admission control in td-cdma networks

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2003; 3:209–223 (DOI: 10.1002/wcm.112)

Admission control in TD-CDMA networks

Jelena Misic*,† and Yat-Kwan TangDepartment of Computer ScienceHong Kong University of Scienceand TechnologyClear Water BayKowloonHong Kong

Vojislav B MisicDepartment of Information Systemsand ManagementHong Kong University of Science andTechnologyClear Water BayKowloonHong Kong

Summary

Quality of service (QoS) at the call or flow level inTime Division Code Division Multiple Access(TD-CDMA) networks is an important topic that hasnot yet received much attention. In this paper, wepropose a two-tier admission and scheduling policy.The scheduling policy is comprised of twoprocedures, the User Prioritizer Procedure (UPP)and Packet Allocator Procedure (PAP). The UPP isused to determine the serving priority of users. Thetask of PAP is to allocate packets to a time slotwithout violating the outage probability by invokingthe tier 1 and tier 2 admission control. The tier 1admission keeps the probability of violating the BitError Rate (BER) limits for new call arrivals belowa specified threshold. The tier 1 admission allocatesbandwidth for newly arrived calls on the basis ofthe outage probability threshold. The tier 2admission reserves bandwidth in all surroundingsectors for future handoff events, which minimizesthe probability that the handoff call from one sectorwill cause an outage condition in the target sector.The performance of the proposed policy is evaluatedthrough simulations in the presence of voice anddata traffic types. We show that the proposedscheme can provide satisfactory quality for a call ordata flow during its lifetime. Copyright 2003John Wiley & Sons, Ltd.

KEY WORDSTD-CDMAQoSadmission control

ŁCorrespondence to: Jelena Misic, Department of Computer Science, Hong Kong University of Science and Technology,Clear Water Bay, Kowloon, Hong Kong.†E-mail: [email protected]

Copyright 2003 John Wiley & Sons, Ltd.

210 J. MISIC, Y.-K. TANG AND V. B. MISIC

1. Introduction

The Wideband Code Division Multiple Access(WCDMA) is the air interface standard of Univer-sal Mobile Telecommunication System (UMTS) [1,2],and therefore may be regarded as one of the main-stream third generation air interface technologies. TheUMTS Terrestrial Radio Access (UTRA) is operatedin two complementary air interface modes: FrequencyDivision Duplex (FDD), also known as WCDMA,and Time Division Duplex (TDD), also known asTD-CDMA. The Time Division Code Division Mul-tiple Access (TD-CDMA) system is designed for usein the unpaired spectrum, unlike WCDMA, and itsmain advantage is improved efficiency in carryingasymmetric traffic such as Internet and multimediatraffic, which are expected to be the main types oftraffic in 3G systems. Consequently, the TD-CDMAsystem has the potential to deliver wideband trafficwith a high degree of asymmetry more efficientlythan the WCDMA system. Another characteristic ofTD-CDMA systems is their smaller cell sizes, whichallow a reduction in average transmitted power, whichin turn leads to smaller out-of-cell interference. At thesame time, however, smaller cell sizes also increasethe handoff rate of mobile users and cause rapidchanges in the network traffic environments makingQoS guarantees difficult to maintain [3–5]. Conse-quently, the problem of providing QoS guaranteesin the TD-CDMA system becomes more challengingthan in comparable WCDMA systems.

In TD-CDMA networks, uplink and downlinktransmissions occur in dedicated, equally sized timeslots, which are further organized in frames. Since thetask of bandwidth management on the uplink is morechallenging, in this work we will focus on the QoS ofuplink transmissions. Because of the multiple accessnature of Code Division Multiple Access (CDMA),multiple users can transmit in the same uplink slot. Adedicated entity, commonly referred to as the sched-uler, executes at the base station in order to managethe transmissions from admitted users by mappingthem into available time slots within the frame insuch a way that the required QoS is maintained. Tothe best of our knowledge, the issue of scheduling andadmission control in the TD-CDMA network has notyet received much attention. Only a few papers [6–8]have discussed the problem of TD-CDMA schedulingand admission control.

Resource management and QoS guarantees in theCDMA system have been investigated by manyresearchers. This work may be loosely classified into

three broad categories: conventional radio resourcemanagement of the CDMA system, TD-CDMA sche-duling schemes, and admission control based onbandwidth reservation scheme.Conventional radio resource management schemesin CDMA systems are based on physical layer mea-surements of power and �Eb/I0� performed at themobile terminals and base stations; the measured val-ues are then used for admission control. In somecases, user mobility is not taken into account [9–20].Other papers model users’ mobility through the meanand the variance of the number of users in a sec-tor, and show that user mobility can violate the QoSrequirements of mobile calls and, thus, substantiallyaffect the capacity of a CDMA system [21–25].TD-CDMA scheduling and admission control hasbeen addressed in [6–8]. In [6], only one power levelis allowed within the slot; this power level corre-sponds to the traffic type with the lowest Bit ErrorRate (BER) requirement. This scheme is further elab-orated by allowing different power levels accordingto the BER requirements of different traffic types[7,8]. However, neither of these papers addresses usermobility.Finally, bandwidth reservation schemes related touser mobility in TDMA fixed channel allocation net-works are discussed in [26–29]. In [26], the authorspropose a wireless bandwidth reservation strategybased on the user mobility specification to control thenew call blocking probability and handoff droppingprobability. It reserves as much wireless bandwidthas requested by the user, while mobility predictionis not used in order to avoid heavy computation. In[27], a differential bandwidth reservation algorithmthat guarantees a certain level of QoS in multimediawireless networks is proposed. The scheme exam-ines a set of cells along the path where a mobileuser may move, and reserves resources based on themovement probabilities of the mobile user. Statisticaluser mobility prediction, used in conjunction with calladmission control and bandwidth reservation algo-rithm, is introduced in [28]. The history data is usedto extract patterns and predict the next movement ofeach mobile user. On the basis of the mobility pre-diction, admission control is invoked and bandwidthis reserved.

The admission control and bandwidth reservationscheme from [29] provides the QoS guarantees byreserving bandwidth in all the cells surrounding thecell in which the original connection is established.When a user moves to one of the surrounding cells,the reserved bandwidth is utilized and the bandwidth

Copyright 2003 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. 2003; 3:209–223

ADMISSION CONTROL IN TD-CDMA NETWORKS 211

reservation process is repeated in the new cell. Thebandwidth reserved in the original surrounding cells,which are no longer neighboring to the new cell, isreleased.

The proposed schemes in [26–29] are conceptuallysimilar to our work about the bandwidth reservationof the handoff calls in Frequency Division Multi-ple Access (FDMA) networks [30,31]. However, theanalytical framework of [30] allows the bandwidthreservation scheme to be integrated—as a separatetier of admission control—on top of the physicallayer admission control. In the latter, the admissionregion (i.e. the number of admissible users with dif-ferent traffic types) and power values for differenttraffic types are calculated given the bound on outageprobability. In the second tier, a portion of availablebandwidth within the network capacity (determinedby the bound on outage probability) is reserved forhandoff calls. To this end, a dedicated statistical pro-cess (run with the rate proportional to the utilizationin surrounding cells) is used to lower the new calladmission threshold imposed by the bound on outageprobability. As a consequence, a number of hand-off calls may be accommodated without violating theoutage rate. Furthermore, admitted calls can freelyexecute handoffs without checking admission con-ditions, and handoff events will not experience orcause poor quality (voice) or retransmissions (data).In this way, the scheduling policy and admission con-trol ensures the following:

1. The probability that the BER bounds of the admit-ted connections in a given time slot will be vio-lated by the admission of a new connection (i.e.the outage probability) will be lower than a prede-termined threshold (e.g. P D 0.01). The predeter-mined threshold (P D 0.01) is derived from [10].

2. The probability that the handoff of a user withbandwidth reservation from one sector to anotherwill violate the BER bounds for connections inthe new sector within the allocated time slot issmaller compared to the probability that the hand-off of a user without bandwidth reservation willviolate the BER bounds. This is accomplished byappropriate bandwidth reservation. Note that timesynchronization between the frames in adjacentsectors is not important since bandwidth reserva-tion for handoffs is performed in all time slots ofthe frame.

3. Finally, the throughput is maximized by schedul-ing the maximum number of connections withinthe slot that will not exceed the prescribed outageprobability threshold.

To the best of the authors’ knowledge, schedulingand admission policy that simultaneously addressesall three of the aforementioned issues has not beenproposed in the literature as yet. In this paper, wepropose an admission control and scheduling policyfor TD-CDMA networks. The proposed policy notonly maximizes the throughput but also keeps theoutage probability under a specified threshold andalleviates the impact of handoffs on cell outage con-ditions.

Of course, call admission control is just oneof the resource management procedures in a TD-CDMA network, and it should be regarded withina wider context that includes link adaptation andother control procedures. However, different aspectsof resource management should be individually eval-uated and properly understood before an integratedapproach is attempted. The integration of variouscontrol procedures will be considered in our futureresearch.

This paper is organized as follows. Section 2describes the call scheduling algorithm. The tier 1and tier 2 admission control schemes are describedin detail in Sections 3 and 4. The Markovian Modelfor single traffic type is presented in Section 5. Thesimulation results are discussed in Section 6. Finally,we summarize the paper and outline some directionsfor further research.

2. A TD-CDMA System with CallScheduling

In this work, we consider a wireless cellular sys-tem that consists of cells arranged in a hexago-nal mesh. Each cell is evenly divided into threesectors with each sector consisting of one antenna

Cell 4

Cell 1

Cell 2

Cell 3

Fig. 1. Sectors within the cells.



having 120° effective beam widths as illustrated inFigure 1.

In a TD-CDMA network, all users share the band-width of the frequency channel by using different timeslots. The transmissions are organized in frames, andeach frame is divided into uplink and downlink slotsof the same length. The number of users that cantransmit simultaneously (i.e. within the same timeslot) is limited by the interference at the receivingbase station, and it depends on the users’ traffic pat-terns, Signal to Interference and Noise Ratio (SINR)requirements and accuracy of power control. Theinterference at the base station at the given time slot isproportional to the number of users in the target cell,as well as in the surrounding cells. A typical framehas a length of 10 ms and consists of 15 time slots,where each time slot has a length of 667 µs [32]. Weassume that static TDD is used; thus in each framethere are n (typically n D 11) time slots for uplinkand q (typically q D 4) time slots for downlink [6,8].In this work, we consider uplink transmissions only.

The goal of the scheduling algorithm is to maxi-mize the throughput without violating the BER of theadmitted users and to reserve bandwidth for potentialhandoffs. (We assume that activity factors of all traffictypes are known.) The scheduling algorithm is per-formed in each base station in a distributed mannerand no central coordination is necessary, as each basestation already keeps track of all mobile users that arecurrently connected to it.

The scheduling algorithm is comprised of twoprocedures: the User Prioritizer Procedure (UPP) andthe Packet Allocator Procedure (PAP). The task ofUPP is to determine the serving priority of newarrival calls and handoff calls, and to handle thedata transmission requests of mobile calls in the nextframe. In the UPP, the calls are classified into threepriority queues based on their cell status instead oftheir traffic types because the performance of differentQoS classes is not the main concern of this paper.The in-cell and handoff calls have higher prioritythan the new arrival calls because the mobile users,who have already existed in the system, should havehigher service priority than the new arrival mobileusers. Only the new arrival calls should be blockedwhen there is not enough bandwidth. The task ofPAP is to determine whether the new arrived calls areblocked or accepted, and to allocate time slots for thenew arrival calls and handoff calls without violatingthe outage probability. Since we want to limit thecomputational complexity of the admission algorithm,we try to distinguish between different traffic types

and allocate them into different time slots wheneverthis is possible. Thus, the PAP will try to allocate (i.e.group) the calls of the same traffic type in the sametime slot and labels that time slot with the traffic typeID. This will not always be possible, and sometimesusers with different traffic types have to share thesame time slot; in that case, the power levels fordifferent traffic types are calculated as part of the calladmission procedure.

At the end of frame, the base station invokes theUPP to schedule the calls in the next frame. Firstof all, the UPP classifies the calls into three queues.The queue of in-cell calls has the highest priority. Thequeue of handoff calls is served next, and the queueof new arrival calls is served last. For handoff calls,PAP is invoked to optimize the time slot allocationafter the handoff is executed since the initial timeslot for handoff call might not have the lowest outageprobability. For new arrived calls, PAP is also invokedto calculate the admission region and decide whetherthe new arrival calls are blocked or accepted. Oncea call is accepted and allocated a time slot, it willpermanently occupy that time slot until it leaves thesector; in other words, the call can freely transmitpackets in the assigned time slot. The main steps ofthe UPP are shown in Figure 2.

As noted above, PAP is invoked by UPP to allocatea time slot for new arrival and handoff calls. Theadmission control proceeds in two tiers: first, wehave to verify that the new arrival call will not

UPP

Leave the admitted calls intheir allocated time slots

Find the optimal time slots for thehandoff calls, which arrived inprevious frame (PAP called)

Make an admission decision forthe new arrival calls, which arrived

in previous frame (PAP called)

Return

Fig. 2. The User Prioritizer Procedure.



PAP - New Call Arrival Allocation(call arrival of traffic type k)

For all timeslots which containtraffic type k only: find the slot

n with the smallest Po,k,n

Pr,n > PrTin the target sector and the

surrounding ring

Tier 2 admission controlof slot n is called

Call is admitted;bandwidth reservation rate is updated

Does there existan empty slot

Tier 2 admission controlof slot n is called

Call is blocked

Yes

No

No

Yes

NoYes

No

Po,k,n < 0.01Yes

Does there exist a slotwhere the call can fit

No

Yes

For timeslots with different traffictypes: execute system of eqs.(5) and find residual capacity

of traffic type k

Pr,n > PrTin the target sector and the

surrounding ring

Return

PAP - Handoff call optimization(handoff call of traffic type k in slot t )

For all timeslots which contain traffictype k only: find the slot n with

the smallest Po,k,n < Po,k,t

Call is rescheduled;bandwidth reservation rate is updated

Find the slot n with the smallest Po,k,n

Yes

Yes

Yes

No

Po,k,n < 0.01

No

No

Does there existan empty slot

For timeslots with different traffic typesexecute system of eqs. (5) and find

residual capacity of traffic type k

Does there exist a slotwhere the call can fit

Return

Fig. 3. The Packet Allocator Procedure: note that Po,k,n defines as the outage probability for traffic type k in time slot n,Po,k,t defines as the outage probability of handoff call for traffic type k in time slot t, Pr,n defines as the reservation

probability in time slot n, and PrT defines as the threshold value of the reservation probability.

violate the outage condition (tier 1), and then, weexamine whether sufficient bandwidth can be reservedin surrounding sectors for future handoffs (tier 2).

For each new arrival call, if the PAP finds a timeslot of the same traffic type (as denoted by theslot ID) that satisfies the tier 1 admission controlrequirement, the tier 2 admission control for that timeslot will be invoked. If the tier 2 admission controlhas been passed, the new arrival call will be accepted;otherwise, the PAP will attempt to find an empty timeslot. In case no empty time slot can be found, PAP willattempt to allocate a time slot of a different traffic typethat can still satisfy both tier 1 and tier 2 admissioncontrol requirements. If no such slot can be found,the new call will be blocked.

When a handoff call enters a neighboring sector,it uses the time slot allocated by the PAP in itsoriginal sector to transmit packets. Since bandwidthwas reserved for the handoff call once it was admitted

Tier 1 admission control

Return

System of equations (5) is solved.Powers and number of admissible

connections are determined

Outage probability iscalculated

Check the number oftraffic types

Single traffic type Multiple traffic types

Fig. 4. The tier 1 admission control.

by the system, outage condition upon handoff willbe prevented. Nevertheless, in the next frame, thePAP of the new target cell will attempt to reallocatethe handoff call to the optimal time slot, that is, theone with the lowest outage probability. Furthermore,when the time slot, which has already been allocatedto the handoff call, is in outage condition, the handoffcall is rescheduled to a nonreserved slot with the



Tier 2 admission control

Return

Calculate the residual capacity of the desired sector

Decrease residual capacity by 1 andcalculate reservation probability

Increment the local value of reservationrate in surrounding sectors

Check whether Pr <PrT

Calculate residual capacity in each ofthe four surrounding sectors

Calculate the reservation probabilities

Check whether Pr <PrT in thesurrounding sectors

Block the call

Block the call

No

Yes

Yes

No

Fig. 5. The tier 2 admission control.

lowest probability of BER to mitigate the outageeffect caused to the system.

Also, the handoff event will cause the bandwidthreservation values to be redistributed: previously allo-cated bandwidth in the neighborhood of the originaltarget sector will be released, while the new reserva-tion values will be distributed to the sectors surround-ing the new target sector.

The flowcharts of the PAP, the tier 1 and tier 2admission control are presented in Figures 3, 4 and5; detailed explanations of the two latter algorithmswill be given in the next two sections.

3. Tier 1 Admission Control

Let us assume that we have J traffic types, and thateach traffic type j 2 �1 . . . J� can be described by

the bandwidth requirement of bj narrowband chan-nels. Therefore, the rate for the jth traffic type isRj D bjRnb, where Rnb represents the rate of thebasic narrowband channel. In the TD-CDMA sys-tem, wideband users achieve the required transmis-sion rates by allocating multiple codes of DedicatedPhysical Channel (DPCH) and/or occupying multipletime slots. For the sake of simplicity of modeling,in this work we assume that one connection is trans-mitting within one time slot using multiple codes.This approach also results in less interference withinthe time slot since codes used by one terminal aremutually orthogonal [6]. Let us consider the time slotn for uplink transmission and assume that there areNt,j,n connections of type j scheduled in time slotn. Every connection of type j is active within itstime slot with probability ˛j. We model the activityof ith connection from traffic type j within its timeslot as the random variable �j,i,n with the followingdistribution:

�j,i,n D{

bj, with probability ˛j

0, with probability 1 - ˛j�1�

It is known that SINR is related to the BER of thechannel. In this work we will assume that traffic typek requires that the bit error rate BERk is boundedby the threshold value TBER,k , which correspondsto the threshold SINR value �Eb/��T

k . Further, wewill denote the received powers in time slot n (pernarrowband channel) at the base station for usersof traffic type k as Sk,n, k 2 �1 . . . J�. For a userfrom traffic type k, in the time slot n, the SINR perone narrowband channel is obtained by extending thework from [10] as(

Eb

�

)k,n

D W

Rnb

1J∑

jD1j6Dk

Sj,n

Sk,n

Nt,j,n∑

iD1

�j,i,n C �s,j,n

Sj,n

C

Nt,k,n�1∑iD1

�k,i,n C �s,k,n

Sk,nC �0W/Sk,n

�2�where W is the width of frequency channel of DS-CDMA system, � is the spectral density of the inter-ference (noise) power, Eb is the energy of the user’sinformation bit, Nt,j,n is the number of traffic type jusers in the target sector in time slot n, �s,j,n is theinterference caused by the mobiles in the surround-ing cells (out-of-cell interference) with flows of typej in time slot n, and �0 is the spectral density of thethermal noise.



Out-of-cell interference �s,j,n is a Gaussian randomvariable; it can be determined by numerical inte-gration [10,13] or through simulations [33]. Averagevalue and variance of the out-of-cell interference fora particular traffic type are dependent on the path lossfunction 10��/10�r�4, where r is the distance of mobileterminal from the base station and � is a Gaussianrandom variable with zero mean and around 8 dBstandard deviation.

If SINR requirement for traffic type k is lowerthan the required threshold, the cell enters the outagecondition for that traffic type. Probability of outagecondition for traffic type k in time slot n (outageprobability) is

Po,k,n D Pr�BERk > TBER,k�

D Pr

J∑

jD1

Sj,n

Sk,n

Nt,j,n∑

iD1

�j,i,n C �s,j,n

Sj,n

>W/Rnb

�Eb/��Tk � �0W/Sk,n

�3�

By including the models for users’ activity and out-of-cell interference, the outage probability for traffictype k is obtained by extending the work from [10]as

Po,k,n DNt,1,n∑i1D0

Ð Ð ÐNt,j,n∑ijD0

Ð Ð ÐNt,J,n∑iJD0

J∏

jD1

(Nt,j,n

ij

)˛

ijj �1 � ˛j�Nt,j,n�ij

ð Q

W/Rnb

�Eb/��Tk

� �0W

Sk�

J∑jD1

ijbjSj,n

Sk,n�

J∑jD1

E��s,j,n�

Sj,n

Sj,n

Sk,n√√√√ J∑jD1

Var��s,j,n�

S2j,n

S2j,n

S2k,n

�4�

where Q�x� D 1p2�

∫ 1x e�y2/2dy. The model that takes

into account the imperfect power control [16,18]could also be used as the basis for Equation (4).However, to limit the complexity and focus mostlyon the handoff dynamics, we do not use this modelin our work.

However, Equation (4) is not suitable to be usedby the scheduler since the scheduler has to determinefuture time slot resource allocation when out-of-cellinterference will be determined by transmissions inother sectors. Therefore, the scheduler has to useaverage out-of-cell interference from the previousframe as the approximation of the future out-of-cell interference. If we denote the average out-of-cell interference from the previous frame as �s,j,Equation (4) becomes

Po,k,n DNt,1,n∑i1D0

Ð Ð ÐNt,j,n∑ijD0

Ð Ð ÐNt,J,n∑iJD0

J∏

jD1

(Nt,j,n

ij

)˛

ijj �1 � ˛j�Nt,j,n�ij

ð Q

W/Rnb

�Eb/��Tk

� �0W

Sk�

J∑jD1

ijbjSj,n

Sk,n�

J∑jD1

E��s,j�

Sj,n

Sj,n

Sk,n√√√√ J∑jD1

Var��s,j�

S2j,n

S2j,n

S2k,n

�5�

This set of J admission equations defines the calladmission system with a statistical QoS bound. Whenthe scheduler checks if new call arrival of class jcan be accommodated in the time slot n, the currentnumbers of admitted users Nt,i,n, i 2 �1 . . . J�, i 6D jare substituted in the admission system. The systemis then solved for J � 1 power ratios and for themaximum admissible number Tj,n of type j users.If Tj,n ½ Nt,j,n C 1 the call can be admitted in slotn. In the rest of the paper, we will refer to thisadmission control as the tier 1 admission control.The difference Cj,n D Tj,n � Nt,j,n is the currentresidual sector capacity within time slot n. In thecase of a single traffic type, the system is simplifiedand the outage probability is calculated with thegiven number of users and compared against thethreshold.

4. Tier 2 Admission Control

Tier 2 admission is executed after the tier 1 admis-sion and its task is to check whether after the call isadmitted in a particular slot n, there is sufficient band-width margin that can accommodate handoff calls thatmay join the sector during the next frame. Therefore,tier 2 admission control has to keep track of the band-width reservation that has to be generated from themobiles in the surrounding sectors. In order to imple-ment the bandwidth reservation to match the handoffarrival rate �h, we create the bandwidth reservationrate in the target sector. The bandwidth reservationrate in the target sector must be created using theentire user population in the first surrounding ring,since it is not known exactly which mobile users willhandoff to the target sector. To this end, every call thatarrives to a sector—either as a new call or a hand-off call—will send a bandwidth reservation value toall the surrounding sectors. When the call leaves thesector, that number is cleared from the host sector, aswell as from all the surrounding sectors. Every basestation maintains the sum of those numbers associatedwith the bandwidth requirement of the call; this sum



is called the bandwidth reservation rate. By analogyto the handoff call arrival rate, the sum of band-width reservation values corresponds to the averagebandwidth reservation rate of a bandwidth reservationprocess .

In the following analysis, we will assume uniformtraffic load in all sectors. Therefore, for a single traffictype (i.e. J D 1, and we will omit index j for clarity),the bandwidth reservation rate in the target sector is

� D 4Ntam �6�

where Nt denotes the average number of calls in thesurrounding sectors and am denotes the bandwidthreservation value sent after the handoff.

In the presence of multiple traffic types, we assumethat bandwidth requirements bj and activity factors ˛j

are known for all traffic types. In order to scale thereservation rates for individual traffic types accord-ing to activity factors and bandwidth requirements,the received sums of reservation values have to bemultiplied by bj˛j. Given that the average numberof users for traffic type j is Nt,j, the total bandwidthreservation rate is equal to

� DJ∑

jD0

Nt,j4am˛jbj �7�

Such creation of total reservation rate means thatadmission threshold for every traffic type has to bedetermined using aggregate bandwidth reservation forhandoff calls. However, reserved bandwidth is notpartitioned but instead, handoff call arrivals of alltraffic types use it in a statistical multiplex. Thisfurther means that narrowband handoff arrivals cantake advantage of wideband handoff calls and there-fore experience lower handoff deteriorating rate thanwideband calls.

In order to achieve sufficient bandwidth reserva-tion for different users’ average dwell times, as wellas for different bounds on the handoff call QoS dete-riorating probability, we must choose the bandwidthreservation values such that the average bandwidthreservation rate is much larger than the handoff callarrival rate �h. In this case we need some regu-lating parameter to determine what portion of thebandwidth reservation rate is needed to achieve therequired QoS. We choose this regulating parameterto be the probability that the number of the arrivalsof the bandwidth reservation process will fit into theresidual capacity of the sector. We call this param-eter the reservation probability, Pr . For example, ifthe unused capacity is small and the reservation rate

is large, the value of reservation probability will besmall. On the other hand, if the reservation rate issmall and the unused bandwidth is moderate, thereservation probability will be large. By fixing thevalue of reservation probability to some thresholdvalue, we get the admission condition that reservesbandwidth for handoff calls. In this case, reservedcapacity will vary according to the changes in reser-vation rate.

Consider the wireless sector at time slot n withvalues of utilization Nt,j,n, and reservation rate �.The result of the tier 1 admission has given theinformation about the residual sector capacity in thetime slot n, which is equal to Cj,n D Tj,n � Nt,j,n.

The tier 2 admission control calculates the sectorutilization when the threshold on reservation proba-bility is reached, that is, when Pr D PrT. Therefore,it has to maintain minimum residual capacity Cmin

j,n DTj,n � Mj,n necessary for future handoffs. Mj,n is thenew limit for the number of admissible calls and itcan be calculated from the following expression:

PrT D e��qDbTj,n�Mj,nc∑

qD0

�q

q!D 1 � 1

�Tj,n � Mj,n�

ð∫ �

0e�xxTj,n�Mj,n dx. �8�

5. Markovian Model for Single Traffic Type

Traffic behavior within single time slot in a sector canbe modeled using one-dimensional Markov chain. Weneed this model in order to analytically determine theimpact of user mobility on the QoS of the connectionsresiding in a sector. Since we analyze the singletraffic type case, we will omit the traffic type indicesfrom expressions. The case of multiple traffic typescan be handled by introducing a multiple-dimensionalMarkov chain; however, the details of this approachwill be omitted for brevity. In our model we assumethe following:

ž The average number of users in each time slot isNt.

ž The probabilities of handoff to any of the surround-ing cells/sectors are equal. Four sectors borderingthe target sector will be referred to as the surround-ing ring in further discussions.

ž The call duration time is exponentially distributedwith parameter .

ž During the process of soft handoff, the mobile ter-minal connects to the new base station as soon as



d = h + m. . . . . .0

λh

1

λ + λh λ + λh λ + λh

Rt

2d

Rt − 1

d Rt d

λh

(Rt − 1)d

λh

T

Td (T + 1)d

Fig. 6. Markov chain for single type of traffic and tier 1 admission control (d D h C ).

its pilot signal power exceeds the handoff thresh-old, and disconnects from the old cell when itspilot’s power drops below the threshold. We willassume that the time from connecting to the basestation or sector X till disconnecting from it isexponentially distributed with parameter h, and wewill refer to it as the cell (sector) dwell time.

ž The sector capacity is not constant but is interfer-ence-limited instead.

The Markov chain that models the number ofcalls in one sector with tier 1 admission controlis presented in Figure 6. The states in the chainrepresent the number of ongoing calls in the targetsector, and the new call arrival rate is represented by�. The threshold state T in Markov chain is imposedby the threshold on outage probability, which ischecked on new call arrivals. Although, in general,the traffic behavior in a single time slot of the TD-CDMA network could be modeled as an infinite sizeMarkov chain, we suggest truncation that retains onlya few states (occupied by handoff calls) beyond thethreshold state.

The arrival rate for the handoff calls �h dependson the finite user population in the surrounding cells.In modeling handoff call arrival rate, we assumethat the neighboring cells operate at the averagenumber of users Nt, regardless of the number ofutilized channels in the target cell. Therefore there arealways Nt potential handoff calls with the ‘arrivingparameter’ h, giving the handoff call arrival rate of

�h D Nth. �9�

The state probabilities of the Markov chain in thiscase are

Pk D1

k!

(� C �h

C h

)k

T∑iD0

1

i!

(� C �h

C h

)i

C(

� C �h

C h

)T Rt∑iDTC1

1

i!

(�h

C h

)i�T

�0 � k � T� �10�

Pk D1

k!

(� C �h

C h

)T (�h

C h

)k�T

T∑iD0

1

i!

(� C �h

C h

)i

C(

� C �h

C h

)T Rt∑iDTC1

1

i!

(�h

C h

)i�T

�T < k < Rt�

The average number of users for this system is

Nt DRt∑

kD0

kPk �11�

The size of the Markov chain Rt corresponds tothe estimated soft capacity of the network in anytime slot. This capacity depends on the admissioncondition. Although tier 1 admission control blocksnew calls if the outage probability is larger thanthe given threshold, handoff events may increasethe sector outage probability above the threshold.Therefore, we will assume that in the steady state,the sector outage probability fluctuates around theaverage value, which is equal to the threshold valuefor new calls. This assumption means that averagevalue of outage probability over all states of Markovchain is equal to the admission threshold value:

Po DRt∑

kD0

Pk

k∑iD0

(ki

)˛i�1 � ˛�k�iQ

ð

W/R

Eb/�� 0W/S � i � E��s�/S

√Var��s�/S2

�12�

where Pk is the Markov chain state probability that kchannels are occupied, while ˛ is the activity factorof the user’s traffic. Average value and variance ofout-of-cell interference �s in a time slot are directlyproportional to the average number of users in thesector within the time slot, that is, E��s�/S D K1Nt

and Var��s�/S2 D K2Nt, where we used K1 and K2

determined in [10].Since the outage probability reaches its threshold

value with T users in the sector, the third relation



between the threshold state T, Markov chain lengthRt, and average number of users Nt is

Po DT∑

iD0

(Ti

)˛i�1 � ˛�T�iQ

ð

W/R

Eb/�� 0W/S � i � E��s�/S

√Var��s�/S2

�13�

Therefore, at the network layer, the TD-CDMA net-work with tier 1 admission control, under uniformtraffic conditions, may be described with the systemof Equations (11), (12), and (13), which can be solvedfor Rt, T, and Nt.

The probability of new call blocking can be definedas the probability that the new call will violate thetier 1 admission condition. Similarly, the probabilityof handoff call QoS deteriorating can be defined asthe probability that the handoff call will cause theoutage condition. These probabilities are given by

PB D Phd DRt∑

kDT

Pk, �14�

Under tier 2 admission control, additional thresholdM is introduced in the Markov chain. The relationshipbetween thresholds M and T is determined by thetier 2 admission equation and under moderate to highloads M < T. This leads to the increased new callblocking probability and smaller handoff deterioratingprobability because the new call blocking probabilityPB and the handoff deteriorating probability Phd ofthe tier 2 admission control becomes

PB DRt∑

kDM

Pk and �15�

Phd DRt∑

kDT

Pk. �16�

6. Simulation Results and Discussion

6.1. Simulation Model

In the simulation, the mobile users are assumed to bemoving on a regular grid of hexagonal cells and themobile users in the wireless cellular system are uni-formly distributed in each sector. The dwell time ofa mobile user in a sector is exponentially distributed.While visiting a sector, a mobile user’s position,

which is represented by a distance parameter anda direction vector from the center of the sector, isassumed to be uniformly distributed over the sectorarea. The position of a mobile user is independentlycalculated by a uniform random number generator.Thus, the moving velocity of a mobile user dependson its dwell time in sectors and the distance betweenthe current sector and the previous sector. When leav-ing the current sector and handoff to a neighboringsector, a mobile user randomly moves to one of thefour adjacent sectors at even chance and its position inthe new sector is recalculated by the random numbergenerator with a new distance parameter and directionvector.

6.2. Simulation Parameters

In the simulation, we assume that �Eb/I0� ½ 5 (forBER < 10�3), W D 1.25 MHz, R1 D 8 kb/s (i.e. b D1 for voice calls), R2 D 16 kb/s (i.e. b D 2 for dataflows), W/R

Eb/� � �0W/S D 30. The call dwell time persector and call duration are exponentially distributedwith average values 100 s (h D 0.01) and 500 s ( D0.002), respectively. The new call arrival rate forvoice calls �1 is varied from 0.15 to 0.3, whichcorresponds to Erlang load in the range of 75 to 150.The new call arrival rate for data flows �2 is variedfrom 0.3 to 0.6, which corresponds to Erlang loadin the range of 150 to 300. The average utilizationper cell can be obtained by multiplying the averageutilization per sector by 3. In the simulation, we setthe bandwidth reservation value am D 0.0395. Thethreshold value of the reservation probability PrT isequal to 0.5. The parameter settings of traffic modelsare shown in Table I.

6.3. Simulation Results

In this section, the simulation results of the proposedscheme are illustrated. The performance of the pro-posed scheme is evaluated by analyzing the average

Table I. Parameter settings of voice and data traffic.

Parameter Value

Voice Data

˛j 0.4 0.8bj 1 2Rj 8 kbps 16 kbpsMean ON period 2.5 s 5 sMean OFF period 3.75 s 1.25 sMax time-out value 2 frames 4 frames



utilization of mobile calls, the throughput of packetsper frame, the handoff call deteriorating probability,the call blocking probability, and the packet droppingprobability for voice and data traffic. For comparisonpurposes, simulations were run with tier 1 admissioncontrol only, as well as with both tiers of admissioncontrol; for simplicity, we will refer to the latter caseas tier 2 only.

The simulation results of average utilization ofvoice and data users per sector are illustrated inFigure 7. Since the calls of all traffic types are statis-tically multiplexed into the system, when more callsof one traffic type are admitted, the utilization of thattraffic type increases while the utilization of the othertype decreases. Hence, the average utilization of voicecalls is proportional to the corresponding arrival rate,but inversely proportional to the arrival rate of dataflows, and vice versa.

Furthermore, the gradient of utilization for voicetraffic is higher than that for data traffic, which isdue to the fact that voice traffic has lower bandwidthrequirement and lower activity factor, and hencelower call blocking probability, than data traffic.

Thus, the number of calls accepted is higher forvoice calls than for data calls. This also explainsthe shape of handoff deteriorating probability shownin Figure 9: owing to different bandwidth require-ments and activity factors, the handoff deteriorat-ing probability of voice traffic (narrowband) is muchlower than that of data traffic (wideband). In otherwords, even though the system does not have suffi-cient bandwidth for data users, it may still admit voiceusers. However, the average utilization is smallerwhen tier 2 admission control is in effect, sincea portion of the total bandwidth is reserved forhandoffs.

Figure 8 illustrates the results of average through-put of packets per frame of voice and data users. Weobserve that throughput is proportional to the numberof users in the system, bandwidth requirement, andactivity factor. Furthermore, we find that the through-put is maximized because in the CDMA system, thecapacity is limited by the SINR instead of the fre-quency channels. To guarantee the outage probabilityunder a specified threshold, the system has to keepthe SINR above a related value. Thus, by holding

Average utilization for voice traffic − tier 1 admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.15

0.2

0.25

0.3λ2

90

110

130

150

170

No.

of v

oice

use

rs

Average utilization for voice traffic − tier 2 admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.15

0.2

0.25

0.3

λ2

6080

100120140160

No.

of v

oice

use

rs

Average utilization for data traffic − tier 1 admission control (simulation)

0.3

0.4

0.5

0.6λ1 0.15

0.20.25

0.3

λ2

40

50

60

70

No.

of d

ata

user

s

Average utilization for data traffic − tier 2admission control (simulation)

0.30.4

0.5

0.6λ1

0.150.2

0.250.3

λ2

35

45

55

65

No.

of d

ata

user

s

Fig. 7. Average utilization of voice and data users, per sector in a TD-CDMA system.



0.3

0.4

0.5

0.6

λ1

0.15

0.2

0.25

0.3λ2

304050607080

Pac

kets

/fram

e

0.3

0.4

0.5

0.6

λ1

0.15

0.2

0.25

0.3

λ2

304050607080

Pac

kets

/fram

e

0.3

0.4

0.5

0.6λ1

0.150.2

0.250.3

λ2

70

9080

100

120

110

Pac

kets

/fram

e

0.30.4

0.5

0.6λ1

0.150.2

0.250.3

λ2

60

80

100

115P

acke

t/fra

me

Throughput for voice traffic − tier 1 admission control (simulation) Throughput for voice traffic − tier 2 admission control (simulation)

Throughput for data traffic − tier 2 admission control (simulation)Throughput for data traffic − tier 1 admission control (simulation)

Fig. 8. Throughput of voice and data users, per sector in a TD-CDMA system.

Handoff deteriorating probability for voice traffic − tier 1admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

0

0.05

0.1

0.15

0.2

Phd

Handoff deteriorating probability for voice traffic − tier 2 admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

00.0020.0040.0060.008

0.010.012

Phd

Handoff deteriorating probability for data traffic − tier 1 admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

00.10.20.30.40.50.6

Phd

Handoff deteriorating probability for data traffic − tier 2 admission control (simulation)

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

0

0.05

0.1

0.15

0.2

Phd

Fig. 9. Handoff deteriorating probability of voice and data users, per sector in a TD-CDMA system.



Call blocking probability for voice traffic − tier 1admission control (simulation)

Call blocking probability for voice traffic − tier 2admission control (simulation)

Call blocking probability for data traffic − tier 1admission control (simulation)

Call blocking probability for data traffic - tier 2admission control (simulation)

0.3

0.4

0.5

0.6

λ10.15

0.20.25

0.3

λ2

0

0.05

0.1

0.15

0.2

Pb

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

0

0.1

0.2

0.3

0.4

0.5

Pb

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

0

0.1

0.2

0.3

0.4

0.5

Pb

0.3

0.4

0.5

0.6

λ1

0.150.2

0.250.3

λ2

0.2

0.3

0.4

0.5

0.6

Pb

Fig. 10. Call blocking probability of voice and data users, per sector in a TD-CDMA system.

Packet dropping probability for voice traffic − tier 1 admission control (simulation)

Packet dropping probability for voice traffic − tier 2 admission control (simulation)

Packet dropping probability for data traffic − tier 1 admission control (simulation)

Packet dropping probability for data traffic − tier 2 admission control (simulation)

0.3

0.4

0.5

0.6

λ10.15

0.20.25

0.3

λ2

0

0.0025

0.005

0.0075

0.01

Pdrop

0.3

0.4

0.5

0.6

λ10.15

0.20.25

0.3

λ2

0

0.001

0.002

0.003

0.004

0.005

Pdrop

0.3

0.4

0.5

0.6

λ10.15

0.20.25

0.3

λ2

0

0.002

0.004

0.006

Pdrop

0.3

0.4

0.5

0.6

λ10.15

0.20.25

0.30

λ2

0.001

0.002

0.003

Pdrop

Fig. 11. Packet dropping probability of voice and data users, per sector in a TD-CDMA system.



the outage probability at the specified threshold, thethroughput of the system can be maximized.

The simulation results for handoff deterioratingprobability and call blocking probability of voice anddata users are shown in Figures 9 and 10, respec-tively. The handoff deteriorating probability of eachtraffic type is proportional to its handoff call arrivalrate and the handoff call arrival rate is proportionalto the sector utilization and user mobility. Hence,under high utilizations and high user mobility, theprobability that handoff will cause the outage in thesector is high. Figures 7 and 9 show this relation-ship. Moreover, we observe that although the callblocking probability of both traffic types in the tier 2admission is comparatively higher than in the tier 1admission, the handoff deteriorating probability of thetier 2 admission is remarkably improved. This showsthat the tier 2 admission can notably improve the QoSfor voice calls and data flows.

The simulation results for packet dropping proba-bility of voice and data users are shown in Figure 11.A packet corresponds to the amount of informationsent during one time slot using one code. We definethe packet dropping probability as the probabilityof packets being dropped when they are lost duringtransmission (due to the outage condition) and cannotbe retransmitted because of time-out. The results inthe tier 1 admission control have shown that the voicetraffic has higher packet dropping probability thandata users due to the tighter time-out values. How-ever, we observe that the situation is much improvedin the tier 2 admission. The improvement of packetdropping probability is around 50% for both traffictypes.

7. Conclusion

In this paper we have proposed a novel admissionand scheduling scheme for TD-CDMA systems. Theadmission scheme maximizes the throughput whilekeeping the outage probability for new call arrivalsunder the specified threshold. The proposed schemealso largely alleviates the impact of handoffs on celloutage conditions. This is achieved by reserving thebandwidth in the surrounding sectors for potentialhandoffs. The admission condition is checked onlyupon new call arrival into the network, so that theadmitted call can freely execute handoffs using thereserved bandwidth. Furthermore, in order to limitcomplexity of the admission algorithm, the schedulertries, whenever possible, to schedule connections withthe same traffic type within the same time slot.

Simulation results show that the tier 2 admission hasmuch smaller handoff deteriorating probability thanthe tier 1 admission while keeping the call blockingprobability at a reasonable level. Thus, this approachprovides satisfactory QoS for voice calls and dataflows.

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Authors’ Biographies

Jelena Misic received her Ph.D.degree in computer engineering in1993, from University of Belgrade,Yugoslavia. She joined Hong KongUniversity of Science and Technol-ogy in 1995 where she is assis-tant professor. Her current researchinterest includes wireless networksand mobile computing. She is themember of IEEE Computer Society.

Yat-Kwan Tang received the B. Eng. degree in computerscience in 1999, from the Hong Kong University of Scienceand Technology (HKUST), Hong Kong. He is currentlyfinalizing the M.Phil. thesis on wireless communicationat HKUST. His current research interest includes resourcemanagement in wireless networks.

Vojislav B. Misic obtained hisPh.D. in computer science fromUniversity of Belgrade, Yugoslavia.Since 1998 he is with the Depart-ment of Information and SystemsManagement of the Hong KongUniversity of Science and Technol-ogy, Hong Kong. His research inter-ests have recently been shifting fromsystems and software modeling (inparticular, measurement and estima-

tion) to modeling and performance evaluation of wirelessnetworks.


admission control in td-cdma networks

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