

, if∑

s′∈SRT

m̄cis′

bcs′ > Cc

i

and s ∈ SRT ,0, if

∑

s′∈SRT

m̄cis′

bcs′ ≤ Cc

i

and s ∈ SRT ,(30)

[a]+ = max[a, 0], and

PScli =

⌊Nc

ibc1

⌋

∑mc

i1=0

· · ·

⌊(Nc

i −∑|S|−1

s′=1mc

is′

bcs′ )

bc|S|

⌋

∑mc

i|S|=0

P ci (mc

imcimci )

mci1∑

m̄ci1

=0

· · ·mc

i|S|∑m̄c

i|S|=0

P (m̄ci1 |mc

i1) · · ·P (m̄ci|S| |mc

i|S|)∑

s′∈Sm̄c

is′bcs′ . (31)

In (30), if the nominal allocation for the RT group is lessthan or equal to the capacity of the cell (i.e., N c

RTi≤ Cc

i ),then the connections from the RT group will not experiencepacket loss due to statistical multiplexing. Finally, the samepacket loss model considering statistical multiplexing is usedfor cell k ∈Me.

E. Joint Connection and Packet-level QoS Optimization

To select the design parameters N ci in cell i ∈ Mc and

Nek in cell k ∈Me, a joint connection-level and packet-level

QoS optimization approach is used. We propose the followingblocking/dropping cost minimization problem with constraintsat the connection-level in terms of blocking and droppingprobabilities and at the packet-level in terms of packet lossprobability. The objective is to minimize all penalty costsinvolved in the blocking and dropping of connections. Notethat cost minimization is equivalent to revenue maximization.We define the objective function which is a linear combinationof the blocking and dropping probabilities:

minimizeNc

i , Nek

∑

s∈S

[ ∑

i∈Mc

(ωc

nisBc

nis+ ωc

hhisDc

hhis+ ωc

vhisDc

vhis

)

+∑

k∈Me

(ωe

nksBe

nks+ ωe

hhksDe

hhks+ ωe

vhksDe

vhks

)]

subject to Bcnis

≤ Γcnis

, ∀ i ∈Mc, s ∈ S,

Benks

≤ Γenks

, ∀ k ∈Me, s ∈ S,

Dchhis

≤ Γchhis

, ∀ i ∈Mc, s ∈ S,

Dehhks

≤ Γehhks

, ∀ k ∈Me, s ∈ S,

Dcvhis

≤ Γcvhis

, ∀ i ∈Mc, s ∈ S,

Devhks

≤ Γevhks

, ∀ k ∈Me, s ∈ S,

Lcis

≤ Γcpis

, ∀ i ∈Mc, s ∈ S,

Leks

≤ Γepks

, ∀ k ∈Me, s ∈ S,

(32)

where ωcnis

and ωenks

denote the cost of blocking a connectionrequest for service s from a new user in cell i and cell k,respectively. Similarly, ωc

hhis, ωe

hhks, ωc

vhis, and ωe

vhksdenote

the costs of dropping a connection request for service s froma horizontal and vertical handoff user in cell i and cell k,respectively. To ensure that higher priority is considered foraccepting connection requests from handoff users rather thannew users, it is reasonable to set ωc

nis< ωc

hhisand ωc

nis<

ωcvhis

for all i ∈ Mc and s ∈ S , and ωenks

< ωehhks

andωe

nks< ωe

vhksfor all k ∈ Me and s ∈ S . At the connection-

level, Γcnis

and Γenks

are the maximum blocking probabilitiesallowed for new connection requests from service s, Γc

hhis

and Γehhks

are the maximum dropping probabilities allowedfor horizontal handoff requests from service s, and Γc

vhisand

Γevhks

are the maximum dropping probabilities allowed forvertical handoff requests from service s in cell i and cell k,respectively. Finally, at the packet-level, Γc

pisand Γe

pksare the

maximum packet loss probabilities allowed for connectionsfrom service s in cell i and cell k, respectively.

IV. PERFORMANCE EVALUATION

We evaluate the performance of an integrated cellu-lar/802.16e system consisting of a 3G RAN with |Mc| =12 cells, and an 802.16e ASN with |Me| = 3 cells. Asshown in Fig. 2, the cells are labeled as follows: Mc ={C1, C2, C3, . . . , C12} and Me = {E1, E2, E3}. At theconnection-level, we consider different traffic patterns byassigning different values to parameters λc

isand λe

ks. Three

different services are considered. Two RT multimedia servicesare offered (i.e., SRT = {1, 2}), and one NRT data service(i.e., SNRT = {3}). The connection durations have means1/υ1 = 1/υ2 = 6 minutes, and 1/υ3 = 4 minutes. In theintegrated system, cells are of the same size [3]. The inter-boundary times in cell i and in cell k have means 1/ηc

i =1/ηe

k = 4 minutes. Different levels of mobility of users arealso investigated.

In each cell i ∈ Mc, the network capacity is 2 Mbps,and the BBU is set to 32 kbps based on the 3GPP supportedmultimedia bearer services [30]. This implies that the capacityof each cell is Cc

i = 62 BBUs. The first service, s = 1, is voiceconnections requiring 32 kbps. The second service, s = 2,is video connections requiring 64 kbps. These values are setaccording to the multimedia codecs for 3GPP [31]. The thirdservice, s = 3, is data connections with HTTP traffic (i.e.,web browsing) at 32 kbps. Thus, the QoS provisioning in celli stipulates that bc

1 = 1 BBU, bc2 = 2 BBUs and bc

3 = 1 BBU.In each cell k ∈Me, according to [23], the network capacity

11

0.5 1 1.5 2 2.5 3 3.5 4

10−4

10−3

10−2

10−1

Arrival rate service 2 (λ2)

New

con

nect

ion

bloc

king

pro

babi

lity

Bcn

C12

(Analytical results)

Bcn

C12

(Simulation results)

Ben

E12

(Analytical results)

Ben

E12

(Simulation results)

Fig. 4. Comparison between analytical and simulation results for newconnection blocking probability in cell C1 and cell E1 versus the arrival rateof connection requests from service 2.

is 6 Mbps. In order to benefit from the additional capacity, therequired data rates of the services are reasonably assumed tobe larger in the 802.16e ASN than the rates in the 3G RAN.We set the BBU to 64 kbps, the voice connections to 64 kbps,video connections to 128 kbps, and data connections to 64kbps. This implies that the capacity of each cell k is Ce

k = 92BBUs, and that the QoS provisioning is be

1 = 1 BBU, be2 = 2

BBUs, and be3 = 1 BBU.

Based on the defined QoS provisioning (i.e., bc1 = 1, bc

2 =2, and bc

3 = 1) and according to [15], we set the nominalallocations in cell i as (N c

RTi, N c

NRTi) = (3, 1)N c

i /4 and δci =

4. The same nominal allocations apply to cell k and δek = 6.

To set the integer values of N ci and Ne

k , we solve the costminimization problem in (32) by using an exhaustive searchalgorithm. Note that to have a statistical multiplexing gain, thesearch is restricted to N c

i > Cci and Ne

k > Cek . As in [20],

we set for all cell i ∈ Mc, k ∈ Me, and s ∈ S , ωcnis

=ωe

nks= 1, ωc

hhis= ωe

hhks= 10, and ωc

vhis= ωe

vhks= 10.

The QoS constraints are as follows: Γcni1

= 0.05, Γchhi1

=Γc

vhi1= 0.05, Γc

ni2/3= 0.20, and Γc

hhi2/3= Γc

vhi2/3= 0.1

for all cell i ∈Mc. The same values are set in cell k ∈Me.Finally, at the packet-level, for service s = 1, the averageTON1 = 7.24 sec and TOFF2 = 5.69 sec. Thus, ρ1 = 0.54[32]. For service s = 2, we assume video frames with anaverage size Favg = 300 bytes, which arrive at periodic timeintervals every 1/24 sec. Thus, ρ2 = (Favg/bc

2)/(1/24) = 0.90[29]. For service s = 3, we assume Web pages with an averagesize of 100 kbytes and an average TOFF3 (i.e., reading time)of 30 sec. Thus, ρ3 = 0.45 [33]. The QoS constraints areΓc

pis= Γe

pks= 5 × 10−5 for all cell i ∈ Mc, k ∈ Me, and

s ∈ S .

A. Effect of Increasing Arrival Rate

For analytical model validation, a discrete event drivennetwork simulator is created using MATLAB. Simulationsresults are then compared with the results obtained from the

0.5 1 1.5 2 2.5 3 3.5 4

10−4

10−3

10−2

10−1

Arrival rate service 2 (λ2)

New

con

nect

ion

bloc

king

pro

babi

lity

Bcn

C12

Ben

E12

Bcn

C13

Ben

E13

Bcn

C11

Ben

E11

Fig. 5. New connection blocking probability in cell C1 and cell E1 versusthe arrival rate of connection requests from service 2.

analytical model. The simulation results are averaged over 500simulation runs. The simulation time for each run is 106 mins.Thus, if the arrival rate is 1.5 requests/min, then there are1.5 × 106 service requests generated in the system for eachcell. The blocking probabilities in each simulation run arecalculated based on the ratio of the total number of rejectedconnection requests and the total number of requests. Fig. 4shows the new connection blocking probability of service 2for cells C1 and E1 versus the arrival rate of service 2. Fig. 4shows that the analytical results closely matched with thesimulation results.

Next, we present the results obtained from the analyticalmodels. Fig. 5 shows the probability of blocking connectionrequests from the new users of the three services in cell C1 andcell E1. The arrival rate of connection requests from service2 is increased from 0.2 to 2 connection requests per minute,while the arrival rates of services 1 and 3 remain constant at1 connection request per minute. For the following results, weset λs = λc

si= λe

sk(i.e., the same increase of connection

requests occurs in the 3G RAN and in the 802.16e ASN). Inboth networks, the blocking probabilities for new connectionrequests of service 2 are the highest, since such connectionsrequire twice more BBUs than services 1 and 3. On the otherhand, note that the blocking probabilities for new connectionrequests of service 1 are lower than those for service 3.The reason is that the connections from service 1 belong tothe RT group (i.e., SNRT = {1, 2}) which can preempt theconnections from the NRT group (i.e., SNRT = {3}). Thus,due to the preemption, the new requests from service 1 areable to obtain access to the integrated cellular/802.16e systemmore often than those from service 3. Fig. 5 also shows thatthe blocking probabilities in cell E1 of the 802.16e ASN arelower than those in cell C1 due to the higher capacity in the802.16e ASN compared to the 3G RAN.

Figs. 6 and 7 show the probabilities of dropping connec-tion requests of the three services in cell C1 and cell E1from horizontal and vertical handoff users, respectively. Inboth networks, the highest probabilities correspond to the

12

0.5 1 1.5 2 2.5 3 3.5 410

−6

10−5

10−4

10−3

10−2

10−1

Arrival rate service 2 (λ2)

Hor

izon

tal h

ando

ff bl

ocki

ng p

roba

bilit

y

Dchh

C12

Dehh

E12

Dchh

C13

Dchh

C11

Dehh

E13

Dehh

E11

Fig. 6. Horizontal handoff dropping probability in cell C1 and cell E1 versusthe arrival rate of connection requests from service 2.

0.5 1 1.5 2 2.5 3 3.5 410

−6

10−5

10−4

10−3

10−2

10−1

Arrival rate service 2 (λ2)

Ver

tical

han

doff

bloc

king

pro

babi

lity

Dcvh

C12

Dcvh

C13

Devh

E12

Dcvh

C11

Devh

E13

Devh

E11

Fig. 7. Vertical handoff dropping probability in cell C1 and cell E1 versusthe arrival rate of connection requests from service 2.

connection requests from service 2, followed by those ofservice 3 and service 1 in decreasing order. Note also thatthe dropping probabilities in cell C1 for horizontal handoffsare lower than those for vertical handoffs. On the other hand,the dropping probabilities in cell E1 for horizontal handoffsare higher than those for vertical handoffs. The reason isthat in cell C1, we have case 1 (αc

C1 > βcC1) from Fig. 3,

while in cell E1, we have case 2 (αeE1 < βe

E1). This isexpected since as described in Section III, each case dependson the handoff rates from the mobility scenario. This canalso be seen from Fig. 2. Cell C1 of the 3G RAN withWc

C1 = {E1} receives horizontal handoffs requests fromsix adjacent cells Ac

C1 = {C2, C3, C7, . . . , C10}, but onlyreceives optional and transferred vertical handoffs requestsfrom two cells Ae

E1 = {E2, E3}. Cell E1 of the 802.16eASN with We

E1 = {C1} receives horizontal handoffs requestsfrom only two adjacent cells and also receives optional andtransferred vertical handoffs requests from six cells.

0.5 1 1.5 2 2.5 3 3.5 410

−12

10−11

10−10

10−9

10−8

10−7

10−6

10−5

10−4

Arrival rate service 2 (λ2)

Pac

ket l

oss

prob

abili

ty

LcC1

3

with exponential inter−arrival time

LcC1

3

with Pareto inter−arrival time

LeE1

3

with exponential inter−arrival time

LeE1

3

with Pareto inter−arrival time

Fig. 8. Packet loss probability in cell C1 and cell E1 versus the arrival rateof connection requests from service 2.

Fig. 8 shows the probability of packet loss for connectionsof service 3 in cell C1 and cell E1. In both networks, thenominal allocations for the connections from the RT group,N c

RTC1= 58 and Ne

RTE1= 87, respectively, are less than the

corresponding capacities of their cells, CcC1 = 62 and Ce

E1 =92, respectively. Such connections do not experience packetloss due to statistical multiplexing. On the other hand, we cansee that as the arrival rate of connection request from service2 increases, service 3 starts to experience a considerableincrease on the packet loss probability in both networks. Recallthat the connections from the NRT group can only transmitwhen

∑s∈SRT

m̄cis

bcs ≤ Cc

i in (30). Fig. 8 also shows theperformance of the algorithm when the packet inter-arrivaltime follows Pareto distribution instead of an exponentialdistribution. The Pareto distribution has two parameters. Weset the shape parameter to be equal to 3 and vary the scalingparameter such that the average packet inter-arrival time forboth Pareto and exponential distributions is equal. Resultsshow that the packet loss probability reduces slightly whenthe packet inter-arrival time follows a Pareto distribution.

B. Performance Comparison with a Fixed Policy

Fig. 9 shows the probability of dropping connection requestsfrom horizontal and vertical handoff users of service 1 in cellC1 of the 3G RAN. We compare the performance of theintegrated cellular/802.16e system operating as described inSection III versus the system operating with a fixed policy.When the fixed policy is used, the system is operating asin case 3 (αc

C1 = βcC1) at all times and independent from

the handoff rates of the mobility scenario. The rest of theparameters are set the same before. In Fig. 9, we can seethat the horizontal handoff dropping probabilities are 170%higher when the fixed policy is used. On the other hand, thevertical handoff dropping probabilities are 55% lower whensuch fixed policy is used. As explained before, since the systemis operating in case 2 (αc

C1 > βeC1) (i.e., cell C1 receives

more requests from horizontal handoff users than from vertical

13

0.5 1 1.5 2 2.5 3 3.5 4

10−4

10−3

10−2

10−1

Arrival rate service 2 (λ2)

Han

doff

drop

ping

pro

babi

lity

Dchh

C11

(αic=β

ic fixed)

Dcvh

C11

Dchh

C11

Dcvh

C11

(αic=β

ic fixed)

55%

170%

Fig. 9. Horizontal and vertical dropping probabilities in cell C1 versus thearrival rate of connection requests from service 2. Performance comparisonwith a fixed policy.

0.5 1 1.5 2 2.5 3 3.5 4

10−4

10−3

10−2

10−1

100

Arrival rate service 2 (λ2)

New

con

nect

ion

bloc

king

pro

babi

lity

Ben

E12

(w/o joint QoS optimization)

Ben

E11

(w/o joint QoS optimization)

Ben

E12

(with joint QoS optimization)

Ben

E11

(with joint QoS optimization)

70%

66%

Fig. 10. New connection blocking probability in cell E1 versus the arrivalrate of connection requests from service 2. Comparison between with andwithout using joint connection and packet-level QoS optimization.

handoff users), higher priority is given to connection requestsfrom horizontal handoff users and less priority to the requestsfrom vertical handoff users. This translates into lowering thehorizontal handoff dropping probabilities at the expense ofslightly increasing the vertical handoff dropping probabilities.This expense is compensated by the fact that fewer verticalhandoff requests will arrive at cell C1. Nevertheless, we cansee that the horizontal handoff dropping probabilities arereduced by 63% when the system operates in case 2 comparedto the case when operates with the fixed policy.

C. Joint QoS Optimization

Fig. 10 shows the probability of blocking connection re-quests from the new users of services 1 and 2 in cell E1. Wecompare the performance of the integrated cellular/802.16esystem without using the joint connection and packet-level

optimization. The parameters N ci and Ne

k are set as the ca-pacities Cc

i and Cek , respectively. In Fig. 10, we can see that the

blocking probabilities increase faster for service 2. The reasonis that we are increasing the arrival rate of service 2 while thearrival rates of service 1 and 3 remain constant. Moreover,connection requests from service 2 require more BBUs. Theblocking probabilities of connections from services 1 and 2are 66% and 70% lower, respectively, when the parameters Ne

k

are set from the joint QoS optimization approach. It is worthmentioning that this decrease in the blocking probabilities atthe connection-level should be traded off against toleratingan increase on the packet loss probabilities. Since for the caseNe

k = Cek , there is no packet loss due to statistical multiplexing

at the packet-level for neither of the groups (i.e., RT andNRT) because we always have that

∑s∈S me

E1sbes ≤ Ce

E1.However, the packet loss probabilities can be bounded into asuitable value due to the packet-level constraints in the jointQoS optimization problem in (32).

V. CONCLUSIONS

In this paper, we first described the mobility scenario for 3Gcellular/802.16e interworking. The scenario specified the hor-izontal and vertical handoffs that can occur in such integratedsystem. We extended the VP with preemption technique to beused for admission control in cellular/802.16e interworking.To this end, we proposed admission control algorithms forconnection requests that consider the class of service (i.e., RTor NRT) and the type of user (i.e., new or handoff). Bothhorizontal and vertical handoffs are considered, and suitablepreemption rules are defined for the RT and NRT connec-tions. We formulated a blocking/dropping cost minimizationproblem following a joint connection and packet-level QoSoptimization approach. The analytical results are validated viasimulations. Results showed the performance of the integratedsystem in terms of the probability of blocking new connectionrequests, the probability of dropping horizontal and verticalhandoff connection requests, and the probability of packet loss.Performance improvement is shown at the connection-levelwhen the proposed admission algorithms are used comparedwith a fixed policy since they properly assign the priorities foradmission control to each type of handoff user based on thedescribed mobility scenario. Improvement is also shown whenpreemption of RT connections over NRT connections and thejoint QoS optimization are used.

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Enrique Stevens-Navarro (S’99-M’09) received theB.Sc. degree from Universidad Autonoma de SanLuis Potosi (UASLP), San Luis Potosi, Mexico in2000, the M.Sc. degree from Instituto Tecnologicoy de Estudios Superiores de Monterrey (ITESM),Monterrey, Mexico in 2002, and the Ph.D. degreefrom The University of British Columbia (UBC),Vancouver, Canada in 2008, all in electrical engi-neering. From 2002 - 2003, he worked as projectmanager at Q-Voz IVR Outsourcing in Monterrey,Mexico. Currently, he is an Assistant Professor at the

Faculty of Science of Universidad Autonoma de San Luis Potosi (UASLP), inSan Luis Potosi, Mexico. His research interests are in mobility and resourcemanagement for heterogeneous wireless networks. He is member of the IEEEand member of the Mexican National Research System (SNI).

Vahid Shah-Mansouri (S’02) is a Ph.D. candi-date in the Department of Electrical and ComputerEngineering at the University of British Columbia,Vancouver, BC, Canada. He received the B.Sc. andM.Sc. degrees in electrical engineering from Uni-versity of Tehran, Tehran, Iran in 2003 and SharifUniversity of Technology, Tehran, Iran in 2005, re-spectively. From 2005 to 2006, he was with Farineh-Fanavar Co., Tehran, Iran. His current research in-terests are in mathematical modeling and protocoldesign for radio frequency identification (RFID)

systems and wireless sensor networks.

Vincent W.S. Wong (SM’07) received the B.Sc. de-gree from the University of Manitoba, Winnipeg,MB, Canada, in 1994, the M.A.Sc. degree from theUniversity of Waterloo, Waterloo, ON, Canada, in1996, and the Ph.D. degree from the University ofBritish Columbia (UBC), Vancouver, BC, Canada,in 2000. From 2000 to 2001, he worked as asystems engineer at PMC-Sierra Inc. He joined theDepartment of Electrical and Computer Engineeringat UBC in 2002 and is currently an Associate Pro-fessor. His research areas include protocol design,

optimization, and resource management of communication networks, withapplications to the Internet, wireless networks, RFID systems, and intelli-gent transportation systems. Dr. Wong is an Associate Editor of the IEEETransactions on Vehicular Technology and an Editor of KICS/IEEE Journalof Communications and Networks. He serves as TPC member in variousconferences, including IEEE Infocom, ICC, and Globecom. He is a seniormember of the IEEE and a member of the ACM.