call requests of different QoS classes. The amount of resources to be reserved is dynamically adjusted by considering neighboring-cell higher-priority calls, ...
Dynamic Call Admission Control Scheme for QoS Priority Handoff in Multimedia Cellular Systems Huan Chen, Sunil Kumar and C.-C. Jay Kuo Integrated Media Systems Center, Department of Electrical Engineering-Systems University of Southern California, Los Angeles, CA 90089-2564 {huan, sunilk, cckuo}@sipi.usc.edu the cost of increasing the blocking rate for other users. To deal with this problem, we introduce a dynamic resource reservation algorithm to efficiently estimate resources needed to be reserved for high priority calls, by using the SNR and the distance information of mobile users in neighboring cells.
Abstract- A dynamic call admission control (CAC) and its associated resource reservation (RR) schemes are proposed in this research based on the guard channel (GC) concept for a wireless cellular system supporting multiple quality of service (QoS) classes. The proposed CAC policy selects the resource access threshold according to the estimated number of incoming call requests of different QoS classes. The amount of resources to be reserved is dynamically adjusted by considering neighboring-cell higher-priority calls, which are likely to handoff. The rationale behind our proposed dynamic CAC and RR scheme is to make an efficient resource reservation for priority calls by considering potential handoff calls in the neighboring cells, based on their signal to noise ratio (SNR) information and the traffic profile for each mobile. A comprehensive service model is developed, which includes not only mobile terminals’ bandwidth requirements but also their different levels of priority, rate adaptivity as well as their mobility. Simulations are conducted by OPNET to study the performance of our proposed scheme in terms of cost function and system utilization under different traffic condition.
The remaining part of the paper is organized as follows. Previous work on priority handoff for wireless multimedia communication is briefly reviewed in Section 2. In Section 3, a service model is described, followed by proposed CAC and associated RR designs. Our simulation results conducted by OPNET are presented in Section 4. Finally, concluding remarks and future work are given in Section 5. II. RELATED WORK A. Preferential treatment to priority and handoff calls A wireless multimedia system cannot always meet different QoS requirements of mobile users, due to resource constraints. Therefore, the system requires rules to decide who will receive the services according to predefined cost function(s), to avoid unwanted call blocking and handoff dropping while maximizing channel utilization. Usually, handoff calls are assigned higher priority over new calls.
I. INTRODUCTION The third generation (3G) wireless communication systems will support multimedia traffic at a target transmission rate of up to 2Mbps for static mobile users and 384kbps for high mobility users. Unlike wired networks, communication entities in wireless networks change their connectivity via handoff when they move from one cell to another. The use of micro or pico-sized cells makes the role of handoff procedures very important in maintaining the service continuity and QoS guarantees to the multimedia applications. Due to the limited bandwidth resources in wireless multimedia system, efficient call admission control (CAC) and resource reservation (RR) schemes are needed to maintain desired QoS. CAC schemes enable the system to provide QoS to new incoming as well as existing calls. The RR scheme, such as the use of guard channels (GC), is adopted to reserve resources for certain higher priority calls. Obtaining a right balance between the two opposing criteria is a big challenge.
How to seamlessly transfer resources between cells during handoff is an important issue. For this, resource reservation and call admission schemes should be integrated with the handoff mechanism to provide more flexibility to all mobile users and better QoS guarantees for premium users. Many different admission control strategies have been discussed in the literature to provide priorities to higher priority-call and handoff requests, without significantly jeopardizing new connection requests. These strategies fall into two categories: Handoff Queue (HQ)[1], and GC [2]-[3] schemes. HQ based methods follow the principle: when resources become available, one of the calls in the handoff queue is served. If there are no available resources, call requests are being queued until resources are available again. HQ scheme needs lot of buffers to deal with real-time multimedia traffic and sophisticated scheduling mechanism is needed to meet the QoS requirement for delay sensitive calls to guarantee that the queued data will not expire before they are transmitted. The basic idea of GC-based admission control strategies is to reserve resources in each cell a priori to deal with handoff requests. In order to provide mobile users with continuous connectivity, a system reserves backup channels referred to as “guard channels” to provide preferential treatment to priority calls and handoff calls. In such a system, call requests with lower priority are rejected if the available
This paper proposes a novel dynamic RR and CAC scheme to increase the access probability for the higher priority calls, while ensuring high overall system efficiency, in the presence of multiple QoS classes such as priority, rate adaptivity as well as different mobility. We adopt the idea of the GC scheme, which gives preferential treatment to the handoff calls by reserving a fixed number of channels exclusively for them. However, such a scheme may lead to poor channel utilization because it decreases the handoff dropping rate at
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resource is less than a certain threshold. GC strategies differ in the number of guard channels to be chosen by a base station.
such priority calls. The reward function is defined in session 4. 4) Mobility: High, moderate and low mobility traffic types are included in our service model. Different mobility traffic will have different weighting factor. More detail will be provided in our proposed resource reservation estimator.
B. Fixed and dynamic GC schemes The concept of Guard Channel was first introduced by Hong and Rappaport [2]. They used a fixed GC scheme to treat new calls and handoff calls differently, by reserving the same amount of resources for the handoff calls in the entire period of simulation cycle. In this paper, only one traffic class was considered. Rapport and Purzynski[4] extended this work to multiple services and platform types. They analyzed the performance based on their proposed mathematical model, with the assumption of stationary traffic. Epstein and Schwartz [5] considered a mixed traffic with calls of narrow and wide-band. Our previous work [6] extended single threshold in the fixed GC scheme to multiple thresholds, to deal with multimedia traffic with different priorities. All the schemes proposed above are static because such GC schemes cannot adapt to quick variation of the traffic pattern.
B. Interactions in Mobile Communication System
Dynamic GC schemes have appeared in the literature, which improve the system efficiency while providing the QoS guarantees to priority calls. These schemes adaptively reserve the actual resources needed for priority calls and, therefore, accept more lower-priority calls as compared to a fixed scheme. Naghshineh and Schwartz [7] proposed an analytical model to estimate the resource requirements for handoff calls. In their model, all connection requests have identical traffic profile and the traffic is under stationary conditions. Ramanathan et al. [8] proposed a dynamic resource allocation scheme based on the estimation of maximum expected resource requirement needed for handoff calls. Acampora et al. [9] applied a linear weighting scheme (LWS) as part of their admission control algorithm. Linear weighting scheme uses the average number of ongoing calls in all cells within the region of awareness to determine the admission. Sutivong and Peha [10] adopted a hybrid scheme by using the weighted sum of ongoing calls in the originating cell as well as other neighboring cells for admission control. III. Proposed call admission control (CAC) and resource reservation (RR) estimator
C. Proposed resource reservation (RR) estimator Our proposed dynamic resource reservation estimator is based on a non-linear weighting sum, which is different from those described in Section 2. Weighting factor (Wi) 1
TTh
Estimated arrival time (Ti)
Fig.1. Resource reservation estimation weighting curve
A. Service Model
Non-linear weighting curve is considered as shown in Eq. (1) and Fig.1, which consider an MT’s distance information and mobility.
We consider multimedia traffic with the following service attributes:
TTh Wi = Ti 1
1) MinBW, MaxBW: Minimum and Maximum Bandwidth Requirements characterize the bandwidth consumption of the traffic. 2) RA: Rate Adaptivity describes whether a connection is flexible in its bandwidth requirements. If a connection is rate adaptive, it can be serviced in a degraded mode when congested. This connection thus has high probability to receive service in either the full or degraded rate.
if
Ti > TTh
if
Ti < TTh
(1)
where Wi is the resource reservation weighting factor for neighboring call i. Ti is a time related factor considering a neighboring call i’s distance to current cell and mobility. TTh is threshold for Ti.
3) Priority Class: Higher priority is assigned to connections that are willing to pay more. They are likely to receive better QoS guarantees in terms of better chance to receive the service and in better quality mode. Similarly, system will gain higher rewards if it provides services to 0-7803-7376-6/02/$17.00 (c) 2002 IEEE.
Mobile communication system usually consists of three key elements: Mobile terminal (MT), Base station (BS) and Main telephone switching office (MTSO). Each active MT connects to one BS at a time. However, each BS will receive signals not only from its associated MTs but also from the MTs in the neighboring cells. If the received SNR is higher than a threshold, BS will send a report message to MTSO to register himself as a handoff candidate of the MT who was heard by it. This is how a mobile terminal will know where to handoff when its signal fades. The SNR signal strength also provides each BS with the distance information of MTs, which are within the awareness range for that BS. Detailed propagation fading model can be found in [11].
The amount of bandwidth to be reserved for incoming call j is set to be BWj_reserve,
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BW j_reserve =
∑ W (MinBW )
i i i ∈{neighbori ng calls | Priority of call i > Priority of call j }
A. System and traffic parameters
(2)
A seven cells system is used in our simulation. Mobile terminals move in the system according to a certain trajectory and calls are generated in each MT, following the Poisson distribution. The call holding time is an exponential distribution. Cell capacity is 60 unit bandwidth for each cell and the maximum bandwidth requirement for each call supporting multimedia is 6 unit bandwidth. Traffic is classified into four priority classes, Priority_1 to Priority_4, where Priority_1 has highest priority level and class Priority_4 has lowest priority. We have assumed that each priority class has equal number of calls in the system. If calls are rate adaptive, they can be serviced in degraded quality mode at any discrete rate within the range of [MinBW, MaxBW]. If calls are not rate adaptive, they can only be serviced at a full rate. The average call holding time (l) is 20 minutes. Three mobility types (Low, Moderate and High speed) are equally distributed in the system.
An incoming call j with bandwidth request BWj_request in the range of [MinBWj, MaxBWj] needs to reserve the amount of resource of BWj_reserve for higher-priority calls i in neighboring cells.
D. Proposed Dynamic Call Admission Control (CAC) Algorithm
Our proposed CAC algorithm for incoming call request j is illustrated below in Fig. 2. If (incoming request is non-rate adaptive) If (Current usage + BWj_request)< (Cell_Capacity− BWj_reserve) Admit call request with BW j_request Else Reject call request End Else /*it is a rate adaptive call*/ If (Current usage + MaxBW j) < (Cell_Capacity − BW j_reserve) Admit call request with MaxBW j Else If (BW j_left > 0) Admit call request with BW j_left Else Reject call request End End
B. Simulation results
1) Performance comparison between proposed dynamic scheme and fixed GC scheme
The performances of proposed dynamic CAC scheme and fixed GC schemes (0%GC, 5% GC) are compared for average call holding time l = 20 (min), and average call generating rate λ=10 (calls/min/mobile). Results for rate adaptive and non-rate adaptive cases are illustrated in Figs. 3 (a) and (b), respectively.
Fig.2 Dynamic CAC algorithm for call request System Reward (Units)
BW j_left = Cell_Capac ity − Current usage − BW j_reserve (3) Finally, we define cost function as system reward function,
R system =
∑ BW
i admitted
i
⋅ w priority + w handoff ⋅
new call requests i
∑ BW
j admitted handoff call requests j
⋅ w priority
j
(1) RA-Dynamic (2) RA-GC 0% (3) RA-GC 5%
(1) (3) (2)
(1) (3) (2)
(1) NonRA-Dynamic (2) NonRA-GC 0% (3) NonRA-GC 5%
(4) where BWadmitted is the bandwidth of admitted call as shown in Fig. 2. Wpriority and Whandoff are the reward weighting factors for priority calls and handoff call requests, respectively.
(a)
(b)
Figure.3 System performance comparison between proposed dynamic scheme and fixed GC scheme in (a) rate adaptive and (b) non-rate adaptive system.
IV. SIMULATIONS Simulations are conducted by using the OPtimized Network Engineering Tool (OPNET)[12], which is a discrete event simulator. We implemented the service model and the CAC algorithm described earlier and compared the traffic under different scenarios. Our goal is to investigate the QoS measures in terms of system utilization and cost function (reward) as defined in (4).
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Simulation time (min)
The result shows that our proposed dynamic scheme outperforms 0% and 5% GC schemes in terms of global system reward Rsystem, in both rate adaptive as well as nonrate adaptive system.
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In future we shall develop a CAC-RR filter bank to accommodate more diverse requirements from users and fast changing system conditions.
2) Performance comparison among different priority classes in proposed service model using dynamic scheme
(1) Priority_1 (2) Priority_2 (3) Priority_3 (4) Priority_4
System Reward (Units)
(4)
(4)
(3)
(3)
(2) (2) (1)
(1) Priority_1 (2) Priority_2 (3) Priority_3 (4) Priority_4
λ(calls/min/mobile) Figure.5 System reward for rate adaptive and non-rate adaptive system
(1)
(a)
RA Non-RA
Simulation time (min)
(b)
System Utilization ( x100% )
Handoff dropping rate (x100%)
The QoS metrics in terms of handoff dropping rate (PHandoff) for each priority class are compared using proposed dynamic CAC scheme for l = 20 (min), and λ=10 (calls/min/mobile). Results for rate adaptive and non-rate adaptive cases are shown in Fig. 4 (a) and (b), respectively.
Figure.4 System performance comparison among different priority classes in the (a) rate adaptive and (b) non-rate adaptive system. The result shows that higher priority class will receive lower handoff dropping rate due to the use of resource reservation. 3) Performance comparison for rate adaptive and non-rate adaptive system The performances between rate adaptive system and nonrate adaptive system are compared under different call generating rate λ (=3,6 and 10). QoS metrics in terms of reward, Rsystem, and system utilization are illustrated in Figs. 5 and 6, respectively. The results show that in rate adaptive system, both system reward and utilization will increase due to calls are allowed to be serviced in a degraded mode when system is congested.
Simulation Time (min) Figure.6 System utilization for rate adaptive and non-rate adaptive system under traffic condition λ=10, l=20 REFERENCES [1] P. -O. Gaasvik, M. Cornefjord, V. Svensson, “Different methods of giving priority to handoff traffic in a mobile telephone system with directed retry,” 41st IEEE Vehicular Technology Conference 'Gateway to the Future Technology in Motion’ , pp.549 -553, 1991. [2] D. Hong and S. S. Rapport, “Traffic model and performance analysis for cellular mobile radiotelephone systems with prioritized and nonprioritized handoff procedures,” IEEE Trans. Vehicular Technology, vol VT35, pp. 77-92, Aug. 1986 [3] T. Kwon; Y. Choi; C. Bisdikian, M. Naghshineh, "Call admission control for adaptive multimedia in
V. CONCLUSION AND FUTURE WORK Proposed dynamic CAC and RR schemes adapt the resource access probability through the use of SNR and distance information of the potential handoff calls in the neighboring cells. A comprehensive service model is proposed for the use of simulation. We have discussed the important issues for the wireless system, e.g. support of multimedia QoS including rate adaptive characteristics, priority differentiation and heterogeneous mobility pattern. Results show that, our proposed CAC and RR schemes work well in many scenarios. 0-7803-7376-6/02/$17.00 (c) 2002 IEEE.
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wireless/mobile networks", IEEE Wireless Communications and Networking Conference vol. 2, pp. 540-544, 1999. [4] S. S. Rapport and C. Purzynski, “Prioritized Resource Assignment for Mobile Cellular Communication Systems with Mixed Services and Platform Types,” IEEE Trans. Vehicular Technology, vol. 45, no. 3, Aug. 1996. [5] B. Epstein and M. Schwartz, “Reservation Strategies for Multimedia Traffic in a Wireless Environment,” IEEE 45th Vehicular Technology Conference, Chicago, IL, July 1995. [6] Huan Chen, Sunil Kumar, and C.-C. Jay Kuo, "Differentiated QoS Aware Priority Handoff in Cell-based Multimedia Wireless Network", Electronic Imaging 2000, IS&T/SPIE’s 12th International Symposium, San Jose, CA, Jan. 2000. [7] M. Naghshineh and M. Schwartz, “Distributed call admission control in mobile/wireless networks,” IEEE J. Select. Areas Commun., vol.14, pp.711-717, May 1996 [8] P. Ramanathan; K. M. Sivalingam, P. Agrawal; S. Kishore, "Dynamic resource allocation schemes during handoff for mobile multimedia wireless networks," IEEE J. Select. Areas in Commun., vol. 17, pp. 1270-1283, July 1999. [9] A. S. Acampora and M. Naghshineh, “Control and Quality of Service Provisioning in High-Speed Microcellular Networks,” IEEE Personal Communications, Second Quarter 1994, pp.36-43 [10] Arak Sutivong and Jon M. Peha, “Novel Heuristics for Call Admission Control in Cellular Systems,” IEEE 6th International Conference on Universal Personal Communications, vol.1, pp 129 -133, 1997 [11] William C. Y. Lee, Mobile Cellular Telecommunications - analog and digital systems, 2nd Edition, McGraw-Hill, 1995. [12] I. Karzela, Modeling and simulating communication networks: a hands-on approach using OPNET, Prentice Hall, New Jersey , August 1998.
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