Traffic management in a multicode CDMA system supporting soft handoffs

52 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 51, NO. 1, JANUARY 2002

Traffic Management in a Multicode CDMA SystemSupporting Soft Handoffs

Duk Kyung Kim, Member, IEEE,and Dan Keun Sung, Senior Member, IEEE

Abstract—A traffic management scheme is proposed in amulticode code-division multiple-access system supporting softhandoff that uses guard channels and a queue for real-time traffic.Preemptive queue control gives priority to queued handoff calls.Handoff traffic is derived as a function of the new call arrivalrate, the size of the soft handoff region, mobile speed, the new callblocking probability, and the handoff failure probability. Systemperformance with types of calls is analyzed by introducing aconcept of effective channel. The effects of the number of guardchannels, the number of effective channels, system capacity, andother factors are numerically investigated. The effectiveness ofthe proposed queue control scheme is also observed in terms ofhandoff processing delay.

Index Terms—Multicode code division multiple access (CDMA),preemptive queue control, soft handoff.

I. INTRODUCTION

FUTURE wireless communication systems, including Inter-national Mobile Telecommunication (IMT)-2000, are ex-

pected to accommodate various services with different bit ratesand different qualities of service (QoSs). Code-division mul-tiple access (CDMA) can be a promising technology for suchsystems. A multicode (MC)-CDMA system [1] is especiallysuitable for flexible multimedia transmission. The MC-CDMAsystem splits a data signal into multiple basic rate streams andspreads each stream with a different spreading code. CDMA hassome unique features that are not shared by time-division mul-tiple access (TDMA). CDMA is interference-limited and canprovide soft handoff [2]. Soft handoff can increase system ca-pacity because interference can be reduced by transmitting adata signal at the minimum power level required by base sta-tions (BSs). Moreover, soft handoff is processed by amake-be-fore-breakmethod, which permits handoff delay of no longerthan the residual time in a soft handoff region for real-timetraffic. However, soft handoff increases handoff traffic and tendsto hold a channel for a longer time than hard handoff.

Traffic management is an important issue in wireless com-munication systems, and many analytical approaches havebeen proposed, especially for handoff problems. Previousstudies [3]–[5] have been primarily based on hard handoff inTDMA systems. Since soft and hard handoffs are considerablydifferent, a new approach is required for modeling and analysis

Manuscript received November 11, 1998; revised August 10, 2001.D. K. Kim was with Korea Advanced Institute of Science and Technology

(KAIST), 305-701 Taejon, Korea. He is now with SK Telecom, 463-020Kyunggi-do, Korea (e-mail: [email protected]).

D. K. Sung is with Korea Advanced Institute of Science and Technology(KAIST), 305-701 Taejon, Korea (e-mail: [email protected]).

Publisher Item Identifier S 0018-9545(02)00420-6.

of soft handoff. Wu and Lin [6] considered CDMA systemsbut simply assumed independence between new call arrivalsand handoff call attempts. Soft handoff was not consideredin their studies. Suet al. [7] analyzed the performance of asoft handoff system with a small capacity and a single type oftraffic. The authors [8] previously derived handoff traffic andintroduced a methodology to obtain the capacity increase dueto soft handoff. Kimet al. [9] considered multiple-chip-ratedirect-sequence (DS)-CDMA systems supporting multiclassservices. However, these works were based on nonprioritizedsystems. This paper further extends the previous works to aprioritized multicode CDMA system.

Thus far, analysis of handoff problems has mainly focusedon a single type of traffic, and voice and data through iden-tical channels have been included for handoff analysis [6], [10],[11]. Epstein and Schwartz [12] considered wide-band trafficbut assumed that this traffic did not require handoff. In gen-eral, high-rate traffic suffers from more severe call blocking andhandoff failure than low-rate traffic. High-rate traffic, however,usually requires high QoS, such as low call blocking proba-bility and low handoff failure probability. Traffic management,including handoff management, needs to be provided to accom-modate high-quality and high-rate traffic in CDMA systems.

Traffic management can be classified into three methods:1) a guard channel method, 2) a queue method, and 3) aqueue/guard method. The guard channel method may wasteradio resources under a light traffic load, while the queuemethod may not be suitable for real-time traffic due to a delayproblem. In the queue/guard method, guard channels havebeen used for real-time traffic, such as voice, and a queue hasbeen used for delay-insensitive data traffic. If soft handoff isavailable, it can mitigate the delay problem of real-time handofftraffic. Thus, different types of traffic management need to beadopted in soft handoff systems.

Soft handoff can be modeled by introducing anoverlap re-gion in addition to a soft handoff region. Applying a flow-basedmobility model in a square cell structure, the handoff attemptrate is derived as a function of the new call arrival rate, cellsize, the size of the soft handoff region, mobile speed, the newcall blocking probability, and the handoff failure probability.The channel holding time is also obtained by considering softhandoff. In an MC-CDMA system, the number of effectivechannels are newly obtained in terms of the number of requiredcodes, the processing gain, and the required . A trafficmanagement scheme using guard channels and a queue isproposed. Guard channels are commonly accessed by handoffcalls and high-quality new calls, and a preemptive queuecontrol is newly proposed to give a priority to the queued

0018-9545/02$17.00 © 2002 IEEE

KIM AND SUNG: TRAFFIC MANAGEMENT IN MULTICODE CDMA SYSTEM 53

Fig. 1. CDMA soft handoff: illustration of regions and boundaries.

handoff calls. The new call blocking probability and handofffailure probability are derived as performance measures usinga recursive calculation. Two traffic types are considered as aspecial example, and a system with preemptive queue control iscompared with a system without preemptive queue control byvarying the number of guard channels, the number of effectivechannels, system capacity, and the ratio of the traffic load oftwo types of traffic. The effectiveness of preemptive queuecontrol is also investigated in view of handoff processing delay.

II. PRELIMINARY

A. Soft Handoff Modeling

When the pilot signal from a BS is stronger than the thresholdvalue T ADD, a new link to the BS is established while main-taining the existing link. In this case, the call is said to be insofthandoff. It is here assumed that a mobile station (MS) can bein soft handoff with two strong BSs. If the pilot signal from athird BS becomes stronger than either of the two strong pilot sig-nals, another handoff occurs and the network drops the weakestlink. When the pilot signal from either the old BS or the newBS weakens to below TDROP, the bad connection is releasedand only a single good connection is maintained after that time.Since an MS in soft handoff is power-controlled by the BS,which requires less power, soft handoff increases system ca-pacity by reducing interference. On the other hand, soft handoffincreases handoff traffic by using multiple channels and also in-

creases signaling traffic, network processing, and the amount ofradio equipment required at the BSs [2], [13].

Soft handoff regions may vary according to handoff-relatedparameters, such as TADD and T DROP, and handoff is alsoaffected by radio propagation characteristics and the required

value. However, in order to take a mathematical approachto performance analysis of CDMA systems, some degree of sim-plification is necessary. Fig. 1 illustrates an example of simpli-fied CDMA systems based on a square cell structure, where theside length of a square cell is 2. For geometrical simplicity,three regions are assumed for the analysis of soft handoff: 1)the inner cellregion, 2) the soft handoff region (SR), and 3) theouter cellregion. They are bounded by aninner boundaryandan outer boundary. The region bounded by a cell boundary iscalled anordinary cell. Even when an MS is in soft handoff, an-other handoff can occur if the pilot signal from a third BS hap-pens to become stronger than either of two existing pilot signals.Thus, anoverlap regionis newly introduced, which is the over-lapped region between two adjacent outer cells. The width of anoverlap region is 2. In this cell structure, the SR is subdividedinto four overlap regions. The region excluding the SR in theordinary cell is called a nonsoft handoff region (NSR).

B. Handoff Traffic

Soft handoff was characterized in [8], and its traffic has beenderived in [9] for types of traffic. New type calls are as-sumed to arrive uniformly in the cell according to a Poisson


process with a rate of , which is the new call arrival rateper unit area. The call holding time and the cell residualtime are exponentially distributed with means of 1and 1 , respectively.

When a new call arrives in the NSR, it is inherently a new callto the corresponding BS. However, if a new call arrives in theSR, it can be considered as a new call to one BS and as a handoffcall to the other BS, based on the received pilot signal powers atthe point. Assuming identical channel propagation throughoutthe service area and identical cell structure as in Fig. 1, it can besaid that half of the new calls arriving in the SR are consideredas new calls to the BS under consideration and the remaininghalf of them are handoff calls to that BS. Then, the new callarrival rate per cell is obtained by

(1)

where is the area of a cell and is given by

(2)

In (1), reflects the area of an NSR and half of the areaof an SR. Handoff attempts occur in the following two cases.First, half of the new calls that arrive in the SR attempt handoffsto the BS under consideration, whose rate is denoted as.However, some of them are not counted as handoff calls if theyare not accepted as new calls to neighboring cells. This is basedon the assumption that both radio links are necessary for softhandoff. Secondly, handoffs occur due to boundary crossingswith a rate of . Then, the handoff attempt rate per cellis given by

(3)

Letting be the new call blocking probability of typetraffic, is expressed as

(4)

where is the area of an SR and is given by

A flow-based model is used to obtain . CommunicatingMSs are assumed to be uniformly distributed, and the move-ment direction of each MS relative to the boundary is uniformlydistributed on . Let and denote the density ofcommunicating type MSs per unit area, the average speed ofa type MS, and the perimeter length of the area, respectively.Then, is given by [14]

(5)

where is the length of an outer cell and is expressed as

The area of an outer cell is given by

(6)

Then, is expressed as

(7)

where and are the successful new call arrival rate andthe successful handoff attempt rate of typetraffic, respectively,and are given by

(8)

(9)

with being the handoff failure probability of typetraffic.When a call is terminated or a communicating user leaves

the outer cell, the occupied channel is released. Thus, channelholding time can be expressed as

(10)

where is the residual time in an outer cell with a meanof 1 . Assuming that is exponentially distributed, themean channel holding time 1 is obtained by

(11)

Now, let us obtain the mean channel holding times of a cell,an outer cell, and an overlap region. When is sim-plified from [3] and [4] as

(12)

Since for and , i.e.,(12) is equal to (5). Hence, is expressed as

(13)

If denotes the radius of an equivalent circle with the samearea as a cell with a side length of 2 is obtained by

(14)

Similarly, the radius for an outer cell is expressed as

Assuming that the cell residual time is proportional to the radiusof equivalent circle [15], the mean residual time 1 in anouter cell is given by

(15)

In case of an overlap region, it has a quite different shapefrom the ordinary cell and outer cell. In general, the residualtime is expected to be shorter in a longish shape like the overlapregion than in a hexagonal or circular shape with the same area.Therefore, a correction factor is needed to reflect the impactof the shape. Thus, the equivalent radius is modified as

(16)


where the area of an overlap region is given by

(17)

The mean overlap residual time can be obtained by sub-stituting for in (15).

C. A Simple Call Admission Criteria

In an MC-CDMA system, all data signals are transmitted atan integer multiple of fixed basic rates and are spread to the samespread-spectrum bandwidth with the same processing gain. TheMC-CDMA system first splits a data signal into a multiple ofbasic rate streams and then spreads each basic rate stream witha different spreading code. Using a subcode concatenation tech-nique, the orthogonality can be preserved, and accordingly, theself-interference can be eliminated among the split signals in thesame data signal.

Type traffic requires codes, and the received power ofthe type traffic is at its home BS. It should be noted herethat differs for different traffic types and tends to be smallerfor the traffic requiring a larger value of due to orthogonalityamong split signals. of type trafficshould be greater than or equal to its targetfor normal op-eration. This can be expressed, with perfect power control, as[16]–[18]

(18)

where is the basic data rate, is the chip rate, is thebackground noise, and is the other-cell interference at thegiven BS. Since the reverse link is more likely to limit the ca-pacity of CDMA systems than the forward link, is con-sidered on the reverse link. The definition in (18) is based on theGaussian density approximation to evaluate bit error probabilityin an asynchronous binary phase-shift keying (BPSK)-modu-lated DS-CDMA system when the channel noise process is as-sumed to be an additive white Gaussian noise (AWGN) processand rectangular chip waveforms are assumed [19]. can beused as a QoS measure, and the target can be deter-mined by considering the bit error probability and the adoptederror correction code, such as forward error correction and au-tomatic repeat request.

Precisely speaking, in (18) denotes the number of typetraffic whose home BS is that under consideration at the mea-surement point and the received power of such traffic is power-controlled to be . If the traffic in SR is power-controlled byother BSs even though it is accepted by the BS under consider-ation, it is considered to generate interference to the BS underconsideration, and counts such interference. However, inSR, particularly around the cell boundaries, the home BS selec-tion may occur frequently, and therefore, represents the in-stantaneous number of typetraffic. This makes the analyticalapproach very complicated, and probably impractical for realenvironments, due to the movement of MS in SR and channelpropagation. One possible and simplified call admission crite-rion is to use the lower bound for , i.e., now repre-

sents not the instantaneous number but the number of acceptedtype traffic, which includes both new calls and handoff calls.This kind of simple call admission will be used for performancecomparison of different handoff management schemes.

Letting for all first-order simulta-neous equations can be obtained from (18). After some manip-ulation, the relationship among the required received powerof type traffic can be found, and then the admissible boundarycan be found so that the required received powerof typetraffic does not exceed the allowed maximum value forall [20]. From this admissible boundary, the admissible setcan be expressed as , satisfying [17]

(19)

where is the processing gain

and is given as

(20)

If we look closely at (19), the left side is the weighted sum offor . The weighting factor is denoted as

(21)

and is called hereafter the number of effective channels of typetraffic. Different from the concept of channels in time- or fre-

quency-division multiple access, is a positive real number(not an integer) that depends on the number of required codes,the target , and processing gain. Letting in(19), the single-cell system capacity can be expressed as

(22)

Since the capacity of a multiple-cell system is inversely pro-portional to , where denotes the average total interfer-ence from other-cell users normalized by the average number ofusers per cell [13], the capacity of a multiple cell system isexpressed as

(23)

is zero in a single-cell system. , actually, can be sta-tistically expressed. Hence, system capacityrepresents thetotal acceptable number of effective channels that maintain theoutage probability at less than a given threshold.

III. H ANDOFF MANAGEMENT AND ITS PERFORMANCE

ANALYSIS

A. New Handoff Management Scheme

Since non-real-time traffic is insensitive to delay, the trafficis usually managed using a queue with a low priority over


real-time traffic. Hence, this paper focuses on real-time traffic.different types of real-time traffic are considered. Without

a special control scheme, high-rate traffic suffers from moreblockings and handoff failures than low-rate traffic. To avoidsevere performance degradation in call blocking and handofffailure, many traffic management schemes have been proposedfor such high-rate traffic. A guard channel method is suitablefor real-time traffic. However, the required number of guardchannels needs to be minimized to prevent excessive waste ofradio resource. guard channels are here commonly accessedby handoff calls of all types of traffic and by new calls requiringless blocking. is used as an indicator function representingwhether new type calls can access guard channels or not

if type traffic can access guard channelsotherwise

The arrival of a new call with or is blocked if the sumof effective channels of communicating MSs after accepting thenew call exceeds or , respectively.

Soft handoff permits a handoff delay of no longer than theresidual time in an overlap region for real-time traffic. Thus, aqueueing method can be used for real-time traffic. A preemp-tive queue control scheme is proposed to give priority to queuedhandoff calls, which preempts released channels until sufficientidle channels become available. This queue control does not re-serve channels at all times but reserves channels on demand.Thus, this queue control reduces waste of radio resources andenables the queued handoff calls to be served faster than non-preemptive queue control.

is an indicator function for a preemptive queue control

if type traffic uses preemptive controlotherwise

A handoff call with is blocked if sufficient idle chan-nels are not available on attempt. A handoff call with isqueued if the sum of communicating effective channels exceeds

after the attempt is accepted. If queued, the handoff call re-serves all the remaining idle channels and waits until sufficientidle channels are preempted.

B. Handoff Analysis

State is expressed as

where is the number of type calls and is defined asfollows:

if no call is queued in the-th positionif a type call

is queued in theth position.

The system has guard channels and a queue of size. isthe last queue position with and .

1) Transition:a) New call arrival: When new type calls with

arrive at a rate of , the state is changed from to if

and . For , the state is changed from toif

and .b) Handoff arrival: When type calls attempt handoffs

at a rate of , the state is changed as follows:

if and

else if and

c) Channel release:The occupied channels are releasedat a rate of . If and , the state is changedfrom to . If and is defined as

the state is changed as follows:

for

...

and the values of and are changed to zero if.

d) Queue defection:A call in the th position in a queuedefects when a call is terminated or when it leaves an overlapregion. Thus, the queue defection rate of type calls isgiven as

(24)

Queue defections occur at a rate of only if and thestate is changed as follows:

2) Performance Measures:The state probability vectorcan be obtained by numerically solving with a con-dition of


is the infinitesimal generator whose terms can be obtainedfrom the previous subsection. Blocking probabilities andhandoff failure probabilities are obtained as performancemeasures.

For is expressed as

(25)

where

For is modified as

The term is included because new calls are blockedif a queue is not empty. This term reflects preemptive queuecontrol.

For , handoff calls are not queued. Thus, is cal-culated from

(26)

where

In this case, handoff calls fail if sufficient idle channels are notavailable upon attempts.

For is obtained by

(27)

where is the probability that a queue is full and the proba-bility is given by

Handoff calls attempted in a state of a full queue can be sup-ported as long as they remain in an overlap region. Since someof these calls may be successfully completed before handofffailure, can be given by

(28)

where an MS returns back to its old BS with a probability of.Finally, (27) can be rewritten as

(29)

where .and are related to each other, and therefore

they are obtained recursively as shown in Fig. 2. Let anddenote the previous value and the newly calculated value

Fig. 2. Calculation flow to obtain� ; P ; andP .

of , respectively. IncDev in Fig. 2 is the incremental devia-tion of and is given by

(30)

IV. NUMERICAL EXAMPLES

Two different types of real-time traffic are considered for nu-merical examples. and are set to zero and and areset to one, respectively. The performance of the proposed traffic


TABLE ISYSTEM PARAMETERS

management scheme is evaluated in terms of handoff failure andhandoff processing delay, and the effects of the number of guardchannels, system capacity, and other factors are also investigatedin this section. Table I shows the system parameters consideredin this paper unless otherwise stated. The number of effectivechannels of both traffic types are calculated from (21) as

The single-cell system capacity is 92.1664 from (22).The factor was previously obtained in [8] for a single type oftraffic. When the path-loss exponent and the standard de-viation of shadowing 8 dB, is calculated to be 1.038, andtherefore comes to 45.24. Although other-cellinterference may be different according to traffic type [20], thefactor for a single type of traffic is adopted here for simplicity.

A microcell environment is considered; km,m, km/h, and s. isset to

(31)

The correction factor in (16) and the factor in (28) are setto one and zero, respectively, for numerical examples.

A. Analysis

The performance analysis for a queue/guard method withpreemptive queue control is similar to the analysis for aqueue/guard method without preemptive queue control, andthus the latter is omitted.

The system performance can be analyzed by using abirth–death process. The state is now simply modified as

where denotes the number of type 1 calls in the system,is thenumber of communicating type 2 calls, andis the number oftype 2 calls in a queue. Detailed state transitions are given by theequations at the bottom of the next page, whereis expressedas

The sum of steady-state probabilities satisfies

(32)

where denotes the greatest integer less than or equal to.

Fig. 3. The probabilitiesP ; P ; P ; andP versus� (C =

7; � = 1=4; andQ = 2).

From (25), the blocking probabilities of type 1 calls andtype 2 calls are given by

(33)

(34)

where

From (26), the handoff failure probability of type 1 calls is ex-pressed as

(35)

where

From (27), the handoff failure probability of type 2 calls is givenby

(36)

where is obtained as

(37)

B. Numerical Results

Fig. 3 shows the probabilities andversus , where and . Since new type 2calls can access seven guard channels, is approximatelythe same as . Handoff failures are reduced for type 2 callsat the expense of increased handoff failures of type 1 callsbecause preemptive queue control reserves released channelsfor queued calls. As traffic increases, the difference between

and becomes narrower because channels are releasedmore frequently, and therefore the preemptive queue controlbecomes more effective.


Fig. 4. The mean waiting time in a queueW versus� (C = 7; � = 1=4;andQ = 1).

Fig. 7. The probabilitiesP andP versus� (c = 2; C = 2; � =

1=4; andQ = 2).

Fig. 4 shows the mean waiting time in a queue as a mea-sure of handoff processing delay, whereis set to one. The de-tailed derivation is described in Appendix A. Preemptive queuecontrol reduces compared with nonpreemptive queue con-trol, and the amount of reduction becomes larger as in-creases. Even though the system has insufficient idle channelsupon handoff attempts, handoff calls are supported during,

Fig. 5. The probabilitiesP andP versus� (C = 7; � = 1; andQ = 2).

Fig. 6. The probabilitiesP andP versus� (C = 4; � = 1=4; andQ = 2).

on average, in an overlap region. Thus, an additional handoffmargin is needed for these handoff calls, and this margin maydegrade the system performance. Since preemptive queue con-trol enables fast handoff, it can reduce the additional margincompared with nonpreemptive queue control.

Fig. 5 shows the handoff failure probabilities versuswhen is increased to one. The blocking probabilities are notshown in the figure (and in the following figures) because they

and

and

and


Fig. 8. Transition diagram for preemptive queue control.

Fig. 9. Transition diagram for nonpreemptive queue control.

are too close to discriminate, and we are focusing on handofffailure probabilities to compare the performances of preemp-tive and nonpreemptive queue controls. A queued handoff callcan be served if a type 2 call is released or if a sufficient numberof channels occupied by type 1 calls are released. Therefore, fora large value of , since the arrival and release of type 1 trafficoccur less frequently, the effectiveness of preemptive queue con-trol decreases, i.e., slight reduction in can be obtained at theexpense of relatively large increase in .

Fig. 6 illustrates the handoff failure probabilities when isreduced to four. The gap between two handoff failure probabil-ities becomes narrower as increases but is still wider thanthe result in Fig. 3. To get a desired performance, the valueneeds to be determined according to the traffic load and the re-quired QoS.

Fig.7 shows the handoff failure probabilities when andare reduced to two. now comes to 3.877. With a small

is approximately the same as even withoutpreemptive queue control. Preemptive queue control constrains

to be less than , and the difference betweenand increases as increases. This suggests that therequired number of guard channels for type 2 calls can bereduced by using the preemptive queue control. Thus, wasteof radio resources can be reduced. By increasing the value of

instead of reducing the values of and , similar resultscan be obtained because more arrivals and departures occur fora larger value of .

V. CONCLUSION

To manage call and handoff traffic with a high rate and highquality, a new traffic-management scheme was proposed basedon guard channels and a queue. The queue can be used even forreal-time traffic in CDMA systems that support soft handoffs.Preemptive queue control was proposed for fewer handoff fail-ures and for faster handoff processing. For the analysis of theproposed traffic-management scheme, soft handoff was newlymodeled and handoff traffic was derived as a function of thenew call arrival rate, the handoff failure probability, the new callblocking probability, cell size, the size of the soft handoff region,

and mobile speed. By using a concept of effective channel in anMC-CDMA system, performance was evaluated in terms of thehandoff failure probability and the handoff processing delay.

From the viewpoint of handoff failure, the preemptive queuecontrol becomes more effective as

1) traffic load increases,2) high-rate traffic decreases,3) system capacity increases.

for was decreased by approximately 10% com-pared with that for , and a value larger than two rarelyaffected . Preemptive queue control reduced handoff pro-cessing delay as well as handoff failures. This fast handoff pro-cessing can reduce the handoff margin, which is required to sup-port handoff calls during , on average, in an overlap regionwhen the system has an insufficient number of idle channels.Accordingly, preemptive queue control can reduce the wasteof radio resources and improve system performance. By prop-erly controlling the number of guard channels for each traffictype and the number of commonly accessible guard channelsaccording to traffic characteristics and system parameters, thesystem can greatly reduce the waste of radio resource and pro-vide a better performance.

APPENDIX IMEAN WAITING TIME IN A QUEUE

The mean waiting time in a queue is derived for .First, when preemptive queue control is used, there are no ar-rivals after queueing. Thus, transitions occur as shown in Fig. 8,where is . The mean residual time instate is given as

(38)

and are the probabilities that statechanges to and to , respectively. They areobtained by

(39)

(40)


Then, the mean queueing time of a queued call in statesatisfies

...

Using the relation , the above equa-tions are simply rewritten as

...

can be obtained by solving the above equations for. If is less than can be simply written as

Finally, can be obtained by

(41)

where is the probability that a queue is not empty.When the preemptive queue control is not used, only type 2

calls are blocked after the queue is occupied, but type 1 calls areaccepted if more than effective channels are available uponarrivals. Type 1 handoff calls are accepted if less than or equal to

effective channels are available upon attempts. Fig. 9 showsa state transition diagram, whereisand is . In addition toand , the probability for state transition from

to is newly introduced. is given as

(42)

and the probabilities and are ob-tained by

(43)

(44)

only if (45)

Then, satisfies the following simplified equations:

...

...

If , then the value needs to be reassigned as

After obtaining by solving the above equations, canbe obtained from (41).

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[17] D. K. Kim and D. K. Sung, “Power allocation and capacity of anMC-CDMA system supporting heterogeneous CBR and ON-OFFtraffics,” in Proc. MoMuC, 1997, pp. 201–204.

[18] S. J. Lee, H. W. Lee, and D. K. Sung, “Capacity calculation inDS-CDMA systems supporting multi-class services,” inProc. PIMRC,1997, pp. 297–301.

[19] F. Adachi and D. K. Kim, “Interference suppression factor in DS-CDMAsystems,”Electron. Lett., vol. 35, pp. 2176–2177, Dec. 1999.

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Duk Kyung Kim (S’93–M’00) received the B.S de-gree in electrical engineering from Yonsei University,Seoul, Korea, in 1992 and the M.S. and Ph.D. de-grees from the Korea Advanced Institute of Scienceand Technology (KAIST), Taejon, in 1994 and 1999,respectively.

From 1999 to 2000, he was a PostdoctoralResearcher at the Wireless Laboratories, NTTDoCoMo, Japan. Currently, he is working at R&Dcenter, SK Telecom, Korea, and is involved in thestandardization in 3GPP and also in 4G system

development in SK Telecom. He was interested in asynchronous transfer mode(ATM) and ATM-based personal communication service (PCS) networks. Hisresearch interests now include system performance evaluation at link/systemlevel, handoff modeling/management, power control, and multimedia provisionin the next generation wireless systems.

Dan Keun Sung(S’80–M’86–SM’00) received theB.S. degree in electronics engineering from SeoulNational University, Seoul, Korea, in 1975 and theM.S. and Ph.D. degrees in electrical and computerengineering from the University of Texas at Austinin 1982 and 1986, respectively.

From May 1977 to July 1980, he was a ResearchEngineer with the Electronics and Telecommunica-tions Research Institute, where he had been engagedin various projects including the developmentof an electronic switching system. In 1986, he

joined the faculty of Korea Advanced Institute of Science and Technology(KAIST), Taejon, where he is currently Professor with the Department ofElectrical Engineering and Computer Science. He was Director of the SatelliteTechnology Research Center (SaTReC) of KAIST from 1996 to 1999. He isEditor of IEEE COMMUNICATIONS MAGAZINE. He is also Editor of theJournalof Communications and Networks. He is currently Vice Chair of ICC 2002Symposium on Global Service Portability & Infrastructure for Next GenerationVirtual Home & Office Environments and Program Cochair of Globecom 2002Symposium on Service Infrastructure for Virtual Enterprise Environments.His research interests include mobile communication systems and networks,high-speed networks, next generation IP-based networks, traffic control inwireless and wireline networks, signaling networks, intelligent networks,performance and reliability of communication systems, and microsatellites. Hehas published more than 250 papers in journals and conferences and has filedmore than 80 patents or patents pending.

Prof. Sung is a member of IEICE and KICS. He is also a member of PhiKappa Phi and Tau Beta Pi.

Documents

Traffic management in a multicode CDMA system supporting soft handoffs