Adaptive Quality of Service Handoff Priority Scheme for Mobile Multimedia Networks

494 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 Adaptive Quality of Service Handoff Priority Scheme for Mobile Multimedia Networks Wei Zhuang, Brahim Bensaou, Member, IEEE, and Kee Chaing Chua, Member, IEEE Abstract For various advantages including better utilization of radio spectrum (through frequency reuse), lower mobile transmit power requirements, and smaller and cheaper base station equipment, future wireless mobile multimedia networks are likely to adopt micro/picocellular architectures. A consequence of using small cell sizes is the increased rate of call handoffs as mobiles move between cells during the holding times of calls. In a network supporting multimedia services, the increased rate of call handoffs not only increases the signaling load on the network, but makes it very difficult for the network to guarantee the quality of service (QoS) promised to a call at setup or admission time. This paper describes an adaptive QoS handoff priority scheme which reduces the probability of call handoff failures in a mobile multimedia network with a micro/picocellular architecture. The scheme exploits the ability of most multimedia traffic types to adapt and trade off QoS with changes in the amount of bandwidth used. In this way, calls can trade QoS received for fewer handoff failures. The call level and packet level performance of the handoff scheme are studied analytically for a homogeneous network supporting a mix of wide-band and narrow-band calls. Comparisons are made to the performance of the nonpriority handoff scheme and the well-known guard-channel handoff scheme. Index Terms Adaptive QoS, handoff priority scheme, mobile multimedia networks. I. INTRODUCTION IN RECENT years, there has been a tremendous effort of research and development expended to realize future mobile communication networks that will support integrated and multimedia services. For example, a large number of projects funded under the European Commission s Advanced Communication Technologies and Services (ACTS) program are focused on research related to third-generation mobile communication systems [1], such as the universal mobile telecommunications system (UMTS) and wireless asynchronous transfer mode (ATM). Due to the scarce bandwidth as well as errors on the wireless channel, efficiency of resource utilization is a key issue in the design and implementation of wireless mobile networks in general and mobile multimedia networks in particular. For various advantages including better utilization of radio spectrum (through frequency reuse), lower mobile transmit power requirements, and smaller and cheaper base station equipment, future mobile multimedia networks will adopt micro/picocellular architectures. A consequence of using small cell sizes is Manuscript received September 11, 1997; revised April 27, 1999. W. Zhuang is with the Department of Electrical Engineering, National University of Singapore, Singapore. B. Bensaou and K. C. Chua are with the Centre for Wireless Communications, National University of Singapore, Singapore (e-mail: cwcbb@cwc.nus.edu.sg). Publisher Item Identifier S 0018-9545(00)02547-0. the increased rate of call handoffs as mobile terminals move between cells during the holding times of their calls. In a network supporting multimedia services, the increased rate of call handoffs not only increases the signaling load on the network, but, and more importantly, may adversely impact the quality of service (QoS) of the calls due to handoff failures. Note that in this paper we are interested in intercells handoff (i.e., handoff due to mobility between cells) rather than intracell handoff where the mobile hands off due to radio interference. From a user s perspective, having a connection terminated in the middle of a call because of a handoff failure is far more annoying than having a new call attempt blocked occasionally. To reduce handoff failures, various schemes have been devised to prioritize bandwidth (channels) allocation to handoff calls rather than accepting new calls into the network [2] [6], [11]. Mainly, two generic schemes are considered in the literature. In the so-called guard channel (GC) scheme (e.g., [7]), a number of channels in each cell are reserved for exclusive access by handoff calls. It is well known that the GC scheme, although simple to deploy, has many disadvantages. First, reserving channels continuously translates into limiting drastically the total carried traffic. Second, it has been shown in [7] that the GC scheme is unable to provide fair QoS to different types of services efficiently. For instance, more bandwidth has to be reserved for wide-band 1 handoff calls; doing so, however, leads to a very low utilization of the scarce radio bandwidth. The second scheme, the so-called handoff-queuing (HQ) scheme, exploits cell overlaps to allow handoff calls to queue and wait for a certain time for channels to become available. This approach is not practically feasible for real-time multimedia services in picocellular networks, since the time interval available for queueing handoff requests might not be sufficiently long enough for bandwidth resources to become available, especially for wide-band handoff calls. In [8], a new handoff scheme which exploits channel subrating has been proposed for personal communications service (PCS) systems with one class of service. The scheme improves system performance in terms of call blocking and handoff dropping probabilities by allowing an occupied full-rate channel to be temporarily divided into two half-rate channels, one to serve the existing call and the other to serve a handoff call when there is no idle channel available at the time the handoff call arrives. For PCS systems, the scheme clearly exploits the ability of users of telephony services to tolerate the use of half-rate vocoders with some degradation in speech quality. Indeed, this is also true for most multimedia services involving audio and video appli- 1 Wideband here refers to calls with high-bandwidth requirements such as those carrying high-quality audio and video traffic. 0018 9545/00$10.00 2000 IEEE

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 495 cations where multilevel or hierarchical source coding schemes can be used. For instance, closed-loop MPEG encoders can be used for implementing services such as videotelephony. In this paper, we propose a similar scheme that supports two classes of services. The first class (wideband) is assumed to be an adaptive QoS class which accepts channel subrating, while the other (narrowband) does not accept QoS degradation. This paper studies the performance of our handoff scheme in the context of a mobile multimedia network. The effects of such a handoff scheme on the packet level performance are also examined. The performance of our system is compared to both the GC scheme and the nonprioritized scheme where handoff and new calls are treated equally. The remainder of this paper is organized as follows. In the next section, the handoff scheme, called adaptive QoS handoff priority scheme, is described. In Section III, the performance of the scheme assuming two types of calls is studied analytically at the call level in terms of call blocking and handoff dropping probabilities. The impact of such a scheme on the packet level performance in terms of packet delay and loss probability are then thoroughly examined in Section IV. In Section V, numerical results and comparison of the performance of the three schemes are presented and discussed. Section VI describes a modified adaptive QoS handoff scheme where the system allows a fraction of both new and handoff wide-band calls to be accepted with degraded QoS. In this latter model, we study the effect of the fraction of calls accepted on the different QoS metrics. Finally, conclusions are drawn in Section VII. II. ADAPTIVE QoS HANDOFF PRIORITY SCHEME The adaptive QoS handoff priority scheme exploits the ability of most multimedia services to adapt to changing network bandwidth availability [9], [10]. Such services, called adaptive services, could use, for example, multilevel or hierarchical coding methods that enable the services to adapt to different amounts of available transmission bandwidth resulting in different QoS levels. For example, closed-loop MPEG encoders with bandwidth renegotiation can be deployed to encode video streams for the adaptive QoS type of service. The handoff prioritization scheme works as follows. New calls of both classes are accepted if and only if the call can secure the full bandwidth requirements. In other words, and particularly for the wide-band service, a new call that cannot obtain the full bandwidth it requires is rejected. On the other hand, upon handoff by an adaptive call, if there is not sufficient bandwidth to meet the normal QoS of the adaptive call, the call will hand off successfully if the new cell to which it is entering is able to provide it with a smaller amount of bandwidth sufficient to meet a lower, but tolerable QoS level. The new cell may obtain the required amount of bandwidth to support the handoff call by lowering the QoS levels of one or more existing adaptive calls that it is already serving. Otherwise, the handoff fails. If the handoff call is nonadaptive, then handoff is successful only if the new cell can provide the handoff call with all the bandwidth needed by the call. Again, this bandwidth may be obtained by lowering the QoS levels of existing adaptive calls served by the cell. Clearly, the handoff scheme requires additional signaling overheads for QoS renegotiations and indications. These are not the subject of the present paper and are thus not discussed here. III. CALL LEVEL PERFORMANCE MODEL The system model used assumes a fixed or quasi-static channel assignment homogeneous network in which a number of channels (which can be time slots, frequencies, spreading codes, etc.) are assigned to each cell [12]. Without loss of generality, we consider two types of calls in this paper. 2 One carries a constant bit rate real-time narrow-band service which always requires the use of one channel The other type of call carries a variable bit rate wide-band service which normally requires the use of two channels, but can tolerate a lower QoS level with the use of only one channel Note that it is not necessary to assume fixed values for and, however, to simplify the mathematical notations later on, we do assume them constant throughout the remainder of this paper. For instance, these quantities can be assumed to be the effective bandwidth required by a source in each class of service, in which case, they would be variable and are functions of the state of the system at the call level. The traffic model makes the commonly used assumptions that new calls are generated according to independent Poisson processes with mean rates and, respectively, for wide-band and narrow-band calls. This assumption is not necessarily simplistic, as calls are generated by end users (humans), and thus the independence assumption and memoryless property of the interarrivals of calls does indeed apply. Furthermore, the mean call holding times are exponentially distributed with means and, respectively, for wide-band and narrow-band calls. Finally, the mean dwelling times in a cell, that is, the time spent in one cell before handing off to a neighboring one, are also exponentially distributed with means and, respectively, for wide-band and narrow-band calls. The handoff rates and of the wide-band and narrow-band calls are shown to be given by [5], [8] where and are the new call blocking probability and the handoff call dropping probability, respectively, for service type Furthermore, [5] and [8] show that the probability that a call of type fails to complete successfully due either to blocking or forced termination (handoff dropping), is given by A. Adaptive QoS Handoff Model Since the network is assumed to be homogeneous, the performance of the system can be deduced from the performance of a single cell analyzed in isolation. The system at a single cell 2 Note that accounting for more types of services is rather a complex, but not complicated process. It can be easily done by increasing the dimensions of the Markovian model. It would only increase the complexity of the numerical procedures used to estimate the performance metrics of the system. (1) (2)

496 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 can be modeled by a two-dimensional (2-D) Markov process. Fig. 1 shows an example state space assuming a total of channels in the cell. In this model, each state can be defined by the 2-tuple such that where is the number of on-going narrow-band calls and is the number of on-going wide-band calls. Let denote the set of feasible states and be an indicator function to indicate the states in i.e., For all, the balance equations are (3) where and are given by Fig. 1. Rate diagram of AQoS handoff priority scheme with four channels. (4) while the expected number of wide-band calls in progress but receiving a lower QoS level is given by (5) Applying the constraint to the set of balance equations, the equilibrium state probabilities can be obtained numerically and used to calculate the call blocking and dropping probabilities using the following: Additionally, the expected numbers of wide-band and narrow-band calls in progress are given by (6) (7) (8) (9) (12) Finally, the probability that a wide-band call experiences a lower QoS level is given by B. Nonpriority Handoff Model With the nonpriority handoff scheme, The channel occupancy time is the minimum of the call holding time and the remaining call residual time [5]. Because these are exponentially distributed for both wide-band and narrow-band calls, the mean channel occupancy times for wide-band and narrow-band calls are Let be the total arrival rates of type calls to a cell. Then, type traffic load is given by. It is well known that the performance of such a system at a single cell can be modeled by a multidimensional loss system, an example state space of which is as shown in Fig. 2, where the cell is modeled with channels. In particular, let be the joint probability that narrow-band and wide-band calls exist in the steady state. It can be shown that the product form solution exists and is given by [13] (10) From the normalization condition, (13) is determined to be (11)

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 497 Fig. 2. Rate diagram of nonpriority handoff scheme with four channels. Fig. 3. Rate diagram of GC handoff priority scheme with four channels. where with denoting the maximum integer value less than or equal to From (13), the probabilities and are given by where (mod (14) (15) C. Guard Channel Handoff Model The analysis of the guard-channel scheme considering multiple types of handoff calls have been considered, e.g., in [7]. Essentially, the system at a single cell can be modeled again using a multidimensional Markov process. Fig. 3 shows an example state space assuming The call blocking and dropping probabilities for both wide-band and narrow-band calls can be determined from the following: (16) (17) (18) (19) IV. PACKET LEVEL PERFORMANCE Intuitively, the AQoS scheme should provide better guarantees than its counterparts in terms of handoff call dropping probability, which should be part of the QoS framework in a wireless mobile multimedia network. However, this improvement is achieved by reducing the bandwidth allocated to both the handoff call and the ongoing adaptive calls. It is obvious that this would have an important impact on the packet level QoS metrics such as packet delay and/or packet loss probability. In this section, we investigate this impact of this call admission policy on the packet level QoS. In order to evaluate the impact of the call level priority scheme on the packet level performance seen by the traffic sources, there are two main scenarios to consider. Scenario 1 considers the case where the traffic flows from the mobile terminals to the base station (i.e., uplink traffic streams). The buffers used to absorb the packet level congestion are located at the different mobile terminals. There is thus no shared buffer architecture, and the only resource shared among the different terminals is the bandwidth. Note, however, that this scenario can also apply to the downlink channel if the base station s architecture segregates the downlink traffic streams destined to different terminals in different buffers. Scenario 2 considers the case where the different terminals share both the buffer and the bandwidth. Typically, this cannot happen for the uplink channels, thus, it concerns mainly the traffic flowing from the base station to the mobile terminals. In both scenarios, the narrow-band service is considered to be a constant bit rate service, and thus deterministic multiplexing is used. The performance of such a service as well as the buffer statistics at the packet level can simply be obtained from classical queueing models such as for Scenario 1 and ([14]) or even ([15]) for Scenario 2. For the wide-band service, we assume it carries a variable bit rate traffic. For mathematical tractability, and without loss of generality, we assume each connection generates traffic according to the well known on/off source model. We assume both the on and off periods to be independent and exponentially distributed with mean 1 and 1/λ respectively. More general burst and silence length distributions can be accounted for (e.g., [17], [18]), however at the expense of more complex numerical procedures.

498 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 Denote by the probability that the queue length exceeds in a buffer, fed by the superposition of independent identically distributed on/off sources and, having a constant service capacity equal to Let be the number of wide-band sources that are on at time and denote by the buffer content at time is not Markovian. However, the joint process is Markovian. The system can thus be solved for the joint stationary probability, and the stationary distribution of the buffer occupancy can be obtained from the marginal distribution of Note that we do not give here the details of such a solution which has been thoroughly and elegantly investigated in [16]. Similar models with generally distributed on and off periods have been considered in the context of ATM networks in [17] and [18]. The input rate to the queue is determined by the state of the finite dimensional Markov process representing the number of on sources at time Let be the total number of sources and the input rate when is in state Denote by the infinitesimal generator of : is the transition rate from state sources on) to state sources on), and Let be the steady-state probability that the rate process is in state and the buffer content is not larger than The system of differential equations governing the dynamics of the joint process is then (20) where diag is the drift matrix (i.e., a matrix giving the rate at which the buffer content increases/decreases for the different states of the input process) and Solving this system would give the stationary buffer overflow probability [16] For the details on how to derive these results as well as how to obtain all the higher order moments of the queue length, we refer the reader to [16]. A. Scenario 1: Shared Bandwidth, Dedicated Buffer In this scenario, we assume that the total bandwidth allocated to the ongoing wide-band connections is shared equally among them (e.g., by using processor sharing or round robin service discipline). Since there is no buffer sharing, if we denote by the average probability that the queue length exceeds for a wide-band connection with a dedicated buffer, approximate the buffer overflow probability by (24) where is the total bandwidth allocated to the wide-band calls when the system, at the call level, is in state When state is a feasible state, can be readily written as The average queue length can also be estimated as (25) Finally, the average packet delay for a given wide-band connection is (26) (21) where and are, respectively, the stable eigenvalues and the associated eigenvectors of and are solutions of the eigensystem (22) and are real coefficients determined from the boundary conditions [16]: and Finally, the expected queue length of such a system can be obtained as B. Scenario 2: Shared Bandwidth, Shared Buffer In this scenario, we assume that both the total bandwidth allocated to the ongoing wide-band connections and the buffer space are shared among all the wide-band connections. If we denote by the average probability that the aggregate queue length exceeds we can write (27) where is the total bandwidth allocated to the wide-band calls when the system at the call level is in state The average queue length can also be estimated as (23) (28)

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 499 Fig. 4. Blocking probability. Fig. 5. Dropping probability. and finally the average packet delay in the aggregate queue V. NUMERICAL RESULTS (29) To assess the performance of the adaptive QoS handoff scheme, comparisons are made with the performance of both the nonpriority and guard-channel schemes using numerical examples. A single cell with channels is considered. The arrival rates of narrow-band and wide-band new calls are assumed to be equal: In this example, we also choose the call completion rates equal calls/min and the dwelling rates calls/min. Throughout this section, for the guard channel scheme, we consider the number of channels reserved for exclusive access by handoff calls to equal seven so that the wide-band call dropping probability is not more than 1% for the traffic load levels considered. A. Call Level Performance Fig. 4 shows the call blocking probabilities of wide-band and narrow-band calls, respectively, against the total call arrival rate. In the figure, the nonpriority scheme, guard-channel scheme with seven guard channels, and adaptive QoS scheme are referred to, respectively, by and AQoS. For both wide-band and narrow-band calls, the call blocking probabilities of AQoS are, as expected, between those of and By allowing wide-band calls to adapt to using fewer channels, AQoS clearly increases the utilization of the available channels in the cell, and this causes new calls to be blocked more often compared to where both new and handoff calls are not differentiated. Nevertheless, the fact that AQoS has call blocking probabilities that are lower than those of shows that AQoS is more flexible in allocating bandwidth to calls than Fig. 5 shows the call dropping probabilities for wide-band and narrow-band calls, respectively, against the total call arrival rate. It can be seen that AQoS performs much better than the other two schemes. It is possible, though, to make achieve lower handoff dropping probability by making much larger, however, this will either make the call blocking probability much worse than AQoS if the number of channels is kept constant or make the overall bandwidth utilization very poor in case the number

500 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 Fig. 7. Expected fraction of time QoS of wide-band calls is degraded. Fig. 6. Call noncompletion probability. of channels is also increased. With AQoS there is no reservation of channel for handoff calls, instead, the probability of call dropping due to handoff is reduced by reducing the QoS levels of calls that carry adaptive traffic. Given that most multimedia traffic such as voice and video are indeed adaptive, AQoS therefore provides a very flexible means of managing bandwidth resources to maximize the utilization. To provide another (perhaps combined) means of comparing the performance of the three handoff schemes being considered in the paper, Fig. 6 depicts the probability of call noncompletion (i.e., the probability that either a new call attempt is blocked or a handoff call is forced to terminate) against the total call arrival rate. For both wide-band and narrow-band calls, AQoS performs better than the other two schemes, indicating that our scheme results in a better utilization of the available bandwidth in the cell (less bandwidth is wasted in supporting calls that are subsequently not successful). Clearly, this better performance in the call level is obtained at th e expense of possible QoS degradation for wide-band calls at the packet level. To determine the extent to which such QoS levels are degraded for wide-band calls, Fig. 7 plots the expected fraction of time in which wide-band calls receive reduced QoS. As can be seen from the figure, wide-band calls experience longer periods of degraded QoS when the traffic load increases. However, for the Fig. 8. Scenario 1: average packet delay. load levels considered, the duration within which QoS is degraded remains under 3.6% of the total call duration. B. Packet Level Performance At the packet level, we assume that each on/off source becomes and remains on for an average duration s and then goes silent for a duration s. During an on period, the source generates data at a constant rate of 1 Mbps. The

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 501 transmission rate per channel is 110 kbps. We discuss the performance of Scenario 1 first. Fig. 8(a) shows the delay against the offered traffic load. With the increase of the traffic load, more wide-band calls share the assigned bandwidth which makes the delay increase. Among the three schemes, the guard channel scheme has the lowest delay because the scheme reserves some bandwidth for exclusive access by handoff calls and thus nondiscontinued calls have access to their full bandwidth requirement. The maximum admissible load in GC is thus less than that can be supported in the other two schemes. In other words, AQoS has the highest delay because it is flexible enough to compromise a little on packet delay to increase the carried traffic load and thus efficiency of resource utilization. By sacrificing some packet delay, it gains with respect to the other two schemes in terms of handoff dropping probability (which can intuitively be considered as the most severe cause of QoS violations). This is confirmed by the results in Fig. 8(b) where we show the normalized delay per carried Erlang against the carried traffic load in Erlang. Here, the carried traffic load is the load of wide-band calls that terminate successfully. AQoS has almost the same average normalized delay as For the normalized delay is slightly lower than AQoS. Nevertheless, we can see that the curve for is shorter than that of which in turn is shorter than that of AQoS. This shows that cannot carry as much traffic load as AQoS because of the guarded channels. There is thus a tradeoff between the carried traffic and the delay. Fig. 9 shows the overflow probability against the buffer size under different traffic loads. It is obvious that the overflow probability increases as the traffic load increases, especially for the AQoS scheme. Although provides better overflow probability than AQoS, it should be noted that the packet level QoS (notably the overflow probability) is meaningful only when the accepted call terminates successfully. Thus, taking into account the achieved handoff dropping probabilities, it can be seen that AQoS is actually much better than both the and schemes. This is mainly because the other schemes are not well suited to deal with different levels of QoS guarantee. Let us consider the following example where the adaptive service should guarantee a packet loss probability of 10 It is obvious that in this case the handoff dropping probability should be less than or at most equal to the packet loss probability, i.e., 10 For an adaptive service such as MPEG video, these values are quite reasonable. Table I compares the performance of AQoS and (Note that the values in the table are rather orders of magnitude than exact values.) It is clear from the table that AQoS can guarantee the same performance as more efficiently. A delay of 100 ms is well within the target delay of many adaptive services. A buffer of 2 Mbits is not uncommon and not extremely expensive. At the expense of these slight increases in buffer size and delay, the network can accept three times more activity per cell in AQoS than it does in Now, we address the performance of Scenario 2. Fig. 10(a) shows the delay against the offered traffic load. The second scenario shows results that are almost similar to those shown Fig. 9. Scenario 1: overflow probability. TABLE I PERFORMANCE COMPARISON AQoS VERSUS GC7 by the first scenario. The GC scheme has the lowest delay, and less admissible load. AQoS has the highest delay with highest admissible load. However, it is obvious that the second scenario has less delay than the first scenario. The reason is that the second scenario uses shared buffer and shared bandwidth. So it can use bandwidth resources more efficiently. Fig. 10(b) shows the normalized delay per carried Erlang against the carried traffic load in Erlang. AQoS has almost the same average normalized delay as has slightly lower normalized delay than AQoS. However, we can see again that the curve for is shorter than that of which in turn is shorter than that of AQoS because cannot carry as much traffic load as AQoS. Fig. 11 shows the overflow probability against the buffer size under different traffic loads. Similar to Scenario 1, the overflow

502 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 Fig. 10. Scenario 2: average packet delay. probability increases as the traffic load increases. has lower overflow probability than AQoS. We can see that the overflow probability is sightly higher than that of Scenario 1. VI. A MODIFIED ADAPTIVE QoS HANDOFF PRIORITY SCHEME As seen from the performance of the AQoS scheme, there is a big mismatch between the handoff dropping probability and the new call blocking probability. For some services, this huge gap is sometimes undesirable. To address this issue, a modified version of the adaptive QoS handoff priority scheme is considered in this section. The modified scheme works as follows. If a wide-band handoff call is unable to secure the total bandwidth it requires, it is accepted with degraded QoS with a probability and is dropped otherwise. In other words, only a fraction of handoff calls is treated in the same manner as in the adaptive QoS scheme. The remaining fraction is dropped. Intuitively, this would relieve the network from being completely jammed by wide-band handoff calls and thus ensures that ongoing wide-band calls receive more than their minimum bandwidth requirement. On the other hand, to decrease the blocking probability of new calls, an adaptive new call which is unable to secure enough bandwidth to meet its normal QoS is accepted with probability if the cell is Fig. 11. Scenario 2: overflow probability. able to provide it with a smaller amount of bandwidth sufficient to meet a lower, but tolerable QoS level. It is blocked otherwise. Nonadaptive calls are admitted only if the cell can provide them with all the required bandwidth. Note that this simply treats new calls and handoff calls in the same way, however, the scheme allows their differentiation through the values and Also, the AQoS scheme is a special case of this modified adaptive QoS (MAQoS) scheme with and The advantages of MAQoS over AQoS are that it provides a system operator with the ability to adjust the parameters and in order to achieve desired blocking and dropping probabilities without having to increase or decrease the number of channels. In other words, MAQoS is much more flexible than AQoS. A. Performance Model of MAQoS Scheme With this scheme, the system at a single cell can also be modeled by a multidimensional Markov process. An example of state transition diagram for MAQoS assuming a total of channels in the cell is similar to that of Fig. 1, except for the transition rates when the system is in congestion state. Only a fraction of handoff calls and a fraction of new calls can be accepted when the system is in any of the congestion states. In the particular example depicted in Fig. 1, these states correspond to the states such that, i.e.,

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 503 the states where wide-band calls are unable to secure their full bandwidth and will thus be accepted with degraded QoS. In the general model, each state can be defined by the same 2-tuple such that where is the number of on-going narrow-band calls and is the number of on-going wide-band calls. The balance equations become TABLE II AQoS PERFORMANCE TABLE III MAQoS PERFORMANCE WITH DIFFERENT f (30) where and are (31) (32) As before, by applying the constraint to the set of balance equations, the equilibrium state probabilities can be obtained numerically and used to calculate the call blocking and dropping probabilities as follows: (33) (34) (35) (36) Finally, the expected numbers of wide-band and narrow-band calls in progress can be calculated from (10) and (11). The expected number of wide-band calls receiving a lower QoS level can also be obtained from (12). B. Numerical Results The performance of the MAQoS scheme is compared to that of AQoS scheme in this section. Under different traffic loads, the service provider can set different and values depending on the QoS requirements. Of course, a more conventional way of choosing these values is to study a network optimization problem with respect to a given objective function (e.g., to maximize a revenue function with different constraints on the QoS). This issue is beyond the scope of this paper. Intuitively, we expect the blocking probabilities of both types of new calls to decrease as increases and the dropping probabilities to increase as decreases. For example, let us set the traffic load of type calls to 2.1 calls/cell/min. The total new calls arrival rate in a cell is thus 4.2 calls/cell/min. Table II shows the average delay, and the blocking and dropping probabilities for the AQoS scheme. In Table III, we set and give the performance of MAQoS with different values of In other words, new calls are treated in the same manner as in AQoS, while handoff calls are accepted with a probability in case of congestion. We can see that the dropping probabilities increase while the blocking probabilities and the delay decrease with the decreasing values of When decreases to zero, the MAQoS scheme corresponds to the scheme described in the previous sections. In Table IV, we set and present the performance of MAQoS for different values of In other words, handoff calls are treated in the same manner as in AQoS, while new calls are accepted with a probability in case of congestion. With increasing values of the blocking probabilities decrease because the system accepts more new calls in the congestion state. However, the delay and dropping probabilities increase. Previously, we gave the performance of MAQoS with different and values. With increasing values of the delay and dropping probabilities increase while blocking probabilities decrease. With decreasing values of the performance reverses. Since the two fractions and have an inverse impact on the three performance metrics, it is clear that by adjusting both fractions simultaneously, one can achieve a wide range of different QoS levels that can only be obtained in all the other schemes by increasing the total bandwidth in a cell. Which means that one of the QoS metrics will be overguaranteed. For example, as shown in rows 2 and 3 of Table V, the dropping probability of wide-band calls can be kept at 1% or so, while the blocking probabilities are decreased, and delay is kept in the same order of magnitude as that of the AQoS scheme.

504 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 49, NO. 2, MARCH 2000 TABLE IV MAQoS PERFORMANCE WITH DIFFERENT f TABLE V MAQoS PERFORMANCE WITH DIFFERENT f VII. CONCLUSIONS AND f An adaptive QoS handoff priority scheme and its modification, which reduces the probability of call handoff failures in a mobile multimedia network with a micro/pico cellular architecture, have been described. The schemes exploit the ability of most multimedia traffic types to adapt and trade off some QoS at the packet level to achieve smaller probability of dropping at handoff which has a dramatic impact on the user-perceived QoS. In this way, calls with adaptive traffic can opt to use lower amounts of bandwidth and handoff successfully while handoff calls with nonadaptive traffic can be accommodated by forcing existing calls with adaptive traffic to adapt to using less bandwidth. The AQoS scheme proved more flexible and efficient in guaranteeing QoS than the so-called guard channel scheme, which guarantees one of them by overguaranteeing the other one. The modification of AQoS, the so-called MAQoS, can admit new calls even when the system is in congestion state, such as emergency calls. In addition to the flexibility inherited from the AQoS scheme, MAQoS is even more flexible in decoupling the different components (dropping, and blocking probabilities) of the grade of service metric. The performances of the adaptive QoS handoff priority scheme and its modification are studied analytically and compared to those of the nonpriority handoff scheme and the guard-channel handoff scheme. [7] S. S. Rappaport and C. Purzynski, Prioritized resource assignment for mobile cellular communication systems with mixed services and platform types, IEEE Trans. Veh. Technol., vol. 45, no. 3, pp. 443 457, 1996. [8] Y. B. Lin, A. Noerpel, and D. Harasty, The subrating channel assignment strategy for PCS hand-offs, IEEE Trans. Veh. Technol., vol. 45, no. 1, pp. 122 130, 1996. [9] K. Lee, Supporting mobile multimedia in integrated services networks, J. Wireless Networks, vol. 2, no. 2, pp. 205 217, 1996. [10] S. Shenker, D. D. Clark, and L. Zhang, A scheduling service model and a scheduling architecture for an integrated services packet network,, 1993. [11] W. Zhuang, B. Bensaou, and K. C. Chua, Handoff priority scheme with preemptive finite queueing and reneging in mobile multimedia networks, in 11th ITC Specialist Seminar: Multimedia and Nomadic Communications, Yokohama, Japan, Oct. 1998, pp. 181 187. [12] J. C. I. Chuang, Autonomous adaptive frequency assignment of TDMA portable radio systems, IEEE Trans. Veh. Technol., vol. 40, no. 3, pp. 627 635, 1991. [13] H. Akimaru and K. Kawashima, Teletraffic Theory and Application. Germany: Springer-Verlag, 1993. [14] J. W. Roberts and J. T. Virtamo, The superposition of periodic cell arrival streams in an ATM multiplexer, IEEE Trans. Commun., vol. 39, no. 2, pp. 298 303, 1991. [15] L. Kleinrock, Queueing Systems Volume I: Theory. New York: Wiley- Interscience, 1975. [16] D. Anick, D. Mitra, and M. Sondhi, Sstochastic theory of a data handling system with multiple sources, Bell Syst. Tech. J., vol. 61, no. 8, pp. 1871 1894, 1982. [17] B. Bensaou, J. Guibert, J. W. Roberts, and A. Simonian, Performance of an ATM multiplexer queue in the fluid approximation using the Bene s approach, Annals Oper. Res., vol. 49, pp. 137 160, 1994. [18] N. Baiocchi, N. Blefari-Melazzi, A. Roveri, and F. Salvatore, Stochastic fluid analysis of an ATM multiplexer loaded with heterogeneous on off sources: An effective computational approach, in IEEE INFOCOM 92, pp. 405 414. Wei Zhuang received the B.E. degree in electronic engineering from the Huazhong University of Science and Technology, Wuhan, China, in 1991, the M.E. degree in communications and electronic systems from Shanghai Jiaotong University, Shanghai, China, in 1994, and the Ph.D. degree from the National University of Singapore, Singapore. He is currently an Engineer at Fujitsu Singapore Pte. Ltd., Singapore. His research interests include mobile radio communications, mobile multimedia, traffic analysis, and queueing theory. REFERENCES [1] J. S. Dasilva, B. Arroyo, B. Barani, and D. Ikonomou, European thirdgeneration mobile systems, IEEE Commun. Mag., vol. 34, no. 10, pp. 68 83, 1996. [2] D. Hong and S. S. Rappaport, Traffic model and performance analysis for cellular mobile radio telephone systems with prioritized and no-protection handoff procedure, IEEE Trans. Veh. Technol., vol. 35, no. 3, pp. 77 92, 1986. [3] S. Tekinay and B. Jabbari, Handoff policies and channel assignment strategies in mobile cellular networks, IEEE Commun. Mag., vol. 29, no. 11, pp. 42 46, 1991. [4] C. H. Yoon and C. K. Un, Performance of personal portable radio telephone systems with and without guard channels, IEEE J. Select. Areas Commun., vol. 11, no. 6, pp. 911 917, 1993. [5] Y. B. Lin, S. Mohan, and A. Noerepel, Queueing priority channel assignment strategies for handoff and initial access for a PCS network, IEEE Trans. Veh. Technol., vol. 43, no. 3, pp. 704 712, 1994. [6] K. C. Chua, B. Bensaou, W. Zhuang, and S. Y. Choo, Dynamic channel reservation (DCR) scheme for handoffs prioritization in mobile micro/picocellular networks, in Proc. IEEE ICUPC 98, Florence, Italy, Oct. 1998, pp. 383 387. Brahim Bensaou (M 97) received the Diplôme d Ingénieur degree from the University of Science and Technology Houari Boumediene of Algiers, Algeria, in 1987, the D.E.A. degree from the Universite Paris XI, Orsay, France, in 1988, and the Ph.D. degree from the University Paris VI, France, in 1993, all in computer science. From 1990 to 1993, he was a member of the Performance of Multiservice Networks research group with France Telecom Research Labs, CNET. From 1995 to 1997, he was a Research Associate at the Broadband Communications Lab, Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Hong Kong. In April 1997, he joined the Centre for Wireless Communications, National University of Singapore, Singapore, where he is currently a Senior Member of Technical Staff and Leader of the Networks Research Group. His research interests are mainly focused on, but not limited to, scheduling and traffic control in both wired and wireless packet networks, QoS and related issues, and mobility issues.

ZHUANG et al.: ADAPTIVE QUALITY OF SERVICE HANDOFF PRIORITY SCHEME 505 Kee Chaing Chua (M 91) received the B.E. degree with First Class Honors in electrical and electronic engineering from the University of Auckland, New Zealand, the M.Eng. degree in electrical engineering from the National University of Singapore (NUS), Singapore, and the Ph.D. degree in engineering from the University of Auckland. He is an Associate Professor in the Department of Electrical Engineering, NUS, and concurrently the Deputy Director of the Centre for Wireless Communications, a national R&D center. His research interests are primarily in computer communication networks, with a current focus on wireless/mobile multimedia networking. He has to date published more than 70 technical papers in both internationally refereed journals and conference proceedings. Dr. Chua has been an active member of the IEEE Communications Society and was the Chairman of the Singapore Chapter of the Society from 1993 to 1994. He also served in the Society s Member Affairs Council and acted as the Region 10 Chapters Chair Coordinator. He also served as the Finance Chair of the IEEE Globecom 95 in Singapore and in the TPC of a couple of IEEE INFOCOM and PIMRC conferences.