Baltzer JournalsA Call Admission and Control Scheme forQuality-of-Service (QoS) Provisioning in NextGeneration Wireless Networks �R. Jayaram, N. K. Kakani and S. K. Das Sanjoy K. SenCenter for Research in Wireless Computing (CReW) Wireless Systems Architecture (2S51)Department of Computer Science Nortel NetworksUniversity of North Texas 2201 Lakeside BoulevardP.O. Box 311366 Richardson, TX 75082 USADenton, TX 76203-1366, USAE-mail: {jayaram,naveen,das}@cs.unt.edu E-mail: sanjoy@nortelnetworks.comAbstractWe propose a framework for quality-of-service (QoS) provisioning for multimedia servicesin next generation wireless access networks. This framework aims at providing a di�er-entiated treatment to multimedia tra�c ows at the link layer, which can be broadlyclassi�ed as real-time (or delay-sensitive) and non-real-time (or delay-tolerant). Variousnovel schemes are proposed to support the di�erential treatment and guarantee QoS.These schemes include bandwidth compaction, channel reservation and degradation, withthe help of which a call admission and control algorithm is developed.The performance of the proposed framework is captured through analytical modelingand simulation experiments. Analytically, the average carried tra�c and the worst casebu�er requirements for real-time and non-real-time calls are estimated. Simulation resultsshow upto 21% improvement in the admission probability of real-time calls and upto 17%improvement in the admission probability of non-real-time calls, when various call con-trol techniques like bandwidth compaction are employed. Using our channel reservationtechnique, we observe a 12% improvement in the call admission probability compared toanother scheme proposed in the literature.1 IntroductionOver the last decade there has been a rapid growth of wireless communication technology.Voice communication over wireless links using cellular phones has matured and becomea signi�cant feature of communications today. Alongside, portable computing devicessuch as notebook computers and personal digital assistants (PDA) have emerged, as aresult of which such applications as electronic mail and calendar/diary programs arebeing provided to mobile or roving users. Observing this trend, it can be predicted thatthe next generation of tra�c in high-speed wireless networks will be mostly generatedby personal multimedia applications including fax, news-on-demand, video-on-demand,WWW browsing, and traveler information systems. For multimedia tra�c (voice, video,�This work is partially supported by Texas Advanced Research Program (TARP-003594-013) andNortel Networks, Richardson, Texas.

Jayaram et.al /QoS Provisioning in Wireless Networks 2and data) to be supported successfully, it is necessary to provide quality-of-service (QoS)guarantees between the end-systems.The QoS provisioning means that the multimedia tra�c should get predictable servicefrom the available resources in the communication system. Typical resources are CPUtime (for the communication software to execute) and network bandwidth. The communi-cation software must also guarantee an acceptable end-to-end delay and maximum delayjitter, i.e., maximum allowed variance in the arrival of data at the destination. In mostcases, QoS requirements are speci�ed by the 3-tuple : hbandwidth; delay; reliabilityi.The QoS provisioning problem for multimedia tra�c in non-wireless networks such asbroadband wire-line networks (e.g., B-ISDN) has been extensively studied (for a good in-troduction to the relevant issues, refer to [Kur93]). The ongoing work in this �eld mainlyconcentrates on the problems of bandwidth management and switch-based scheduling toprovide deterministic guarantees on end-to-end delay, throughput and packet losses.However, the two major di�erences between wire-line and wireless networks are due tolink characteristics and mobility [NSA96]. The broadband wire-line network transmissionlinks are characterized by high transmission rates (in the order of Gbps) and very low errorrates. In contrast, wireless links have a much smaller transmission rate (Kbps-Mbps) anda much higher error rate. For example, the commercially available wide area wireless datanetworks such as ARDIS or Mobitex o�er a channel rate of 8Kbps - 2Mbps and similarlocal area wireless networks such as Motorola's Altair-II o�er about 6 Mbps [NT95].Additionally, wireless links experience losses due to multipath dispersion and Rayleighfading. The second major di�erence between the two networks is the user mobility.In wire-line networks, the user-network-interface (UNI) remains �xed throughout theduration of a connection whereas the UNI in a wireless environment keeps on changingthroughout the connection. Therefore, it is necessary to re-design or revise the usual QoSprovisioning techniques for wireless networks.Figure 1 shows the characteristics of various tra�c types in wireless networks, in termsof the bandwidth usage and typical tolerable delay. Since the tra�c varies signi�cantlywithin a wide range of parameters, the QoS provisioning becomes even more challenging.From this viewpoint, a multimedia tra�c can be broadly classi�ed as real-time and non-real-time [AN95]. Real-time tra�c (e.g., video and voice) is highly delay sensitive, whilenon-real-time tra�c (e.g., TCP packets and text data transfers) can tolerate large delays.Multimedia tra�c is also bursty and stream-oriented in nature, and keeps a long and con-tinuous load on the network. Existing public data networks such as cellular digital packetdata (CDPD), general packet radio service (GPRS), and high speed circuit switched data(HSCSD) utilize the unused voice capacity to support low-priority, non-real-time data.In case of scarcity of available bandwidth, the transmitted data packets are bu�ered orsuitable ow control techniques are used leading to an increase in the transmission delay.In spite of the recent auction of 1850-2000 MHz band by the FCC for personal com-munication services (PCS) users, the bandwidth is still the major bottleneck in mostreal-time multimedia services which can substantially di�er in bandwidth requirements,e.g. 9.6 Kbps for voice service and 76.8 Kbps for video. Most of the earlier research onwireless bandwidth allocation concentrated on the problem of optimizing the frequencyreuse, and hence the carried tra�c, for only one class of service such as voice. For amulti-class wireless service provider, the carried tra�c for each class in the system has tobe considered individually.

Jayaram et.al /QoS Provisioning in Wireless Networks 3

Voice

Data filetransfer

Packetizedvideo

Low speedinteractive data

Traffictype

1

10

100

1000

10000

1K 10K 100K 1M 10M 100M

Bandwidth

Delay

Figure 1: Characteristics of multimedia tra�c1.1 Related WorkRecently, some work has been proposed for guaranteeing QoS for multimedia tra�c inwireless cellular networks [OKS96, AN94, AN95, NSA96, Sur96a, Sur96b, PTP94, RP96,Schw95]. Rappaport and Purzynski [RP96] have developed analytical models for a cel-lular mobile environment consisting of mixed platform types with di�erent classes ofchannel and resource requirements. Prioritizing hand-o� calls over ordinary ones and in-corporating quotas for each type of resources, various performance measures like carriedtra�c, blocking and forced termination probabilities for each platform and call type, arenumerically computed from the analytical models.Based on the minimum resource requirement criteria provided by the users, Oliveira,Kim and Suda [OKS96] proposed a bandwidth reservation algorithm for guaranteeingQoS to multimedia tra�c. For real-time tra�c, the call is admitted only if the requestedbandwidth can be reserved in the call-originating cell and all its neighbors. For a non-real-time call, the requested bandwidth is reserved only in the originating cell. Although thisscheme guarantees QoS, the main drawbacks are: (i) bandwidth is reserved redundantlysince the user moves only to one of the six neighboring cells (assuming hexagonal cellgeometry), and (ii) the stringent call admission procedure might not admit many real-time requests in a highly overloaded system.The carried tra�c in a wireless network can be increased by the graceful degradation ofsome or all of the existing services in the system [Sur96a, Sur96b]. Seal and Singh [Sur96b]identi�ed two QoS parameters namely, graceful degradation of service and guaranteeof seamless service. With the help of user-supplied loss pro�les, bandwidth usage of

Jayaram et.al /QoS Provisioning in Wireless Networks 4applications that can sustain loss is degraded in situations where user demands exceed thenetwork's capacity to satisfy them. A new transport sublayer is proposed to implementloss pro�les by selectively discarding data from special applications like a compressedvideo stream.Acampora and Naghshineh [AN94] proposed a virtual connection tree (i.e., a cluster ofbase stations) approach for call routing, call admission and resource allocation. In [AN95],a call admission control algorithm is proposed for QoS provisioning for multimedia tra�c,based on an adaptive resource sharing policy among real-time and non-real-time tra�c,where the former tra�c class has preemptive priority over the latter. A simple analyticalmodel based on Poisson call arrivals and departures, and threshold rules for each classof tra�c is proposed to capture various QoS parameters like call blocking and droppingprobabilities and the probability for a minimum bandwidth availability for non-real-timerequests which share the available bandwidth equally among themselves.1.2 Contributions of this WorkMost of the works in the existing literature deal separately with resource reservationapproaches and call admission control in wireless multimedia networks. The most im-portant contribution of this work is the development of an integrated framework for QoSprovisioning at a low layer (e.g., the radio link layer) combining various novel approachesfor call admission control, channel reservation, bandwidth degradation and a new tech-nique of improving spectrum utilization called the bandwidth compaction. The dynamic,error-prone behavior of the wireless physical link necessitates such a low level QoS controlscheme.One of the important motivations behind this QoS framework is to provide di�erentialtreatment at a low layer to the two important classes of wireless multimedia tra�c { thosegenerated respectively by real-time (delay-sensitive) and non-real-time (delay-tolerant)applications. This is achieved through sorting the real-time and non-real-time packetsby a packet sorter and providing for packet marking and various priority schedulingtechniques, as required. Driven by the industry's policy-driven networking paradigm, wekeep our framework exible enough to incorporate various QoS provisioning policies atthe discretion of the network operator.The performance of this QoS provisioning framework is captured through variousanalytical models and simulation experiments. Analytical models compare our reservationand call admission schemes with an existing scheme proposed in the literature. Signi�cantimprovement is predicted which is validated with simulation experiments. By analyticalmodeling, we also estimate the carried tra�c and the worst case bu�er requirementsfor real-time and non-real-time calls. Simulation studies show a signi�cant improvementof the system performance using some of the functionalities proposed within our QoSprovisioning framework. A preliminary version of this paper appeared in [DSJ97].The rest of the paper is organized as follows. The general framework for QoS provi-sioning is described in Section 2. In Section 3, we develop some analytical models, whileSection 4 deals with the details of the simulation experiments. Section 5 concludes thepaper.

Jayaram et.al /QoS Provisioning in Wireless Networks 52 A General Framework for QoS ProvisioningFigure 2 depicts a framework for low layer QoS provisioning in wireless multimedia sys-tems. Let us assume that during the call setup period, the application speci�es the follow-ing parameters to the system : (i) average bandwidth required, (ii) minimum bandwidthrequired, and (iii) whether it is delay-tolerant (non-real-time) or delay-sensitive (real-time). The user can use an RSVP-like signaling protocol to convey to the system of itsrequirement pro�le (RP) consisting of the above three parameters. The admission con-troller allocates one or more channels matching the speci�ed bandwidth requirements, ifcertain tra�c-related conditions are satis�ed. The functionalities of the admission con-troller is described in some details in Section 2.3...

AdmissionController

RequirementProfile

LayersHigher

RadioResourceAdaptationSub-Layer

...Radio link

Frame

PacketsTraffic Packet

Sorter

Call Control Block

Queue information

Control information

decision

SchedulingDegradationReservationCompaction

Admission Non-Real time packet queues

Real time packet queues

SublayerQoS Link LayerFigure 2: A Framework for Low Level QoS Provisioning in Wireless Multimedia SystemsAll multimedia tra�c can be broadly classi�ed into two classes: real-time and non-real-time. Tra�c generated by video and voice telephony is highly delay-sensitive andbelong to the real-time class. Whereas, tra�c such as e-mail and fax can tolerate largedelays. Hence they constitute non-real-time tra�c and can be bu�ered in case of unavail-ability of bandwidth. If bandwidth is available, a �xed amount which we call a bandwidthpage, is allocated to each non-real-time tra�c on a time-sharing basis. Real-time tra�c,on the other hand, cannot be delayed beyond a certain duration and is assumed blockedor dropped if the minimum speci�ed bandwidth, which we call a bandwidth segment, isnot available. The usage of the terms bandwidth segment and page will be justi�ed inSection 2.2 while describing our bandwidth compaction algorithm. When a real-time callrequest arrives and �nds all channels occupied, it may, under certain circumstances, forceone (or more) ongoing non-real-time calls to be temporarily bu�ered so that the releasedchannel can be used to admit the real-time request. Therefore, a real-time tra�c haspreemptive priority over a non-real-time tra�c.Recall here that a channel is a �xed block of communication medium such as a 2-tuple htime slot, carrier frequencyi in the TDMA systems or simply a �xed block of radio

Jayaram et.al /QoS Provisioning in Wireless Networks 6frequency as in the FDMA systems. Multiple channels can be allocated to a single userto satisfy higher bandwidth requirements, which is equivalent to a bandwidth segment byour de�nition. Otherwise, slots larger than a certain minimum duration are de�ned tosupport higher bandwidth tra�c. For example, in the FRAMES multiple access proposalfor UMTS (GSM evolution for 3G wireless systems), three kinds of slot structure arede�ned. These are 116 th and 18 th rate slots which correspond to bandwidth segments, anda 164 th rate slot corrresponding to the bandwidth page. IS-136 is another wireless datastandard which speci�es allocation of multiple time slots to high bandwidth users. TheIS-136 protocol is an improvement over the existing AMPS system by incorporating sixusers instead of one, in one channel.Once the admission controller admits the call (in the cell), it passes the requirementpro�le of the user to the packet sorter. Our proposed framework di�erentiates betweenthe packets generated by the real-time and non-real-time tra�c sources at the link layer.These packets are directed towards the mobile user from the base station controller (BSC)in the forward link. In the proposed architecture, we emphasize on such service di�erenti-ation in the forward link, because the next generation wireless applications are expectedto be likewise assymetric. It can be shown that a similar concept, in a slightly modi�edform, will also be applicable to the reverse link.At the link layer, the upper layer packets, depending on their size, may have to besegmented into link layer packets. The link layer packets are classi�ed as belonging to real-time or non-real-time applications and are treated di�erentially depending on their class.We will explore more on this di�erential treatment later. The packet sorter interfaceswith two queues in the system { the real-time packet queue (RTQ) and the non-real-timepacket queue (NRTQ), the former having a higher scheduling priority over the latter. Notethat, both RTQ and NRTQ are abstractions of actual physical bu�er implementations ofwhich there can be multiple instances under each type. The packet sorter has two mainfunctionalities :� Sorting : Sorting real-time packets from the other type and routing them towardsthe appropriate queue. This is done using the requirement pro�le received from thecall admission controller, and identifying packets based on user ows. The iden-ti�cation process is, however, implementation dependent. The packets are thensegmented and marked (i.e., classi�ed) if necessary, and routed towards the appro-priate queue.� Classi�cation : The packets are marked (by setting certain bit pattern in theirheader) to distinguish them further, such that the scheduler (and also the radioresource manager in the system) can treat them di�erentially while allocating radioresources (e.g., time slot) or while faced with resource constraints. One exampleof this service di�erentiation is the priority of real-time packets over non-real-time;in case of bu�er over ow, the non-real-time packets are discarded �rst. Anotherexample might be to allocate smaller rate time slots (bandwidth pages or 164 th rateslots) to packets generated by degradable applications and allocate larger time slots( 18 rate) to non-degradable ones. This concept leads to a more e�cient bandwidthusage.The call control block in Figure 2 is mainly responsible for various policy-drivenschemes like (i) scheduling di�erent classes of packet; (ii) call degradation or reduc-ing bandwidth allocation to degradable applications in face of scarcity of available radio

Jayaram et.al /QoS Provisioning in Wireless Networks 7Local

Departing

RSS < Rt

RSS > Rt

R

peripheral region sector

r

R = cell radius

r = threshold distance

Rt = threshold RSS

Figure 3: Classi�cation of users in a cellchannels; (iii) bandwidth reservation for delay-sensitive, high priority applications; (iv)bandwidth compaction to maximize utilization of available channel resources; and (v)radio resource usage monitoring. Due to the importance of the call control block and theadmission controller, their functionalities are described in Subsections 2.3 and 2.4.Let us now describe two important schemes { bandwidth reservation and bandwidthcompaction { to be used in our call admission and call control blocks.2.1 Bandwidth Reservation Based on User Location PredictionFor frequent and arbitrary movement of the user in a cellular system, the problem ofguaranteeing QoS in case of hand-o� becomes a critical issue. This is particularly true forhigh bandwidth applications using multiple channels (bandwidth segment) in a cellularenvironment. A plethora of reservation schemes have been proposed in the literature invarious forms [OKS96, AN94, Sur96a, Schw95], to guarantee user connectivity and servicequality in case of frequent hand-o�. In order to guarantee QoS, most of these schemes re-serve resources in all the neighboring cells of the user's current cell. This over-engineeringhas its price not only in blocking scarce resources, but in all probability, making the cus-tomer pay a lot more than the corresponding service in the wire-line systems. We proposea resource reservation technique based on predicting the user mobility in a cell which cansigni�cantly reduce the cost of bandwidth reservation.The mobile users in a cell can be classi�ed as departing or local depending on whetheror not they are located within the shaded region in Figure 3. This entails an accuratelocation prediction of the user within the cell boundary (error margin of less than 100sq. meters for a typical cell of 5 sq. miles). A simple way to predict user locationis based on the received signal strength (RSS) from the mobile equipment to the basestation. Another technique is based on the phase delay of the received signal. Both theschemes can lead to severe inaccuracies due to obstacles in the signal propagation pathand various radio propagation conditions like shadowing or multipath fading. Hence, it islikely that a scheme which predicts user location by combining the received signal strengthmeasurement (by the BS) with a knowledge-base of the terrain conditions in that cell, willoutperform both of the above schemes. From �eld measurements, a few points (typically,a hundred of them) with known values of RSS are chosen as anchor points within the

Jayaram et.al /QoS Provisioning in Wireless Networks 8cell. Based on the RSS from the user and the RSS at the user from neighboring basestations, the anchor point closest to the user is computed. Then, the location of the useris predicted relative to its closest neighboring anchor point whose coordinates are assumedto be known. As a byproduct of this approach, three neighboring cells (or sectors) closestto the current location of the user can also be determined. Let these cells be called theset of destination cells of the user (e.g., cells D1; D2 and D3 of the cell A in Figure 7).If an error in the user location prediction is tolerable, then the destination cell(s) forthe user can also be computed from only RSS measurements as follows. The neighboringbase station from which the RSS is the maximum, is most likely the closest neighboringcell of the user. After the base station detects that a user has entered the peripheralregion (by comparing its RSS with certain threshold, Rt), it instructs the mobile stationto send the RSS measurements from all the neighboring BSs and the corresponding cellids. Thus, the three closest neighboring cells of the user are predicted. In this prediction,however, we do not consider the e�ects of terrain conditions of the cell.Once a real-time user enters the departing region (see Figure 3), the system initiates aprocedure by which the same amount of average bandwidth requested in the requirementpro�le (RP) at call setup time, is attempted to be reserved in its destination cells. Toreduce the probability of reservation (and hence hand-o�) failure, various other schemeslike bandwidth degradation, compaction and channel borrowing are resorted to.2.2 Bandwidth CompactionWe assume that a high bandwidth application will be assigned contiguous multiple chan-nels (e.g., time-slots, frequency band) by the system. This is usually the case for a realsystem like GPRS, employing multirate channels. Based on the bandwidth requested inthe requirement pro�le, a bandwidth segment is allocated to a real-time application user.Similarly, bandwidth pages will be allocated to the non-real-time tra�c successively fromthe rear end of the spectrum allocated to the cell (see Figure 4). The system will havethe provision of \removing" an active bandwidth page depending on the tra�c load. Thismeans that the amount of bandwidth allocated to non-real-time applications decreasesbecause the scheduler schedules the non-real-time packets less often. Bandwidth alloca-tion in this fashion ensures that the bandwidth segments and pages form two disjoint setsfor e�cient utilization of the spectrum.Since real-time users are assigned bandwidth segments, there can be unutilized band-width \holes" in the frequency spectrum caused by the call terminations. For example,in Figure 4(a), call terminations for users 2 and 4 lead to the creation of \holes". In asystem requiring contiguous allocation of bandwidth for multimedia tra�c, such a holecan only be �lled again by a call request of equal or smaller bandwidth. We describe belowa method, called bandwidth compaction, for e�cient utilization of the available spectrumin such a system.The idea behind bandwidth compaction is to shu�e the ongoing communications toplace all free bandwidth together in one contiguous block. For example, the spectrumwith holes as in Figure 4(a) becomes as in Figure 4(b) after compaction.Moving a bandwidth segment to a di�erent part in the frequency spectrum implies \re-tuning" the channels (constituting the segment) to a di�erent set of channels. This mightmean hopping to di�erent carrier frequencies in the FDMA systems or re-synchronization

Jayaram et.al /QoS Provisioning in Wireless Networks 9����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

����

��������

��������

��������������������

��������������������

��������

��������

����������������

����������������

1 3 5 6

Bandwidth Segments Bandwidth Pages

(b) After Bandwidth Compaction

��������

��������

��������������������

��������������������

��������

��������

����������������

����������������

������������

������������

����������������

����������������

1 2 3 4 5 6

Bandwidth PagesBandwidth Segments

holes

(a) Before Bandwidth Compaction

Figure 4: Bandwidth allocation pattern showing segments and pagesto di�erent time slots in the TDMA systems. The bandwidth compaction algorithm willsequentially shu�e the existing bandwidth segments towards the lower frequency side toabsorb the holes, requiring the communicating users to \tune" to new channels almostinstantaneously. This scheme can sometimes be very expensive and time consuming as alarge number of channel re-assignments need to be done.A call request will generally not tolerate long delays to receive an acknowledgment.Usually, the user will hang-up and try again later. Therefore, a partial bandwidth com-paction algorithm will be executed during the call admission phase. The partial com-paction scheme checks the spectrum allocation pattern to determine if by moving a few(in the range of 2 or 3) bandwidth segments to adjoining holes, su�cient contiguous spec-trum can be released in order to satisfy the user request. This scheme only involves veryfew channel reassignments and sustains lower delays.2.3 Call Admission AlgorithmThe call admission algorithm is presented as a owchart in Figure 5. As discussed earlier,the originator of the request speci�es a requirement pro�le specifying its average and min-imum bandwidth requirements, and whether it is a real-time or non-real-time application.The call admission criteria will be di�erent for each class. For real-time users, admissionis primarily based on the availability of bandwidth and compaction may have to be re-sorted to. This is shown in Figure 5. The real-time user is classi�ed as local or departingbased on the location prediction scheme. If the user is departing, bandwidth reservationis initiated in the predicted destination cells. If these reservations are successful, thenonly the call is admitted. The call of a local user is admitted based on the bandwidthavailability in the current cell only.For non-real-time users, the admission is primarily based on the availability of bu�er

Jayaram et.al /QoS Provisioning in Wireless Networks 10space in the non-real-time packet queue. This criteria may be set against certain bu�eravailability threshold, in order to prevent bu�er over ow once the new call is admitted.Once the non-real-time call is admitted, one or more bandwidth pages (if available) areassigned to transmit the packets depending on its requirement pro�le.requested BW< available BW?

Do partial BW compaction

requested BW <new available BW?

Y

N

Send message todestination cellsto reserve BW

Ack. received?

Classify user

Departing?

Admit request

Y

N

Y

N

N

Y

request = real-time?Y

N

Y

N

Reject request

user requirement profile

Admit request

Buffer available < threshold

<RP>

Figure 5: Flowchart of the call admission algorithmThere is a QoS monitor function as part of the call admission controller. Its functionis to monitor the QoS parameters a�ecting system-wide performance, e.g., interferencelevel in the cell, number of hand-o� drops etc., and also to provide feedback to the calladmission controller to help it make certain policy-based call admission decisions. Thesepolicies will be determined largely by the network operators.In case of a fully loaded system (a sector/cell with all channels occupied), there is aprovision for admitting non-real-time calls by \stealing" channel capacity from the real-time users. During the period of inactivity of the application source, the real-time useris not using the channels assigned to it, therefore, the channel can be used to transmitpackets for non-real-time users. The scheme is as follows. In a fully loaded system, achannel can be owned by multiple users comprising of at most one real-time user andone or more non-real-time users. The maximum number of such users will depend on the

Jayaram et.al /QoS Provisioning in Wireless Networks 11policy enforced by the network operator. The real-time user will have true ownership ofa channel, while the non-real-time users sharing the same channel will have a restrictedownership. This implies that a real-time user sharing a channel with another non-real-timeuser, will have preemptive priority over the latter and will continue packet transmissionwhen the source is active again.2.4 Call Control BlockThe call control block is the supervisory algorithm that manages and monitors the progressof the ongoing sessions. This block, depicted in Figure 6, performs the following functions:execute various scheduling and call degradation policies as well as initiate bandwidthreservation in one or more neighboring cells. Also, it periodically executes the bandwidthcompaction algorithm in the background and monitors the sanity of the radio channels(e.g., bit error rate performance).Policy based

Scheduler

DegradationPolicy based Compaction

Reservation

Figure 6: The call control blockWe describe brie y the scheduling, degradation and channel reservation functionalitiesof the call control block.� Policy based Scheduling : The simplest packet scheduling approach is a prioritybetween the two queues, where a packet from a non-real-time queue (NRTQ) isnever scheduled until the real-time queue (RTQ) is empty. This might lead tostarvation of the non-real-time packets (leading to queue build-up and ultimately,bu�er over ow) if the real-time applications continue to proliferate packets. Aweighted fair queueing or a multi-level feedback queue based approach may be moresuitable for implementation.� QoS Degradation : For this framework, the following two types of degradedbehavior are considered.{ Bandwidth degradation [SJBD98, Sur96a] implies that an application occupy-ing multiple channels releases some of them to enter a degraded mode. If theaverage rate speci�ed in the requirement pro�le equals the minimum rate for

Jayaram et.al /QoS Provisioning in Wireless Networks 12the application, then the application is not degradable. For example, someforms of compressed video may exhibit this behavior. Bandwidth degradationmay be precipitated by various circumstances, e.g., when the mobile user facessudden resource constraint while trying to hand over to a heavily loaded cell.Degradation can be achieved using various ow control techniques, e.g., dis-crding packets at link and transport layers, or by applying hierarchical codingschemes [Schw95].{ Delay degradation implies an increase in the delay jitter or delay variance ofa session. Typically, the non-real-time applications are expected to exhibit alarge amount of delay degradation if the number of higher priority real-timeapplications grow in the system. An analysis of queue build-up (and delaydegradation) for non-real-time packets in a mixed tra�c scenario is presentedin [SJBD98].� Reservation through Channel Borrowing : Referring to Figure 7, a departinguser in cell A has the set D = fD1; D2; D3g of three destination cells. If theuser requested bandwidth is not available in any one of these destination cells,bandwidth is borrowed from one of their neighboring cells selected according to achannel borrowing function. We impose the restriction that a cell in the set Dcannot borrow channels from another cell in the same set or the current cell of theuser.

A

D

D

D

B

B

B

B

B

B

B

B

B

1

2

3

1,2

2,1

2,2

2,3

3,1

3,2

3,3

1,3

1,1

Figure 7: Channel borrowing in destination cellsTo make the set of lenders for each destination cell uniform and disjoint (implyingbetter load balancing among these cells), the lender set of the destination cell Di,for 1 � i � 3, is restricted to the set of three cells marked as Bi;j where 1 � j � 3.The selection of the lender cell from the lender set is determined as follows.

Jayaram et.al /QoS Provisioning in Wireless Networks 13Let T and Nbw be the tra�c load in Erlangs and the amount of available band-width in a probable lender cell. We select that cell as the lender whose parametersmaximize the value of the function F = NbwT . Thus the objective is to select a cellwith a large amount of available bandwidth and a low average tra�c load.To enforce the degradation and compaction policies, the call control block interactswith the radio resource manager of the local system, while the channel reservation mech-anism may necessitate communication with a remote system (e.g., another BSC).3 Performance AnalysisIn this section, we derive analytical models to evaluate the performance of our call ad-mission scheme using predictive channel reservation in the neighboring cells. Also, weevaluate expressions for the average carried tra�c under call degradation, and the aver-age size of the real-time and non-real-time packet queues.3.1 Call Admission and Successful Hand-o� for Real-time CallsLet us �rst derive a suitable QoS parameter for bandwidth reservation for real-time callsso that we can compare the performance of our scheme with an existing scheme proposedin [OKS96]. The metric is the probability of admitting a call and its successful hand-o�to one of the neighboring cells, assuming that the user changes cell at least once dur-ing the session. Since bandwidth reservation takes place only in case of real-time calls,these performance models are applicable to this class of calls only. Let us also assume forsimplicity that all calls, on the average, require the same amount of bandwidth; and letPav be the stationary probability that this bandwidth is available in any cell. Let us de�neC � Event denoting call admission,S � Event denoting successful hand-o� to a neighboring cell.Thus, we want to derive an expression for the probability P (CS).Assuming that unavailability of bandwidth is the only reason behind a hand-o� fail-ure, let us determine the conditional probability for a successful hand-o� of a call to aneighboring cell, given that the call is admitted. The algorithm in [OKS96] admits areal-time call only if there is enough bandwidth to be reserved in the originating cell andits six neighbors. Thus, P (SjC) = 1. The call admission probability is P (C) = P 7av , andthe call blocking probability is given by P (B) = 1 � P (C). Hence, for the scheme in[OKS96], the probability of call admission and successful hand-o� is given byP (CS) = P (C)P (SjC) = P 7av: (1)The admission policies in our scheme are di�erent for a local and a departing userinitiating the call request. For a local user, a call is admitted if there is enough bandwidthavailable in the cell, implying that P (C)local = Pav . After admission, the probability ofsuccessful hand-o� of the call to a given neighboring cell is P (SjC)local = P (BWD),where BWD denotes the event that su�cient bandwidth (through borrowing, if required)is available in the set D of destination cells for the user. For a departing user, the call

Jayaram et.al /QoS Provisioning in Wireless Networks 14is admitted if enough bandwidth is available in the cell and the set D of its destinationcells. Thus, P (C)departing = PavP (BWD). Once the call is admitted, the probability ofsuccessful hand-o� is P (SjC)departing = 1. Thus,P (CS)departing = P (C)departingP (SjC)departing =PavP (BWD) = P (C)localP (SjC)local = P (CS)local (2)implying that P (CS) is the same for both local and departing users.In order to derive P (BWD), we consider separately the cases where bandwidth isavailable in all three, two, one and none of the three destination cells in the set D. There-fore,P (BWD) = P 3av + 3P 2av(1� Pav)(3Pav) + 3Pav(1� Pav)2(3Pav)2 + (1� Pav)3(3Pav)3= [Pavf1 + 3(1� Pav)g]3Therefore, the probability of call admission and a successful hand-o� in our scheme isP (CS) = P 4avf1 + 3(1� Pav)g3 (3)To compare our scheme with that in [OKS96], the ratio � of the two probabilities inEquations (3) and (1) is obtained as� = P 4avf1 + 3(1� Pav)g3P 7av = � 4Pav � 3�3 (4)Since Pav � 1, we set � � 1 which implies that the probability of call admission andsuccessful hand-o� in our scheme is always greater than or equal to that obtained fromthe approach proposed in [OKS96]. The values of � as a function of Pav are plotted inFigure 8.It is observed that with low probabilities of resource availability, our scheme showsa huge improvement over [OKS96]. As the probability Pav increases, the performanceof both the schemes tend to be similar, and �nally, the ratio � converges to unity whenPav = 1.3.2 Carried Tra�c under Bandwidth DegradationRecall that the real-time tra�c has preemptive priority over non-real-time tra�c, andbandwidth degradation is applicable only to a fully loaded system, where all the channelsare occupied. Let us now consider the case where all the channels are occupied by real-time users. A new real-time call can be admitted either if bandwidth degradation leadsto the availability of su�cient channels or another user terminates a call. Assumingsu�cient bu�er space is available, the admitted non-real-time users \steal" bandwidthfrom real-time users who are inactive. Hence, the average number of non-real-time usersin the system will depend on the number of such \idle" channels of real-time users. In thefollowing analysis, we assume a TDMA system where a channel is equivalent to a timeslot in a frame.Let N be the total number of slots in a frame. Let the call arrival process for real-timeusers be Poisson with average rate �r , and the service time be exponentially distributed

Jayaram et.al /QoS Provisioning in Wireless Networks 15

0500100015002000250030003500400045005000

0:2 0:3 0:4 0:5 0:6 0:7 0:8 0:9 1�

PavFigure 8: � versus Pavwith rate 1�r . Also, assume that the real-time users are using Navgr number of slots onthe average. Let us denotepi = probability that user i is degradable (5)ni = number of slots by which user i can degrade (6)(7)The average number of slots obtained by degrading the existing real-time users is givenas Sdegrade = Xi =2 S1 pini (8)where, S1 is the set of non-degradable, real-time users using a single slot. We also assumethat a new user is never admitted into the system in a degraded mode [SJBD98]. Theaverage real-time tra�c per carrier is given asUr = N + SdegradeNavgr (9)Note that, the non-real-time users are allocated the \idle" slots of the real-time users.Let ai denote the average activity of the user i occupying Ni slots. Hence, the probabilitythat a slot is idle is given as pidle = mXi=0(1� ai)NiN (10)where m is a random variable denoting the number of real-time users in the system. Theaverage value of m in this case can be equated to Ur as given by Equation (9).

Jayaram et.al /QoS Provisioning in Wireless Networks 16The probability of k slots being idle is given by the Binomial distribution, �Nk �pkidle(1�pidle)N�k. Hence, the average number of idle slots is N �pidle. Let the average bandwidthrequirement for non-real-time users be Navgnr . Then, the total carried non-real-time tra�cis given by Unr = N � pidleNavgnr (11)Thus the total carried tra�c in the system is given by U = Ur + Unr.3.3 Average Queue Lengths for RTQ and NRTQFor this analysis, we assume that the real-time users are admitted depending only uponthe available bandwidth. Hence, the maximum number of real-time users that can beserved is Cr = NNavgr (12)Let the average activity of real-time users be denoted by �ar, while the arrival and servicerates of real-time requests (both Poisson distributed) be denoted by �r and �r, respec-tively. Since the non-real-time users are accommodated by \stealing" bandwidth frominactive real-time users, the average number of non-real-time requests served is given byCnr = N � �ar ��r�r �NavgrNavgnr (13)We model both classes of users with the help of M=M=C queueing model, where Cis approximated by Cnr for non-real-time calls and as Cr for real-time calls.Average RTQ LengthAccording to the M=M=C model the probability that the queue length is n is givenas [NEL96] Pnr = �rnn! �nr P0; for 1 � n � Cr= �rnCn�Crr Cr! �nr P0; for n � Crwhere P 0r = �PCr�1i=0 1i! ��r�r �i + 1Cr ! ��r�r �Cr � Cr�rCr�r � �r ���1 :The length Lqr of the real-time queue of requests waiting to be served is thenLqr = 1Xn=Cr+1 (n � Cr) Pnr= " (�r�r )Cr�r�r(Cr � 1)!(Cr�r � �r)2 #P 0r (14)

Jayaram et.al /QoS Provisioning in Wireless Networks 17The waiting time in the queue is given by Little's formula, W qr = Lqr�r .Assuming that the tra�c generated by an admitted call has a Poisson-distributed packetburst size with an average rate �pr , the number of packets (Br) that need to be bu�eredhas a simple upper bound given byBr � Lqr �W qr � �pr (15)Here, Lqr � �pr gives the maximum packet arrival rate when all the admitted real-timeusers are simultaneously active.Average NRTQ LengthFor non-real-time tra�c, let �nr be the request rate and �nr be the service ratefor each request, both Poisson distributed. Using the M=M=C queueing model with Capproximated by Cnr given by Equation 13, the number of non-real-time packets, Bnr,that need to be bu�ered can be bounded asBnr � Lqnr �W qnr � �pnr (16)where the parameters Lqnr and W qnr are derived similar to the corresponding parametersfor the real-time case.4 Simulation ExperimentsIn this section, we provide details of our simulation model and the experimental resultsthat are obtained from the simulation.4.1 Simulation ModelOur simulation is comprised of applications that generate requests and data packets, theadmission controller (AC) that admits these requests, the packet sorter (PS) that sortsthe data packets, the queue manager (QM) that keeps track of the length of the real-timeand non real-time queues, the compaction manager (CM) that performs compaction on aframe, and the scheduler that schedules packets onto a frame. Hand-o� control and reser-vation messaging for hand-o� calls are taken care of by the call control manager (CCM).Admission ControlUser requests are modeled as requirement pro�le, which is a 3-tuple: <average band-width, minimum bandwidth, and delay tolerance>. Figure 9 shows the sequence of eventsthat are initiated by the AC when a new request has to be admitted. Once an applica-tions' request has been admitted and data packets are received from the application, thePS appends a single degradation information bit (DIB) to convey the degradation in-formation to the scheduler according to a simple algorithm that checks if the minimumbandwidth is less than the average bandwidth. If so, we set DIB = 1, otherwise DIB = 0.

Jayaram et.al /QoS Provisioning in Wireless Networks 18Frame Structure and SchedulingOur scheduling mechanism is adapted from the FRAMES radio interface proposal[FRAMES98]. In FRAMES mode-1 (called FMA1), a unit TDMA frame length is 4.615msec and can consist of 1/64 slots (72 �s), and 1/8 slots (577 �s) �tting together in theTDMA frame. We denote a 1/64 slot by SA and a 1/8 slot by SB . Thus SA will map toan indivisible unit time slot and SB = 8SA per frame. For a bit rate of 64 Kbps, either asingle SB slot or eight SA slots can be used in a single radio link frame. For the purposeof link layer scheduling, we abstract that the slot SA can carry K bytes of data and SBslot can carry 8K bytes of data.Our scheduling algorithm considers the time slots of a frame indexed by increasingtime. A unit frame is represented as St0; St1; : : : ; Stn. Two indices are maintained whilepopulating a frame. These indices indicate the last time slot index (Stmin) occupiedbefore the \hole", and the �rst slot index (Stmax) occupied at the end of the \hole",respectively.AC QMApplication PS

New call RP(avg bw, min bw, class)

QLenResponse(RTQ length,NRTQ length)

CMCCM

QLenQuery()

NewCall(uid, class)

BWAvlQuery()

BWAvlResponse(current bw)

CompactionInvoke()

(bw savings)CompactionResponse

(accept, reject)

Seq. No.

1

2

3

4

5

6

AdmissionDecision

Figure 9: Admission Control MessagingThe link layer scheduling algorithm is implemented as follows:ALGORITHM SchedulerWHILE (Real time queue (RTQ) not empty) DODequeue a packet from RTQp = d(packet size in bytes=K)eq = d(packet size in bytes=nK)e

Jayaram et.al /QoS Provisioning in Wireless Networks 19CASE (packet's DIB)0: /* not degradable */IF ((Stmin + (q � SB)) < Stmax) THENAssign q SB slots at StminStmin = Stmin + (q � SB)ELSE /* cannot assign RTQ packet */BREAK; /* exit RTQ loop and schedule NRTQ packets */ENDIF1: /* degradable */IF ((Stmax � (p � SA)) > Stmin) THENAssign p SA slots at StmaxStmax = Stmax � (p � SA)ELSE /* frame is full */EXIT;ENDIFENDCASEENDWHILEWHILE (Non-real time queue not empty) DOIF ((Stmax � (p � SA)) > Stmin) THENAssign p SA slots at StmaxStmax = Stmax � (p � SA)ELSE /* frame is full */EXIT;ENDIFENDWHILEEND ALGORITHM SchedulerTable 1 shows a random mix of tra�c packets and Figure 10 shows how the packetsare mapped on to an FMA1, mode-1 frame. We have assumed that K = 8 bytes.Table 1: Example of a tra�c mixPacket # Packet size (bytes) Packet source DIB1 100 RTQ 12 170 RTQ 03 3 RTQ 04 20 NRTQ -5 40 NRTQ -Compaction ImplementationThe compaction algorithm checks for idle slots (from terminated calls) and reassignsslots to ongoing calls. The implementation for compacting the lower end of a frame isgiven below. The compaction on the upper end of the frame is carried out in a similiar

Jayaram et.al /QoS Provisioning in Wireless Networks 20...

Packet 1

Packet 2

Packet 3

Packet 4

Packet 5

1 5432

Figure 10: Segmenting packets to FMA1 frameloop.ALGORITHM CompactorFOR (i = 0 TO Stmin) DOIF (CALLSTATUS of Sti is TERMINATED) THENFOR j = (Sti + q TO Stmin) DOAssign CONTENTS(Sti) = CONTENTS(Stj)Sti = Sti + 1ENDFORStmin = Stmin � qENDIFENDFOREND ALGORITHM Compactor4.2 Simulation parameters1. Data generation: Admitted requests act as bursty sources of data, with inter-arrival time of the bursts being exponentially distributed with mean 1 .2. Requests origination and termination: Call arrival is programmed as a Poissonprocess with inter-arrival time exponentially distributed with mean 1� . The callholding time is also programmed as a exponentially distributed random variablewith mean 1� .3. Mobility modeling: The number of hand-o� requests is programmed as an ex-ponentially distributed random variable with mean 1�h . No di�erentiation is madebetween real-time and non real-time users for hand-o� calls.4. User classi�cation: In order to classify the users correctly, we need to modelthe signal strength received by the base station from the user. Since actual signalstrengths cannot be generated, we overlay on each cell a grid of size 100� 100. Auser position within a cell is given by a pair of co-ordinates (x; y) in this grid. A�xed set of co-ordinates de�nes the peripheral shaded region of a cell as shown inFigure 3.

Jayaram et.al /QoS Provisioning in Wireless Networks 21

0:20:30:40:50:60:70:80:911:1

0 0:02 0:04 0:06 0:08 0:1PAR

Arrival rate of users �

with compaction bb b b b b b bwithout compaction ++ + + + + + +

Figure 11: Call admission probability (PAR) for real-time users versus call arrival rate forqueue size = 100 and service rate � = 0:034.3 Simulation ResultsThe simulation results are shown in Figures 11 - 15. From Figure 11, we observe that thesystem capacity is fully utilized only when compaction technique is used. With arrivalrate less than the service rate, the call admission probability is approximately unity whenthe compaction is used. Without compaction, some incoming requests are blocked dueto insu�cient bandwidth in the left-over \holes". The observed improvement varies from21% for an arrival rate of 0.03 to about 4% for an arrival rate of 0.07.Figure 12 shows an improvement in the call admission probability of non-real-timecalls with compaction over that without compaction. The percentage improvement variesfrom 4% at an arrival rate of 0.05 to about 17% at an arrival rate of 0.09. The improvementis greater at higher call arrival rates due to the fact that at high arrival rates, most ofthe incoming real-time calls are blocked providing a scope for scheduling the bu�erednon-real-time calls.Figure 13 demonstrates the call admission probability with varying queue sizes withand without compaction. The admission probability for non-real-time users is observedto be higher with compaction at small queue lengths over that without compaction.Note that, at some threshold queue size, the bene�t of compaction is lost. This impliesthat, given an average call arrival and service rates, there exists a queue size beyondwhich the compaction algorithm should not be executed, as compaction is generally verycomputation intensive.Figure 14 shows that as the service rate is increases, the real-time calls are terminatedquickly leading to an increase of higher priority real-time calls in the system. This reduces

Jayaram et.al /QoS Provisioning in Wireless Networks 22

0:60:70:80:911:1

0:5 0:55 0:6 0:65 0:7 0:75 0:8 0:85 0:9 0:95 1PANR

Request rate of the users � � 10

with compaction bb b b bwithout compaction ++ + + +Figure 12: Call admission probability (PANR) for non-real-time users versus call arrivalrate for queue size = 100 and service rate � = 0:03

0:80:850:90:9511:05

20 30 40 50 60 70 80 90 100 110PANR

Queue Length

with compaction bb b b bwithout compaction ++

+ + +

Figure 13: Call admission probability for non-real-time users versus queue size for arrivalrate � = 0:08 and service rate � = 0:03

Jayaram et.al /QoS Provisioning in Wireless Networks 23

0:80:850:90:9511:051:1

0:05 0:06 0:07 0:08 0:09 0:1 0:11PANR

Arrival rate of requests �

� = 0:01 bb b b b � = 0:03 ?? ? ? ?� = 0:05 44 4 4 4

Figure 14: Call admission probability for non-real-time users versus call arrival rate forqueue size = 100

00:050:10:150:20:250:30:350:40:450:5

0 0:2 0:4 0:6 0:8 1PCD

Hando� rate �h � 100

with degradation b

bb bwithout degradation +

++ +

Figure 15: Hand-o� call drop probability (PCD) versus hand-o� rate for queue size = 100,service rate � = 0:01 and call arrival rate � = 0:1

Jayaram et.al /QoS Provisioning in Wireless Networks 24the admission probability of the non-real-time calls in the system.Figure 15 shows that the hand-o� dropping probability also decreases with com-paction. The observed improvement is around 17% for a hand-o� rate of �h = 0:001 and11% for a rate of 0.009.Comparison of Reservation SchemesFigure 16 shows the comparison of our scheme with hat in [OKS96] in terms of theprobability, P (CS), of call admission and successful hand-o�. As expected from theanalytical results in Section 3.1, the P (CS) values for both the schemes tend to mergefor high values of �. An improvement of about 12% is observed for � = 0:9.

0:30:40:50:60:70:80:91

0:1 0:2 0:3 0:4 0:5 0:6 0:7 0:8 0:9P (CS)

�

"our-scheme""OKS96"

Figure 16: Call admission and successful hand-o� probability, P (CS), versus tra�c load5 ConclusionsIn this paper, we have proposed a framework for low layer (e.g., radio link layer) QoS pro-visioning in next generation wireless access networks. The most important contributionof this work is the development of an integrated framework by combining various novelapproaches to call admission control, channel reservation, bandwidth degradation and atechnique of improving spectrum utilization called bandwidth compaction. The dynamic,error-prone behavior of the wireless physical link necessitates such a low level QoS controlmechanism.The performance of our QoS provisioning framework is captured through variousanalytical models and simulation experiments. Analytical models compare our reservation

Jayaram et.al /QoS Provisioning in Wireless Networks 25and call admission schemes with an existing scheme [OKS96], which is substantiated bysimulation experiments that show an improvement of about 12%. Simulation experimentsalso show upto 21% improvement in the call admission probability of real-time calls andupto 17% improvement in the admission probability of non-real-time calls, when variouscall control techniques (e.g., bandwidth compaction) are employed within this framework.References[Kur93] J. Kurose. \Open Issues and Challenges in Providing Quality of Service Guarantees in High-SpeedNetworks", Computer Communication Review, 23 (1), January 1993.[OKS96] C. Oliveira, J.B. Kim, T. Suda. \Quality-of-Service Guarantee in High-Speed Multimedia Wire-less Networks", IEEE International Communications Conference 1996, Dallas Texas, pages 728-734.[AN94] A.S. Acampora, M. Naghshineh. \Control and Quality-of-Service Provisioning in High-Speed Mi-crocellular Networks", IEEE Personal Communications Magazine, Vol. 1, No. 2, Second Quarter1994 .[AN95] A.S. Acampora, M. Naghshineh, \QOS Provisioning in Micro-Cellular Networks Supporting Mul-timedia Tra�c", IEEE INFOCOM, 1995.[NSA96] M. Naghshineh, M. Schwartz, A.S. Acampora. \Issues in Wireless Access Broadband Networks",Wireless Information Networks, edited by J.M. Holtzman, Kluwer Academic Publishers, 1996.[DSJ97] S. K. Das, S. Sen, R. Jayaram, \Call Admission and Control for Quality-of-Service Provisioningin Cellular Networks", Proceedings of ICUPC, 1997, San Diego, California.[SJBD98] S. Sen, J. Jawanda, K. Basu, S. K. Das, \Quality-of-Service Degradation Strategies for Multi-media Wireless Networks", Proceedings of Vehicular Technology Conference, Ottawa, Canada, pp.1884-1888, May 1998.[NT95] D. Newman, K. Tolly. \Wireless LANs: How Far? How Fast?", Data Communications on theWeb, http://www.data.com/Lab Tests/Wireless LANs.html.[RW94] D. Raychaudhuri, N.D. Wilson. \ATM-Based Transport Architecture for Multiservices WirelessPersonal Communications Networks", IEEE Journal on Selected Areas in Communications, Vol.12(8), October 1994.[Sur96a] S. Singh, \Quality of Service Guarantees in Mobile Computing", to appear in IEEE Journal ofComputer Communications.[Sur96b] K. Seal, S. Singh, \Loss Pro�les : A Quality of Service measure in mobile computing", Journalof Wireless Computing, Vol. 2, pp 45-61, 1996.[PTP94] S. Papavassiliou, L. Tassiulas, P. Tandon, \Meeting QOS requirements in a cellular network withreuse partitioning", Proceedings of INFOCOM '94, pp 4-12.[RP96] S. Rappaport, C. Purzynski, \Prioritized resource assignment for mobile cellular communicationsystems with mixed services and platform types", IEEE Transactions on Vehicular Technology, vol.45, no. 3, pp 443-457, Aug. 1996.[Schw95] M. Schwartz, \Network Management and Control Issues in Multimedia Wireless Networks",IEEE Personal Communications Magazine, June 1995.[NS96] M. Naghshineh, M. Schwartz, \Distributed Call Admission Control in Mobile/Wireless Networks",IEEE Journal on Selected Areas in Communications, Vol. 14, No. 4, May 1996.[FRAMES98] E. Nikula, A. Toskala, E. Dahlman, L. Girard, A. Klein, \FRAMES Multiple Access forUMTS and IMT-2000", IEEE Personal Communications Magazine, April 1998.[LEE96] Mobile Cellular Telecommunication Systems, Analog and Digital Systems, W.C.Y. Lee, Secondedition, McGraw-Hill, 1996.[RS90] Multiple Access Protocols, Performance and Analysis, R. Rom and M. Sidi, Springer-Verlag, 1990.[NEL96] Probability, Stochastic Processes and Queueing Theory, R. Nelson, Springer-Verlag, 1996.

Jayaram et.al /QoS Provisioning in Wireless Networks 26Authors' BiographiesRajeev Jayaram received his bachelors degree in computer science and engineeringfrom the Bangalore University, India, in 1992 and his Masters degree in Computer Sciencefrom the University of North Texas, Denton TX. From 1992 to 1994, he worked on formalmodels for distributed systems at the Indian Institute of Science, Bangalore. Currently, heis working as a senior software engineer in the wireless communications division of EricssonInc in Richardson, Texas. His current research interests include channel assignment,call admission and control, Quality-of-Service provisioning in wireless networks and alsodistributed systems and networking.Mr. Jayaram is a student member of the IEEE Communications Society.E-mail: jayaram@cs.unt.eduNaveen K. Kakani received his B.Tech. degree from the department of Electronicsand Communication Engineering, Indian Institute of Technology Kharagpur, India in1995 and M.S. degree from the department of Computer Science, University of NorthTexas in 1997 where he is currently pursuing his Doctoral degree. His current area ofresearch is Quality-of-Service provisioning in wireless networks.Email: naveen@cs.unt.eduSajal K. Das received the B.Tech. degree in 1983 from Calcutta University, the M.S.degree in 1984 from the Indian Institute of Science, Bangalore, and the Ph.D. degree in1988 from the University of Central Florida, Orlando, all in Computer Science. Currentlyhe is a Full Professor of Computer Science and also the Director of the Center for Researchin Wireless Computing at the University of North Texas, Denton. Dr. Das is a recipientof Honor Professor Awards from UNT in 1991 and 1997 for best teaching and scholarlyresearch, and UNT's Developing Scholars Award in 1996 for outstanding research. Hehas been a Visiting Scientist at the the Council of National Research in Pisa, Italy, andSlovak Academy of Sciences in Bratislava, and also a Visiting Professor at the IndianStatistical Institute, Calcutta.Besides his passion for parallel computing, Dr. Das' current research interests includevarious subareas in wireless networks and mobile computing such as location management,dynamic channel assignment, QoS management, intercommunication protocols, wirelessinternet and data networking. He has published over 100 research papers in these areas.He serves on the executive committee of the ACM SIGMOBILE and is the founding Pro-gram Chair of the First ACM International Workshop on Wireless Mobile Multimedia(WoWMoM'98). He has been a member of Program Committees for numerous confer-ences including MobiCom'98, INFOCOM'98 and ICUPC'97. He was also the GeneralCo-Chair of the Sixth International Symposium on Modeling, Analysis and Simulationof Computer and Telecommunication Systems (MASCOTS'98), sponsored by the IEEEComputer Society. Dr. Das serves on the editorial boards of the Journal of Paralleland Distributed Computing, Parallel Processing Letters and the Journal of Parallel Algo-rithms and Applications. As an Executive Committee member of IEEE TCPP, he is alsothe Co-editor of the TCPP Newsletter. Dr. Das is a member of the IEEE and the ACM.Email: das@cs.unt.edu

Jayaram et.al /QoS Provisioning in Wireless Networks 27Sanjoy K. Sen received his B.Tech degree in Electronics and TelecommunicationsEngineering from Jadavpur University, India in 1991, and M.S. and Ph.D. in ComputerScience (Wireless Communications) from the University of North Texas, Denton, in 1995and 1997 respectively.Between 1991 and 1993, Dr. Sen was involved with Siemens Telematiks India Ltd. asa senior projects engineer in the switching hardware division. He joined Northern Tele-com, Richardson, Texas, in January 1998, where he is currently involved in developingan architectural framework for next generation wireless networks in the Wireless AccessArchitecture group. His research interests include Internet protocol suites and their re-lationship with mobile computing (location management, call admission control, QoS),computer architecture, distributed systems and parallel discrete event simulation.Email: sanjoy@nortelnetworks.com