Utility and Game-Theory Based Network Selection Scheme in ...

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2009 proceedings.

Utility and Game-Theory Based Network Selection Scheme in Heterogeneous Wireless Networks Chung-Ju Chang and Tsung-Li Tsai

Yung-Han Chen

Department of Communication Engineering National Chiao Tung University Hsinchu 300, Taiwan [email protected]

Information and Communication Research Laboratories Industrial Technology Research Institute Chutung Hsinchu 310, Taiwan [email protected]

Abstract − The paper proposes a utility function and game theory (UGT) based network selection scheme to maximize accommodated number of calls, minimize handoff occurrence frequency, and fulfill quality of service (QoS) requirements in heterogeneous wireless networks. When a new call or a handoff call arrives, UGT would calculate the utility value and preference value for each candidate network based on the QoS satisfaction of the call request and the cooperative game computation. Finally, the network, which has the maximum result of linearly combined utility value and preference value, would be selected as the most suitable network for the call request. Simulation results show that UGT has good QoS satisfaction. It also allows system to accommodate more calls than iterative TOPSIS scheme. In addition, UGT can reduce the handoff occurrence frequency by about 30% as compared to iterative TOPSIS.

be regarded as a kind of competition behavior. In [4] and [5], Niyato and Hossain used cooperative game and noncooperative game to find the solutions of the bandwidth allocation problem for non-real-time services in heterogeneous wireless networks. However, the mentioned literatures do not consider MS mobility, which is a very important factor in the network selection scheme. A high mobility MS is more likely to experience more handoffs, which also implies more overhead and higher risk of call dropping, especially in WLAN systems because of the smaller coverage. In this paper, a utility and game theory (UGT) based network selection scheme is proposed for heterogeneous wireless access networks, where multiple classes of traffic and MS mobility are considered. The UGT scheme intends to maximize the accommodated number of calls, minimize handoff rate, and support QoS. After obtaining the candidate networks for the call request, the utility value of each candidate network would be calculated to represent the satisfaction of QoS requirements for a call request. The cooperative game is also adopted to calculate the preference value of each candidate network. Finally, the network with the maximum result of linearly combined utility value and preference value would be selected as the most suitable network for the call request. Simulation results show that UGT obtains lower new call blocking rate and fewer forced terminated calls. Besides, UGT reduces the handoff occurrence frequency about 30% than iterative TOPSIS. The rest of the paper is organized as follows. Section II describes the system model. Section III introduces game theory. Section IV is the proposed utility and game theory (UGT) based network selection scheme. Section V shows simulation results and discussions. Finally, Section VI gives the conclusions.

I. INTRODUCTION Broadband, multimedia capability, and mobility are the major concerns in modern communication technologies. Several broadband wireless network standards, such as wideband code division multiple access (WCDMA) cellular network, IEEE 802.16 wireless metropolitan area network (WMAN), and IEEE 802.11 wireless local area network (WLAN), have been developed to seek for better efficiency and functionality to support Internet applications. In order to provide mobile stations (MSs) with seamless Internet access in the heterogeneous wireless networks, the connection capability to various radio access networks is also necessary. An MS with multi-mode capability is required to access the proper network. Each network, however, has different features, it is necessary to consider essential system information of each network and quality of service (QoS) requirements of MS to make appropriate network selections in heterogeneous wireless networks. The network selection is an essential step before the system entry of a new call or the handoff of an existing call. In the heterogeneous networks, a new call or a handoff call might have multiple options of access networks. It means that an existing call could hand over to a different system, which is called the vertical handoff, based on the network selection to obtain better service continuity. Stevens-Navarro, Lin, and Wong [1] proposed a Markov decision process based vertical handoff decision algorithm for heterogeneous wireless networks, which can achieve low expected number of vertical handoffs per connection. Bari and Leung [2] used multi attribute decision making (MADM) method to formulate the network selection problem in the heterogeneous networks and adopted iterative TOPSIS scheme to solve this problem. Game theory [3] is also considered as a good approach to solve the resource allocation and network selection problems, because allocating suitable resource and networks for MSs can

II. SYSTEM MODEL Assume that the heterogeneous access network contains a WCDMA cellular system, an IEEE 802.16 WMAN system, and an IEEE 802.11 WLAN system. As shown in Fig. 1, WCDMA services are available at any place, while WMAN and WLAN services are only available regionally. It is also assumed that WLANs are deployed only at some places for high-speed data services in the urban area. The proposed utility and game-theory (UGT) based network selection scheme is installed in a radio network controller (RNC), which can gather information from base stations (BSs) in control for network selection. In WCDMA cellular networks, the achievable bit rate for MSj, denoted by Rj, can be obtained by [6]

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This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2009 proceedings.

candidate networks selection determines which networks are available for the call request. For each candidate network, the UGT scheme obtains values from the computations of utility function for QoS satisfaction and cooperative game for network preference. The utility function for QoS satisfaction computes the utility value based on the satisfaction of QoS requirements of the call request. The cooperative game for network preference computes the preference value from the network point of view. Its main goal is to decrease the number of handoff and achieve load balancing. Finally, the utility value and the preference value of each candidate network are combined linearly, and the network which has the maximum combined value is the most suitable one for the call request.

Fig. 1. The network topology of WMAN, WCDMA and WLAN systems Rj =

Pj W , × v j ⋅ ( Eb / N 0 ) j I total − Pj

(1)

where W is the chip rate, vj is the activity factor of MSj, (Eb/N0)i is the signal energy per bit divided by noise spectral density that is required to meet a predefined QoS of MSj, Pj is the signal power from MSj received at BS, and Itotal is the total power including thermal noise power received at BS. In IEEE 802.16 WMAN systems [7], the orthogonal frequency division multiple access is adopted. Suppose that there are K sub-channels, and each sub-channel consists of q spread out sub-carriers. Assume that each frame includes L OFDMA symbols, and T is the frame duration. From [8], an approximation of modulation order M can be obtained by M =

1.5 × SINRa( A, k) − ln(5 × BERa* )

+ 1,

(2)

where SINRa( A,k) is the received signal-to-interference-noise ratio (SINR) of service request a on sub-channel k at the Ath OFDMA symbol and BERa* is the required bit error rate of service request a. In WLAN systems, the standard IEEE 802.11e [9] proposes the enhanced distributed channel access (EDCA), which allows the access point (AP) to initiate a period of transmission opportunity in the contention period. This standard amendment also defines differentiated priorities to support QoS. In this paper, EDCA is used for data transmissions. III. GAME THEORY Game theory is often studied in the strategic interactions between agents, where the agents are players in the models of optimization problem [3]. A game consists of a set of players, a set of strategies available to the players, and a specification of payoffs for each combination of strategies. In strategic games, agents choose strategies which can maximize their payoffs. Two kinds of games, cooperative and non-cooperative, are usually used. In a cooperative game, the players choose their strategy with joint considerations of other players. In the noncooperative game, each player selects his/her strategy individually without any joint considerations among players. If there is a set of strategies that makes every player obtain the best payoff, the set of strategies satisfies Nash equilibrium. In heterogeneous networks, a cooperative game is defined for the network selection problem, and the goal is to find a set of strategies which satisfies the Nash equilibrium for the network selection decision. IV. UTILITY AND GAME-THEORY BASED NETWORK SELECTION SCHEME Before the utility and game-theory (UGT) based network selection scheme, the candidate networks selection is performed first when a new call or a handoff call arrives. The

A. Candidate Networks Selection There are two constraints, the signal strength constraint and network load constraint, in the candidate networks selection. If the pilot signal strength of network i received at the MS exceeds the candidate networks constraint, the network i will be classified as a candidate network. The network load constraint is used to ensure that the admittance of a call request will not affect the quality of the ongoing connections, which can be expressed by (3) Η E + ηa ≤ ηth , where Η E is the current load intensity of a candidate cell, η a is the load intensity increment by the call request a, and ηth is the predefined load intensity threshold. In WCDMA networks, η a can be estimated from [6] 1 (4) , η = (1 + f ) a

1 + W /(Ca ⋅ ea )

where f is the factor of other cells interference, W is the chip rate of the WCDMA system, Ca is the estimated mean rate of call request a, and ea = Eb / N0 is the required bit-energy-tonoise-ratio corresponding to the desired link quality of the call request a. In IEEE 802.16 WMAN system, according to the system model described in Section II, the mean capacity of WMAN can be estimated as 4 × K × L × q / T (bps). Then η a can be estimated as

η a = Ca /(4 × K × L × q / T ).

(5)

In WLAN systems, the measurement-based network load intensity estimation is used. During an observation duration Td , let Ts be the total busy time occupied by successful and collided data transmissions. Therefore the load intensity could be defined as H E = Ts / Td . If Η E ≤ ηth,WLAN ,where ηth,WLAN is a predefined load intensity threshold for WLAN, the network will be regarded as a candidate network. B. Utility Function for QoS Satisfaction A utility function Ui for each candidate network i is defined to represent the QoS satisfaction degree of the MS, which is defined as (6) U i = f B ,i × f D ,i × f R ,i , where f B ,i , f D,i , and f R ,i are the QoS evaluation functions of data rate, packet delay, and packet dropping rate for access network i, respectively. If a candidate network has greater QoS measures to fulfill the QoS requirements of the call request during the capability negotiation, then the evaluation values calculated from the evaluation functions would be higher. Thus, the evaluation function of data rate could be defined as

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2009 proceedings.

addition, the penalty weight wi is used to avoid the situation that high preference values are allocated to some unsuitable f B ,i (7) networks. Generally, if the remaining available resource of a candidate network is higher, its preference value would be where Bi is the allowed data rate measured in the candidate higher. On the other hand, if the penalty weight of a candidate network i, Breq is the data rate requirement of the call request, network is higher, its preference value would be lower. The penalty weight of network i is affected by three factors, and Bth is a threshold used to represent whether the QoS is highly satisfied. Moreover, the evaluation function of packet the call holding time, the dwell time, and the mobility of the MS. For a high mobility MS, the penalty weight wi can be delay could be defined as defined as ⎧1.5, if Di ≤ Dth ⎪⎪ (8) if xi ≤ 0.75 ⎧0, f D ,i = ⎨1 + 0.5 × ( Dreq − Di ) /( Dreq − Dth ), if Dth < Di ≤ Dreq , ⎪( x − 0.75), (12) ⎪ 2 2 if 0.75 < x ≤ 1 , ⎧1.5, if Bth ≤ Bi ⎪⎪ = ⎨1 + 0.5 × ( Bi − Breq ) /( Bth − Breq ), if Breq ≤ Bi < Bth , ⎪ 2 2 ⎪⎩exp[−( Bi − Breq ) /(4 × Breq )], if Bi < Breq

⎪⎩exp[−( Di − Dreq ) /(4 × Dreq )],

if Dreq < Di

where Di is the average packet delay measured in the candidate network i, Dreq is the maximum delay tolerance of the call request, and Dth is a threshold used to represent whether the QoS is highly satisfied. Similarly, the evaluation function of packet dropping rate could be defined as f R ,i

⎧1.5, if Ri ≤ Rth ⎪⎪ = ⎨1 + 0.5 × ( Rreq − Ri ) /( Rreq − Rth ), if Rth < Ri ≤ Rreq , ⎪ 2 2 ⎪⎩exp[−( Ri − Rreq ) /(4 × Rreq )], if Rreq < Ri

(9)

where Ri is the average packet dropping rate measured in the candidate network i, Rreq is the maximum allowable packet dropping rate of the call request, and Rth is a threshold used to represent whether the QoS is highly satisfied. Only real-time traffic classes have delay bound, hence the non-real-time traffic classes need not take f D,i and f R ,i into consideration. Once the value of utility function of every candidate network is computed, the normalized utility value for the candidate network i could be obtained by n

NU i = U i / ∑U j ,

(10)

j =1

where n is the number of candidate networks.

⎪ i i wi = ⎨ + × < ≤ x if x 0.25 3 ( -1), 1 1.25 i i ⎪ ⎪⎩1, if 1.25 < xi

where xi is the ratio of the estimated call holding time and the dwell time of the MS in the candidate network i. The estimated call holding time can be obtained from the traffic statistical results, and the estimated dwell time can be obtained by [10]. For a low mobility MS, the candidate network i will be more suitable if the MS is near to the BS of the candidate network i. Thus, the penalty weight wi can be defined as if d bm ≤ cri ,th ⎧0, ⎪ wi = ⎨(dbm − crth ) /(cri − cri ,th ), if cri ,th < dbm ≤ cri , ⎪1, if cri < d bm ⎩

(13)

where dbm is the distance between the BS of candidate network i and the MS, cri is the cell radius of the candidate network i, and cri,th is a pre-defined threshold. If dbm does not exceed cri,th, the penalty weight would be zero. With Ai and wi , the set of strategies which satisfies the Nash equilibrium for the network preference game can be found. From the definition, the pure strategy {NP1* , NP2* ,..., NPn*} is in a Nash equilibrium if POtotal ( NP*1, NP*2 ,..., NP*n ) ≥ POtotal (NP1, NP2 ,..., NPn ),

C. Cooperative Game for Network Preference A network preference game is formulated and the cooperative game is adopted to achieve load balancing and reduce the handoff occurrence frequency. There are three components in the network preference game, players, strategies, and payoffs. The players of this game are the n candidate networks denoted by {N1, N2 , …, Nn}. Since every candidate network has its own strategy, there are total n strategies, denoted by {NP1, NP2 , …, NPn}, in this game, where NPi is the preference value for Ni , i=1, 2, …, n. Note n that ∑ i =1 NPi = 1 and 0 ≤ NPi ≤ 1 , for i=1, 2, …, n. The payoff for the total candidate networks is defined as n

POtotal ( NP1 , NP2 ,..., NPn ) = ∑ Ai × ( NPi − wi × NPi 2 ),

(11)

i =1

where Ai = (1 − ρi / ηi ,th ), ρi is the current load intensity of network i before accepting the call request, ni ,th is the predefined load intensity threshold of network i , and wi is the penalty weight of network i. The main goal of the cooperative game is to find the set of strategies that would maximize the payoff function for each candidate network. When considering the load balancing in the heterogeneous networks, it is more suitable for the call request to choose the underloaded access network. The value calculated by Ai represents the ratio of remaining available resource for the call request. However, allocating more preference value to a candidate network implies that other candidate networks would have less preference values. In

(14)

∀ NP1,..., NPn .

And the network preference game can be formulated as an optimization problem described as Maximize

POtotal ( NP1 , NP2 ," , NPn )

subject to

∑ NP = 1,

(15)

n

i

i=1

NP1 ≥ 0, NP2 ≥ 0," , NPn ≥ 0.

To solve the problem subjecting to equality and inequality constraints in (15), the Karush-Kuhn-Tucker (KKT) condition [11] is used to find the solution. D. Candidate Networks Decision Finally, one candidate network which has the maximal compromised evaluative value is selected in the candidate networks decision. This decision issue can be formulated as an optimization problem given by i* = arg max [α NU i + (1 − α ) NPi ],

(16)

i

where α is a constant value between 0 and 1, and i is the index of the selected candidate network for the call request. *

V. SIMULATION RESULTS AND DISCUSSIONS A. Simulation Environment There are 7 WCDMA cells, 7 WMAN cells, and 28 WLAN cells in the simulation environment, as shown in Fig. 1. The

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2009 proceedings.

Number of cells 7 Chip rate (W) 3.84M bps Ratio of inter-cell interference to the total interference in the 0.55 referenced cell (f) Number of subchannels (K) Number of data subcarriers per subchannel (q) Number of slots per frame (L)

WMAN 1.6 Km 5 ms 2.5 GHz 1

WLAN 0.1 Km 9 us 2.4 GHz 0.75

7

28

4 48 16

The wireless fading channel is composed of large-scale fading and small-scale fading. The large-scale fading is caused by the path loss and shadowing effect, while the small-scale fading is caused by multipath reflection. The path loss is modeled as Lpathloss = 128.1 + 37.61× log dbm ( dB) [12], where dbm is the distance between the BS and the MS in kilometers. Assume that mean and standard deviation of the log-normal shadowing are zero and 8dB, respectively. For small-scale fading, the Jakes model [13] is used to simulate the fading channel. Furthermore, the channel is assumed to be fixed within a frame and varies independently from frame to frame. Two models for high and low mobility MSs are used in this paper. For a high mobility (50 km/hr and 80 km/hr) MS, it is assumed that the moving velocity and direction are constant. For a low mobility (3 km/hr) MS, it is assumed that the moving velocity is constant, but the direction will be changed randomly. B. Traffic Models and QoS Requirements There are four classes of traffic in the simulations, including the conversational, streaming, interactive, and background class traffic. The conversational class represents real-time multi-media applications such as voice telephony. The streaming class includes the real-time video stream applications. The interactive class is composed of non-realtime applications for HTTP web browsing or chat room. The background class is the service using best effort transmissions, such as file transfer protocol (FTP). In the simulations, the models of these four classes of traffic are according to [12], [14], and [15]. The QoS requirements of each traffic class are listed in Table II. Table II QoS requirements of each traffic class Traffic class Conversational (Voice)

Streaming (Video) Interactive (HTTP) Background (FTP)

Requirement Required BER Required Eb/No Max. delay tolerance Max. allowable packet dropping rate Required BER Required Eb/No Max. delay tolerance Max. allowable packet dropping rate Required BER Required Eb/No Required BER Required Eb/No

Value 10-3 4 dB 40 ms 1% 10-4 3 dB 100 ms 1% 10-6 2 dB 10-6 1.5 dB

C. Performance Evaluation The proposed UGT algorithm is compared with the iterative TOPSIS algorithm with the multi attribute decision making

0.12

0.035

UGT iterative TOPSIS

0.1 0.08 0.06 0.04 0.02 0

UGT iterative TOPSIS

0.03 Forced terminated rate

WCDMA 2 Km 10 ms 2 GHz 0.75

New call blocking rate

Parameters Cell radius Frame (time slot) duration Carrier frequency load intensity threshold ηth

(MADM) method [2]. The attributes in the MADM are system capacity, allowed data rate, utilization, packet delay, and packet dropping rate. For each cell, the new call arrival rate of the conversational, streaming, interactive, and background class traffic in the heterogeneous network are AR ×1/10 , AR ×1/ 30 , AR ×1/ 30 , and AR × 1/ 60 users per second, respectively, where AR is the equivalent arrival rate and would be from 1 to 5. Fig. 2(a) and Fig. 2(b) show the new call blocking rate and the forced terminated rate, respectively. The forced terminated rate is defined as the probability that a call will be forced terminated during call conversation. It can be found that the new call blocking rate and the forced terminated rate are lower when UGT is adopted. The reason is that UGT can provide better load balancing. It also implies that the system with UGT can accommodate more number of calls than the system with iterative TOPSIS.

0.025 0.02 0.015 0.01 0.005

1

0

2 3 4 5 Equivalent arrival rate (AR)

1

2 3 4 5 Equivalent arrival rate (AR)

(a) (b) Fig. 2. (a) New call blocking rate, and (b) forced termination rate Fig. 3 shows the handoff occurrence frequency, which is defined as the number of handoff per call conversation. The total handoff occurrence frequency in UGT is about 30% lower than that in iterative TOPSIS. The reason is that UGT takes the mobility of MS into consideration, but iterative TOPSIS does not. It can be found that the non-real-time call has higher handoff frequency in UGT than that in iterative TOPSIS. Since the real-time call is more sensitive to the occurrence of handoff, UGT will select a suitable access network to prevent frequent handoff for the real-time call. Therefore the penalty weight would be more influential for real-time call than nonreal-time call in UGT. 1.2

Handoff occurence frequency

system parameters in the heterogeneous network are listed in Table I. Table I System parameters for WCDMA, WMAN, and WLAN

UGT : total UGT : real-time call UGT : non-real-time iterative TOPSIS : total iterative TOPSIS : real-time iterative TOPSIS : non-real-time

1

0.8

0.6

0.4

0.2

1

2

3

4

5

Equivalent arrival rate (AR)

Fig. 3. Handoff occurrence frequency The average delay for voice and video calls in the heterogene-ous network are shown in Fig. 4(a) and Fig. 4(b), respectively. It can be found that the results of average delay

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the WCNC 2009 proceedings.

40 Delay requirement UGT : WCDMA UGT : WMAN UGT : WLAN iterative TOPSIS : WCDMA iterative TOPSIS : WMAN iterative TOPSIS : WLAN

30

20

10

0

1

2

3

4

5

Average delay of Video traffic (ms)

Average delay of Voice traffic (ms)

are almost the same because voice and video calls are the highest priority, and the scheduler will serve the real-time calls first. 100 Delay requirement UGT : WCDMA UGT : WMAN UGT : WLAN iterative TOPSIS : WCDMA iterative TOPSIS : WMAN iterative TOPSIS : WLAN

80 60 40

REFERENCES

20 0

1

2

3

4

5

Equivalent arrival rate (AR)

Equivalent arrival rate (AR)

(a) Voice (b) Video Fig. 4. Average delay of voice traffic and video traffic

Maximum acceptable dropping rate UGT : WCDMA UGT : WMAN UGT : WLAN iterative TOPSIS : WCDMA iterative TOPSIS : WMAN iterative TOPSIS : WLAN

0.8 0.6

0.4 0.2 0

1

2

3

4

5

Equivalent arrival rate (AR)

Packet dropping rate of Video (%)

Packet dropping rate of Voice (%)

The packet dropping rate for voice and video calls are shown in Fig. 5(a) and Fig. 5(b), respectively. It can be found that the maximum packet dropping rate requirement is satisfied for each scheme. However, the packet dropping rate is higher in UGT than that in iterative TOPSIS. It is because that iterative TOPSIS would select the access network which has the most available resource for the call request. Hence the link quality would be better for the real-time call. But UGT would jointly consider the QoS requirements and handoff frequency of the MS, the candidate network which has the most available resource for the real-time call is not necessarily the most suitable one. Therefore, the system packet dropping rate would be sacrificed slightly, but the QoS requirement is still satisfied and the total handoff occurrence frequency would be improved significantly, as shown in Fig. 3. 1

1 Maximum acceptable dropping rate UGT : WCDMA UGT : WMAN UGT : WLAN iterative TOPSIS : WCDMA iterative TOPSIS : WMAN iterative TOPSIS : WLAN

0.8

0.6

0.4

0.2

0

1

2

3

4

reduced significantly. But the packet dropping rate of the realtime call in UGT is higher than that in iterative TOPSIS because the network with the most available resource for the real-time call is not necessarily the most suitable one if the mobility of MS is considered.

5

Equivalent arrival rate (AR)

(a) Voice (b) Video Fig. 5. Packet dropping rate of voice traffic and video traffic

VI. CONCLUSIONS In this paper, a utility and game-theory (UGT) based network selection scheme is proposed for heterogeneous wireless networks. After obtaining the set of candidate networks, a utility value is computed in UGT to represent the satisfaction degree of QoS requirements. In order to achieve load balancing and less handoff overhead, a cooperative network preference game is defined in UGT to obtain the preference value for each candidate network. Finally, the utility value and the preference value of each candidate network would be linearly combined, and the most suitable candidate network for the call request can be decided according to the combined values the candidate networks. Simulation results show that UGT can achieve lower new call blocking rate and forced termination rate than iterative TOPSIS, which means that the heterogeneous system can accommodate more calls when UGT is adopted. In addition, the total handoff occurrence frequency in UGT is about 30% lower than that in iterative TOPSIS because UGT takes the mobility of MS into consideration. Accordingly, the handoff overhead can be

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