Dynamic RAT selection for multiple calls in heterogeneous wireless networks using group decision-making technique

Computer Networks 56 (2012) 1390–1401

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Dynamic RAT selection for multiple calls in heterogeneous wireless networks using group decision-making technique Olabisi E. Falowo ⇑, H. Anthony Chan Department of Electrical Engineering, University of Cape Town, South Africa

a r t i c l e

i n f o

Article history: Received 17 June 2011 Received in revised form 12 December 2011 Accepted 15 December 2011 Available online 29 December 2011 Keywords: Dynamic RAT selection Group decision-making Multiple calls Heterogeneous wireless network TOPSIS method Radio resource management

a b s t r a c t Existing radio access technology (RAT)-selection algorithms for heterogeneous wireless networks (HWNs) do not consider the problem of RAT selection for a group of calls from a multimode terminal (MT). Multimode terminals (MTs) for next generation wireless networks have the capability to support two or more classes of calls simultaneously. When a new call is initiated on an MT already having an ongoing call in an HWN, the current RAT may no longer be suitable for the two calls (incoming call and the existing call). Thus, a new RAT may be more suitable for the two calls. The problem of RAT selection for two or more calls from an MT in an HWN is a group decision problem. This paper addresses the problem of RAT selection for a group of calls from an MT in an HWN by using the modified TOPSIS group decision-making technique. The paper proposes a dynamic RAT-selection algorithm that selects the most suitable RAT for a single call or group of calls from an MT in an HWN. The algorithm considers users’ preferences for individual RATs, which vary with each class of calls, in making RAT selection decisions in an HWN. A user’s preference for each of the available RATs is specified by weights assigned by the user to RAT selection criteria for different classes of calls. Based on the assigned weights, the proposed algorithm aggregates individual calls’ weights specified by the user to make a RAT-selection decision for a group of calls. In order to reduce the frequency of vertical handover, the proposed algorithm uses RAT preference margin in making RAT selection decisions. RAT preference margin is a measure of the degree to which the newly preferred RAT is better than the current RAT. Performance of the proposed algorithm is evaluated through numerical simulations. Results are given to show the effectiveness of the proposed RAT-selection algorithm.  2012 Elsevier B.V. All rights reserved.

1. Introduction Joint radio resource management (JRRM) has been proposed for efficient radio resource utilization and enhanced QoS provisioning in heterogeneous wireless networks (HWNs), and a number of JRRM algorithms have been developed for HWNs [1–3]. The radio access technology (RAT) selection algorithm is one of the JRRM algorithms. The purpose of a RAT selection algorithm is to select the

⇑ Corresponding author. E-mail addresses: [email protected] (O.E. Falowo), h.a.chan@ ieee.org (H. Anthony Chan). 1389-1286/$ - see front matter  2012 Elsevier B.V. All rights reserved. doi:10.1016/j.comnet.2011.12.013

most suitable RAT for an incoming call in an HWN. Fig. 1 illustrates the problem of RAT selection for a single call in HWNs. A number of RAT-selection algorithms have been proposed in the literature and these algorithms can be classified as single-criterion or multi-criteria RAT-selection algorithms. Single-criterion RAT selection algorithms use a single criterion for RAT selection whereas multi-criteria RAT selection algorithms use two or more criteria for RAT selection. Multi-criteria RAT-selection algorithms have been shown to be more efficient than single-criterion RAT selection algorithm. Thus much effort has been concentrated on developing multi-criteria RAT selection algorithms for HWNs [1,3–7].

O.E. Falowo, H. Anthony Chan / Computer Networks 56 (2012) 1390–1401

Fig. 1. RAT selection for a single call in an HWN.

In [1], Giupponi and Pérez-Romero proposed a JRRM algorithm based on fuzzy neural approach for selecting the most appropriate RAT for an incoming call in HWNs. In [3], Alkhawlani and Hussein proposed an intelligent RAT-selection algorithm for next generation wireless networks. The proposed algorithm uses a combined parallel fuzzy logic control and multi-criteria decision-making technique to select the most appropriate RAT for an incoming call in HWNs. In [4], Zhang proposed a fuzzy multiple attribute decision-making (MADM) RAT-selection algorithm that uses fuzzy logic to represent imprecise information of some RAT-selection criteria. The fuzzy MADM method operates in two steps. The first step is to convert the imprecise fuzzy variables to crisp numbers. The second step is to use classical MADM technique to determine the ranking order of the candidate networks. The highest-ranking RAT is then selected for the call. In [5], Xavier et al. presented a Markovian approach for RAT selection in an HWN. They developed an analytical model for a RAT-selection algorithm in an HWN comprising GSM/EGDE and UMTS. The proposed algorithm selects just one RAT for each incoming call. In [6], Guo et al. proposed a RAT selection algorithm that uses a fuzzy multiple objective decision-making technique to select the most suitable RAT for each incoming call in an HWN. In [7], Wu and Sandrasegaran conducted a study of RAT selection algorithm in a heterogeneous UMTS-GSM network. All the RAT-selection algorithms reviewed above were designed to select the most suitable RAT for just one incoming call in HWNs. None of the RAT-selection algorithms considered the problem of RAT selection for a group of calls (multiple calls) from a multimode terminal (MT) in HWNs.

Fig. 2. RAT selection for multiple calls in an HWN.

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Multimode terminals (MTs) for next generation wireless networks have the capability to support two or more classes of calls simultaneously. Thus in a multi-service HWN, a subscriber using a multimode terminal (MT) will be able to access multiple services (such as voice, web session, video streaming, etc.) simultaneously, through any of the available RATs. Therefore, a group-call RAT selection problem occurs when a single RAT is to be selected for multiple classes of calls initiated from a multimode terminal in an HWN or when multiple classes of calls from a multimode terminal are to be handed over from one RAT to another. Fig. 2 illustrates the problem considered in this paper, where a RAT is to be selected for multiple calls from an MT in an HWN. As shown in Fig. 2, a user having an MT can initiate multiple calls from the MT, and a RAT has to be selected for the multiple calls in the HWN based on some RAT selection criteria. Different classes of calls have different QoS requirements, and different RATs in HWNs usually have different capabilities in terms of available data rate (bit per second), battery power consumption, security level provided, delay, etc. Selecting the most appropriate RAT for multiple calls from the MT in an HWN is a group decision problem. Existing RAT selection algorithms were not designed to select a RAT for multiple calls in HWNs. Therefore, this paper addresses the problem of RAT selection for multiple calls from an MT using the modified TOPSIS group decision-making technique [8,9]. The following are some reasons why it may be necessary to select a single RAT for multiple calls from a multimode terminal in HWNs: (1) to reduce handoff complexity, (2) to reduce signaling overhead, (3) to reduce battery power consumption, and (4) to accommodate low-capability multimode terminals. If multiples calls from an MT are admitted into different RATs in an HWN, coordination of handover procedure among the different RATs will be complicated, and incur excessive signaling overhead. Moreover, multiple RAT interfaces of the multimode mobile terminals will be activated, which may increase the overall battery power consumption of the multimode terminal. In addition, some multimode terminals can be connected to only one RAT at a time. If these low-capability multimode terminals are to support multiple services, group decision is inevitable. Thus, it is necessary to develop an algorithm that will select the most suitable RAT for a group of calls from an MT in HWNs. The objective of this paper is to develop a dynamic RATselection algorithm for making group call RAT-selection decisions in HWNs. The main contributions of this paper are threefold. The first is the conceptualization of groupcall RAT-selection problem in HWNs. The second contribution is the development of a dynamic RAT selection algorithm and application of the modified TOPSIS group decision technique to solve the problem of RAT selection for multiple calls in HWNs. The third contribution is the investigation of the effect of RAT preference margin on the frequency of vertical handoff in HWNs. To the best of our knowledge, this is the first paper considering the problem of dynamic RAT selection for a group of calls from a

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multimode terminal in HWNs, and it is the first attempt to solve the problem. The rest of this paper is organized as follows. In Section 2, multi-criteria group decision-making techniques are briefly reviewed. In Section 3, the proposed RAT selection algorithm is described. In Section 4, the modified TOPSIS group decision-making technique is applied to solve the problem of RAT selection for multiple calls in HWNs. Performance evaluation and simulation results are presented in Section 5. 2. Multi-criteria group decision-making techniques In multi-criteria group decision-making (MCGDM) techniques, preference information on alternatives provided by decision-makers (experts) is aggregated to form a collective opinion. Then, ranking of the alternatives or selection of the best alternative is based on the derived collective opinions of the decision-makers [10]. The essence of group decision-making is to find the alternative among a set of feasible alternatives, which best reflects the preferences of the group of decision-makers as a whole [11]. A number of MCGDM techniques have been proposed in the literature [8,11–15]. These techniques have been applied in different fields such as politics, economics, engineering, medicine, management, etc.

the third category, aggregation is performed across multiple criteria and across multiple decision makers simultaneously to form a collective opinion [18]. In this paper, the decision-makers’ opinion aggregation method used falls under the second category described above. This approach is used because is effective and not very complicated. 2.3. Dynamic MCGDM Generally, resolution methods for MCGDM problems are static, in which it is assumed that the number of alternatives and experts that act in the GDM problem remains fixed throughout the decision-making process. However, in many real decision-making situations, there exist dynamic MCGDM problems in which the number of alternatives and/or decision-makers varies during the decisionmaking process [19]. The problem of RAT selection for multiple calls from an MT in an HWN, described in this paper, can be best modeled as a dynamic group decision-making problem in which the number of available alternatives (RATs) and/or decision-makers (on-going calls) from an MT varies with time. The RAT-selection algorithm proposed in this paper is described in the next section. 3. Description of the proposed RAT-selection procedure

2.1. MCGDM Problem Generally, in MCGDM problem, there is a finite set of feasible alternatives, X = {x1, x2, . . . , xn}, (n P 2), to be ordered from best to worst, based on a set of criteria, C = {c1, c2, . . . , ck}, (k P 2), by a set of decision-makers, d = {d1, d2, . . . , dm}, (m P 2). Each decision-maker gives his/ her preference information on alternatives, and the preference information on alternatives provided by each expert is then aggregated to form a collective opinion (decision). In practical MCGDM problems, the preference information provided by decision-makers can be expressed in multiple formats, such as utility values, multiplicative preference relations, fuzzy preference relations, linguistic variables, interval numbers, preference rankings, and ordinal interval numbers [12].

We consider a dynamic RAT selection problem in which a RAT is to be selected for a call or multiple calls from an MT. The number of available RAT may not be fixed over time; it depends on the location of the MT in the HWN. The number of simultaneous calls from an MT is not fixed over time. 3.1. Triggering of RAT selection algorithm in HWN There are two events that can trigger the proposed RAT selection algorithm in an HWN, namely (1) call event and (2) RAT availability event. A call event occurs when there is a change in the number of ongoing calls (decision-makers) from an MT in an HWN. This change in the number of ongoing calls will

2.2. Aggregate of decision-makers’ opinions In MCGDM, it is essential to aggregate the opinions of decision-makers. Different ways have been proposed in the literature for aggregating the decision-makers’ opinion. These methods can be broadly divided into three categories [13]. In the first category, aggregation is performed across multiple criteria for each decision maker, which results in global preference for each decision maker. The global preference information is then aggregated for all decision makers to form a collective opinion [16]. In the second category, aggregation is performed across multiple decision makers in order to obtain a collective opinion per criterion. The collective information per criterion is then aggregated across multiple criteria to form a collective opinion [17]. In

Fig. 3. RAT selection for multiple calls in an HWN.

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trigger the RAT selection algorithm, and can lead to reselection of RAT for an MT in two ways: (i) when a new call is initiated from an MT and (ii) when an ongoing call, from a group of calls, is terminated from an MT. Fig. 3 illustrates reselection of RAT for an MT when a new call is initiated or terminated from an MT in a three  RAT HWN. In Fig. 3(a), the MT has one ongoing call s1t connected through RAT-3, which is the most suitable RAT  for the call. In Fig. 3(b), the MT initiates a new call s2t . RAT-2 is the most suitable for the group of two calls (s1t and s2t Þ. Therefore, RAT-2 is selected for the two calls. In   Fig. 3(c), the MT initiates a new call s3t . RAT-3 is the most suitable RAT for the group of three calls (s1t , s2t , and s3t Þ. Therefore, RAT-3 is selected for the three calls. In Fig. 3(d), call, s1t has ended, remaining two calls, s2t and s3t , RAT-2 is the most suitable RAT for calls s2t and s3t . Therefore RAT-2 is selected for the two calls. The above example shows how call initiation and call termination from an MT can lead to reselection of RAT in an HWN. On the other hand, a RAT availability event occurs when there is a change in the number of RATs accessible to an MT in an HWN. Change in the number of RATs accessible to an MN can lead to reselection of RATs for the MN in two ways: (i) availability of a new RAT may lead to selection of the new RAT for ongoing call(s), and (ii) unavailability of the current RAT due to loss in signal-to-noise ratio which will lead to necessary selection of another RAT for the MT (necessary vertical handoff). Fig. 4 illustrates reselection of RAT for an MT when a new RAT is available or a current RAT is unavailable in a three-RAT HWN. In Fig. 4(a), only two RATs (RAT-1 and RAT-2) are available, and RAT-2 is more suitable for the call  1 st . Therefore, RAT-2 is selected for s1t . In Fig. 4(b), a new RAT, RAT-3 is now available. RAT-3 is more suitable for s1t than RAT-1 and RAT-2. Therefore, RAT-3 is selected for s1t . In Fig. 4(c), probably due to the user’s mobility in the HWN, RAT-3 is no longer available. Therefore, RAT-2 is selected for s1t . The above example shows how RAT availability and RAT unavailability can lead to reselection of RAT for an MT in an HWN.

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3.2. Reducing the frequency of vertical handoffs in HWNs The problem of frequent vertical handoffs is obvious in Figs. 3 and 4 due to reselection of RATs for call(s) from an MT by the dynamic RAT selection algorithm. In order to reduce the frequency of vertical handoffs, we introduce the use of RAT preference margin between the current RAT and the newly preferred RAT, for cases when vertical handoff is not compulsory. RAT preference margin (PM) is the value by which the newly preferred RAT must be higher than the current RAT before the newly preferred RAT can be selected for the call(s) from an MT, 0 6 PM < 1. For example, in Fig. 3(b), if P2 is the preference value of RAT2 for the two calls (s1t and s2t Þ and P3 is the preference value of RAT-3 for the calls (s1t and s2t Þ, then, P3 must be greater than (P2 + PM) before RAT-3 can be selected for the two calls. If P3 < (P2 + PM), the current RAT (RAT-2) is maintained for the two calls. The procedure for calculating the preference value for each RAT is given in Section 4. The higher the value of PM, the less the frequency of vertical handover in the HWN will be. However, if the value of PM is set too high, less-preferred RATs will be selected for calls most of the time. 3.3. Proposed dynamic RAT algorithm Fig. 5 shows the proposed dynamic RAT selection algorithm, where N is the number of ongoing call (s) and PM is the preference margin. As shown in Fig. 5, when a RAT selection event occurs, the algorithm determines whether it is a call event or a RAT availability event. It then decides whether a new RAT should be selected or the current RAT be maintained. In the next section, we apply the modified fuzzy TOPSIS technique for evaluating the preference of each of the available RATs in HWNs. 4. Application of the modified fuzzy TOPSIS group decision-making technique for RAT selection in HWNs In this section, we apply the modified fuzzy TOPSIS procedure [8] to solve the problem of RAT selection for a single or multiple calls from a multimode terminal in HWNs. The basic function of the proposed RAT selection algorithm is to select the most suitable RAT for a single or multiple calls from an MT in HWNs. 4.1. RAT selection problem definition Let R = {r1, . . . , rl, . . . , rjRj}, jRj P 2, be a set of RATs in an HWN, and let S = {s1, . . . , si, . . . , sjSj}, jSj P 1, be the set of calls (services) supported in the HWN. n o t j ; jSt j P 1; be a group of calls Let St ¼ s1t ; . . . ; skt ; . . . ; sjS t (decision- makers) from a multimode terminal, Mt, that must jointly select a RAT from a set of available RAT, Rt, that can support the group of calls from Mt. n o t j Rt ¼ r1t ; . . . ; rjt ; . . . ; rjR ; jRt j P 1. t

Fig. 4. RAT selection for call(s) in an HWN with varying number of available RATs.

Let C = {c1, . . . , cu, . . . , cjCj}, jCj P 1, be the set of criteria for selecting the most suitable RAT for the incoming call(s) in HWN. Note that jXj denotes the cardinality of X.

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Fig. 5. Flow chart of the proposed RAT selection algorithm.

n

Let W t;i ¼ w1t;i ; . . . ; wut;i ; . . . ; wjCj t;i

o

denote the set of user-

specified weights for the RAT-selection criteria, where wut;i is the weight for criterion, cu, for call sit from mobile terminal Mt. Each user will provide his/her preference information for a particular RAT for each class of calls in terms of weights attached to RAT-selection criteria. The weight represents the relative importance of each criterion for each class of call to the user. n o t j Let P t ¼ p1t ; . . . ; pit ; . . . ; pjS denote the priorities of t t

each call in S . The call priorities represent the degree of importance of each call in St to the user. If the degrees of t

j ¼ importance of calls are equal, then p1t ¼ pit ¼ pjS t t

t

1=jS j; 8i 2 f1; . . . ; jS jg. Fig. 6 is a block diagram of RAT evaluation procedure for a group of calls in an HWN, using the modified fuzzy TOPSI group decision technique. The evaluation of available RAT is carried out in four stages, namely specification, evaluation, aggregation, and selection stages. During stage 1, a multimode terminal requesting RAT selection for a single call or multiple calls sends a RAT selection request to the RAT selection algorithm, specifying the number and classes of calls, bandwidth requirements of the calls, the weight assigned to each RAT selection criterion for each class of calls by the user, as well as the priority of each call. During stage 2, based on current loads in each of the available RATs in the HWNs, bandwidth requirements of call(s), and current user’s location in the HWNs, the RAT selection algorithm determines the set,

Rt, of available RATs that can support the group of calls. During stage 3, the proposed RAT selection algorithm aggregates the user-specified weights for the call(s) per criterion. It then aggregates the multiple criteria to determine the most suitable RAT for the call(s). During stage 4, the most suitable RAT is selected for the group of calls. 4.2. Criteria specification Different RATs in a heterogeneous wireless network have different features with respect to maximum data supported, security level provided, QoS provisioning, battery power consumption, service cost, etc. RAT selection criteria are specified by users in terms of these different features. Some of the selection criteria used in determining a user’s preference for a particular RAT (e.g. data rate) are specified using real numbers, whereas some other criteria (e.g. security level) are specified using linguistic values such as very high, high, medium, low, very low. The linguistic terms are first converted to fuzzy numbers using a conversion scale. Then, the resulting fuzzy numbers are converted to crisp numbers. For instance, if five linguistic terms are used to represent the possible user preference – very low, low, medium, high and very high – these linguistic terms are first converted to fuzzy numbers using a conversion scale in [20], where both the performance score and membership function are in the range from 0 to 1. A fuzzy scoring method is then used to convert each fuzzy number to a corresponding crisp value. For example, the five fuzzy numbers (very low, low, medium, high, and very high)

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Fig. 6. Stages involved in evaluation of available RATs using the modfied fuzzy TOPSIS technique.

Fig. 7, for each class of calls (e.g. voice call, video call, video streaming, web session, etc.) each user assigns weights to the RAT selection criteria. The weights assigned to the criteria are on a standard scale, which is known to users. For example, the weights can be on a ten-point scale (0–9), where 0 represents minimum weight and 9 represents the maximum weight. For example, for call si, if a criterion C1 has weight 1 and criterion C2 has weight 5, then C2 is considered to be five times more important than C1 for call si, to the user. For a particular call, if a criterion is assigned weight 0 by a user, the criterion is not important to the user for the call. The assignment of weights for a particular class of calls is done once and it will always be used in selecting a RAT for that class of calls for the user. However, users can make changes to the weights they have assigned to a selection criterion. The weight represents the relative importance of each RAT selection criterion to each user for each class of calls. For example, network security may be very important to a user when making voice calls, whereas network security may be less important to the same user during video stream (e.g. streaming of video from the internet). In addition to the assignment of weight to RAT selection criteria, the priorities of individual calls are also specified. Call priority shows the relative importance of each call to users. Table 1 is an example of priority level that can be assigned to different classes of calls. If the same value is assigned to all calls from a multimode terminal, then all the calls have equal priority. 4.4. RAT selection procedure using TOPSIS

are converted to 0.091, 0.283, 0.5, 0.717, 0.909, respectively [20]. Chen and Hwang have proposed eight different conversion scales with different number of linguistic terms [20]. 4.3. Criteria weights and call priorities specification Fig. 7 shows the assignment of weight to each of the RAT selection criteria for each class of calls by a user. As shown in

Using modified fuzzy TOPSIS group decision-making procedure, the proposed RAT selection algorithm is described in the following ten steps: Step 1: Specify the set of call(s), St, from Mt for which a RAT is to be selected. Specify Pt and Wt,i "i 2 {1, . . . , jstj} and determine Rt. Step 2: Construct the decision matrix, Dt for jRtj available RATs and jCj RAT-selection criteria. The decision matrix is given as:

ð1Þ

where mj,u represents the performance rating of RAT, rtj ðj ¼ 1; 2; . . . ; jRt jÞ on criterion, cu(u = 1, 2, . . . , jCj). Note that Table 1 Call priority values.

Fig. 7. Criteria weight (preference) specification for each call by user.

Call priority

Priority value

Very high High Medium Low Very low

5 4 3 2 1

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the decision matrix contains both linguistic terms and crisp values. Step 3: Convert the linguistic terms in the decision matrix to crisp values using a linguistic-termto-fuzzy-number conversion scale, and then normalize the decision matrix. It is necessary to normalize decision matrix Dt. Usually, there are benefit criteria and cost criteria in MCGDM problems, and the ‘dimension’ of the criteria may be different. In order to measure all criteria in dimensionless units and to facilitate their comparison, normalization is necessary.  tj;u of the normalized Each normalized value m t  decision matrix ðD Þ is calculated as follows:

 j;u m

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ,v u jRt j uX ¼ mj;u t ðmj;u Þ2 ;

of calls, the priority of each call in the group is taken into consideration. If all the calls have equal priority, then t

xt;u ¼

jS j 1 X ðuÞ  : w jSt j i¼1 t;i

ð11Þ

t Step 7: Aggregate the normalized decision matrix, D and the group weighting vector, Xt to obtain the weighted normalized decision matrix, H , which is given as:

ð12Þ

j ¼ 1; . . . ; jRt j;

x¼1

u ¼ 1; . . . ; jCj;

ð2Þ

 tj;u is the normalized performance rating of RAT r tj where m on criterion, cu. Step 4: Normalize the user-specified criteria weights. The weighting vector, Wt,i is given as follows:

n o ð1Þ ðuÞ ðjCjÞ W t;i ¼ wt;i ; . . . ; wt;i ; . . . ; wt;i :

ð3Þ

 j;u 8j 2 f1; . . . ; jRt jg; hj;u ¼ xt;u  m u 2 f1; . . . ; jCjg:

Step 8: Determine the ideal solution, A and the negative-ideal solution A of H :

h i    A ¼ h1 ; h2 ; . . . ; hjCj ;   0 00 h A ¼ ðmax ju ju 2 C Þ; ðmin hju ju 2 C Þ ; t t

The weighting vector is normalized as follows:

W

t;i

n o ð1Þ  t;i  ðuÞ  ðjCjÞ ; ¼ w ;...;w t;i ; . . . ; wt;i

 ðuÞ w t;i ¼

ðuÞ wt;i PjCj ðxÞ x¼1 wt;i

;

8u ¼ 1; 2; . . . ; jCj:

h

ð4Þ ð5Þ

o ð1Þ ðiÞ ðjSt jÞ Pt ¼ pt ; . . . ; pt ; . . . ; pt :

ðiÞ p t ¼

ðiÞ pt PjSt j ðxÞ x¼1 pt

;

8i ¼ 1; 2; . . . ; jSt j:

ð7Þ ð8Þ

Step 6: Aggregate the different call weights per criterion to obtain a group weight per criterion. The importance of each criterion for the group of calls from Mt is obtained as:

xt;u ¼

1 jSt j

jSt j X

ðiÞ  ðuÞ w t;i  pt ;

u ¼ 1; 2; . . . ; jCj:







i

A ¼ h1 ; h2 ; . . . ; hjCj ;   0 00 h A ¼ ðmin ju ju 2 C Þ; ðmax hju ju 2 C Þ ; t t j2R

ð14Þ

j2R

ð15Þ

j2R

ð6Þ

The call priority vector is normalized as follows:

n o t ð1Þ ðiÞ ðjS jÞ ; Pt ¼ p t ; . . . ; pt ; . . . ; pt

j2R

where C0 is the set of benefit criteria, and C00 is the set of negative criteria. Note that C = C0 + C00 .

Step 5: Normalize the call priority vector, Pt.

n

ð13Þ ⁄

ð9Þ

i¼1

Step 9: Calculate the closeness coefficient of each of the available RATs to the ideal solution, and negative ideal solution. The basic principle of TOPSIS is that the selected RAT should have the shortest distance from the ideal solution and the farthest distance from the negative ideal solution.

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u jCj uX    2 dt;j ¼ t hj;u  hu ;

j ¼ 1; . . . ; jRt j;

j¼1

u ¼ 1; . . . ; jCj; vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u jCj uX    2 hj;u  hu ; dt;j ¼ t

ð16Þ

j ¼ 1; . . . ; jRt j;

j¼1

u ¼ 1; . . . ; jCj:

The group weighting vector, Xt is obtained as:

X t ¼ fxt;1 ; . . . ; xt;u ; . . . ; xt;jCj g:

ð10Þ

Note that in aggregating the weight per criterion specified for each call into a collective weight per criterion for group

ð17Þ

The closeness coefficient of RAT, rjt denoted as ftj is obtained as: 

ftj ¼

dt;j





dt;j þ dt;j

;

8j ¼ 1; 2; . . . ; jRt j:

ð18Þ

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Step 10: Rate the available RATs based on the closeness coefficients, and select the RAT that has the highest closeness coefficient for the group of calls. If two or more RATs have the same highest closeness coefficient, select the least loaded of the RATs for the group of calls from Mt. 5. Performance evaluation and results In this section, we evaluate the performance of the proposed RAT-selection algorithm through numerical simulations conducted in MATLAB. We use three-service threeRAT network as an example of HWN supporting multimode terminals. Only vertical handoffs due to call-events are considered in the simulations. To study the performance of proposed algorithm based on call events, it is assumed that the three RATs are available for all the calls considered in the simulations. A RAT is available when it has enough signal strength to support an incoming call in the location of the mobile terminal where the call originates. The mobile terminals considered in the simulations support the three types of calls in the HWN, namely voice (denoted as call, s1), file download (call s2), and video streaming (call s3). We consider 1000 multimode terminals in the three-RAT HWN, under different scenarios which are described in the subsequent subsections. In the three-RAT HWN, five criteria are considered for selecting a RAT for a call or a group of calls from a multimode terminal. The criteria are service price (C1), data rate (C2), security (C3), power consumption (C4), and network delay (C5). The objective of the RAT selection algorithm is to select the most suitable RAT for each call or group of calls, based on the priority of each call and the weight assigned to the criteria for each call by the user. Table 2 shows the RAT selection criteria for the RATs. From Table 2, the decision matrix D is obtained as:

Using the conversion scale described in Section 3 [20], the fuzzy variables are converted into crisp values.

Using (2), the normalized decision matrix is obtained as:

Using the normalized decision matrix obtained above and Eqs. (3)–(18), the closeness coefficient of each RAT is obtained for each of the 1000 multimode terminals in the three-RAT HWN under different scenarios, and the most suitable RAT is selected for the call (or group of calls) using the proposed algorithm. In each of the scenarios, call weights for RAT selection criteria are randomly generated (so as to mimic real-life situation), to indicate different users’ preferences. In the following subsection, we investigate the proportion of calls (or group of calls) admitted to each of the three RATs using the proposed RAT selection algorithm. For comparison purposes, we also investigate the performance of a dynamic random selection algorithm. The dynamic random selection algorithm randomly selects one of the available RATs for a new call or a group of calls whenever a new call is initiated or call(s) are to be handed over from one RAT to another.

5.1. Proportion of calls admitted into each RAT In this subsection, we investigate the proportion of calls admitted into each RAT by the proposed RAT-selection algorithm in different scenarios shown in Table 3. For example, in Scenario 1a, each of the 1000 multimode terminals initiate a voice call in the 3-RAT HWN. In Scenario 4a, each of the multimode terminals has two calls: voice and file download. In Scenario 7a, each of the 1000 multimode terminals has three calls: voice, file download, and video streaming. In Scenarios 1a to 7a, all the calls have equal priorities but call weights for RAT selection criteria are randomly generated. Fig. 8 shows the proportion of single calls admitted into each RAT for Scenarios 1a, 2a, and 3a. It can be seen that voice calls are mostly admitted into RAT-2, (3G HSPA) whereas they are least admitted into RAT-1 (WLAN). In Scenario 2a, file downloads are mostly admitted into (WLAN) because of the high data rate available in WLAN, whereas file downloads are least admitted into 3G HSPA. Video streaming are mostly and equally admitted into 3G and WiMAX, and are least admitted into WLAN because WLAN has a higher delay than 3G and WiMAX. Fig. 9 shows the proportion of double calls from each multimode terminal admitted into each RAT for Scenarios 4a, 5a, and 6a. It can be seen that the proportion of double calls admitted into each RAT in the HWN depends on the

Table 2 RAT-selection criteria used in the numerical example. RATs

RAT 1 (WLAN) RAT 2 (3G HSPA) RAT 3 (Mobile WiMAX)

Criteria Price

Maximum data rate (Mbps)

Security

Battery power consumption

Network delay

Low High Medium

54 7.5 25

Low Very high High

Medium High High

High Very low Low

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Table 3 RAT-selection criteria used in the numerical example. Scenario

Calls considered

1a 2a 3a 4a 5a 6a 7a

Voice only File download only Video streaming only Voice + file download only Voice + video streaming only File download + video streaming only Voice + file download + video streaming

Number of calls admitted

1398

700 600 500

WLAN 3G HSPA Mobile WiMAX

400 300 200 100 0

7a Scenario

Number of calls admitted

Fig. 10. Number of triple calls admitted into each RAT (Scenario 6a). 900

WLAN

800

3G HSPA

700

Mobile WiMAX

600 500

5.2. Effect of call priority on RAT selection decisions

400 300 200 100 0 1a

2a

3a

Scenario

Fig. 8. Number of single calls admitted into each RAT (Scenarios 1a, 2a, and 3a).

Number of calls admitted

In the following subsection, we investigate the effect of call priority of RAT selection decisions.

800 700 600

WLAN 3G HSPA Mobile WiMAX

500 400 300 200 100 0 4a

5a

6a

Scenario

Fig. 9. Number of double calls admitted into each RAT (Scenarios 4a, 5a, and 6a).

combination of the two calls. For example, voice and file downloads are mostly admitted into Mobile WiMAX. Voice and video streaming are mostly admitted into 3G HSPA, whereas file download and video streaming are mostly admitted into WLAN. Fig. 10 shows the proportion of triple calls from each multimode terminal admitted into each RAT in Scenario 7a. It can be seen that Mobile WiMAX is mostly preferred for the three calls. Fig. 11 shows the proportion of calls from each multimode terminal admitted into each RAT for Scenarios 1a– 7a, using a dynamic random RAT selection algorithm. It can be seen that the number of calls admitted into each RAT is almost the same for all the seven scenarios. The dynamic random selection algorithm does not consider users’ preferences in making RAT selection decsions but selects one of the available RATs with a probability of 1/Rt. Therefore, probability of selecting the same RAT when a new call is initiated in the three-RAT heterogeneous wireless network is 1/3 whereas the probability of selectin a different RAT is 2/3.

In this subsection, we consider 1000 active multimode terminals in the 3-RAT HWN with each multimode terminal having triple calls. We investigate the effect of using different call priority levels on RAT selection decisions. The priority levels used are shown under 10 different scenarios in Table 4. Fig. 12 shows the number of group of three calls admitted into each RAT in Scenarios 1b–4b. It can be seen that call priority considerably affects the choice of RAT for each group of calls. For example, in Scenario 1b, all the calls have equal priority. In Scenario 2b, voice call has the highest priority among the group of three calls. Thus, the greatest number of the group of calls is admitted into the 3G network, which is mostly preferred for voice. In Scenario 3b, file download has the highest priority among the three calls. Thus the greatest number of the group of calls is admitted into WLAN in order to take advantage of the high data rate in WLAN. Fig. 13 shows the number of group of three calls admitted into each RAT in Scenarios 5b–10b. It can be seen that call priority considerably affects the choice of RAT for each group of calls. In the next subsection, we investigate the effect of RAT preference margin on vertical handoff frequency in the 3RAT HWN. 5.3. Effect of RAT preference margin of vertical handover frequency In this subsection, we study the effect of RAT preference margin on the frequency of vertical handoff in HWNs. In order to capture the effect of RAT preference margin, we use the same priority for each group of three calls from a multimode terminal. Different scenarios with a different call sequence are investigated in this subsection. These scenarios are shown in Table 5. For example, in Scenario 1c, each multimode terminal initiates voice calls (illustrated in Fig. 3(a)–(c)). The proposed algorithm selects the most suitable RAT for voice based on the user’s specified weights. Shortly after, the user initiates another call (file download). The proposed algorithm makes RAT selection decision for the group of two calls (voice + file download), and selects the most suitable RAT for the two

1399

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WLAN

Number of calls admitted

400

3G HSPA

Mobile WiMax

350 300 250 200 150 100 50 0 1a

2a

3a

4a

5a

6a

7a

Scenario

Table 4 Call priority values for groups of three calls. Scenario

Call priority values

Number of calls admitted

1b 2b 3b 4b 5b 6b 7b 8b 9b 10b

Voice

File download

Video streaming

5 5 1 1 5 5 3 3 1 1

5 1 5 1 3 1 5 1 5 3

5 1 1 5 1 3 1 5 3 5

400 300 200 100 6b

7b

8b

9b

10b

Fig. 13. Effect of call priority on RAT selection for triple calls (Scenarios 5b–10b).

3G HSPA Mobile WiMAX

500 400 300 200 100 0 3b

500

Scenario

700

2b

600

5b

WLAN

1b

WLAN 3G HSPA Mobile WiMAX

700

0

800 600

Number of calls admitted

Fig. 11. Number of calls admitted into each RAT for the seven scenarios using the dynamic random selection algorithm.

4b

Scenario

Fig. 12. Effect of call priority on RAT selection for triple calls (Scenarios 1b–4b).

calls. Shortly after, the user initiates another call (video streaming). The proposed algorithm makes RAT selection decision for the group of three calls (voice + file download + video streaming), and selects the most suitable RAT for the three calls. For each scenario shown in Table 5, we investigate the frequency of vertical handoffs for 1000 multimode terminals when RAT decisions are made from single call (call 1) to double calls (call 1 + call 2), and from double calls to triple calls (call 1 + call 2 + call 3) by each of the multimode terminals in the HWN. Fig. 14 shows the effect of varying RAT preference margin on frequency of vertical handoffs for Scenario 1c. The graph with the label ‘‘1st–2nd’’ indicates the number of vertical handoffs when file download is initiated in addi-

tion to the existing voice call. The graph with the label ‘‘2nd–3rd’’ indicates the number of vertical handoffs when video streaming is initiated in addition to the existing voice call and file download. The graph with the label ‘‘1st–2nd–3rd’’ indicates the total number of vertical handoffs from the initiation of ‘‘voice’’ to ‘‘voice + file download’’ to ‘‘voice + file download + video streaming’’. It is the summation of the two previous values. In Fig. 14, the graph with label ‘‘Random’’ indicates the number of vertical handoff calls when file download is initiated in addition to the existing voice call, using the dynamic random selection algorithm. ‘‘Random also indicates the number of vertical handoffs when video streaming is initiated in addition to the existing voice call and file download. In Scenario 1c, it can be seen for three graphs that the number of vertical handoff decreases as RAT preference margin increases. It can also be seen that the number of vertical handoffs is higher from ‘‘1st–2nd’’ than from ‘‘2nd–3rd’’. The reason is that in group call decision making, when going from call-1 (one existing call) to call-2 (one new call), the probability of selecting the current RAT is about 0.5 and the probability of selecting a different RAT (having vertical handoff) is about 0.5, given that call 1 and call 2 have the same priority level and different preferences. Whereas when going from call-2 (two existing calls) to call-3 (one new call), the probability of selecting the current RAT is greater than 0.5, and the probability of selecting a new RAT is less than 0.5. In generally, in group decision making, it is easier for one decision maker to influence the decision of another decision maker than for one

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Table 5 Call priority values for groups of three calls. Sequence of call initiation Call 1 + call 2 + call 3

Voice + file download Voice + video streaming File download + voice File download + video streaming Video streaming + voice Video streaming + file download

Voice + file download + video streaming Voice + video streaming + file download File download + voice + video streaming File download + video streaming + file download Video streaming + voice + file download Video streaming + file download + voice

1st-2nd 2nd-3rd 1st-2nd-3rd Random

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

RAT preference margin

Fig. 14. Effect of RAT preference margin on number of vertical handoffs for Scenario 1c.

Number of vertical handoffs

decision maker to influence the group decision of two decision makers, if all the decision makers have equal weights. For the dynamic random selection algorithm, the frequency of vertical handoff is higher than that of the proposed algorithm for RAT preference margin greater than 0.02. Moreover, the numbers of vertical handoffs for ‘‘1st–2nd’’ and for ‘‘2nd–3rd’’ are the same because the probability of selecting a new RAT when a new call is initiated is 2/3. Similarly, it can be seen from Scenarios 2c, 3c, 4c, and 5c that the number of vertical handoff decreases as RAT preference margin increases. It can also be seen that the number of vertical handoffs is higher from ‘‘1st–2nd’’ than from ‘‘2nd–3rd’’. Figs. 14–19 show that using a RAT preference margin substantially reduces the frequency of vertical handoff due to multiple calls from a multimode terminal in HWN. In addition, different RAT preference margins will be most suitable for ‘‘1st–2nd’’, ‘‘2nd–3rd’’, etc. RAT selection decisions in HWNs. The figures also show that the probability of having a vertical handoff due to initiation of a new call from a multimode terminal decreases as the number of existing calls from the mobile terminal increases. 1000 900 800 700 600 500 400 300 200 100 0 0.00

Number of vertical handoffs

1000 900 800 700 600 500 400 300 200 100 0 0.00

Call 1 + call 2

Voice Voice File download File download Video streaming Video streaming

1000 900 800 700 600 500 400 300 200 100 0 0.00

1st-2nd 2nd-3rd 1st-2nd-3rd Random

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

RAT preference margin

Fig. 16. Effect of RAT preference margin on number of vertical handoffs for Scenario 3c.

Number of vertical handoffs

Number of vertical handoffs

1c 2c 3c 4c 5c 6c

Call 1

1st-2nd 1000 2nd-3rd 900 1st-2nd-3rd 800 Random 700 600 500 400 300 200 100 0 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

RAT preference margin

Fig. 17. Effect of RAT preference margin on number of vertical handoffs for Scenario 4c.

Number of vertical handoffs

Scenario

1st-2nd 1000 2nd-3rd 900 1st-2nd-3rd 800 Random 700 600 500 400 300 200 100 0 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20

RAT preference margin 1st-2nd 2nd-3rd

Fig. 18. Effect of RAT preference margin on number of vertical handoffs for Scenario 5c.

1st-2nd-3rd Random

0.02

0.04

0.06

0.08

0.10

RAT preference margin

Fig. 15. Effect of RAT preference margin on number of vertical handoffs for Scenario 2c.

Figs. 14–19 also show that the frequency of vertical handoffs resulting from initiation of a new call from a multimode terminal also depends on the sequence of call initiation. For example, the frequency of vertical handoffs in Fig. 15 is much less than the frequency of vertical handoffs in Fig. 14. For the dynamic random selection algorithm,

Number of vertical handoffs

O.E. Falowo, H. Anthony Chan / Computer Networks 56 (2012) 1390–1401 1000 900 800 700 600 1st-2nd 500 2nd-3rd 400 1st-2nd-3rd 300 Random 200 100 0 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 RAT preference margin

Fig. 19. Effect of RAT preference margin on number of vertical handoffs for Scenario 6c.

Figs. 14–19 also show that the numbers of vertical handoffs for all the scenarios are almost constant. 6. Conclusion Next generation multimode terminals have the capability to simultaneously support two or more different classes of calls in HWNs. Existing RAT selection algorithms do not consider the problem of RAT selection for multiple calls in HWNs. In this paper, the problem of RAT selection for multiple calls in HWNs has been conceptualized. A RAT selection algorithm has been proposed and the fuzzy TOPSIS group decision-making technique has been applied to solve the problem of RAT selection for single and multiple calls in HWNs. Numerical simulations have been conducted to illustrate the performance of the proposed RAT-selection algorithm, using a three-RAT HWN, and considering different call priorities and different RAT preference margins. Simulation results show the call priorities significantly affect RAT selection for each group of calls in HWNs. Results also show that frequency of vertical handoffs reduces as the RAT preference margin increases. Moreover, results show that the probability of having a vertical handoff due to initiation of a new call from a multimode terminal decreases as the number of existing calls from the mobile terminal increases. References [1] L. Giupponi, J. Pérez-Romero, A novel approach for joint radio resource management based on fuzzy neural methodology, IEEE Transactions on Vehicular Technology 57 (3) (2008) 1789–1805. [2] O.E. Falowo, H.A. Chan, Joint call admission control algorithms: requirements, approaches, and design considerations, Elsevier Journal: Computer Communications 31/6 (2007) 1200–1217. [3] M.M. Alkhawlani and A.A. Hussein, Intelligent radio network selection for next generation networks, in: Proceedings of the 7th International Conference on Informatics and Systems (INFOS), Cairo, Egypt 28–30 March, 2010. [4] W. Zhang, Handover decision using fuzzy MADM in heterogeneous networks, in: Proceedings of IEEE WCNC’04, Atlanta, GA, March, 2004. [5] X. Gelabert, J. Pérez-Romero, O. Sallent, R. Agustı´, A Markovian approach to radio access technology selection in heterogeneous multiaccess/multiservice wireless networks, IEEE Transactions on Mobile Computing 7 (10) (2008). [6] Q. Guo, X. Xu, J. Zhu, H. Zhang, A QoS-guaranteed cell selection strategy for heterogeneous cellular systems, ETRI Journal 28 (1) (2006) 77–83. [7] L. Wu, K. Sandrasegaran, A study on RAT selection algorithms in combined UMTS/GSM networks, ECTI Transactions on Electrical Engineering, Electronics, and Communications 6 (2) (2008). [8] Y. Wanga, H. Lee, Generalizing TOPSIS for fuzzy multiple-criteria group decision-making, Computers and Mathematics with Applications 53 (11) (2007) 1762–1772. [9] B. Vahdani, S.M. Mousavi, R. Tavakkoli-Moghaddam, Group decision making based on novel fuzzy modified TOPSIS method, Applied Mathematical Modelling 35 (9) (2011) 4257–4269.

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Olabisi E. Falowo received his PhD in Electrical Engineering at the University of Cape Town in 2008. He has published over 25 papers in reputable conferences and journals including Computer Communications, EURASIP Journal on Wireless Communications and Networking, Telecommunications Systems, International Journal of Communications, and Wireless Communications and Mobile Computing. He is currently a senior lecturer in the Department of Electrical Engineering, University of Cape Town. His primary research interest is in radio resource management in heterogeneous wireless networks. Olabisi is member of the IEEE and the IET.

H. Anthony Chan (M’94–SM’95–F’08) received his PhD in physics at University of Maryland, College Park in 1982 and then continued post-doctorate research there in basic science. After joining the former AT&T Bell Labs in 1986, his work moved to industryoriented research in areas of interconnection, electronic packaging, reliability, and assembly in manufacturing, and then moved again to network management, network architecture and standards for both wireless and wireline networks. He had designed the Wireless section of the year 2000 state-of-the-art Network Operation Center in AT&T. He was the AT&T delegate in several standards work groups under 3rd generation partnership program (3GPP). During 2001–2003, he was visiting Endowed Pinson Chair Professor in Networking at San Jose State University. In 2004, he joined University of Cape Town as professor in the Department of Electrical Engineering. He was Administrative Vice President of IEEE CPMT Society and had chaired or served numerous technical committees and conferences. He is distinguished speaker of IEEE CPMT Society and is in the speaker list of IEEE Reliability Society since 1997.