A Novel Vertical Handoff Algorithm Based on

J. Shanghai Jiaotong Univ. (Sci.), 2012, 17(1): 25-30 DOI: 10.1007/s12204-011-1137-0

A Novel Vertical Handoff Algorithm Based on Fuzzy Logic in Aid of Grey Prediction Theory in Wireless Heterogeneous Networks LIU Xia1,2∗ (

), JIANG Ling-ge1 ()

(1. Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai 200240, China; 2. College of Merchant Marine, Shanghai Maritime University, Shanghai 200135, China)

© Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg 2011 Abstract: To coordinate the various access technologies in the 4G communication system, intelligent vertical handoff algorithms are required. This paper mainly deals with a novel vertical handoff decision algorithm based on fuzzy logic with the aid of grey theory and dynamic weights adaptation. The grey prediction theory (GPT) takes 4 sampled received signal strengths as input parameters, and calculates the predicted received signal strength in order to reduce the call dropping probability. The fuzzy logic theory based quantitative decision algorithm takes 3 quality of service (QoS) metric, received signal strength (RSS), available bandwidth (BW), and monetary cost (MC) of candidate networks as input parameters. The weight of each QoS metrics is adjusted along with the networks changing to trace the network condition. The final optimized vertical handoff decision is made by comparing the quantitative decision values of the candidate networks. Simulation results demonstrate that the proposed algorithm provides high performance in heterogeneous as well as homogeneous network environments. Key words: fuzzy logic theory, grey prediction algorithm, quantitative decision, vertical handoff CLC number: TN 929.5 Document code: A

0 Introduction In recent years, the exponential growth of wireless communication services has resulted in a widespread success of various wireless technologies. Today’s wireless communication networks (such as wireless LAN (WLAN), 3G cellular and satellite networks) will coexist to provide ubiquitous wireless access and various traffic services[1] . Therefore, how to coordinate and interoperate the various types of networks available has become a major concern in the mobility management of heterogeneous wireless system[2] . Vertical handoff (VHO) is introduced and the optimized vertical handoff decision algorithm is required. Compared to horizontal handoff, vertical handoff is defined as a handoff between base stations (BSs) that are using different wireless access technologies, such as WLAN and 3G cellular networks[3]. The traditional horizontal handoff research is mainly based on the evaluation of received signal strength (RSS) for the mobile host (MH). However, in the case of vertical handoff, RSS evaluations and comparisons are insufficient for making an optimized vertical handoff decision. Many Received date: 2009-11-26 Foundation item: the National Natural Science Foundation of China (Nos. 60832009, 60872017 and 60772100) ∗E-mail: [email protected]

other metrics, such as service type, monetary cost, network conditions, system performance, mobile node conditions, and user preferences, should be taken into consideration[4]. Without the loss of generality, RSS, available bandwidth (BW), and monetary cost (MC) are chosen as input parameters in our fuzzy logic theory based quantitative decision algorithm (FQDA). At the same time, the grey prediction theory (GPT) takes other factors into account, such as fast fading environment. In the proposed intelligent vertical handoff scheme, we use the GPT to predict the future RSS. The GPT can tell whether and when to start a handoff. After a handoff procedure is triggered, the FQDA is implemented to quantitatively evaluate the input parameters of candidate networks.

1 Related Works In heterogeneous wireless networks, it is critical to design an efficient VHO algorithm for providing seamless service. The complicated VHO algorithm is based on the fuzzy logic system, neural network or neuro-fuzzy are valid approaches to solve VHO problems. In Ref. [5], an adaptive fuzzy based VHO algorithm was presented. It used the fuzzy logic system to decide the handoff hysteresis value of RSS determined by user speed and traffic load. It only considered the RSS as the input parameter of the VHO decision

26

mechanism. Reference [6] optimized the VHO decision mechanism through the combination of service type and RSS. At the same time, the fuzzy logic system was used to certify the cell selection. But the RSS threshold for VHO decision is fixed, and the ping-pong effect cannot be alleviated. Reference [7] provided a multi-criteria decision-making algorithm based on fuzzy theory for access network selection, but it only gave the theoretic descriptions for mobility management in heterogeneous networks. The neuro-fuzzy predictor was used to predict the RSS in cellular network and WLAN in Ref. [8]. Based on the fuzzy predictor, it proposed the fuzzy inference mechanism to determine the possibility of handoff according to the fuzzy decision algorithm. On the other hand, the system complexity has to be solved before it can be utilized widely. In Ref. [9], a fuzzy logic VHO scheme was explored, using the differential prediction and pre-decision method to start a VHO trigger. The optimized VHO decision is made by the normalized quantitative decision value, without referring to the enumerative fuzzy rule base. However, the differential prediction is not precise enough in a fast fading environment. RSS and BW as input parameters for the fuzzy logic system are not enough to make handoff decisions. The GPT can be used to predict the future RSS indicator values under a lognormal fading environment[10] . Reference [11] applied the grey prediction technique for RSS by the algorithm based on relative signal strength with threshold and hysteresis in horizontal handoff procedure, but not considering the heterogeneous wireless communication environment. And RSS was the only parameter to make handoff decision. In this paper, GPT is implemented to make an accurate handoff trigger and reduce the call-dropping probability. We use fuzzy theory to quantitatively evaluate three input parameters RSS, BW and MC in the FQDA. The advantage of the FQDA over traditional fuzzy VHO algorithms will be presented in Section 3.

2 Grey Prediction Theory In order to reduce the call-dropping probability in VHO, the GPT is implemented to get the predicted RSS (PRSS). The PRSS is used to decide whether and when to start a VHO. If and only if PRSS is lower than the threshold RSS value of the current network, the VHO procedure will be triggered in a fast fading environment. The GPT can achieve precise predicted results with only a few data available. It also yields satisfactory predicted results even if the input data are interfered under random noise. Both of the advantages are very desirable in wireless communication systems. Using a few sampled RSSs (here we take n=4, n is the length of sequence) stored in database of MH, PRSS can be deduced by the GPT. As the input sample size

J. Shanghai Jiaotong Univ. (Sci.), 2012, 17(1): 25-30

is only 4, it takes less time to get the predicted result. The GPT is developed as follows. 2.1 Development of the GM(1, 1) Model One of the most widely used grey prediction models is the GM(1, 1) model. We define the sequence X (0) = {x(0) (1), x(0) (2), · · · , x(0) (n)} to be the n sampled RSSs stored at MH. Since the n original data are random and they contain noise, further processing has to be applied to them. The accumulated generating operation (AGO) is employed to process the original data. x(1) (k) =

k

x(0) (i),

k = 1, 2, · · · , n,

(1)

i=1

where x(1) (k) is called the first order AGO sequence. Then, we use the linear dynamic model to approximate the first order AGO sequence[12] . x(0) (k) + ax(1) (k) = b, where, a and b are the to-be-decided coefficients of the differential equation, whose solution is given by the following equation; a is also named as developed parameter while b is named as grey input. b −an b x(1) (n + 1) = x(0) (1) − e + . (2) a a The coefficients a and b can be decided by the least square approximation. The vector representation is given by c = [a b]T = (B T B)−1 B T γn ,

(3)

where, the value of B and γn are determined by ⎤ ⎡ −0.5[x(1) (1) + x(1) (2)] 1 ⎥ ⎢ ⎢ −0.5[x(1) (2) + x(1) (3)] 1 ⎥ ⎥ ⎢ B=⎢ ⎥, .. ⎥ ⎢ . ⎦ ⎣ −0.5[x(1) (n − 1) + x(1) (n)] 1 γn = [x(0) (2) x(0) (3) · · · x(0) (n)]T . Therefore, the predictive value can be achieved by b −an x ˆ(0) (n + 1) = x(0) (1) − e (1 − ea ), (4) a and PRSS=ˆ x(0) (n + 1). 2.2 Improving the Precision of Prediction by Time Sequence Error Mode The time sequence error[12] can be defined as εt = tε − t,

(5)

where t is the time sequence corresponding to the original sampled data, and tε = 1 +

1 x(0) (1) − b/a ln . a x(1) (t) − b/a


27

Thus, time error sequence can be achieved by (0)

εt

(0)

(0)

(0)

= {εt (1), εt (2), · · · , εt (n)}.

(6)

Being similar to the development of the GM(1, 1) (0) model of X (0) , the GM(1, 1) model of εt can be (0) developed. The predicted value εˆt (n + 1) can be (0) achieved. Inserting εˆt (n + 1) into Eq. (5), the time tˆε can be obtained, which corresponds to the (n+1)th to-be-sampled data. Finally, inserting n = tˆε into Eq. (4), then the improved predicted value x ˆ(0) (n+1) is obtained.

(such as RSS= Q in Fig. 1) or two (such as RSS= P in Fig. 1) of the 5 fuzzy sets resulting in corresponding membership degrees μRSS . For example, when the ini put value is RSS= P , the membership degree of P is [0 0 0 0.8 0.3]. RSS i

1.0 0.8

L

M

H

VH

0.3 0 RSS-THi M1

3 Fuzzy Logic Theory Based Quantitative Decision Algorithm There are 3 sub-procedures in the proposed FQDA: fuzzification, quantitative evaluation, and quantitative decision. The 3 input parameters are restated: RSS at MH from candidate networks, BW and MC of candidate networks. The advantage of the FQDA over traditional fuzzy handoff algorithms can be summarized as follows. (1) The FQDA is able to get the quantitative decision value (QDV) of a certain candidate network. The QDV tells the probability that the certain network becomes the target one to handoff. There is no need to establish a database to store the enumerative handoff rule bases, which may occupy a large memory of MH. For example, if 5 fuzzy sets are established for each of the 3 input parameters, the number of cases in rule bases will be 53 =125. (2) The final VHO decision is solely based on the comparison of QDVs of the candidate networks. There is no need to search within the rule bases, which may take a relatively longer time. (3) The FQDA realizes better distinguishability among candidate networks. Since more and more wireless networks will become available in a heterogeneous environment, it is possible that the rule bases established by fuzzy theory may be too obscure to evaluate and select out the optimized candidate network. The proposed FQDA with better distinguishability can solve this problem. 3.1 Fuzzification of Input Parameters We define THi to be the minimal keepalive-value of input factors in a certain candidate network i, and MAXi to be the maximal available value of input factors that is provided by a candidate network. Here, RSS is taken as an example to describe the membership function as shown in Fig. 1. For the fuzzy variable RSS, we take RSS-THi and RSS-MAXi as the minimal and the maximal RSS value of the candidate network i. It has the fuzzy set: VL (very low), L (low), M (medium), H (high), VH (very high). If the real time measurement of RSS in a candidate network is fed into its membership function, it can be classified into one

VL

Fig. 1

M2

M3 P

RSS M4 Q RSS-MAXi

Membership function of RSS

In heterogeneous network environments, the constants THi , M1 , M2 , M3 , M4 can be specified with different values according to the specific characteristics of the certain type of network. We give the membership function presentations of the three fuzzy variables: RSS RSS [μRSS μRSS μRSS i-VL μi-L i-M μi-H i-VH ], BW BW BW BW [μBW i-VL μi-L μi-M μi-H μi-VH ], MC MC MC MC [μMC i-VL μi-L μi-M μi-H μi-VH ].

Moreover, in order to quantitatively evaluate the input factors, a specific quantitative evaluation index has to be assigned to each fuzzy set. Quantitative evaluation indexes of the five fuzzy sets can be described as [QVL QL QM QH QVH ], which can be specified with different values according to the specific characteristics of the certain network type in heterogeneous network environment. It is pointed out that, the quantitative evaluation indexes of the five fuzzy sets of MC are different from the other two. The lower the MC is, the more desirable the network is on behalf of the users. In order to simplify the complexity of VHO decision process, we fix the set indexes as RSS [QRSS QRSS QRSS QRSS VL QL M H VH ] = [0 0.25 0.5 0.75 1],

[QBW VL

QBW L

QBW M

QBW H

QBW VH ]

=

[0 0.25 0.5 0.75 1], [QMC VL

QMC L

QMC M

QMC H

[1 0.75 0.5 0.25 0]. 3.2

(7) (8)

QMC VH ]

= (9)

Quantitative Evaluation (QEV) of Input Parameters Based on the membership degrees and quantitative evaluation indexes for each input metric, the QEVs of RSS, BW, and MC for a candidate network i can be

28


weights can be defined as

calculated by QEVRSS = [QRSS QRSS QRSS QRSS QRSS VL L M H VH ]× i RSS RSS RSS T [μRSS μRSS i-VL μi-L i-M μi-H μi-VH ] ,

(10)

BW = [QBW QBW QBW VL QL M H BW BW BW BW T μi-L μi-M μi-H μi-VH ] , MC = [QMC QMC QMC VL QL M H MC MC MC T μMC μ μ μ i-L i-M i-H i-VH ] .

QBW VH ]× (11) QMC VH ]× (12)

3.3

Vertical Handoff Decision Making Using QDVs After the calculation of QEVRSS , QEVBW , and i i MC QEVi in a candidate network i, the final QDVi of network i should be achieved by integrating the three QEVs. In order to optimize the handoff decision, each metric weight of a candidate network should be adjusted to reflect this metric priority relative to the other attributes in different candidate networks. Thus, the weights should dynamically reflect the importance and relationships of the continuously changing QEVs under unpredictable wireless environments, and should magnify the dominant-difference among candidate networks. We modify the dynamic weights as follows. Suppose that the QEVs (E) of RSS, BW, and C of the n candidate networks have been calculated and organized as E RSS = [QEVRSS QEVRSS · · · QEVRSS 1 2 n ],

(13)

E BW = [QEVBW QEVBW · · · QEVBW 1 2 n ],

(14)

E MC = [QEVMC QEVMC · · · QEVMC 1 2 n ].

(15)

Taking the QEVs of RSS of the n candidate networks RSS for example, we define QEV and σ RSS as follows. RSS

=

σ RSS

=

1 QEVRSS , i n i=1

1 n−1

n

φBW , Φ

wMC =

φMC , (19) Φ

QDVi = wRSS QEVRSS +wBW QEVBW +wMC QEVMC . i i i (20) By comparing the QDVs of the n candidate networks, the candidate network with the largest QDV can be selected. This is the final target network to handoff.

4 Simulation Results 4.1 Simulation of GPT As shown in Fig. 2, we predict the RSS in WLAN as an example. The continuous curve represents the actual sampled RSSs and the discrete dots represent the predicted RSSs. It is shown that the GPT based predictive dots matches the actual sampled RSS curve very well. The GPT is accurate to predict the RSS trend of wireless communication in future time. 4.2 Simulation of FQDA The simulation model of the FQDA is a certain mobile telephone system as shown in Fig. 3, in which the 0 _

20

_

40

_

60

_

80

_

100

_

n

QEV

wBW =

where Φ = φRSS + φBW + φMC . For a certain candidate network i, the QDV of it can be calculated as

RSS/(dB·m)

QEVBW i BW [μi-VL QEVMC i [μMC i-VL

φRSS , Φ

wRSS =

120

(16)

_

140

_ RSS 2 ) ,

(QEVRSS − QEV i

160 0

(17)

20

Fig. 2

i=1

40 60 80 Predicted samples

100

120

Simulation of GPT

RSS

where QEV is the average of the QEVs of RSS. It reflects the importance of the factor RSS. The smaller RSS QEV is, the more important the factor RSS is, and thus the larger the weight of RSS should be assigned. And σ RSS is the standard deviation of the QEVs of RSS. The larger σ RSS is, the larger the weight of RSS should be assigned. Therefore, the key factor of RSS φRSS can be defined as RSS

φRSS = exp(−QEV

+ σ RSS ). BW

A M BS1

AP1 O

AP2 P Q AP3

B BS2

(18)

AP—Access point

MC

The key factors of BW and MC (φ , φ ) can be defined in a similar way. Finally, the desired dynamic

Fig. 3

Mobile model in heterogeneous networks


29

where Pt is the transmitted power and Pl is the path loss at distance d. It is assumed that the MC of a traffic unit for a certain candidate network is assumed to be constant, and the cost of cellular networks is much higher than that of WLAN cells. With the movement of MH in the heterogeneous wireless networks, the changing curves of available bandwidths from the candidate networks along the path from A to B are shown in Fig. 4, which are varying with the changing of the MH location. Table 1 shows the simulation parameters. After the calculation of the FQDA according to the proposed VHO procedure, the QEVs and QDVs of the current and candidate networks at the provided scenarios are shown in Table 2. And the target network at each handoff location is also pointed out. The locations where handoffs have occurred are shown in Fig. 5, where 0 of the vertical axis indicates there is no handoff and 1 indicates one handoff happens. It has to be pointed out that the handoffs that occurred at location O and Q are vertical handoffs; however, the handoff occurred at location P is a horizontal handoff. Here, we present the results of our proposed algorithm (GPT-VHO) for the HO number under different conditions compared to the other two VHO algorithms, namely the basic fuzzy-logic VHO (BF-VHO)[6] and the differential prediction based VHO (DP-VHO)[9] . This simulation has been done 5 times and the average was considered. The HO number in different velocities is

Table 1 Parameter Cellular network

Radius of P /W each cell/km t

MC

0.80 BS1

0.75 0.70

AP3

0.65 BW

RSS(d) = Pt − Pl (d),

shown in Fig. 6. Due to the precise prediction of the GPT and the dynamic weights of multiple metrics, the HO number is less than the other two. It is worth mentioning that the VHO number of GPT-VHO is hardly affected by the MH velocity, so the proposed algorithm is able to be applied at high mobility in fast fading environments.

0.60

BS2

AP2

0.55 0.50

AP1

0.45 0.40 0.35 0

Fig. 4

0.5

1.0 1.5 2.0 2.5 3.0 3.5 Distance from location A/km

4.0

The curve of bandwidth changing in candidate networks O

1

P Q

HO occurence

cells of WLAN and cellular network coexist in the same service area. The channel propagation model used for RSS at distance d is given by Ref. [6]:

O

Fig. 5

0.5

1.0 1.5 2.0 2.5 3.0 3.5 Distance from location A/km

4.0

The locations where handoffs have occurred

Simulation parameters

RSS-THi /(dB·m) RSS-MAXi /(dB·m) BW-THi BW-MAXi MC-THi MC-MAXi

1.0

1.0

(0.8/0.8)

−125

−105

0.2

0.9

0.1

1

0.1

0.1

(0.5/0.4/0.5)

−105

−85

0.2

0.9

0.1

1

(BS1 /BS2 ) WLAN (AP1 /AP2 /AP3 )

Table 2 Location O

P

Q

QEVs and QDVs of candidate networks

Networks

ERSS

EBW

EMC

QDV

Target AP2

BS1 (Current)

0.000

0.660

0.430

0.304

BS2 (Candidate)

0.310

0.533

0.400

0.396

AP2 (Candidate)

0.392

0.580

0.380

0.437

AP2 (Current)

0.000

0.610

0.420

0.311

BS2 (Candidate)

0.508

0.376

0.400

0.389

AP3 (Candidate)

0.760

0.697

0.900

0.786

AP3 (Current)

0.000

0.620

0.280

0.236

BS2 (Candidate)

0.860

0.480

0.830

0.760

AP3

BS2

J. Shanghai Jiaotong Univ. (Sci.), 2012, 17(1): 25-30 Average handoff number

30 8

parameters to see if these changes can affect the service quality in wireless communication networks. For the space constraint, the performance evaluations of handoff delay and system load will not appear in this paper. We will discuss these problems further in other articles.

GPT-VHO DP-VHO BF-VHO

7 6 5 4 3 2 1 0

Fig. 6

References 5

10

15 20 25 30 _ Velocity/(m·s 1)

35

40

The average HO number versus velocity

1.1

_

Throughput/(Mb·s 1)

Throughputs of the three algorithms are compared when MH is moving from location A to location B which is 1 km at the speed of 10 m/s. It is assumed the data rates of cellular network and WLAN are 384 kb/s and 1 Mb/s, respectively. As shown in Fig. 7, the throughputs get the peak value in WLAN cells of AP1 , AP2 and AP3 . And GPT-VHO has the timely and accurate handoff to get the most throughput, which is the area under the curve.

0.9 GPT-VHO DP-VHO BF-VHO

0.7 0.5 0.3 0

Fig. 7

20

40 60 80 Average connection time/s

100

The throughputs comparison versus connection time

5 Conclusion This paper proposes a novel vertical handoff algorithm across heterogeneous wireless networks. Grey theory based prediction algorithm is used to get the predicted RSS, which can tell when to start a handoff. It can effectively reduce Ping-Pong effect. Fuzzy theory based FQDA is applied to each of the candidate networks. The final optimized handoff decision can be made based on the resulted QDVs. The FQDA saves the memory to store the rule bases, saves the time to search within the rule bases. The simulation results show that the proposed vertical handoff scheme provides high performance in generic handoff scenarios of both heterogeneous and homogeneous network environments. The handoff scheme can be carried out easily through a simple intelligent software controller without complicated system hardware. Our proposed vertical handoff algorithm is not taking service types, security and other network factors into account. We plan to choose different factors as input

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