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Dynamic Threshold-based Call Admission Framework for Prioritized Multimedia Traffic in Wireless Cellular Networks Nidal Nasser and Hossam Hassanein Telecommunications Research Laboratory, School of Computing, Queen’s University Kingston, ON, Canada K7L 3N6 {nasser, hossam}@cs.queensu.ca generation of wireless cellular networks will use micro/pico cellular architectures in order to provide higher capacity. Therefore, one of the most important connection-level QoS issues is how to reduce/control handoff drops due to lack of available resources in the new cell, since mobile users should be able to continue their ongoing connections. Since it is practically impossible to completely eliminate handoff drops, the best one can do is to provide some form of probabilistic QoS guarantees by keeping HCDP below a predetermined value [2]. In earlier study [3], we attempted to overcome the above difficulties through an adaptive multimedia framework. In this paper, however, we present a dynamic Call Admission Control (CAC) framework for wireless cellular networks with non-adaptive multimedia services where the bandwidth of a call is fixed throughout the call lifetime.

Abstract – Next generation wireless cellular networks aim at

supporting wireless multimedia services with different classes of traffic that characterize by diverse Quality of Service (QoS) and bandwidth requirements. They will use micro/pico cellular architectures in order to provide higher capacity. However, small-size cells increase the handoff rate drastically. As a result, it is a challenge to provide stable QoS in these networks. In this paper, we present a novel Dynamic Call Admission Control (DyCAC) framework for next generation wireless cellular networks. The framework consists of the following components: (i) a threshold-based bandwidth reservation policy, (ii) a threshold update processing module, and (iii) an admission controller module. In this work, each base station locally, independently of other base stations in the network, differentiates between new and handoff calls for each class of traffic by assigning a threshold to each class according to its QoS requirements. The threshold values change dynamically and periodically in order to respond to the varying traffic conditions. The main feature of the proposed framework is its ability to simultaneously achieve several design goals. Numerical results show that our proposed DyCAC framework can guarantee the connection-level quality of service of individual traffic classes while maximizing resource utilization. As well, DyCAC requires low communication overhead, and is highly scalable.

CAC is a key factor that affects the bandwidth utilization efficiency and QoS guarantees provided to users [4]. Beside the functionality of CAC, bandwidth reservation mechanism helps CAC to decide how much bandwidth is needed to be reserved in order to provide QoS guarantees to mobile users. The bandwidth reservation module dynamically changes the amount of bandwidth to be reserved by referencing traffic conditions in neighboring cells periodically.

I. INTRODUCTION

Several proposals that combine bandwidth reservation and CAC mechanisms to guarantee the connection-level QoS parameters of multimedia traffic were introduced in the literature. Existing proposals fall into two broad groups. The first group [5-8] assumes that each base station has some knowledge about the traffic conditions in neighboring cells. Such conditions are expressed in terms of the number of new and handoff calls present in each base stations, new and handoff arrival rates and/or channel holding time, for each class of traffic. The actual implementation of these proposals will, therefore, introduce a large communication and processing overhead in order to keep up-to-date information about the state of the neighboring cells. However, in our work we argue that for a given base station, state information about neighboring cells that is necessary for the call admission decision may be deduced from the local information available at the base station itself. Proposals in the second group [9-12] use probabilistic computations derived from analytical models. In most of these studies, a base station estimates (approximates) the number of handoff calls for each class of traffic that may handoff to it

Next generation of wireless cellular networks, including 3G and 4G technologies are envisaged to support more mobile users and variety of high-speed Wireless Multimedia Services (WMSs). A WMS enables the simultaneous transmission of voice, data, text and images through radio links by means of the new wireless technologies. Different WMSs have diverse bandwidth and Quality of Service (QoS) requirements from their users that need to be guaranteed by wireless cellular networks. In wireless cellular networks, user’s QoS requirements can be quantitatively expressed in terms of probabilistic connection-level QoS parameters such as new call blocking probability (NCBP) and handoff call dropping probability (HCDP) [1]. The NCBP is the probability of a new arriving call being rejected while the HCDP is the probability that an accepted call is terminated before the completion of its service, i.e., the probability that a handoff attempt fails [1]. Provisioning connection-level QoS in wireless cellular networks becomes complex due to (i) the limited radio link bandwidth, and (ii) the high rate of handoff events as the next

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and use this to compute the handoff call dropping probability so as to maintain a target handoff call dropping probability. Proposals in this group have several drawbacks. They have high implementation complexity, due to probabilistic estimations for the potential number of handoff calls from neighboring cells. Moreover, most of these proposals [10, 12] suffer from state-space explosion problem, which prohibits exact analysis. However, in this work, described in section 3, we show that by obtaining the handoff rate through local measurements, rather than analytical modeling, it is possible to decouple the state of the system, and model each cell individually. Thus, reducing the analytical complexity and avoiding the problem of state-space explosion.

The rest of this paper is organized as follows. The system model is described in Section II. The detailed Dynamic Call Admission Control (DyCAC) framework with its main components is presented in Section III. As well, the analytical framework model and derivations of connection-level QoS metrics are provided. Numerical results and performance comparisons are reported in Section IV. Finally, conclusions drawn from the paper are discussed in Section V. II. SYSTEM MODEL Our approach is based on decomposing the wireless cellular network into individual sub-systems, each comprising a single cell. The correlation between these sub-systems (models), results from handoff connections among corresponding cells. Under this formulation, each cell can be modeled and analyzed individually. A same model is used for all cells in the network, but the model parameters may be different, reflecting the mobility and traffic conditions in individual cells.

This paper proposes the integration of bandwidth reservation with CAC into a novel framework, which we call Dynamic CAC (DyCAC) for next generation wireless cellular networks. DyCAC operates at the connection-level where the connection of ongoing calls can be carefully maintained. This framework supports different types of multimedia traffic with diverse QoS requirements. In this work, each base station, independently of other base stations in the network, reserves a number of handoff channels for each class of handoff traffic in order to guarantee that a predetermined maximum probability of handoff call dropping of the corresponding class is not exceeded and to give them priority over the new calls. This then results in lower handoff call dropping probability. The number of reserved handoff channels for each class changes dynamically in order to respond to the varying traffic conditions. We argue that for a given base station, state information about neighboring base stations that is necessary for the call admission decision may be deduced from local information available to the base station itself.

We assume the system uses Fixed Channel Allocation (FCA), which means the cell has a fixed amount of capacity. Hereafter, whenever we refer to the bandwidth of a call, we mean the number of basic bandwidth units (bbu) that is adequate for guaranteeing desired QoS for this call with certain traffic characteristics. Consider a cell that has a total capacity of B bbu. Two types of calls share the bandwidth of the cell: new calls and handoff calls. Traffic arriving at the cell is partitioned into K separate classes based on bandwidth requirements. The bandwidth of a class-i is given by bi. The classes are indexed in an increasing order according to their bandwidth requirements, such that: b1 ≤ … ≤ bi ≤ bi+1 ≤ … ≤ bK.

In order to demonstrate how our framework can eliminate the requirement for state information exchange among base stations, while still being able to respond to varying traffic conditions, we introduce the following example. Consider a test base station, and suppose that one or more of its neighboring base stations is experiencing higher traffic loads. The effect of the higher load in the congested base stations will be observed in terms of increased arrival rate of handoff calls. According to our threshold update module, described in section 3.2.1, the number of handoff reserved channels is increased thus limiting the number of new calls allowed into the network. This, in turn will reduce the future number of calls requesting handoff into the congested base station(s), which should reduce their congestion level.

III. DYNAMIC CALL ADMISSION CONTROL FRAMEWORK Our Dynamic Call Admission Control (DyCAC) framework consists of three main components – a thresholdbased bandwidth reservation policy, a threshold update processing module and an admission controller module. A. Threshold-based Bandwidth Reservation Policy 1) Policy Description: The main principle of our bandwidth reservation policy is based on reserving bandwidth for aggregate handoff connections, thus giving them priority over new connections, and providing them with lower handoff call dropping probability. In addition, the policy prioritizes between different classes of handoff connections according to their QoS constraints by assigning a series of bandwidth thresholds t0, t1, …, tK, such that

Our specific objectives in designing the DyCAC framework are: 1- establishing a priority mechanism for handoff calls over new calls for each class of traffic; 2- maintaining the handoff dropping probability below a target level for each class of traffic; 3- maximizing the bandwidth utilization; 4- reducing the information exchange among base stations; 5- ease of implementation.

t0 ≤ … ≤ ti ≤ ti+1 ≤ … ≤ tK where, t0 denotes the maximum number of total basic bandwidth units that can be allocated to new connections, and ti, 1 ≤ i ≤ K, denotes the maximum number of total basic bandwidth units that can be allocated to class-i handoff connections. Note that, if the different handoff connections were allowed to completely share the bandwidth, then connections with lower bandwidth requirements will have a better chance at occupying the bandwidth than those with

Unlike previous work, our proposed DyCAC framework aims at fulfilling the objectives above simultaneously.

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higher bandwidth requirements. In this work, threshold values are tuned to satisfy the handoff dropping probabilities for each individual traffic class.

S = {s = (m1 , m2 ,..., mK , n1 , n2 ,..., nK ) |

2) Analytical Formulation of Threshold-based Bandwidth Reservation Policy: In this section, we show that our proposed policy can be modeled as a multi-dimensional Markov chain where each dimension is modeled as M/M/∞ queuing system.

Let ρnci denote the load generated by class-i new connections, and let ρhi denote the load generated by class-i handoff connections. Then, λnc (5) ρ nci = i , and µ nci

K

∑mb

i i

i =1

a) Assumptions: We assume that an arriving connection (new or handoff) that is not admitted immediately is blocked or dropped, i.e., a call is never buffered. We also assume the arrival processes for both new and handoff calls to be independent.

ρh = i

(6)

i

µh

K

a(s ) as, a (s ) = ∏ i =1

bi

i

∑

i

+ ni )bi ≤ B

i

0

∑

j

j

j

j =1

(11)

∑ a(s )

is given by: Pb = i

s∈S bi

is given by: Pd = i

s∈S d i

,and (12) G the dropping probability for class-i handoff connections, Pd i ,

∑ a(s ) G

(13)

B. Measurement-based Call Admission Control Scheme In this section, we provide a distributed measurementbased CAC scheme for different classes of traffic to satisfy QoS requirements and to utilize the system resources efficiently. The proposed CAC scheme is distributed since each base station in the network runs an instance of the same algorithm and utilizes information collected locally. The CAC scheme is divided into two modules: 1- Threshold Update Processing (TUP) module 2- Admission Controller (AC) module

(1)

The TUP module is responsible for dynamically updating the threshold values. Whereas, the AC module decides to accept or reject an arriving call (new or handoff) to the given cell. Both modules are operating in a time independent manner as shown in Figure 1. The processing of the TUP module does not interfere with the AC module, and therefore, does not cause any call setup delay.

(2) (3)

i =1

Thus, the state space of the policy is given by:

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j

Thus the blocking probability for class-i new connections, Pbi ,

i =1

K

j

j =1

j =1

The non-negative integers mi and ni denote the number of ongoing new call and handoff call class-i connections, respectively. Let S denote the state space required to represent our bandwidth policy. Then, s will belong to S if and only if the following conditions are satisfied:

ni bi ≤ t i , ∀i

(7)

mi ! ni !

K

s = (m1 , m2 ,..., mK , n1 , n2 ,..., nK )

≤ t0

i

S d i = {s ∈ S | bi + ni bi > t i ∨ bi + ∑ ( m j + n j )b j > B}

The state of the system (cell) is defined by the vector:

i i

i

i

s∈S

b) Analysis and Derivations: Based on the above assumptions, our policy can be modeled as a truncation of 2K independent M/M/∞ queues, and admits, therefore, a product form solution.

∑m b

i

Let Sbi ⊂ S denote the set of states in which a class-i new connection is blocked, and let Sdi ⊂ S denote the set of states in which a class-i handoff connection is dropped. According to our bandwidth reservation policy, K K (10) S = {s ∈ S | b + m b > t ∨ b + (m + n )b > B} ,and

i

K

ρ ncm ρ hn

Then the steady state probability that the system is in state s, p (s ) , is given by: p (s ) = a (s ) , (8) G where G is a normalization constant given by G = a (s ) (9) ∑

The channel holding time is the minimum of the CHT and the CRT. As the minimum of two exponentially distributed random variables is also exponentially distributed, then the channel holding time for new calls, as well as for handoff calls for class-i is, therefore, assumed to be exponentially distributed with means 1/µnci and 1/µhi, respectively, where µ nc = µ h = µ b + h .

∑ (m

λh

Define

To characterize mobility, we assume the following simple model. The cell residence time (CRT), i.e., the amount of time during which a mobile terminal stays in a cell during a single visit, is assumed to follow an exponential distribution with mean 1/h [13]. We assume that the CRT is independent of the service class. Hence, connections in any class follow the same CRT distribution. Note that the parameter h represents the call handoff rate.

i

(4)

i =1

i

As for traffic characterization, new call arrivals of class-i (i = 1, 2, …, K) into a cell are assumed to be Poisson with mean arrival rate λnci. The call holding time (CHT) of a class-i call is assumed to follow an exponential distribution with mean 1/µbi. As a simplifying assumption, handoff call arrivals of class-i (i = 1, 2, …, K) are also Poisson with mean arrival rate λhi.

i

K

≤ t 0 ∧ ni bi ≤ ti ∀i ∧ ∑ (mi + ni )bi ≤ B}

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1) Threshold Update Processing Module: The threshold values, t0, t1, …, tK, of the bandwidth reservation policy are dynamically updated to react to changes in nodal mobility and traffic conditions in individual cells. This update is done locally and periodically. We refer to the time between two consecutive threshold updates as Fixed Threshold Update Period (FTUP). The FTUP is chosen to be relatively long compared to a typical call holding time. The threshold update takes place at the end of each FTUP as shown in Figure 1. The new threshold values are used by the AC module in the next FTUP.

usually requires more bandwidth, bi +1 ≥ bi , and may potentially encounter higher dropping rates. 2) Admission Controller Module: The admission controller uses the threshold values that obtained by the threshold update processing module described in Section 3.2.1, to make the admission/rejection decision for a given call request. As shown in Figure 1 the AC module is run upon a call request. Let mi denote the number of class-i new calls, and let ni denote the number of class-i handoff calls that are present in the system at the time of the call request. Also, let Bnci

Let r(s) be the revenue rate when the call is in state s. If ri is the revenue rate of class-i, the total revenue rate for the system (cell) is calculated by: K

denote the total bandwidth allocated for class-i new calls, and let Bhi denote the total bandwidth allocated for class-i

(14)

r ( s ) = ∑ ri (mi + n i )

handoff calls. Note that, B nci = m i bi , and

i =1

Assuming that revenue is given by the number of basic bandwidth units assigned, the total revenue rate in state s is equal to the system bandwidth utilization in state s as follows: K

r ( s) = ∑ bi ( mi + ni )

The available bandwidth in the cell, B A , is then given by

(15)

K

i =1

Given the threshold values, t0, t1, …, tK, a class-i new call is accepted if K (22) b + B ≤ t and bi ≤ B A

In order to satisfy the QoS constraints of all connection classes and to maximize the bandwidth utilization, optimal threshold values are obtained as follows:

i

K

∑ b (m + n )

PDK ≤ PDK −1 ≤ ... ≤ PD1

(18)

FTUP

Time

FTUP

Figure 1. TUP module and AC module time axis function

IV. NUMERICAL RESULTS This section evaluates the performance of the proposed Dynamic Call Admission Control (DyCAC) framework. We first describe the simulation model that is used in this paper. We then validate the accuracy of our analytical model, developed in Section 3, which we have based our DyCAC framework on. Finally, using simulation, we present how our DyCAC framework can guarantee QoS to users and compare its performance simulation results to the complete sharing policy (t0 = t1 = … = tK). The connection-level QoS metrics: the new call blocking probability (NCBP) and the handoff call dropping probability (HCDP) are examined for different system parameters as described below. Since the call arrival rate and mobility (handoff rate, h) could significantly affect QoS metrics, therefore, different experiments are run to obtain NCBP and HCDP results under various settings of the system parameters.

i

PDi +1 always has a lower value than PD (i.e., PD ≤ PD ). The i

rationale for such condition is that a higher traffic class

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AC

: An arriving call (new or handoff)

predetermined value, PD . The constraint in (18) ensures that i i +1

TUP

Legend:

AC

where PD denotes the maximum allowable handoff call i dropping probability of class-i connections, due to their QoS constraints. The constraint in (17) is guaranteed to keep the calculated handoff call dropping probability, Pd , below the

i

(23)

…

Subject to the constraints that for every i = 1, 2, …, K (17)

0

TUP

i =1

Pd i ≤ PDi

nci

while a class-i handoff call is accepted if bi + B hi ≤ t i and bi ≤ B A

(16)

i

∑ i =1

Step 1: During the FTUP, thresholds of different classes remain fixed and each base station measures the new and handoff call arrival rates, as well as the new and handoff call service rates for each class of traffic. Step 2: The new call blocking probability (equation 12) and the handoff call dropping probability (equation 13) are calculated for each class-i, i = 1, …, K, given the traffic parameters provided from Step 1. Step 3: At the end of the FTUP and using the results obtained from Step 2, optimal threshold values, t0, t1, …, tK, are obtained such that the QoS constraints of all connection classes are satisfied, while maximizing the bandwidth utilization. In other words, search for the optimal threshold values, t0, t1, …, tK, that i

(21)

B A = B − ∑ ( Bnci + Bhi )

where ri in (14) is replaced by bi.

i

(20)

B hi = n i b i

i =1

Maximize

(19)

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bandwidth of handoff connections is constant, the NCBP decreases in response to the decreased load while the HCDP increases in response to the increased handoff load.

A. Simulation Model The following describe the assumptions used in our simulation. The simulated system consists of 7 cells arranged on a circle with identical mobility and traffic conditions (homogenous traffic environment). The diameter of each cell is 1 km (micro cellular environment). The base station resides at the center of each cell. The total capacity of each cell is B (bbu). Two classes are considered with class-1 bandwidth requirement b1 and class-2 bandwidth requirement b2. Maximum number of total basic bandwidth units that are allocated to new connections is t0 and to class-i handoff connections is ti for i = 1, 2. Call requests are generated according to a Poisson process with rate λ (calls/second) in each cell. A newly generated connection can appear anywhere in the cell with an equal probability. Note that λ =λnc1 =λnc2 and the handoff call arrival rate of class-i is assumed to be proportional to the new call arrival rate of class-i by λh = ( h / µb )λnc . Mobiles can travel in one of eight directions with equal probability. A constant randomly selected speed is assigned to a mobile when it enters a cell either at call initiation or after handoff. The speed is obtained from a uniform probability distribution function ranging between Vmin = 10 k m/hr and Vmax = 60 k m/hr. The lifetime of a class-i connection is exponentially distributed with mean 1 / µb seconds. i

i

C. Performance Evaluation Here we show how the proposed framework (DyCAC) can satisfy the desired QoS for handoff calls of tested classes. Threshold values are dynamically adjusted, and are updated periodically at the end of the fixed threshold update period by invoking the threshold update processing module. For comparison purposes, we simulated the complete sharing policy (t0 = t1 = t2) as well, whereby a call of class-i is admitted whenever a cell has enough available bandwidth to accommodate the call. Figures 3 and 4 illustrate the connection-level QoS parameters, NCBP and HCDP, versus the call arrival rate of both traffic classes. The system parameters in this case are as follows: B = 30, b1 = 1, b2 = 3, t0 = t1 = t2 = 30, λnc1 = 3λ, λnc2 = λ, h = 0.5, µb1 = µb2 = 0.5. Figure 3 shows the performance of the complete sharing policy of the above system. Note that both new and handoff calls of the individual classes have the same performance, and that class-2 calls suffer from a higher blocking and dropping probabilities than class-1 calls due their higher bandwidth requirement.

i

Figure 4(a) shows the performance of our dynamic CAC framework for a maximum handoff call dropping probability for class-1, PD = 10 −2 and for class-2, PD = 10 −3 . Figure 5(b)

i

B. Analytical Model Validation Table I presents a numerical comparison of the NCBP and the HCDP obtained from the analytical model (denoted by ANA) and simulation (denoted by SIM). The threshold update processing module is not invoked as our purpose here is to verify the accuracy of the analytical model. The parameters of the system considered in this experiment are as follows: B = 30, b1 = 1, b2 = 3, t0 = 15, t1 = 30, t2 = 30, λnc1 = λnc2 = λ, h = 0.5, µb1 = µb2 = 0.5. The results in Table I show the effect of varying the call arrival rate on the performance measures. The comparison illustrates that difference between the two models are negligible, thereby validating the ability of the analytical model to accurately capture the behavior of the system. Moreover, the results in Figure 2 also compare the analytical and simulation results with negligible differences observed. The simulation results obtained in all experiments have a 95% confidence level with 5% confidence intervals.

1

1

i

i

i

V. CONCLUSIONS In this paper, a novel Dynamic Call Admission Control (DyCAC) framework for wireless cellular networks is proposed. The proposed framework considers multiple classes of wireless multimedia services with different QoS requirements. Three related components comprise the main building blocks of the framework: (i) a threshold-based bandwidth reservation policy, (ii) a threshold update processing module, and (iii) an admission controller module. The underlying goals of our DyCAC framework are achieved simultaneously. Our work is hence of a pioneering nature as it is the first to simultaneously address these issues. Simulation results show that the system is able to guarantee the connection-level QoS handoff call dropping probability for each class of traffic. Thus, it satisfies mobile users’ needs resulting in a stable performance levels during heavy load periods. Furthermore, the DyCAC provides a low call blocking probability of new calls, which translates into high resource utilization. This is a highly desirable property from the service provider point of view.

i

i

also increases, while ρnci decreases. Since the reserved

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2

bound of handoff dropping probability can be met for this system, even at the lowest call arrival rate considered here, if calls are allowed to completely share the bandwidth. However, from Figures 4(a) and Figure 4(b), our proposed DyCAC framework achieves an almost constant HCDP that is below but yet close to the maximum allowable HCDP for both classes, even under extremely high loading conditions.

Figure 2 shows the effect of varying the handoff rate, h, on the connection-level of QoS. The parameters of the system considered here are as follows: B = 30, b1 = 1, b2 = 3, t0 = 15, t1 = 30, t2 = 30, λnc1 = λnc2 = 3, h = h, µb1 = µb2 = 1. We can see that at low handoff rates, there is a large difference between the HCDP and the NCBP of both traffic classes. As the handoff rate increases, the NCBP decreases while the HCDP increases until they converge to the same value. This is because the load generated by class–i new connections ρnci approximately equals ρnc = λnc /( µb + h) , and the load generated by the handoff connections ρhi can be approximated by ρ h = ( h / µb ) ρ nc . As the cell handoff rate h increases, ρhi i

2

shows the performance of our dynamic CAC framework for a maximum handoff call dropping probability for both classes of PD = PD = 10 −3 . From Figure 3, it is clear that neither upper

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[8] Malla, A., El-Kadi, M., Olariu, S. and Todorova, P., “A fair resource allocation protocol for multimedia wireless networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 14, no. 1, Jan. 2003, pp. 63-71. [9] Ramanathan, P.; Sivalingam, K.M.; Agrawal, P.; Kishore, S.; “Dynamic resource allocation schemes during handoff for mobile multimedia wireless networks”, IEEE Journal on Selected Areas in Communications, vol. 17, no. 7 , July 1999, pp. 1270-1283. [10] Si Wu, K. Y. Michael Wong and Bo Li, “A dynamic call admission policy with precision QoS guarantee using stochastic control for mobile wireless networks”, IEEE/ACM Transactions on Networking, vol. 10 , no. 2, April 2002, pp. 257–271. [11] Ganguly, S.; Nath, B.; Goyal, N.; “Optimal bandwidth reservation schedule in cellular networks”, Proceedings INFOCOM , vol. 3, March 2003, pp. 1591-1602. [12] N. Nasser and H. Hassanein, “Semi-Markov Decision-Based Call Admission Control for QoS Support in Wireless Cellular Networks", submitted for publication, 2004. [13] K. Yeung and S. Nanda, “Channel Management in Microcell/macrocell Cellular Radio Systems”, IEEE Transactions on Vehicular Technology, Vol. 45, Nov. 1996, pp. 601-612.

REFERENCES [1] S. S. Rappaport, “The Multiple-Call Hand-off Problem in High-Capacity Cellular Communications Systems”, IEEE Trans. Veh. Tech., vol. 40, no. 3, Aug. 1991, pp. 546-557. [2] Naghshineh, M. and Schwartz, M., “Distributed call admission control in mobile/wireless networks”, IEEE Journal on SAC, vol. 14 no. 4, May 1996, pp. 711-717. [3] N. Nasser and H. Hassanein, “Prioritized Multi-class Adaptive Framework for Multimedia Wireless Networks”, Proceedings of the IEEE 2004 International Conference on Communications (ICC), Paris, France, June 2004, pp. 4295-4300. [4] J. Zander, S.-L. Kim, M. Almgren, and O. Queseth, Radio Resource Management for Wireless Networks. Artech House Publishers, 2001. [5] Taekyoung Kwon, Yanghee Choi, Bisdikian, C. and Naghshineh, M., “Measurement-based call admission control for adaptive multimedia in wireless/mobile networks”, IEEE WCNC, vol.2, Sept. 1999, pp. 540-544. [6] Dongxu Shen and Chuanyi Ji, “Admission control of multimedia traffic for third generation CDMA network,” Proceedings INFOCOM, vol. 3 , March 2000, pp.1077 -1086 [7] Huan Chen, Kumar, S. and Kuo, C.-C.J., “Dynamic call admission control scheme for QoS priority handoff in multimedia cellular systems,” Proceeding of the IEEE WCNC, vol. 1, March 2002, pp. 114-18.

TABLE I: COMPARISON OF ANALYTICAL AND SIMULATION RESULTS. NCBP

Call arrival rate (λ) 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

HCDP

ANA Pb1 1.80E-06 4.34E-05 2.55E-04 8.55E-04 0.002122 0.004349 0.007795 0.012651 0.018997 0.026728

SIM Pb2 1.17E-05 2.79E-04 0.001591 0.005074 0.011813 0.022595 0.037809 0.057445 0.081127 0.107947

Pb1 2.00E-06 4.34E-05 2.85E-04 8.55E-04 0.002122 0.004349 0.007795 0.012651 0.018997 0.026728

ANA Pb2 1.90E-05 3.00E-04 0.001591 0.005074 0.011813 0.022595 0.037809 0.057445 0.081127 0.100047

SIM

Pd1 0 0 3.00E-07 5.10E-06 2.75E-05 1.29E-04 3.77E-04 9.56E-04 0.002039 0.003721

Pd2 0 0 3.0E-06 2.80E-05 1.66E-04 6.52E-04 0.00192 0.004575 0.009251 0.012023

Pd1 0 0 3.00E-07 6.10E-06 3.15E-05 1.49E-04 4.37E-04 1.04E-03 0.002339 0.004021

Pd2 0 0 3.10E-06 3.30E-05 1.67E-04 7.02E-04 0.00222 0.005329 0.009751 0.012023

1.0E+00

1.0E+00

Pb1( Analyt ical)

Pb1( Simulat ion) Pb2( Analyt ical)

Pb2( Simulat ion)

Blocking/Dropping Probability

Blocking/Dropping Probability

1.0E-01

Pd1( Analyt ical)

1.0E-02

Pd1( Simulat ion) Pd2( Analyt ical)

Pd2( Simulat ion)

1.0E-03

1.0E-04

1.0 E-05

1.0E-06

1.0E-01 1.0E-02 1.0E-03

Pb1

1.0E-04

Pb2 Pd1

1.0E-05

Pd2 1.0E-06

1.0 E-07 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

0

Handoff Rate

Figure 2. Effect of varying the handoff rate on the connection-level QoS. Pb1

Pb2

Pd1

1

1.5

2

2.5 3 3.5 4 Call Arrival Rate (λ)

4.5

5

5.5

6

6.5

Figure 3. Connection-level QoS vs. call arrival rate for the complete sharing policy. Pb1

Pd2

Pb2

Pd1

Pd2

1.0E+00

Blocking/Dropping Probability

1.0E+00

Blocking/Dropping Probability

0.5

1.0E-01

1.0E-02

1.0E-03

1.0E-01

1.0E-02

1.0E-03

1.0E-04

1.0E-04 0

0.5

1

1.5

2

2.5 3 3.5 4 Call Arrival Rate ( λ)

4.5

5

5.5

0

6

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

6

Call Arrival Rate (λ)

(a) PD1 = 10-2 and PD2 = 10-3

(b) PD1 = 10-3 and PD2 = 10-3 Figure 4. DyCAC connection-level QoS vs. call arrival rate.

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