Fuzzy-Based Call Admission Control Scheme for Mobile Networks ...

Fuzzy-Based Call Admission Control Scheme for Mobile Networks Jing-Long Wang and Shu-Yin Chiang Department of Information and Telecommunications Engineering, Ming Chuan University, Taipei 11120, Taiwan [email protected]

Abstract. The fuzzy-based call admission control (CAC) scheme is presented in this paper to offer adaptive services for multimedia stream in next generation wireless networks. Since the contemporary wireless networks will provide a variety of services and each service has multiple levels of quality requirements, but the resource is always scarce. Thus, the adaptive resource allocation for bandwidth is an important issue. In this paper, the proposed CAC scheme considers the adaptive resource requirements to enhance the efficiency of channel utilization in wireless networks. When a new call is arriving, the CAC scheme will evaluate if the available bandwidth can satisfy the requirement of incoming call. Whenever the available bandwidth is not sufficient to meet the requirement, the CAC scheme is based on fuzzy logic to choose an existing call, of which the allocated bandwidth will be degraded in order to release some bandwidth for the incoming call. From the results of simulation, the proposed scheme is superior to the existing scheme. Keywords: Fuzzy, CAC, QoS, Wireless Networks.

1 Introduction Recently, as the increased demand of bandwidth capacity and QoS for multimedia, the main resource constraint in the wireless network is the bandwidth available for transmission, due to the inherent bandwidth scarceness. Moreover, the traffic load changes drastically with time and position and thus the demand of channels is dependent of time and position of cells. Therefore, it is important to develop an effective method for efficiently assigning bandwidth to each call [1]. There are two main parameters, the blocking rate and the dropping rate, that are used to evaluate the quality of service in wireless networks [2]. The blocking rate denotes the rejection rate of new calls, while the dropping rate denotes the cancellation rate of handoff calls. The new call is the new initiated call, while the handoff call is the existing and working call that is going to transfer from one cell to the other cell due to the movement of mobile station. Obviously, to interrupt a working call, the handoff call, will bring more inconvenience than to disallow a new initiated call, the new call. Thus, most channel assignment methods treat the handoff call with higher priority than the new call. In addition to protect handoff call, an effective method has I. Lovrek, R.J. Howlett, and L.C. Jain (Eds.): KES 2008, Part II, LNAI 5178, pp. 958–965, 2008. © Springer-Verlag Berlin Heidelberg 2008

Fuzzy-Based Call Admission Control Scheme for Mobile Networks

959

to offer the required QoS for mobile stations as well as refine the utilization of bandwidth. In order to provide the adaptive QoS provision, each connection that requires QoS guarantee is given an adaptable profile according to its traffic characteristics when the connection is initialized, including the adaptable range of QoS requirements such as maximum and minimum QoS requirements. In the stage of connection establishment, the BS (base station) employs the call admission control to determine whether to accept or reject a new connection according to available bandwidth and the adaptable profile. During the run-time period, the BS has to monitor both changes of each connection and the whole network system, and dynamically regulate the bandwidth allocation of wireless spectrum according to QoS constraints of each call [3, 4]. In next generation wireless networks, CDMA has emerged as one of the most promising multiple accesses and widely adopted fourth-generation air interface. In this paper, a new adaptive CAC method for CDMA system are presented to satisfy differentiated QoS requirements as well as to maximize high system utilization. The proposed method firstly determines if there is available bandwidth for an incoming call. If there is not sufficient bandwidth for the incoming call, it employs the fuzzy logic to decide which on-going call is able to reduce the quantity of occupied bandwidth. In this way, more calls can be accepted to work in wireless networks, while the existing calls only reduce some tolerable bandwidth. From the simulation results, the proposed method is able to carry more connections and improve system utilization. There are five sections in this paper. In the section 2, the CAC methods of previous works are introduced. Some comparisons of their advantages and disadvantages are also illustrated. The section 3 presents the proposed method. The section 4 describes the system model and the results of the simulation. Finally, a conclusion is given in the section 5.

2 Previous Works CAC methods have a great impact on the efficiency and performance of system throughput. The design of an efficient resource management for CAC method is a difficult task that typically involves many conflicting considerations which have to be analyzed to find a smooth and balanced solution. A lot of CAC methods have been proposed to satisfy various QoS requirements. A multimedia QoS provision exploits the concepts of Static Priority (SP) and Minimum Set (mSet). Each component within a multimedia is assigned a significance at call set-up stage that indicates a level of SP. For example, a videophone application contains two media, including voice and video, in which reasonably voice component has higher SP than video components. Minimum Set indicates the minimum bound of QoS requirements. For example, if a teleconference session contains voice, audio, and video, users may decide not to progress the call when audio or video cannot be transmitted. In [3], it mainly proposes an algorithm to allocate the resources at CAC period. Each connection is assigned an acceptable range of transmission rate and also preprioritized according to its traffic characteristics. An adaptive scheme of penalty-based adaptable reservation and admission (PARA) was presented in [4]. Each connection may contain more than one set of QoS

960

J.-L. Wang and S.-Y. Chiang

requirement, in which one for normal mode and others for degrade modes. With the penalty information, the BS based on the traffic conditions provides acceptable service quality to all the connections by minimizing the aggregate penalty. The BS accepts a new connection only if the penalty of blocking the new connection is larger than the sum of penalty caused by the degradation of other existing connections.

3 Adaptive QoS Scheme Basically, mobile stations talking to each other are going through a base station. A mobile device can send a QoS request to the base station while a new call is issued. In the QoS request, there could have a lot of valuable parameters, including the required bandwidth and the priority level. When the CAC mechanism receives this request from a mobile device, these parameters will be evaluated in order to determine whether the incoming call is accepted or not. Whenever the available bandwidth is currently not available to satisfy the requirement of incoming call, the degradation scheme is employed to dynamically adjust the bandwidth allocated to the existing calls. The occupied bandwidth of an existing call can be reduced such that some of bandwidth can be released for reallocating to the new incoming call. The priority of a call is denoted as Pi, in which the higher the value of i, the higher the priority. In the QoS definition of 3GPP, there are four classes of priority, including P1, P2, P3, and P4, representing the background, the interactive, the streaming, and the conversation service class, respectively. Among these four classes, the conversion service class has the highest priority, while the background service class has the lowest priority. In addition, the bandwidth required by a call may be different, which is indicated by Bi. Similarly, the higher value of i represents the higher bandwidth requirement. In this paper, it is assumed that every call at least should have two kinds of bandwidth requirement, in which Bmin indicates the minimum bandwidth requirement, and Bmax indicates the maximum bandwidth requirement.

4 Fuzzy Logic Degradation CAC Algorithm The proposed scheme perform the bandwidth degradation decision is based on the fuzzy logic and three important factors, including the resource, the bandwidth and the priority. In this section, the fuzzy logic degradation scheme is described, in which there are three main steps, including the fuzzification, the rule evaluation, and the defuzzification. 4.1 Fuzzification The first step of the proposed degradation scheme is fuzzification, in which the membership function should be defined. Firstly, two input variables and four membership functions are defined. Two input variables are BW(Bandwidth) and PC(Priority Class). Four membership functions are MBW(Maximum BW), NBW(Minimum BW), HPC(High PC), and LPC(Low PC). The membership values can be obtained by applying the values of input variables to different membership functions. Assume the

Fuzzy-Based Call Admission Control Scheme for Mobile Networks

961

membership values under consideration are generalized from triangular membership functions, in which the membership value is located at the range from 0 to 1. The triangular membership function μA(x) is defined by ⎧ ⎪ ⎪ μA(x) = ⎨ ⎪ ⎪ ⎩

( x−a1 )ha , a2 −a1 (a3 −x)ha a3 −a2

0

a1 ≤ x ≤ a2 a2 ≤ x ≤ a3

,

(1)

otherwise

where 0≤ha≤1. 4.2 Rule Evaluation The fuzzy rules are defined according to the membership value, in which there are a lot of IF-THEN rules for deciding which path should be chosen. Three membership values can be used to produce eight combinatorial rules. The fuzzy number (FN) of each rule is defined as the minimum value of the two membership values, as shown in equation (2). FNi=min{MBW/NBW, HPC/LPC}.

(2)

FN is the minimum value of each combination of membership values, while i is the value from 1 to 4. If MBW=0.2, and HPC=0.5, the fuzzy number will be 0.2. Each rule may have four kinds of different results, including Yes(Y), Probably Yes (PY), Probably No (PN), and No (N). Thus, the bandwidth degradation decision (BDD) can be defined as the largest fuzzy number of individual results, as shown in the equation (3), in which the value of p is one of the four possibilities, including Y, PY and PN or N. BDD(p)=Max{FN1, FN2,…….,FNt}.

(3)

4.3 Fuzzy Logic Routing Decision In the phase of defuzzification, the process of transforming the result of fuzzy inference into an exact number is called fuzzy decision (FD). A weight value is set for each possible result, including Y, PY, PN and N. The final fuzzy decision can be obtained by the equation (4), in which the value of FD is between 0 and 1. The call with the minimum value of FD will be the most feasible choice. FD =

∑M ×W ∑M m

m

(4)

m

Mm: BDDm value, Wm: the weight value for the result m, m: Y, PY, PN or N. 4.4 Fuzzy Logic Degradation Model Before the proposed model is introduced, some parameters are defined. Bavail is the total available bandwidth. Bmax is the required maximum of bandwidth of a call in the

962

J.-L. Wang and S.-Y. Chiang

cell and Bmin is the acceptable minimum bandwidth of a call. Dfd is fuzzy decision that decides whether the allocated bandwidth will be degraded or not. Trel is threshold value and Dfd is less than Trel that the call can be degraded. When a new call or a handoff call arrives, the CAC is triggered by the request of mobile terminal. If the available bandwidth is abundant and greater than the maximum requirement of an arriving call, the maximum bandwidth would be allocated to this arriving call. However, when the cell is fully loaded, one or some of the existing calls may be degraded to minimum. The fuzzy decision algorithm will choose the candidate calls that are able to release some bandwidth for the arriving call. The CAC and fuzzy algorithms are shown in the following CAC() /* Call Admission Control */ { while ( incoming(calli) ) { if ( Bavail > Bmax(calli) ) Allocate(calli, Bmax); else if ( Bavail > Bmin(calli) ) Allocate(calli, Bmin); else { Bavail =Fuzzy(Bmin); if ( Bavail > Bmin(calli) ) Allocate(calli, Bmin); else Block( calli ); } } } fuzzy(Bmin) { while (Bavail < Bmin) { k = min{ FDk

=

∑ M ×W ∑M k

k

};

k

if (k = 0) return(0); { Bavail = Bavail +(B(k) - Bmin(k)); B(k) = B(k)- Bmin(k); } } Return(Bavail) }

5 Simulations The wireless network with 64 cells is studied to investigate the performance, as shown in Figure 1. Each base station is assumed to have 32 channels. The arrival rates

Fuzzy-Based Call Admission Control Scheme for Mobile Networks

963

Fig. 1. The simulated wireless network with 64 cells

of new calls ( λn ) are assumed to be 0.1~0.8 (call/sec). The arrival rates of handoff calls (λh ) are in the range of 0.08~0.25 (call/sec). The service rates of new calls (μn ) and handoff calls (μh ) are 1/60 (call/sec) and 1/60 (call/sec). The mean serve time of a call is 60 seconds. It is assumed that the tolerable dropping rate (ptd)is 103. The fuzzy method is compared with the fixed allocation methods, including the Bmax and Bmin allocation methods. The Bmax method is based on the maximum bandwidth to assign the required bandwidth, while the Bmin method is based on the minimum bandwidth to assign the required bandwidth. Figure 2 show the bandwidth utilization for different arrival rates of handoff calls when the arrival rate of new call is from 0.1 to 0.8. From the results, the Bmin method has the worst bandwidth utilization, while the Bmax method and the fuzzy method have the better performance for the utilization. However, when the arrival rate is higher than 0.6, the utilization of the Bmax method cannot have any further improvement, while the fuzzy method is still able to accept new calls and so the utilization can be increased continuously. Figure 3 illustrates the dropping rate versus arrival rate. Evidently, the fuzzy method and Bmin method are capable of controlling the dropping rate under the tolerable dropping rate (10-3), while Bmax method cannot satisfy the requirement of the tolerable dropping rate when the arrival rates of handoff calls become larger. This is due to the fact that the fuzzy method can adaptively degrade the bandwidth of some existing calls, and thus result in the lower dropping rate. Figure 4 presents the blocking rate versus the arrival rate of handoff calls. The blocking rate increases with the arrival rate. This is due to that most bandwidth will be occupied by the handoff calls. As a result, new calls cannot acquire the required

Fig. 2. Utilization versus Arrival Rate

964

J.-L. Wang and S.-Y. Chiang

Fig. 3. Dropping Rate versus Arrival Rate

Fig. 4. Blocking Rate versus Arrival Rate

bandwidth. From the results, the blocking rate of the fuzzy method and the Bmin method are better than that of the Bmax method. This is because the Bmax method allocates too much bandwidth for existing calls, and therefore less bandwidth can be allocated to new calls. On the other hand, the fuzzy method adaptively allocates the bandwidth. When the arrival rate is high and the available bandwidth is small, the fuzzy method will release some bandwidth that originally are allocated to existing calls, and then reallocate the released bandwidth to new calls. Thus, the blocking rate can be effectively reduced.

6 Conclusions In this paper, a new CAC method is proposed for mobile networks. This method based on the fuzzy logic takes into account both the bandwidth requirement and the priority level. The fuzzy method adaptively allocates the bandwidth. When the arrival rate is high and the available bandwidth is small, the fuzzy method will release some bandwidth that originally are allocated to existing calls, and reallocate the released bandwidth to new calls. In addition, the proposed method provides QoS guarantee by keeping the handoff dropping rate below the desired level. The simulation results show that the proposed scheme can effectively overcome the problems that the traffic load changes very fast and is distributed non-uniform in the wireless network. Moreover, the proposed method is capable of confining the dropping rate below the predefined limitation, and enhancing the blocking rate significantly in comparison with other methods.

Fuzzy-Based Call Admission Control Scheme for Mobile Networks

965

References 1. Wang, J.-L., Chiang, S.-Y.: Adaptive Channel Assignment Scheme for Wireless Networks. Journal of Computers and Electrical Engineering 30(6), 417–426 (2004) 2. Chou, C.-T., Shin, K.G.: Analysis of Adaptive Bandwidth Allocation in Wireless Networks with Multilevel Degradable Quality of Service. IEEE Transactions on Mobile Computing 3(1), 5–17 (2004) 3. Wang, J.-L., Chen, C.-H.: Adaptive Two-stage QoS Provisioning Schemes for CDMA Networks. Computer Systems Science and Engineering 22(6), 56–64 (2007) 4. Wang, J.-L., Chen, C.-W.: Fuzzy Logic Based Mobility Management for 4G Heterogeneous Networks. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 888–895. Springer, Heidelberg (2006)