Periodical_zngydxxb-e201001018.aspx

J. Cent. South Univ. Technol. (2010) 17: 110?116

DOI: 10.1007/s11771?010?0018?2

A novel dynamic call admission control policy for wireless network

HUANG Guo-sheng(黄国盛)1, 2, CHEN Zhi-gang(陈志刚)1, LI Qing-hua(李庆华)1,

ZHAO Ming(赵明)1, GUO Zhen(郭真)1

1. School of Information Science and Engineering, Central South University, Changsha 410083, China;

2. College of Physics Science and Information Engineering, Jishou University, Jishou 416000, China

? Central South University Press and Springer-Verlag Berlin Heidelberg 2010

Abstract: To address the issue of resource scarcity in wireless communication, a novel dynamic call admission control scheme for wireless mobile network was proposed. The scheme established a reward computing model of call admission of wireless cell based on Markov decision process, dynamically optimized call admission process according to the principle of maximizing the average system rewards. Extensive simulations were conducted to examine the performance of the model by comparing with other policies in terms of new call blocking probability, handoff call dropping probability and resource utilization rate. Experimental results show that the proposed scheme can achieve better adaptability to changes in traffic conditions than existing protocols. Under high call traffic load, handoff call dropping probability and new call blocking probability can be reduced by about 8%, and resource utilization rate can be improved by 2%?6%. The proposed scheme can achieve high source utilization rate of about 85%.

Key words: wireless network; call admission control; quality of service; Markov decision process

1 Introduction

With the rapid development of wireless network communication, effective use of the limited wireless resources and QoS support in wireless network has become more and more important, and considerable efforts have been focused on call admission control (CAC). The lack of wireless resources is the major bottleneck in wireless networks to provide QoS support. The next generation of wireless cellular networks will use micro/pico cellular architectures in order to provide higher capacity, mobile nodes handovering frequently among cells brings new challenges to the call admission control [1?3]. An ongoing call in the current cell may have to be handed over to another cell. During this process, the call may not be able to obtain enough resources to continue its communication due to the limited available resources in the new cell, which will lead to call dropping. Because mobile users are more sensitive to ongoing call dropping than new call blocking, handoff calls are normally assigned higher priority over new calls [4?5].

There are two important connection level QoS parameters in wireless communication: new call blocking probability P nb and handoff call dropping probability P hd. CAC optimization goal is to maximize resource utilization while reduce P hd and P nb. However, in order to reduce P hd, the existing CAC policies usually result in obvious decrease of resource utilization and may lead to a high P nb [6].

The existing CAC schemes can be divided into two major categories: reservation-based schemes and threshold-based schemes [7?8].

In Ref.[9], when a handoff occurred, bandwidth was allocated in the new cell and reserved in the new cell’s neighboring cells, according to the call connection number in neighboring cells and the mobility direction of the mobile node. The disadvantage of this scheme is that the reserved bandwidth in the neighboring cells would cause a waste of system resources, resulting in the increase of P nb in these cells. GWENDAL and ERIC [10] used mobile IP reservation protocol (MIR) to reserve a fixed bandwidth in each cell for switching users, carrying out CAC according to the reserved bandwidth-size. However, MIR solution did not give an algorithm to determine the reserved bandwidth-size. It is difficult for this solution to adapt the dynamic changing of network conditions.

Guard channel (GC) solution [11?13], is the most widely used threshold-based CAC solution. New call bounding (NCB) scheme, proposed by FANG and ZHANG [13], is a typical representative of GC solutions. The scheme works as follows: when a new call arrives, if the number of new calls in a cell exceeds a threshold K,

Foundation item: Project(60873082) supported by the National Natural Science Foundation of China; Project(09C794) supported by the Natural Science Foundation of Education Department of Hunan Province, China; Project (S2008FJ3078) supported by the Science and Technology

Program Foundation of Hunan Province, China; Project(07JJ6109) supported by the Natural Science Foundation of Hunan Province, China Received date: 2009?02?28; Accepted date: 2009?07?09

Corresponding author: CHEN Zhi-gang, Professor; Tel: +86?731?88830797; E-mail: czg@https://www.360docs.net/doc/b614868111.html, cn

the new call will be blocked; otherwise it will be

accepted. The handoff call will be rejected only when all channels in the cell are used up. In fact, GC solutions are equivalent to specifically reserve part of system bandwidth for handoff calls. In threshold-based solution, P hd is reduced at cost of increase of P nb.If the reserved bandwidth for handoff calls is too large, although P hd is reduced, a rapid increase of P nb will cause a decline in integral performance of system connection level QoS. Furthermore, part of GCs may be left to be idle, resulting in the decrease of system resource utilization. On the contrary, if reserved bandwidth for handoff calls is too small, the P nb will come down while P hd will go up, which is difficult to meet the QoS demand of mobile users [14?17].

WANG et al [18] proposed an adaptive call admission control solution. The solution calculated the optimal call number of each cell adaptively according to the system state vector, call end vector and call transfer matrix. It performed CAC based on NCB scheme. The advantage of this solution is that it can adapt to the changes of network load. However, this solution needs to calculate the system call transfer matrix and overload probability to find the ideal system state [18]. With the increase of the cell number of a domain, the computational load will increase considerably. Although some adaptive GC based CAC solutions have been proposed, it is difficult for existing schemes to reduce P hd and P nb effectively and improve system resources utilization [19?20].

In this work, a reward mechanism based dynamic optimization scheme on CAC (RBDO-CAC) for wireless mobile network was proposed. The scheme established a reward model of call admission control of wireless cell based on Markov decision process, dynamically optimized call admission process according to the principle of maximizing the average system rewards, and had a good adaptability to changes in network flow conditions.

2 System model analysis

RBDO-CAC was based on NCB scheme [13]. It adapted to the changes of system call traffic load, adjusted new call admission threshold K of a cell according to the principle of maximizing system reward, consequently, realized the dynamic optimization of CAC.

As shown in Fig.1, calls in each wireless cell can be divided into two types: new calls and handoff calls. New call is initiated from mobile nodes (MNs) of current cell. Handoff call is an ongoing call moving from adjacent

Fig.1 Structure model of wireless cell

cell to current cell. Assume the effective capacity of a cell is C, then C represents the maximum connection requests that the cell can accept [21?22].

To simplify the problem description, a cell was chosen as the research object. Assume arrival process of new calls and handoff calls follow the Poisson distribution, and their arrival rates are λ1 and λ2 respectively. Service time of new calls and handoff calls follows the exponential distribution, and their average service time is 1/μ1 and 1/μ2, respectively. The cell channel occupied state can be expressed by a two- dimensional Markov chain, and the system state space can be written as follows:

S={(n1, n2)│0≤n1≤K, n2≥0, n1+n2≤C} (1) where n1 denotes the number of new calls accepted in the cell, and n2 is the number of handoff calls in the cell.

For the purpose of simplicity, the following definitions are given.

Definition 1 New call traffic intensity ρ1 is the multiplication of new call arrival rate and new call average service time, i.e., ρ1=λ1/μ1.

Definition 2 Handoff call traffic intensity ρ2 is the multiplication of handoff call arrival rate and handoff call average service time, i.e., ρ2=λ2/μ2.

According to the above definitions, the larger the ρ1 and ρ2 are, the heavier the system load is.

Assume the system rewards of accepting a new call and handoff call are R n and R h, respectively. When the system is in state (n1, n2), the total reward R T can be written as follows:

R T=n1R n+ n2R h (2) In RBDD-CAC, the new call admission threshold K was dynamically optimized to maximize the system average reward )

(T K

R. Thus, it can be known that

max(

T m

R K

K∈[1, C](3) where K m is the optimal new call threshold to maximize the system average reward. Obviously, to give priority to handoff calls, the reward of accepting a new call and a handoff call (i.e., R n and R h) must satisfy:

R h＞R n (4) The larger the ratio of R h to R n, the higher the priority P of handoff calls, that is

P∝R h/R n(5) The system state transition diagram is shown in Fig.2, where K stands for new call threshold, C stands for system capacity.

Fig.2 Transition diagram of system state

The schematic diagram of system state transition rate between adjacent states is shown in Fig.3, where (i, j) stands for a state in state space S, i denotes the number of new calls initiated in the cell and j is the number of handoff calls in the cell, i∈[1, K?1] and j∈[1, C?1].

Fig.3 Schematic diagram of system state transition rate

Assume P(n1, n2) denotes the steady state probability

of n1 new calls and n2 handoff calls in the cell, then the system state balance equation can be written as

λ1P(i?1, j)+(i+1)μ1P(i+1, j)=iμ1P(i, j)+λ1P(i, j),

i∈[1, K?1], j∈[1, C?2] (6) λ2P(i, j?1)+(j+1)μ2P(i, j+1)=jμ2P(i, j)+λ2P(i, j),

i∈[1, K?1], j∈[1, C?2] (7) λ1P(i, 0)=μ1P(1, j), j∈[0, C?1] (8) λ2P(i, 0)=μ2P(i,1), i∈[0, K] (9) Cμ2P(0, C)=λ2P(0, C?1) (10) From Eqs.(6)?(10), it can be known that

λ1P(i , j)=(i+1)μ1P(i+1, j), 0≤i≤K?1, 0≤j≤C(11) λ2P(i , j)=(j+1)μ2P(i, j+1), 0≤i≤K, 0≤j≤C?1 (12) According to system state balance equation, by using recursive method, the steady state probability P(n1,n2) can be expressed as [13]

(,)(0, 0),

n n

P n n P

n n

ρρ

=0≤n1≤K, n1+n2≤C (13) Then, the normalization equation can be written as 12

(,)1

K C K

n n

P n n

∑∑(14)

According to Eqs.(13) and (14), it can be known that

(0, 0)

C n

n n

ρρ

=??

∑∑(15)

According to Eqs.(13) and (15), new call blocking probability P nb and handoff call dropping probability P hd in the cell can be written as

1212

211

!!!()!

n C n

K K

C K K

n n

C n

n n

K n n C n

n n

ρρ

∑∑

(16)

111

112

1212

1112

000

()

!()!!!

n C n C n

n n

K K

n n n

n C n n n

ρρρρ

===

∑∑∑(17)

According to Eq.(13), the mathematical expectation of new call number and handoff call number in the cell can be expressed as

121

111

!!!

n n n

C n

K K

n n n

C n

n n

n n n

n n

ρρρ

ρρ

???

===

∑∑∑

∑∑

(18)

122

111

!!!

n n n

C n

K C

n n n

C n

n n

n n n

n n

ρρρ

ρρ

???

===

∑∑∑

∑∑

(19)

According to Eqs.(2), (18) and (19), the system average reward T R of the cell can be expressed as T1n2h

= +

R n R n R

??=

121

121n 2h 1211

12120

0()

n C n n K n

h n n C n n n K

n n n R n R n n n n ρρρρ?

?==?==++∑∑

∑

2121

1212

121

12120

n n K

n n C n n n K

n n n R n R n n n n ρρρρ?

==?==+

∑∑∑

∑

(20)

According to Eqs.(3) and (20), it is not difficult to find the optimal new call threshold K m to maximize system average reward.

3 Reward mechanism based dynamic optimization on call admission control

In RBDO-CAC, the optimal call admission threshold K m was calculated, according to the model established in section 2 based on the principle of maximizing system average reward, and K m was dynamically optimized in time period τ in accordance with the real-time call traffic load.

Because the system obtains more reward accepting a handoff call than accepting a new call, so the system will give priority to accept handoff calls, that is, handoff call has a higher priority than new call. At the same time, in order to obtain the largest average reward, the system will make full use of available network resources to accept new calls as many as possible. Consequently, RBDO-CAC can improve system resource utilization and optimize the system performance.

When a call request arrives, the system first checks the call type, for new calls, only when the new call number system accepted is less than threshold K m , it can be accepted; for handoff calls, as long as the system has available resources, it will be accepted.

According to the call admission model given in section 2, reward mechanism based dynamic optimization on call admission control algorithm is described as follows:

// Pseudo code of dynamic optimization CAC // Input:

// C = effective capacity of a cell;

// τ = time period for updating threshold K m ; // λ1 = arrival rate for new call; // λ2 = arrival rate for handoff call;

// 1/μ1= average channel holding time for new call; // 1/μ2 = average channel holding time for handoff call;

// Temporary variables:

// R max : a temporary variable to store system reward; // K 1: a temporary variable to store call threshold;

// K m : the optimal call admission threshold for new call;

// C A : number of accepted new call in the cell; // C B : total number of accepted calls in the cell; for ( ; ; )

// Find the optimal call admission threshold K m { for (j = 1; j ≤C ; j ++) {k = j ;

compute T R according to Eq.(20); if (R max ＜T R ) {R max =T R ; K 1=k } }

K m =K 1;

// K m is the optimal call admission threshold // Call admission control with threshold K m if (Connection type = = new call) {if (C A +1＜K m and C B ＜C )

{accept; C A = C A +1; C B = C B +1} else reject; }

else // Connection type is handoff call {if (C B +1＜C )

{accept; C B = C B +1;} else reject; }

Delay time-interval τ;

//Update threshold K m periodically }

In this algorithm, the optimal new call admission threshold K m is calculated dynamically. The algorithm can maximize system reward, improve system resource utilization, and adapt to the dynamic changes of network traffic.

4 Simulation and performance analysis

RBDO-CAC was simulated in Red Hat Linux. The effective capacity of a cell is 50. To simplify the calculation, the system reward of accepting a new call is given as R n =1, by changing R h to adjust the priority of handoff calls. The optimal new call admission threshold K m , new call blocking probability P nb , handoff call dropping probability P hd and system resource utilization R u under different call traffic intensity (ρ1and ρ2) and different handoff call reward R h , were measured, and the simulation results were compared with NCB solution [13].

4.1 Optimal new call admission threshold K m

Fig.4 shows the relationship between system average reward and new call admission threshold K when maintaining handoff call reward R h =3 and new call traffic intensity ρ1=35 unchanged, and it also shows the effect of changing handoff call traffic intensity ρ2 on the optimal threshold K m . It can be seen from Fig.4 that when handoff call traffic intensity ρ2 is 22, 24 and 26, the new call admission threshold K m of obtaining the

maximum system average reward is 26, 22 and 20, respectively, which indicates that the greater the ρ2, the smaller the K m. This is because the greater the ρ2, the more the handoff call admission request. The system needs to reduce new call threshold K m to accept more handoff calls to obtain the largest reward.

Fig.4 Optimal call threshold K m under different handoff call traffic intensities ρ2

Fig.5 shows the relationship between system average reward and new call admission threshold when maintaining ρ1=35, ρ2=25 unchanged. As shown in Fig.4, when handoff call reward R h=2.5, 3.0 and 3.5, the system optimal call threshold K m=23, 21 and 20, respectively. It is found that the greater the R h, the smaller the K m. This is because, according to the principle of maximizing system reward, the greater the R h, the higher the priority of handoff calls, the system will reduce K m to accept more handoff calls.

Fig.5 Optimal call threshold K m under different handoff call reward R h

4.2 Handoff call dropping probability P hd and new

call blocking probability P nb

Fig.6 shows handoff call dropping probability of RBDO-CAC and NCB when new call traffic intensity ρ1=20, and handoff call traffic intensity ρ2 changes from 21 to 35. As can be seen from Fig.6, with the increase of handoff call traffic intensity ρ2, handoff call dropping probabilities P hd of the two solutions both increase, but P hd of RBDO-CAC has a more gentle change. It is found that RBDO-CAC has a better adaptability to network call traffic changes, and P hd of RBDO-CAC is lower than that of NCB. This enhancement is expected as ρ2 increases, DT-CAC adaptively reduces new call admission threshold K m, accepting more handoff calls to maximize system reward, consequently, limiting the increase of P hd. In addition, as shown in Fig.6, for RBDO-CAC, the larger the handoff calls reward R h, the smaller the P hd. This is because the greater the R h, the higher the priority of handoff call, and the more the handoff call accepted.

Fig.6 Handoff call dropping probability P hd vs handoff call traffic intensity ρ2

Fig.7 shows the relationship between P nb and ρ1 of RBDO-CAC and NCB when handoff call traffic intensity ρ2=21. As shown in Fig.7, for RBDO-CAC, the greater the handoff call reward R h, the greater the P nb. This is because in accordance with the principle of maximizing system average reward, the greater the R h, the higher the priority of handoff calls, consequently, the larger the P nb. Instead, the smaller the handoff call reward R h, the smaller the P nb. So a good trade-off between P hd and P nb can be achieved by adjusting handoff call reward. From Fig.7, it can also be seen that probabilities P nb of two solutions both increase when ρ1 increases, the increase of P nb of NCB is more significant, and P nb of RBDO-CAC is less than P nb of NCB. This is because in NCB, when new call traffic intensity ρ1 increases, call admission thresholds remain unchanged. RBDO-CAC can adapt to changes of network load and dynamically optimize call admission threshold according to the principles of maximizing system average reward, accepting more new calls, thus restricting the increase of P nb.

Fig.7 New call blocking probability P nb vs new call traffic intensity ρ1

4.3 Resource utilization under different call traffic

intensities

Fig.8 depicts system resource utilization of RBDO-CAC and NCB under different new call traffic intensity ρ1, where handoff call traffic intensity is given as ρ2=20, and handoff call reward of RBDO-CAC is given as R h=2.0. As shown in Fig.8, when ρ1 increases, resource utilizations of the two solutions both increase, and resource utilization of RBDO-CAC is significantly higher than that of NCB. Because when ρ1 increases, call admission threshold of NCB remains unchanged, which cannot adapt to the changes of network load. On the contrary, RBDO-CAC can dynamically optimize new call admission threshold K m, take full advantage of available channels in the cell to accept more calls, maximize system average reward, and thus improve system resources utilization.

Fig.8 System resource utilization of RBDO-CAC and NCB under different new call traffic intensities ρ1

5Conclusions

(1) The call admission control scheme proposed dynamically calculates call admission threshold according to network load conditions, and can adapt to the dynamic changes of call traffic.

(2) Realization of maximizing system rewards enables system to accept calls request as many as possible, and effectively improves system resources utilization.

(3) The scheme effectively reduces system handoff call dropping probability and new call blocking probability, achieves a good trade-off between handoff call dropping probability and new call blocking probability by adjusting the reward of handoff call and new call, and prevents unrestricted increase of new call blocking probability caused by the priority of handoff call.

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(Edited by CHEN Wei-ping)

A novel dynamic call admission control policy for wireless

network

作者：HUANG Guo-sheng， CHEN Zhi-gang， LI Qing-hua， ZHAO Ming， GUO Zhen

作者单位：HUANG Guo-sheng(School of Information Science and Engineering, Central South University,

Changsha 410083, China;College of Physics Science and Information Engineering, Jishou

University, Jishou 416000, China)， CHEN Zhi-gang,LI Qing-hua,ZHAO Ming,GUO Zhen(School of

Information Science and Engineering, Central South University, Changsha 410083, China)

刊名：

中南大学学报（英文版）

英文刊名：JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY(ENGLISH EDITION)

年，卷(期)：2010，17(1)

被引用次数：0次

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22.CHATZIPERIS S.KOUTSAKIS P.PATERAKIS M A new call admission control mechanism for multimedia traffic over next-generation wireless cellular networks 2008(1)

相似文献(10条)

1.外文期刊Chi-Jui Ho.Chin-Tau Lea.Gordon L. Stuber Call admission control in the microcell/macrocell overlaying system

Call admission control can significantly affect the performance of a cellular system by adding additional bandwidth to a wireless network. In this paper, we show how call admission control can be used to optimize the performance of a hierarchical cellular system. The hierarchical system we study is based on a novel frequency planning scheme, whereby both the micro and macro layers can share the same spectrum and a hard partition of frequency spectrum is not needed. The original analysis of the hierarchical system showed that a significant capacity gain can be achieved by the scheme. However, this capacity is gained at the expense of the carrier-to-interference ratio (C/I) performance of the macrocells. In this paper, we show that call admission control can be used in hierarchical cellular systems to achieve a capacity gain without sacrificing the C/I performance of the macrocells.

2.外文会议Joutsensalo. J..Hamalainen. T..Sayenko. A..Paakkonen. M.Call admission control for quality of service and

revenue maximization

Fair resource allocation in the wireless domain poses significant challenges due to the unique issues in the wireless channel such as location dependent and bursty channel error. This paper presents an adaptive scheduling and call admission control model for the capacity optimization of the wireless network. The model shares limited resources to different traffic flows in a fair way, and at the same time it maximizes the revenue of the service provider. In addition, the flat pricing scenario for the different traffic classes has been used. Presented algorithm is derived from the linear type of revenue target function, and a closed form globally optimal formula is presented. The method is computationally inexpensive, while still producing maximal revenue. Due to the simplicity of the algorithm, it can operate in the highly nonstationary environments. In addition, it is nonparametric and deterministic in the sense that it uses only the information about the number of users and their traffic flows, not about call density functions or duration distributions.

3.外文期刊Xinbing Wang.Do Young Eun.Wenye Wang A Dynamic TCP-Aware Call Admission Control Scheme for Generic Next

Generation Packet-Switched Wireless Networks

Traditional call admission control (CAC) schemes only consider call-level performance and are mainly designed for circuit-switched wireless network. Since future wireless communications will become packet-switched systems, the packet-level features could be explored to improve the system performance. This is especially true when the TCP-type of elastic applications are running over such packet-switched wireless networks, as the elasticity of TCP applications has more tolerance toward the throughput/delay variation than non-elastic traffic does. In order to efficiently utilize the system resource from an admission control perspective, we propose a TCP-aware CAC scheme to regulate the packet-level dynamics of TCP flows. We analyze the system performance under realistic scenarios in which (i) the call holding time for non-elastic traffic like voice is independent of system states and (ii) the call holding time for TCP type of traffic depends on the system state, i.e., on the TCP flow's transmission rate. Extensive simulations are presented under different scenarios to show that the proposed scheme can effectively improve the system performance in terms of call blocking probability, call-level throughput (call/min) and link utilization,

in accordance with our theoretical results.

4.期刊论文黄国盛.陈志刚.赵明.李庆华.梁平原.Huang Guosheng.Chen Zhigang.Zhao Ming.Li Qinghua.Liang Pingyuan分层移动

IPv6中呼叫接入控制的动态优化-高技术通讯2010,20(1)

对分层移动IPv6中的无线资源管理问题进行了研究,提出了一种基于动态阈值的呼叫接入控制(DT-CAC)策略.DT-CAC通过建立小区呼叫接入的马尔可夫排队模型,根据网络负载状态的变化对切换呼叫和新呼叫的接入阈值进行动态调整,在减少切换呼叫掉线率的同时限制新呼叫阻塞率的增加,从而在切换呼叫掉线率和新呼叫阻塞率之间取得平衡.大量仿真结果表明,DT-CAC与现有协议相比,在减小新呼叫阻塞率和切换呼叫掉线率以及提高资源利用率等方面具有较好的性能.当呼叫流量较大时,DT-CAC的新呼叫阻塞率和切换呼叫掉线率可以减少约6%左右,资源利用率可以达85%以上.

5.会议论文Yu Qin.Wu Ye.Suili Feng A Call Admission Control Scheme Using Statistical Priority Queue2006

In modern wireless networks, call admission control (CAC) has received a lot of attention because of its central role for Quality of Service (QoS) provision. In this paper, a new admission control scheme using statistical priority queue (SQCAC) is proposed. Its objective is to get an optimal overall satisfaction for the customers in terms of an objective function defined in this paper. In SQCAC, handoff calls and new calls occupy the queue in basic

station (BS) statistically. Handoff calls are given a higher probability to occupy the queue than new calls when the available bandwidth is used out. SQCAC can also provide a tradeoff between call block probability (CBP) and call dropping probability (CDP)

6.外文会议Falowo. Olabisi E..Chan. H. Anthony.ISWCS Fuzzy Logic Based Call Admission Control for Next Generation

Wireless Networks

Different radio access technologies (RATs) such as UMTS, WiMax, WLAN, etc, will coexist in next generation wireless networks (NGWN). This coexistence of RATs necessitates joint radio resource management (JRRM) for efficient radio resource utilization and improved users'' satisfaction. Admitting a call into the most appropriate RAT based on many selection criteria is major challenge in NGWN. This paper focuses on joint call admission control (JCAC) algorithm which is one of the JRRM algorithms. We propose a Fuzzy Logic based JCAC scheme for NGWN. The JCAC scheme consists of local CAC algorithms and a RAT selection algorithm. Each RAT has a local CAC algorithm which select the most appropriate cell for an incoming call, and determine whether the call can be admitted into the cell or not. A RAT selection algorithm then selects the most appropriate RAT for the incoming call among the RATs whose selected cell meets the local call admission condition. The proposed JCAC scheme is illustrated using two local CAC criteria and five RAT selection criteria.

7.外文会议Mohamed. N.O..Deniz. D.Z.Dynamic partitioning based call admission control for integrated services wireless

mobile networks

Call admission control for multimedia wireless networks providing integrated services is considered. Performance evaluation of the reserve channels CAC strategy with dynamic partitioning for a wireless cell serving three customer classes is carried out. Each customer class has different bandwidth and channel holding time requirements which correspond to voice, data and video respectively. The wireless cell serves newly arriving as well as customers handed over from nearby cells. A number of channels are dynamically reserved for the sole use of handoff customers to reduce the probability of handoff call dropping. A solution technique is developed and the performance of the system is obtained. It is found that new and handoff customers which require more channels for

their service are blocked more. Also, server utilization is much improved when the arrival rate of higher bandwidth requiring customers is high.

8.外文会议Yu Qin.Wu Ye.Suili Feng A Call Admission Control Scheme Using Statistical Priority Queue

9.外文会议Elbatji. A.Y..Rachidi. T..Bouzekri. H.A testbed for the evaluation of QoS provisioning in WCDMA based 3G

wireless networks

This paper presents the design patterns and architecture of a testbed for the evaluation of quality of service (QoS) provisioning in WCDMA (wideband code

division multiple access) based third generation wireless networks. The testbed allows for the evaluation of QoS adaptation techniques that supercedes to the traditional power control in the radio resource manager (RRM). It handles classes of service defined for the Universal Mobile Telecommunication System (UMTS), as well as mobility and handoffs.

10.外文会议Elbatji. A.Y..Rachidi. T..Bouzekri. H.A testbed for the evaluation of QoS provisioning in WCDMA based 3G

wireless networks

This paper presents the design patterns and architecture of a testbed for the evaluation of quality of service (QoS) provisioning in WCDMA (wideband code division multiple access) based third generation wireless networks. The testbed allows for the evaluation of QoS adaptation techniques that supercedes to the traditional power control in the radio resource manager (RRM). It handles classes of service defined for the Universal Mobile Telecommunication System (UMTS), as well as mobility and handoffs.

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授权使用：北方工业大学(bfgyds)，授权号：2b691e9a-32ab-4b86-9560-9e9400f278ee

下载时间：2011年2月24日