Call Admission Control for Voice/Data Integrated ... - Semantic Scholar

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 22, NO. 4, MAY 2004

Call Admission Control for Voice/Data Integrated Cellular Networks: Performance Analysis and Comparative Study Bin Li, Senior Member, IEEE, Lizhong Li, Bo Li, Senior Member, IEEE, Krishna M. Sivalingam, Senior Member, IEEE, and Xi-Ren Cao, Fellow, IEEE

Abstract—In this paper, we propose a new call admission control scheme called dual threshold bandwidth reservation, or DTBR scheme. The main novelty is that it builds upon a complete sharing approach, in which the channels in each cell are shared among the different traffic types and multiple thresholds are used to meet the specific quality-of-service (QoS) requirements. We present a detailed comparative study based on mathematical and simulation models, and quantitatively demonstrate that the DTBR is capable of providing the QoS guarantee for each type of traffic, while at the same time leading to much better channel efficiency. We further show that the DTBR scheme with elastic data service can offer both service guarantee and service differentiation for voice and data services, and enhance the bandwidth utilization. Index Terms—Call admission control, quality-of-service (QoS), wireless cellular networks.

I. INTRODUCTION

T

HIS PAPER studies the problem of call admission control in integrated cellular networks supporting multiple traffic types. Call admission control in single-service wireless cellular networks has been extensively studied. For conversational services such as voice, the guarded channel (GC) schemes have been shown to be effective for providing the necessary

Manuscript received February 23, 2003; revised October 6, 2003. The work was support in part by grants from the Hong Kong Research Grants Council (RGC) under Contract HKUST6196/02E and Contract HKUST6402/03E, a Natural Science Foundation of China and Hong Kong Research Grants Council (NSFC/RGC) joint grant under Contract N_HKUST605/02, a grant from Microsoft Research under Contract MCCL02/03.EG01, and in part by grants from the U.S. Air Force Office of Scientific Research (AFOSR) under Contract F-49620-97-1-0471 and Contract F-49620-99-1-0125. B. Li was with the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. He is now with China Motion NetCom, Ltd. (e-mail: [email protected]). L. Li was with the Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. He is now with the National Key Laboratory of Modern Signal Processing, Chengdu, China. B. Li is with the Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China (e-mail: [email protected]). K. M. Sivalingam is with the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore, MD 21250 USA (e-mail: [email protected]). X.-R. Cao is with the Department of Electrical and Electronic Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China (e-mail: [email protected]). Digital Object Identifier 10.1109/JSAC.2004.825987

quality-of-service (QoS) guarantee in terms of both call termination and call blocking probabilities [2], [3]. For streaming media services where rate adaptation is present, graceful bandwidth degradation schemes have been proposed to provide low handoff dropping probability and forced termination probability [4]–[6]. A system that supports multiple traffic types has to incorporate the varying traffic characteristics in its admission control mechanism. One of the challenges in a multiservice system is that the limited bandwidth has to be efficiently shared among multiple traffic types. The basic mechanism is to partition the available channels in a cell among the different traffic types. This partitioning can be rigid or adaptive. The former is simpler to implement but will result in poor performance when the traffic patterns do not conform to the partitioning. Thus, flexible schemes that change the allocation of channels to different traffic types can help improve performance. In [7], the complete sharing (CS) and complete partition (CP) schemes are investigated for two types of traffic with different bandwidth requirements. Huang et al. propose a movable boundary allocation scheme for voice and data traffic [8], in which bandwidth is divided into two subpools by two dynamically adjustable thresholds. This facilitates bandwidth provisioning for different QoS requirements and provides adaptation to changing traffic requirements. In this paper, we build upon our dual threshold bandwidth reservation (DTBR) scheme for a voice and data integrated system that was presented in [10] and [11]. In this scheme, two thresholds are employed to block the data and new voice traffic, respectively. The main feature of this scheme is that it enables the CS of the overall bandwidth, thus leading to efficient usage of the scarce wireless resources. The analysis and simulation results presented in [10] reveal that, by proper adjustment of threshold values, the DTBR scheme can guarantee target handoff dropping probability while maintaining the relative new call blocking probability. The results obtained in [10] reveal that the maximum channel utilization is less than 0.8 for the studied traffic conditions. This is due to the fact that data calls specify fixed bandwidth requirements (e.g., channels per call). When the number of available channels is less than , new data calls cannot be accepted and these idle channels will be wasted, thus leading to relatively low total channel utilization. In order to improve total channel utilization, we extend in this paper the DTBR scheme to include variable or elastic data traffic. Here, data calls specify band. Ideally, it is width needs as a range of values

0733-8716/04$20.00 © 2004 IEEE

LI et al.: CALL ADMISSION CONTROL FOR VOICE/DATA INTEGRATED CELLULAR NETWORKS

assumed that each data connection is always provided with the if available channels can accommomaximum bandwidth date the request. Otherwise, data calls share the bandwidth left for them equally. This data traffic model may cover the interactive service class defined in Universal Mobile Telecommunications System (UMTS)/IMT-2000 [1] and also the background is set to near zero. This variable data traffic service class if model was also studied by the call admission control schemes in [18] and [23], but because voice calls are provided with preemptive priority, the voice and data subsystem were assumed to be independent. In this paper, we will study the scenario where voice calls cannot preempt existing data calls. The contributions of this paper are two-fold. Since there has been little study on the performance comparison of the different bandwidth allocation schemes, we first present quantitative performance measures for the different bandwidth allocation schemes presented in [7]–[10] and then investigate their respective design tradeoffs. We then extend the DTBR scheme to support variable (or elastic) data bandwidth requirements and study its performance without the preemptive voice call priority. The performance analysis is based on a detailed mathematical model followed by numerical results. The rest of the paper is organized as follows. We describe the traffic models and DTBR strategy in Section II. We extend the DTBR scheme to include variable or elastic data traffic in Section III. We present the analytical models in Section IV, followed by the numerical studies in Section V. We conclude the paper and present possible avenues for further study in Section VI. II. NETWORK ARCHITECTURE In this paper, we consider a wireless mobile network with a cellular infrastructure. The network comprises a number of base stations connected by a wireline backbone network. Each base station covers a certain geographical area called cell. A mobile, while residing within a cell communicates with the rest of the network through the base station. When a mobile moves across the cell boundary, a handoff occurs. If there is insufficient bandwidth in the new cell that the mobile is moving into, the handoff could be dropped. One of the key requirements for the call admission control scheme is to ensure that the handoff dropping probability be maintained at a prespecified threshold, irrespective of the traffic conditions. The network supports different types of traffic, where each traffic type specifies its bandwidth requirements that can be fixed (e.g., one channel per voice call) or variable. In this paper, without loss of generality, we focus on two traffic types, namely, voice and data. The derivations and conclusions are generally applicable to arbitrary number of traffic types. We assume that each cell has a total of channels; and that the bandwidth required for voice and data calls are 1 and units (channels), respectively. In general, there are three types of bandwidth reservation schemes: complete partition, in which the bandwidth within a cell is divided into separate pools for each traffic type; CS in that the entire bandwidth is shared among all traffic types; and hybrid schemes that combine both. As mentioned earlier, the work in [7] studies the complete partition and CS; the work in

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Fig. 1. DP control scheme.

[8] presents a scheme based on movable boundary. This scheme provides variable bandwidth allocation, based on a threshold mechanism, to two different traffic types. In this section, we first extend the movable boundary scheme in [8] to a new dynamic partition (DB) scheme, and then we build our earlier proposed model called DTBR scheme presented in [9] and [10]. Below, we present the details of the DP scheme and the DTBR schemes. A. DP Scheme In [8], Huang et al. proposed a voice and data integrated system with finite buffers, in which bandwidths assigned to voice and data are separated (i.e., complete partition). In this scheme, the boundary for the partition is “movable” and, thus, can effectively deal with the traffic changes in the system. However, handoff calls are not differentiated from new calls and the specified requirement of handoff dropping probability is not met. Furthermore, it is assumed that the data requires one unit of bandwidth. In this paper, we extend this scheme to differentiate between handoff calls and also take into account the different bandwidth requirements. In the new scheme, called DP scheme (see Fig. 1), out of channels are reserved exclusively for new/handoff voice calls (voice-only-area) and channels are reserved exclusively for new/handoff data calls (data-only-area). The other ( ) channels (shared-area) are shared in fair manner by both voice and data calls. In order to maintain a low handoff dropping probability for voice calls, we further restrict that new voice calls can only out of voice channels (i.e., guarded channel use the policy). However, handoff voice calls can use all the channels. The admission control of DP scheme is described as below. For a new voice call request, a channel is searched for in the voice-only-area. If there is no available channel there, the shared-area will be searched. Likewise, a data call assignment will be first attempted in the data-only-area and if that is not possible, the shared-area will be examined. When a handoff voice call arrives, if there is no channel available in both the voice only area and the shared area, it will be dropped. When a new voice call arrives, if the channel occupancy exceeds the threshold in the voice only area and there is no idle channel in the shared area, it will be blocked. When a data call (new or handoff) arrive, if the number of idle channels in the data only area or in the

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Fig. 2. DTBR control scheme.

shared area is less than that is the fixed number of channels for data service, it will be blocked. The next section presents the DTBR scheme, which we originally proposed in [10] and [11]. B. DTBR Scheme The proposed DTBR scheme [11] (see Fig. 2) is based on a CS approach. The reason is that this can achieve the maximum channel efficiency, while providing the necessary service guarantee and differentiation for diverse traffic. In the DTBR scheme, the channels of each cell are divided into three reand ( ). When the gions by two thresholds , then number of channels occupied is less than the threshold both data and voice traffic can be admitted into the system; when , no the number of channels occupied is over the threshold data traffic is allowed; when the number of channels occupied , then only handoff voice calls can is more than the threshold be allowed. The handoff voice call will be dropped only if there is no channel available. Under this basic control model, the handoff voice gets highest priority, while data receives lowest service. The reason is that the data traffic can tolerate certain degree of service degradation, while voice cannot. There can be a number of variations using this basic model, for example, whether or not to use a queue to further buffer the handoff request when the requested channels are not available. This has been shown to reduce the handoff dropping probability [2], [12]. In this paper, for simplicity and for fair comparison with the DP scheme, we consider the DTBR scheme without queueing. III. DTBR SCHEME FOR ELASTIC DATA SERVICE In this section, we describe the need for variable (or elastic) data bandwidth specifications and then present how the DTBR scheme can be enhanced to incorporate this need. A. Data Traffic Model and Assumptions Since voice calls are delay and delay-jitter sensitive, fixed bandwidth (one channel unit in this paper) should be allocated for the entire call duration. On the other hand, data calls are loss sensitive but tolerant to delay. Data loss can be handled using

packet retransmission at the upper layers, while delay tolerance is equivalent to the acceptance of a variable service rate. As an example, we consider the transport control protocol (TCP). When a wireless data connection encounters a congested area wherein the total available bandwidth is smaller than the total instantaneous capacity required by wireless terminals in the area, the end-to-end TCP entity would reduce the flow of new packets into the networks by reducing the window size. In other words, the wireless bandwidth of that cell will be shared by all wireless connections within that cell [18]. Thus, in this paper, we assume data calls are elastic, i.e., they can take bandwidth from a range . Further underlying assumptions about of values data calls are made as follows. 1) Data calls are greedy, in that they will always occupy the if there is available channel maximum bandwidth capacity. If this capacity is not available, each data call will be allocated their minimum need and any remaining capacity equally shared among all the data calls. 2) Whenever the system changes its state (i.e., due to call arrival or departure), the wireless terminals can adjust their transmission rate after an infinitesimal amount of time. 3) The buffers at the IP packet layer are large enough to absorb the IP packets until upper layer protocol (e.g., TCP) throttles the senders. The above data traffic model can cover a wide range of applications such as the interactive service class defined approaches in UMTS/IMT-2000 [1]. Specifically, if zero, it will be the commonly used best effort type. B. DTBR for Elastic Data Service As described earlier, in the DTBR scheme, the channels of and each cell are divided into three regions by two thresholds . In order to guarantee the target handoff voice call dropping out of the total probability, new voice calls can only use channels, while data calls (new or handoff) can only use out of the total channels. Assume currently there are voice calls and data calls in the system. The new admission control scheme is described as follows. • When a handoff voice call arrives, if , it will be accepted. Otherwise, it will be dropped. , • When a new voice call arrives, if it will be accepted. Otherwise, it will be blocked. • When a data call (new or handoff) arrives, if , it will be accepted. Otherwise, it will be blocked. When a completed call (voice or data) releases all its channels, all the data calls still in-progress in the cell will share the released bandwidth equally. If all the active data calls have the or reach it with part of the released maximum bandwidth channels, the channels not used are made available for future requests. IV. ANALYTIC PERFORMANCE MODELS This section presents the analytic performance models for the DP, DTBR/basic, and DTBR/elastic data schemes. We consider a homogeneous wireless network where all cells have the same number of channels and experience the same new


and handoff call arrival rates. In each cell, the arrivals of new voice calls, new data calls, handoff voice calls, and handoff data , , and calls are Poisson distributed with arrival rate respectively. Thus, the total voice call arrival rate and data and , call arrival rate are respectively. Since data can usually tolerate some degree of service degradation, new data calls and handoff data calls are not distinguished. Call duration times or call holding times of voice and data are exponentially distributed with the average call duand . In addition, the cell residence time ration time for voice and data calls is exponentially distributed with mean and , respectively. Thus, the channel occupancy times for voice and data calls are exponentially distributed with and , mean and likewise for voice respectively. Also, define calls. The above set of assumptions have been found reasonable as long as the number mobiles is much larger than the number of channels in a cell, and have been widely used in literature [2], [3], [7], [8], [11]–[13]. The exponential call holding time has also been shown to be valid for a wide range of systems [14]–[16]. A. DP Scheme Model The DP scheme can be modeled as a three-dimensional Markov chain. It is ergodic [21], [22] if (refer to Section II-A for definitions). If a Markov chain is ergodic, then it is possible to find the stationary distribution of the states (i.e., the equilibrium state probabilities) in the be the steady probability that there are Markov chains. Let new voice calls, handoff voice calls, and data calls in the denote the greatest integer less than or equal to system. Let . The steady-state balance equations of DP scheme are shown below. Case 1) If , then (1) , and , all voice calls (new and handoff) and data calls will be accepted, thus, we have

Case 2) If or

and

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and , only Case 4) If handoff voice calls will be accepted, new voice calls and data calls will be rejected, thus, we have

(4) Case 5) If

and

, or and , or and , all new/handoff voice calls and data calls will be rejected, thus, we have

(5) Case 6) If and , data calls will be accepted, but new and handoff voice calls will be rejected, thus, we have

(6) The above balance equations can be solved using a recursive technique developed by Herzog et al. [17]. This technique is based on the typical feature of Chapman–Kolmogoroff equations that there exists a subset of the state probabilities, called boundary states, such that all other states can be expressed as a linear combination of boundary states. The basic idea of this technique is to choose the boundaries first and to derive the expressions for all remaining state probabilities as functions of the boundary values, and then solve a reduced system of equations for these boundaries. After that, all state probabilities are determined from the boundary states. This has been shown to be suitable for solving a wide class of queueing problems. Compared with the traditional matrix inversion technique, this technique requires significantly reduced computer time and/or memory. ( Choose state probabilities and ) as the boundaries. We introduce the following substitution as in [17]: (7)

(2) Case 3) If and , voice calls (new and handoff) will be accepted, but data calls will be rejected, thus, we have

(3)

where if if

and or

(8)

The coefficients can be solved recursively. First, rewrite the balance equation in a general format, as shown in (9) at the , ( ) can be bottom of the page, where obtained from (1)–(6) accordingly. After some manipulation,

(9)

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can be expressed as linear combinations of , thus, we have

, where

(13)

(14) (10) Substituting all state probabilities in (10) according to (7), we have (15)

B. DTBR/Basic Scheme Model

(11) , where Then, for every fixed pair of and , can be determined by solving linear (10) recursively by assuming and for or . , the boundary states’ After obtaining all coefficients probabilities can be determined by solving reduced system of independent equations along with the normalizing condition

The DTBR scheme can also be modeled as a two-dimensional denotes the steady probability that Markov chain, where there are voice (new and handoff) calls and data calls in the . system. The Markov chain is ergodic [21], [22] if The steady-state balance equations of DTBR scheme are shown in (16) and (17). , see (16) at the bottom of the page. If If if if if if if

Having solved the boundaries, all steady-state probabilities can be determined from (7). Thus, the voice call blocking , the handoff voice call dropping probability , probability , and the total channel utithe data call blocking probability can be derived as lization

if if

(17) The balance equations can be solved recursively as in the DP , scheme. After obtaining all the steady-state probabilities the voice call blocking probability , the handoff voice call , the data call blocking probability , dropping probability can be derived as and the total channel utilization

(12)

(18)

if if if if

(16)

if if if if

and or


(19)

(20)

(21) C. DTBR/Elastic Scheme Model represent the probability that there are voice calls Let and and data calls in the system, where . Here, denotes the maximum number of data calls that the system can accommodate. Thus, the general balance equations are described as

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quality that can be endured by users [4]–[6]. On the other hand, for data services such as FTP, HTML, etc., such compression is not possible. Thus, when the network is in congestion, either the packet is sent in its entirety or it is dropped. The result is that the holding time of this class of data calls typically depends on the throughput. For example, the transfer of a file would take half of the time with doubled throughput. So, in this paper, we assume [19] if that data calls are serviced with ideal departure rate . But the real they are provided with maximum bandwidth instantaneous departure rate of data calls is proportional to the actual bandwidth of each call. So, when data calls are serviced , the average service rate is with the maximum bandwidth . , all data calls will be When the system is in state . So, served with the bandwidth of for and , and considering jointly will not change that the average cell residence duration along with the changing of serving bandwidth, the state depenand are dent voice and data call service rate

(22) (28) , , and are state dependent voice call where arrival rate, data call arrival rate, voice call service rate, and data call service rate, respectively. denote the maximum number of data Let ) that the system can supcalls with maximum bandwidth ( port. If we assume there are only data calls in the system (i.e., ), then, when is less than or equal to , all data calls will be served with the maximum bandwidth ; when is greater than , all data calls will be served with allowed bandand the total number of width , where . Thus, when the system channels occupied by data calls is is in state, the number of busy channels in the system is

(29) After solving balance (22) together with the normalization condition , we can obtain the system steady probabilities . Then, handoff voice call dropping ), voice call blocking probability ( ), data probability ( call blocking probability ( ), and average channel utilization ( ) can be obtained as

(30) if if

and and

(23) For the convenience of presentation, we introduce the following two indicator functions: if otherwise if otherwise

(31) (24) (25) (32)

Then, for and dent voice and data call arrival rates, are

, the state depenand , respectively,

(26) (27) (33) For real-time services such as variable bit rate video, when the network is congested, some less important part of the video packet can be compressed with graceful degradation of video

These performance measures are greatly influenced by , . In order to evaluate how these the threshold values,

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threshold values impact the total system performance, we introduce following system award function Award

(34)

. We do not include handoff voice call where in the above equation since handoff dropping probability voice call dropping probability is more important than the other performance measures. Thus, throughout this paper, we always keep handoff voice call dropping probability below a given . On the other hand, voice/data call target value, namely blocking probability and channel utilization are treated equally in (34). This is because efficient call admission control schemes should seek to maximize the average channel utilization while satisfying the users’ QoS requirements. The weighting factors and are selected based on the relative importance of voice and data call blocking probability as determined by the system’s overall revenue and service objectives.

Fig. 3. Handoff voice call dropping probability of DP scheme when

0:2 .

=

V. NUMERICAL RESULTS AND REMARKS In this section, we present numerical results and compare the above three schemes. In order to validate the accuracy of the analysis, we have also developed an event-driven simulation. To alleviate the transient effects of simulation, the simulation was run for a long duration in order to reach the steady-state, and the system performance measures were obtained by averaging over the results of ten independent rounds of simulations. One of the key QoS measures in wireless cellular networks is the handoff voice call dropping probability since dropping a call-in-progress is generally not considered acceptable or userfriendly. In order to compare the performance of DP and DTBR scheme, we set the target of the handoff voice call dropping probability to be 10 [3]. The system parameters that were used in the simulation are as follows. The total number of chan; maximum bandwidth requirement of data nels per cell ; data call intensity ; average data arrival calls , and average service rate , rate where and , respectively. These assumptions are considered reasonable for typical Internet type of traffic [20]. For voice calls, the average service (equivalently, the avrate is assumed to be erage voice call holding time is about 2 min), while the voice call intensity is varied from 6 to 16. The performance metrics studied include the total channel utilization. In wireless cellular networks, radio resources are scarce and, hence, a good bandwidth allocation scheme should provide the specified QoS while fully utilizing the scarce bandwidth. A. Performance of DP Scheme We first present the performance of the DP allocation scheme with different sets of thresholds that can guar) for antee target handoff voice call dropping probability ( . Then, we choose the set of that achieves the highest channel utilization as the representative to compare values. with DTBR schemes having different should be less For the DP allocation scheme to be ergodic, than , where . Figs. 3–6 illustrate

Fig. 4. Channel utilization of DP scheme when

= 0 :2 .

the performance of DP scheme when (i.e., lower user mobility), while Figs. 7–10 plot the performances of DP (i.e., higher user mobility). scheme when Fig. 3 indicates that a certain number of channels ( ) needs to be designated exclusively for voice calls in order to maintain the target handoff voice call dropping probability. But due to the nature of complete partition, these voice-only channels cannot be shared by data calls. Thus, when the voice call intensity is low, much lower handoff voice call dropping probability can be achieved. From Fig. 4, we can see that, among all the sets of values, the combination achieves the highest channel utilization when the traffic intensity is high. This is because in this case only channels are reserved exclusively for handoff voice calls, and no channels are reserved exclusively for data calls. In all other cases (here for clarity we only present four cases), more than three channels need to be reserved for handoff voice calls to maintain target QoS. Thus, more ) can be used by channels ( new voice calls resulting in higher channel utilization. On the other hand, when the voice call intensity is low, the case of


Fig. 5.

Data call blocking probability of DP scheme when

Fig. 6.

Voice call blocking probability of DP scheme when

= 0:2 .

= 0 :2 .

provides many more channels (15 in this case) than what handoff voice calls need, resulting in relative lower channel utilization compared with most other cases. values while keeping Fig. 5 illustrates that, increasing constant results in lower data call blocking probability. This is because more channels can be used exclusively by data calls. However, there is a corresponding increase in voice call blocking probability (as indicated in Fig. 6) since fewer channels can be shared by voice calls. Fig. 5 also indicates that, the has the lowest DP scheme with new voice call blocking probability. This is due to the fact that, ) in this case, the maximum of ( channels can be used by new voice calls. While in all other cases, fewer channels can be used by new voice calls. ), For DP scheme with higher user mobility (i.e., similar results are obtained as illustrated in Figs. 7–10. The only values. We observe difference is in the choice of that the combination achieves . Thus, in folthe highest channel utilization for lowing performance comparison between DP and DTBR allo-

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Fig. 7. Handoff voice call dropping probability of DP scheme when

0:4 .

Fig. 8. Channel utilization of DP scheme when

= 0 :4 .

Fig. 9. Data call blocking probability of DP scheme when

cation schemes, the DP scheme with is used for and for .

= 0 :4 .

=

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Fig. 10.


Voice call blocking probability of DP scheme when

= 0:4 .

Fig. 11. Handoff voice call dropping probability of DP and DTBR schemes when = 0:2 .

B. Performance of DTBR/Basic and Comparison to DP In this section, we compare the performance of DTBR/Basic and DP schemes. For the DTBR allocation scheme to be ergodic, should be greater than , where it was noted earlier that . Thus, in the determination of values for DTBR scheme, we first set the values for , and vary the value of from high ( ) to low ( ). Figs. 11–14 depict the system performance of DP and DTBR , while Figs. 15–18 plot the results when when . Fig. 11 shows that the DP scheme usually provides lower handoff voice call dropping probability. This is because in the DP scheme, the voice-only channels needed to maintain target handoff voice call dropping probability cannot be shared by data calls. This results in an over-provisioning of the bandwidth especially when is low. On the contrary, in the DTBR scheme, no channels are dedicated to voice calls. Other ) channels needed to meet target handoff voice than the ( , all other channels can be call dropping probability at shared by both new voice calls and data calls. Thus, the handoff voice call dropping probability of the DTBR scheme cannot be as low as in the DP scheme.

Fig. 12.

Channel utilization of DP and DTBR schemes when

Fig. 13.

Data call blocking probability of DP and DTBR schemes when

0 :2 .

= 0 :2 .

=

Fig. 12 shows that, among all the cases (here, for clarity, we only present three cases) of the DTBR scheme, the combinaresults in highest channel utilization. The tion , only three (i.e., ) reason is that, when channels are needed to be reserved for voice calls; in the other DTBR cases more channels [for example, seven for the case of ] are needed. This figure also indicates that compared with the best DP scheme combination, DTBR with has much higher channel utilization. The reason , 15 is that, in DP scheme with channels are designated exclusively for voice calls; data calls can share only the remaining 15 channels. While in DTBR with , only three channels are reserved for voice calls and data calls can share all the other 27 channels leading to higher channel utilization. This can further explain that DTBR can achieve the lowest data call blocking with probability as observed from Fig. 13. The high channel utilization and low data call blocking probais obtained at the expense bility for DTBR with of higher new voice call blocking probability, as indicated in Fig. 14. This is because in this case, no channels are reserved for new voice calls. They have to contend with handoff voice


Fig. 14.

Voice call blocking probability of DP and DTBR schemes when

Fig. 16. Channel utilization of DP and DTBR schemes when

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= 0 :4 .

= 0:2 .

Fig. 17.

Data call blocking probability of DP and DTBR schemes when

Fig. 18.

Voice call blocking probability of DP and DTBR schemes when

0 :4 . Fig. 15. when

=

Handoff voice call blocking probability of DP and DTBR schemes = 0:4 .

calls and data calls to get access. Furthermore, because DP with achieves the lowest voice call values, we blocking probability among all sets of can conclude that the DTBR scheme with two thresholds can achieve much lower new voice call blocking probability among all the cases of DP and DTBR schemes. to (i.e., higher user mobility), When we increase similar results are obtained as illustrated in Figs. 15–18. The main difference is the selection of the control thresholds in both schemes. From the above results, we can draw the following conclusions. 1) DP scheme has the lowest handoff voice call dropping probability and relative lower data call blocking probability, while at the same time keeping relative higher channel utilization. 2) DTBR scheme with one threshold (i.e., there is no channel reserved for new voice call) can achieve the highest channel utilization and the lowest data call dropping probability.

= 0 :4 .

3) If more stringent handoff voice call dropping probability and voice call blocking probabilities are required, DTBR schemes with two thresholds yield the best performance. This is achieved at the expense of lower channel utilization and potentially higher data call blocking probability.

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Fig. 19.

Fig. 20.


Handoff voice call dropping probability of DTBR scheme.

Fig. 21.

System award of DTBR scheme when ( = 0:9; = 0:1).

Fig. 22.

System award of DTBR scheme when ( = 0:1; = 0:9).

Channel utilization of DTBR scheme.

C. Performance of DTBR/Elastic Data Scheme In this section, we present numerical results of the DTBR/Elastic data scheme. The system parameters are as outlined earlier in Section V-A. Figs. 19–22 depict the system performance when (i.e., 20% of voice call arrivals are handoff) for combinations, varying from 1 to 2, different and for varying voice call intensity values. The value indicates that data calls require constant (i.e., nonelastic) bandwidth during the entire call duration. Fig. 19 demonstrates can provide lower handoff that the system with voice call dropping probability compared with the system with . This is due to the fact that the admission control policy can reduce the bandwidth of in-progress data calls for and, thus, accommodate more handoff voice calls. Fig. 20 shows that the average channel utilization can be significantly improved by adopting the elastic data traffic model. For the given system configuration, the maximum channel utilization is seen to be improved from 0.78 to nearly 0.9, an increase of nearly 15%. For the constant data bandwidth system, new data calls cannot be accepted when the number of available . Thus, these idle channels will channels is less than be wasted leading to relatively low total channel utilization. For the elastic case, the bandwidth of in-progress data calls can be reduced to accept new data calls, thus reducing channel wastage and improving channel utilization. From Fig. 20, we can observe that, among different values of , the case with one threshold ( ) has the

highest channel utilization. The reason is that, in this case, no priority is given to new voice calls. Except for a small amount of channels that are reserved for handoff voice calls to meet the , all the other available channels are available for target both voice and data calls. Thus, leads to higher channel utilization. Figs. 21 and 22 present the total award of the system for and , respectively. We observe that, much higher system award can be achieved by adopting elastic data traffic model. At the same time, if handoff voice call dropping probability and new voice call blocking probability are the most preferred performance measures (e.g., , , as indicated in Fig. 21), system award will increase with decreasing . This is due to the fact that fewer channels are available for data calls resulting in lower new voice call blocking probability. On the other hand, if handoff voice call dropping probability and data call blocking probability are the most preferred performance measures (e.g., , , as indicated in Fig. 22), system award will in. This is because now more crease by increasing the value of channels are available for data calls resulting in lower data call blocking probability. Thus, we can adjust the values of to satisfy the different QoS requirements of voice and data users, while obtaining maximal total system award. VI. CONCLUSION In this paper, we study and compare the performance of three call admission control schemes for multiservice cellular networks. The schemes are based on partitioning the channels in a


cell with CS between different traffic types and the use of thresholds to meet specified QoS needs. A system that considers voice and data traffic was considered. Detailed analytic models were developed to study the system performance in terms of metrics such as call blocking probability and channel utilization. The paper presents a dual-threshold based partitioning scheme and also an extension to include elastic (or variable) bandwidth demands for data calls. The performance results obtained from both analysis and simulation show that: 1) both DP and CS schemes can maintain the prespecified voice handoff dropping probability; 2) the proposed DTBR scheme can achieve lower overall call blocking probability; and 3) the DTBR with elastic data scheme can further improve the channel efficiency. There are a number of issues that will be considered in our future research: 1) The handoff rates for both voice and data are assumed to be fixed in this paper, as there are no adequate models that can derive such rates based on the known system parameters such as new arrival rates and user mobility. We are currently exploring the iterative techniques used in [16] to calculate the handoff rates. 2) It has been shown that finding the optimal thresholds is a complex optimization problem and in [22] we have shown that simulated annealing is a possible solution. We are currently considering using a Markov decision process to derive the optimal setting. 3) The call admission control discussed in this paper is static. We will explore the dynamic call admission control scheme such as the one proposed in [3] to handle multiservice systems.

REFERENCES [1] Services and System Aspects: QoS Concept and Architecture, 1.0.0 ed., 3GPP TS 23.107, 2000. [2] R. Guerin, “Queueing-blocking systems with two arrival streams and guarded channels,” IEEE Trans. Commun., vol. 36, pp. 153–163, Feb. 1988. [3] S. Wu, K. Y. M. Wong, and B. Li, “A dynamic call admission policy with precision QoS guarantee using stochastic control for mobile wireless networks,” IEEE/ACM Trans. Networking, vol. 10, pp. 257–271, Apr. 2002. [4] A. K. Talukdar, B. R. Badrinath, and A. Acharya, “Rate adaptation schemes in networks with mobile hosts,” in Proc. Conf. IEEE/ACM MobiCom’98, Oct. 1998, pp. 169–180. [5] S. K. Das and S. K. Sen, “Quality-of-service degradation strategies in multimedia wireless networks,” in Proc. Conf. IEEE Vehicular Technology Conf., May 1998, pp. 1884–1888. [6] Y. Xiao, C. L. P. Chen, and B. Wang, “Bandwidth degradation QoS provisioning for adaptive multimedia in wireless/mobile networks,” Elsevier J. Comput. Commun., vol. 25, pp. 1153–1161, 2002. [7] B. Epstein and M. Schwartz, “Reservation strategies for multimedia traffic in a wireless environment,” in Proc. Conf. IEEE Vehicular Technology Conf., July 1995, pp. 165–169. [8] Y.-R. Huang, Y.-B. Lin, and J. M. Ho, “Performance analysis for voice/data integration on a finite mobile systems,” IEEE Trans. Veh. Technol., vol. 49, pp. 367–378, Mar. 2000. [9] H.-L. Wu, L.-Z. Li, B. Li, L. Yin, I. Chlamtac, and B. Li, “On handoff performance for an voice/data integrated cellular system,” in Proc. IEEE PIMRC’2002, Sept. 2002, pp. 2180–2184. [10] L.-Z. Li, B. Li, B. Li, and X.-R. Cao, “Performance analysis of bandwidth allocations for multi-service mobile wireless cellular networks,” in Proc. IEEE WCNC, Mar. 2003, pp. 1072–1077.

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[11] L. Yin, B. Li, Z. Zhang, and Y.-B. Lin, “Performance analysis of a dualthreshold reservation (DTR) scheme for voice/data integrated mobile wireless networks,” in Proc. IEEE WCNC, Sept. 2000, pp. 258–262. [12] B. Li, C. Lin, and S. Chanson, “Analysis of a hybrid cutoff priority scheme for multiple classes of traffic in multimedia wireless networks,” ACM/Baltzer J. Wireless Networks, vol. 4, pp. 279–290, Aug. 1998. [13] R. Ramjee, R. Nagarajan, and D. Towsley, “On optimal call admission control in cellular networks,” in Proc. IEEE INFOCOM, Mar. 1996, pp. 43–50. [14] Y. Fang and I. Chlamtac, “Teletraffic analysis and mobility modeling of PCS networks,” IEEE J. Select. Areas Commun., vol. 17, pp. 1062–1072, July 1999. [15] Y. Fang, Y.-B. Lin, and I. Chlamtac, “Channel occupancy times and handoff rate for mobile computing and PCS networks,” IEEE Trans. Comput., vol. 47, pp. 679–692, June 1998. [16] Y.-B. Lin, “Performance modeling for mobile telephone networks,” IEEE Networks, vol. 11, pp. 63–68, Nov/Dec 1997. [17] U. Herzog, L. Woo, and K. Chandy, “Solution of queueing problems by a recursive technique,” IBM J. Res. Dev., vol. 19, pp. 295–300, May 1975. [18] M. Naghshineh and A. S. Acampora, “QoS provisioning in micro-cellular networks supporting multiple classes of traffic,” ACM/Kluwer J. Wireless Networks, vol. 2, pp. 195–203, Aug. 1996. [19] S. Racz, M. Telek, and G. Fodor, “Call level performance analysis of 3rd generation mobile core networks,” in Proc. IEEE Int. Conf. Commun. (ICC’01), June 2001, pp. 456–461. [20] M. Cheng and L.-F. Chang, “Wireless dynamic channel assignment performance under packet data traffic,” IEEE J. Select. Areas Commun., vol. 17, pp. 1257–1269, July 1999. [21] Kleinrock, Queueing Systems, Vol. I: Theory. New York: Wiley, 1975. [22] I. Chau, K. Y. M. Wong, and B. Li, “Optimal call admission control for multi-service cellular system using simulated annealing,” in Proc. IEEE GLOBECOM, Nov. 2002, pp. 809–813. [23] C. W. Leong and W. H. Zhuang, “Call admission control for voice and data traffic in wireless communications,” Elsevier J. Comput. Commun., vol. 25, pp. 972–979, June 2002.

Bin Li (M’96–SM’03) received the Bachelor degree in industrial electric automation from Huazhong University of Science and Technology, Wuhan, China, in 1991, and the M.Phil. and Ph.D. degrees in electrical and electronic engineering from Hong Kong University of Science and Technology (HKUST), Kowloon, China, in 1996 and 2003, respectively. After that, he became a Network System Engineer with the China Telecom Guangdong Branch, Guangzhou, China. He is now the Executive Director and Chief Operating Officer of China Motion NetCom, Ltd., Hong Kong, a dually-listed company in Hong Kong and Singapore. His current research areas include computer and communications systems, wireless technology and network, IP technology and network, in which he has published 30 papers.

Lizhong Li received the B.S., M.S., and Ph.D, degrees in the telecommunications and information systems from University of Electronic Science and Technology of China, Chengdu, in 1987, 1990, and 2000, respectively. From 1990 and 1996, he worked on SDH, ATM switches and wireless ATM MAC protocol with the Southwestern Institute of Telecommunications Technology, Chengdu, China. Between 2000 and 2001, he worked as a Research Assistant with the Department of Electronic Engineering, City University of Hong Kong, Kowloon. Between 2002 and 2003, he worked as a Research Associate with the Department of Computer Science, Hong Kong University of Science and Technology (HKUST), Kowloon, China. He is currently a Senior Research Engineer at the National Key Laboratory of Modern Signal Processing, Chengdu, China. His current research interests include PCS and wireless mobile networking supporting multimedia, especially in the area of dynamic bandwidth allocation and media access control. He has published about 20 papers in above areas.

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Bo Li (S’89–M’92–SM’99) received the B.Eng. (summa cum laude) and M.Eng. degrees in computer science from Tsinghua University, Beijing, China, in 1987 and 1989, respectively, and the Ph.D. degree in electrical and computer engineering from University of Massachusetts, Amherst, in 1993. From 1993 and 1996, he worked on high-performance routers and ATM switches in IBM Networking System Division, Research Triangle Park, NC. Since 1996, he has been with the Department of Computer Science, Hong Kong University of Science and Technology (HKUST), Kowloon, China, where he is now an Associated Professor and Co-Director for the ATM/IP Cooperate Research Centre, a government sponsored research center. Since 1999, he has also held an Adjunct Researcher position at Microsoft Research Asia (MSRA), Beijing, China. He has been on the Editorial Boards of ACM/Kluwer Journal of Wireless Networks (WINET), ACM Mobile Computing and Communications Review (MC2R), Elsevier Ad Hoc Networks, and SPIE/Kluwer Optical Networking Magazine (ONM). He has served as a Guest Editor for ACM Performance Evaluation Review Special Issue on Mobile Computing (December 2000), SPIE/Kluwer Optical Networks Magazine for the Special Issue on Wavelength Routed Networks: Architecture, Protocols, and Experiments (January/February 2002), and ACM/Kluwer Mobile Networks and Applications (MONET) Special Issue on Energy Constraints and Lifetime Performance in Wireless Sensor Networks (4th Quarter of 2004). His recent research focuses are on adaptive video multicast, routing in optical networks, resource management in wireless cellular systems, and content replication. He has published 60 some journal papers and held several patents in above areas. Dr. Li has been on the Editorial Boards of the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (JSAC)–Wireless Communications Series, and KICS/IEEE JOURNAL OF COMMUNICATIONS AND NETWORKS (JCN). He served as a Guest Editor for the IEEE Communications Magazine for the Special Issue on Active, Programmable, and Mobile Code Networking (April 2000), the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS for the Special Issue on Protocols for Next-Generation Optical WDM Networks (October 2000), Special Issue on Recent Advances in Service-Overlay Networks (January 2004), and on Quality-of-Service Delivery in Variable Topology Networks (3rd Quarter of 2004). In addition, he has been involved in organizing over 40 conferences, especially the IEEE INFOCOM since 1996. He is the Co-TPC Chair for the IEEE INFOCOM 2004.

Krishna M. Sivalingam (M’95–SM’00) received the B.E. degree in computer science and engineering from Anna University, Chennai (Madras), India, in 1988, the M.S. and Ph.D. degrees in computer science from the State University of New York (SUNY), Buffalo, in 1990 and 1994, respectively. He is an Associate Professor in the Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore. From 1997 to 2002, he was with the School of Electrical Engineering and Computer Science at Washington State University, Pullman, and with the University of North Carolina, Greensboro, from 1994 to 1997. He has also conducted research at Lucent Technologies’ Bell Laboratories, Murray Hill, NJ, and at AT&T Laboratories, Whippany, NJ. While at SUNY, he was a Presidential Fellow from 1988 to 1991. His work has been supported by AFOSR, Laboratory for Telecommunication Sciences, NSF, Cisco, Bellcore, Alcatel, Intel, and Washington Technology Center. He has published an edited a book on Optical WDM Networks (Norwell, MA: Kluwer, 2000). He has served as Guest Co-Editor for the special issue on Wireless Sensor Networks for the ACM MONET and an issue on Recent Advances in Optical Networking for SPIE Optical Networks Magazine, both in 2003. He is a Member of the Editorial Board for KICS Journal of Computer Networks and ACM Wireless Networks Journal. He holds three patents in wireless networks and has published several research articles including more than 25 journal publications. His research interests include wireless networks, optical wavelength division multiplexed networks, and performance evaluation. Dr. Sivalingam has served as Guest Co-Editor for the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS for the issue on Optical WDM Networks (2000). He is a Member of the Editorial Board for the IEEE TRANSACTIONS ON MOBILE COMPUTING. He is corecipient of the Best Paper Award at the IEEE International Conference on Networks 2000 held in Singapore (jointly with M. Mishra of Intel Corporation). He has also served as General Co-Chair for Opticomm 2003 (Dallas, TX) and for ACM International Workshop on Wireless Sensor Networks and Applications 2003 (San Diego, CA), and as Program Chair for the Second International Workshop on Trusted Internet 2003 (Hyderabad, India).

Xi-Ren Cao (S’82–M’84–SM’89–F’96) received the M.S. and Ph.D. degrees from Harvard University, Cambridge, MA, in 1981 and 1984, respectively, where he was a Research Fellow from 1984 to 1986. He then worked as a Principal and Consultant Engineer/Engineering Manager at Digital Equipment Corporation, MA, until October 1993. Since then, he has been a Professor with the Hong Kong University of Science and Technology (HKUST), Kowloon, China. He is the Director of the Center for Networking, HKUST. He held visiting positions at Harvard University, University of Massachusetts at Amherst, AT&T Laboratories, University of Maryland at College Park, University of Notre Dame, Shanghai Jiaotong University, Nankei University, Tsinghua University, University of Science and Technology of China, and Tongji University. He has 2 patents in data communications and published two books in the area of discrete event dynamic systems. His current research areas include discrete event dynamic systems, communication systems, signal processing, stochastic processes, and system optimization. Dr. Cao received the Outstanding Transactions Paper Award from the IEEE Control System Society in 1987 and the Outstanding Publication Award from the Institution of Management Science in 1990. He is an Associate Editor at Large of the IEEE TRANSACTIONS OF AUTOMATIC CONTROL and he is/was on the Board of Governors of the IEEE Control Systems Society, associate editor of a number of international journals, and chairman of a few technical committees of international professional societies.