QoS Aware Fuzzy Rule Based Vertical Handoff Decision

Algorithm for Wireless Heterogeneous Networks

K. Vasu, Sumit Maheshwari, Sudipta Mahapatra, C. S. Kumar* Department of Electronics and Electrical Communication Engineering

*Department of Mechanical Engineering Indian Institute of Technology Kharagpur, India

Abstract— Increasing demands of ubiquitous services in wireless networks and evolution towards heterogeneous solutions has driven the necessity of developing efficient QoS aware vertical handoff (handoff between different networks) mechanisms. This paper proposes a QoS aware fuzzy rule based vertical handoff mechanism that makes a multi-criteria based decision, found to be effective for meeting the requirements of different applications. The QoS parameters considered are available bandwidth, end-to-end delay, jitter, and bit error rate (BER). A new evaluation model is proposed using a non birth–death Markov chain, in which the states correspond to the available networks. Simulation results show that compared to other vertical handoff algorithms, the proposed algorithm gives better performance for different traffic classes.

Keywords: Heterogeneous networks, Vertical handoff, Fuzzy Rule Based algorithm, Traffic Classes, QoS parameters.

I. INTRODUCTION

Next Generation heterogeneous networks require seamless mobility among the different access networks while maintaining the Quality of Service (QoS) for applications like high-speed data services, video, and multimedia applications. To meet different QoS requirements for different traffic classes while maintaining fair and high utilization of wireless resources at the same time, it is necessary to employ efficient mobility management strategies. This is achieved with a good vertical handoff mechanism in heterogeneous wireless networks. In [1], the authors have proposed a new handoff scheme for reducing handoff delay using the concepts of Received Signal Strength and threshold management. In order to reduce the total interference in CDMA, a vertical handoff decision algorithm among the CDMA networks and wireless Local Area Networks (WLANs) is proposed in [3]. Vertical handoff mechanisms involve three different phases of operations: system discovery, handoff decision process and handoff execution. In system discovery phase, the system may periodically monitor for a better network to which a mobile can be handed over. The handoff decision phase is

very crucial in vertical handoff using available information. There are several algorithms proposed for implementing the vertical handoff decision process. A comparison between different vertical handoff algorithms has been presented in [2]. The handoff decision may depend on various parameters including available bandwidth, bit error rate, jitter, average battery lifetime, access cost, transmit power, and end-to-end delay. A combination of some of the criteria can be considered for making a decision in handoff [4]. A multi-criteria based handoff has a greater advantage for achieving good performance in heterogeneous wireless networks. Multi-criteria based approach for hand off decision using the fuzzy inference system (FIS) and a modified Elman neural network (MENN) is proposed in [5]. FIS considers bandwidth, velocity, and number of users as decision parameters. Unlike horizontal handoff in homogeneous networks, vertical handoff (VHO) is not reversible, i.e., the handoff from UMTS to WLAN differs from that of WLAN to UMTS [5]. Fuzzy control theory based vertical handoff decision procedure presented in [6] considers three main input parameters, namely received signal strength, cost, and bandwidth. These parameters are dynamically evaluated and computed to achieve optimal handover. By addressing the knowledge about context of mobile devices, users, and networks, a context-aware handover decision, based on user perceived quality of service trigger, is presented in [7]. However, the context information used here is not enough to maintain the required QoS for various kinds of applications, which might have widely different QoS requirements in terms of data rates, delay bounds, and bit error rates. For example, unlike non real-time data packets, video services are very sensitive to packet delivery delay, but can tolerate some frame losses and transmission errors. In wireless networks, the scenario is changing from best effort to QoS aware networks.

The various handoff strategies used for executing handoff may in general be classified into: Mobile-Controlled Handoff (MCHO), Network-Controlled Handoff (NCHO), and Mobile-Assisted Handoff (MAHO). In MCHO, the mobile node continuously monitors the signal of access points and initiates the handoff procedure when some handoff criteria

are satisfied. NCHO is a centralized handwhich network makes handoff decismeasurements of the signal quality of mobilea number of base stations (BS). In MAHOmeasures the signal strength of surrounding bthen decides whether or not to initiate the hanMCHO has a low complexity in terms of netHowever, latency and loss of large number ointer-subnet handoff can be high. Handoff deis made by the network for coordination amoand global optimization. However, in heterogaccess networks only the mobile nodes havabout the kind of interfaces they are equippnetwork dependency on the mobile node is MCHO with some assistance from the nesuited for implementing vertical handoff.

Analytical modeling of the blocking and probability in wireless cellular networks proposed in [8], where performance and avare developed. In that model authors considvirtual channels as the state parameter for wIn [9] & [10], a similar approach is considhandoff in wireless networks, where autperformance, availability and performabilityof performance and reliability) model. between various vertical handoff decisionheterogeneous wireless networks, authors Markov model with birth-death process.

The QoS aware fuzzy rule based hanproposed in this paper assumes MCHO and from the network, where a mobile will perithe available networks and using a fuzzy ruleit determines the best network. This infocommunicated to the current network fohandoff. The evaluation model used in the simbirth-death Markov chain where a statenetworks available at any time. The rest organized as follows: the proposed algorithmSection II, followed by theoretical evaluationmodel in Section III. Section IV containsresults and Section V concludes this paper.

II. QoS AWARE FUZZY RULE BASHANDOFF DECISION ALGORITHM

Although, the authors in [5] consinference system based handoff decision wparameters are bandwidth, velocity, and nummethod did not consider various QoS servicdifferent traffic classes as per IMT-2000 Q[14]. The fuzzy rule based algorithm proposefour QoS parameters namely available bandwdelay, jitter and bit error rate (BER). In

doff protocol, in ion based on e station (MS) at

O, a mobile node base stations and ndoff procedures. twork equipment. of packets during ecision in MAHO ong mobile nodes geneous wireless

ve the knowledge ped with; so the high. Therefore,

etworks is better

packet dropping with handoff is ailability models dered number of

wireless networks. dered for vertical thors propose a y (a combination For comparison

n algorithms for in [2] used a

ndoff mechanism some assistance iodically monitor e based algorithm ormation is then or executing the mulation is a non

e represents the of the paper is

m is presented in n of the proposed s the simulation

ED VERTICAL

sidered a fuzzy where the input

mber of users, the ce parameters for QoS classes [13], ed here considers width, end-to-end n heterogeneous

networks, the vertical handoff betwto be more efficient while maintainfor different types of traffic classeTherefore, selecting the best netwnetworks is always an important tarule based algorithm that gives network to be selected, is depicted i

In the proposed QoS aware futhe network types assumed are an network, and a WLAN. The pararates for bandwidth vector, and typfor these networks are based on Bandwidth vector for UMTS: [32,2048] kbps, for GPRS: [21, 42, 64kbps and for WLAN: [1000, 2000,to-end delay (E2EDelay) vector respectively [190, 160, 130, 100, 70135, 110, 85, 60, 35, 10]msec, andAll networks have the same set ovectors. The values for jitter and bi11]msec, and [0.01, 0.001, 0.00respectively. The membership funparameters are considered as triangdifferent regions: low, medium andThe output membership function triangular function as shown in the f

Fig. 1: Fuzzy Rule Based H

Fig. 2: Fuzzy member

ween networks is required ning the QoS requirements es even after the handoff.

work among the available ask. The QoS aware fuzzy a decision regarding the in Fig. 1.

uzzy rule based algorithm, UMTS network, a GPRS

ameters like possible data pical delay values assumed

the standard values [2]. , 64, 128, 256, 512, 1024, 4, 85, 107, 128, 149, 171] , 5500, 11000] kbps; End-

for these networks are 0, 40, 10]msec, [185, 160, d [160, 110, 60, 10]msec. of jitter and bit error rate it error rate are [3, 5, 7, 9, 001, 0.00001, 0.000001] nctions for different input gular functions with three d high as shown in Fig. 2. is also assumed to be a

figure.

Handoff Mechanism

rship functions

The fuzzy rules are made as per the requirQoS classes [13, 14]. Eighty one rules aretraffic class (for four input parameters and thregions, 81 possible combinations of rules ceach traffic class). Due to space limitation rules for each traffic class in Table I. The system is as follows: IF (a set of conditionTHEN (a set of consequents) [12, 15]. handoff score value, which can be drawn fromindividual consequents of each rule, and irange of 0 to 100 with triangular membersthree regions as shown in Fig. 2. The Centroifor defuzzification [12]. When handoff is recalculates the handoff score value for all avwith a set of input parameters by using the scheme proposed above, and selects the information about the best network is then cthe current network to execute the handoff.

TABLE I. RULE BASE FOR EACH TRAFFI

Conversational

Rule No.

BER E2EDelay Jitter Band

1 Low Low Low Lo25 Low High High Lo50 Medium High Medium Med81 High High High Hi

Streaming Rule No.

BER E2EDelay Jitter Band

1 Low Low Low Lo25 Low High High Lo50 Medium High Medium Med81 High High High Hi

Interactive Rule No.

BER E2EDelay Jitter Band

1 Low Low Low Lo25 Low High High Lo50 Medium High Medium Med81 High High High Hi

Background Rule No.

BER E2EDelay Jitter Band

1 Low Low Low Lo25 Low High High Lo50 Medium High Medium Med81 High High High Hi

III. EVALUATION MODEL

In our model, we consider a non birth-deawhere a state corresponds to the available neta state to represent the unavailability of anconnection lifetimes of all states are independent identically distributed (iid) ra

rements of 3GPP e made for each hree membership can be made for we show only 4 fuzzy rule based ns) are satisfied The output is a m a rule base and s defined in the

ship functions of id method is used equired, a mobile vailable networks fuzzy rule based

best one. The communicated to

IC CLASS

dwidth Handoff score

ow High ow Low dium Low igh low

dwidth Handoff score

ow low ow Low dium High igh Medium

dwidth Handoff score

ow Medium ow Low dium Low igh Low

dwidth Handoff score

ow Medium ow Medium dium Medium igh Medium

L

ath Markov chain tworks, including ny network. The assumed to be

andom variables.

The primary reason for consideringchain is that a mobile device can network or more networks and go state as depicted in Fig. 3.

In each state, the best network available networks and connectionnetwork. The connection life time ofollow an exponential distribution, lifetime (µ) is varied from 1 to 10 malso assumed to follow an exponentequal to λi (where i is 1 for UMTWLAN). For UMTS and GPRS ne4min. whereas for WLAN networksdifferent networks are considered inSection 2. Thus, there are eight networks available to the mobile aThe Markov chain is shown in Figfor the markov chain is obtained bconditions [11]. State changes eMarkov chain with the adaptive statgenerator matrix G and state transitwhere λ equals λi for network i basefrom the previous state. (Where i is and 3 for WLAN)

6

2 53 4

μ λ λ λμ λ μ λ λμ μ λ μ λμ μ μ λ μμ μ μ μμ μ μ μμ μ μ μμ μ μ μ

−⎡⎢ − −⎢⎢ − −⎢ − −⎢⎢⎢⎢⎢⎢⎢⎣

7

T = (G/q) +I Where q>max (|Gij|), 0 ≤ i, j ≤ 7, Isize 8×8.

State 0 = {no network}; StaState 2 = {GPRS}; State 3

State 4 = {UMTS, GPRS}; State State 6 = {UMTS, WLAN}; State 7 =

We considered four traffic classwhich are conversational, stre

Fig 3: Markov ch

G =

a non birth-death Markov be in a region having no from a state to any other

will be selected from the ns will be assigned to that of the mobile is assumed to where average connection

min. State changing time is tial distribution with mean S, 2 for GPRS, and 3 for etworks, we use λ1 = λ2 = s, we use λ3 = 1min. Three n our model as specified in possible combinations of

at any instant of the time. g. 3. The generator matrix by solving the equilibrium evolve according to the te transition matrix T. The tion matrix T is as follows ed on the network selected 1 for UMTS, 2 for GPRS,

4 35 2

67

λ λ λ λλ λ λ λλ λ λ λ

μ λ λ λ λλ μ λ λ λ

μ λ μ λ λμ μ λ μ λμ μ μ λ

⎤⎥⎥⎥⎥⎥⎥− −⎥

− − ⎥⎥− −⎥

− ⎥⎦

I is the identity matrix of

ate 1 = {UMTS}; 3 = {WLAN}; 5 = {GPRS, WLAN}; {UMTS, GPRS, WLAN};

ses defined by 3GPP [14], eaming, interactive and

hain

background. Each traffic class is associated with different QoS parameters. The QoS parameters considered are available bandwidth, end-to-end delay (E2E delay), bit error rate (BER), and jitter, with the corresponding importance weight for each traffic class computed using the Analytical Hierarchical Processing (AHP), are shown in TABLE II [2]. We compare three existing vertical handoff algorithms: SAW (Simple Additive Weight), TOPSIS (Techniques for Order Preference by Similarity to Ideal Solution) and MEW (Multiplicative Exponent Weighting) with our fuzzy rule based algorithm. The results are presented in Section IV.

TABLE II. IMPORTANCE WEIGHTS OF EACH QOS PARAMETERS

FOR DIFFERENT TRAFFIC CLASSES

Traffic Class BER E2EDelay Jitter Bandwidth Conversational 0.04998 0.45002 0.45002 0.04998 Streaming 0.03737 0.11380 0.42441 0.42441 Interactive 0.63593 0.16051 0.04304 0.16051 Background 0.66932 0.05546 0.05546 0.21976

IV. RESULTS

The simulation is carried out using Matlab and results are

plotted in Fig. 4. For conversational, streaming, and interactive traffic class, average end-to-end delay and availability are obtained for various vertical handoff algorithms. Availability is defined as one minus the probability of the mobile being in the zero state. The mean value is obtained by averaging over 10000 connections. No network condition is assumed with bandwidth of zero, delay of 500msec, BER and Jitter of zero values. It is likely that in heterogeneous wireless networks, numerous types of access networks will prevail to support wireless services that have varied QoS requirements. From the requirements of IMT2000 QoS classes [13, 14], conversational, streaming and interactive traffic classes expect less delay. Applications like conversational, interactive video conferencing and live streaming require more network availability, less E2EDelay with tolerable bandwidth. Results obtained using the proposed technique show better performance for E2EDelay, availability of the network. The Average available bandwidth is also obtained for Streaming and background classes. The available bandwidth performance is moderate while satisfying the E2EDelay and availability requirements. Rules can be more properly tuned to get even better performance in available bandwidth. Fuzzy rule based approach based on awareness of QoS requirements of the traffic classes makes clear decision to do the handoff among the networks, which uses the fuzzy membership functions defined in the Fig. 2. The fuzzy membership regions helps to make a clear distinction among the parameter values of the network and fuzzy rule base will be used to infer the result of handoff score value. SAW, TOPSIS, and MEW mechanisms follow a simple additive or multiplicative approach with weighted parameter set based on the QoS requirements of various

traffic classes. These techniques loose the intelligence to make decision of handoff among the networks while satisfying QoS. The assumption of available networks as a state in heterogeneous network environment is also more realistic.

V. CONCLUSIONS

In this paper, we considered two sets of networks for simulation. The paper proposes QoS aware fuzzy rule based algorithm with multi-criteria of bandwidth, delay, jitter and bit error rate by considering different traffic classes. The paper also proposes a new evaluation model using a Markov chain with state parameters corresponding to the available networks. Simulation results show that fuzzy rule based approach gives better delay, availability, with moderate performance of available bandwidth. Hence, QoS aware fuzzy rule based algorithm gives good QoS performance for delay sensitive applications like conversational, interactive and live streaming applications. The evaluation model using non birth-death markov chain with available networks as a state can be used for comparing vertical handoff mechanisms. Our future work will consider minimal fuzzy rule set based vertical handoff algorithms for heterogeneous wireless networks.

ACKNOWLEDGEMENT

This work was carried out under the Vodafone Essar sponsored research project at IIT Kharagpur, India.

REFERENCES

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[13] 3GPP, “IMT -2000 QoS classes”, TSG-SA #17 Meeting September 2002. [14] 3GPP, “QoS concepts and architecture”, TS 23.107 v9.0.0 (2009-12). [15] Asli Celikyilmaz and I. Burhan Turksen, “Modeling Uncertainty with Fuzzy Logic with Recent Theory and Applications” , Springer 2009, ISBN 978-3-540-89923-5. [16] http://www.radioelectronics.com/info/cellulartelecomms/evdo/ev-do.php

Fig. 4: (a) Avg. E2EDelay (b) Availability (c) Avg. Available Bandwidth

(a)

(b)

(c)