Bandwidth based PHO algorithm for B4G heterogeneous wireless networks

(1)

BANDWIDTH BASED PHO ALGORITHM FOR B4G

HETEROGENEOUS WIRELESS NETWORKS

S. Neeraja and A. Abhishiktha

Department of Electronics and Communication Engineering, GIT, GITAM (Deemed to be University), Visakhapatnam, India E-Mail: [email protected]

ABSTRACT

In wireless communications, accessing the world wide information by the end user is being a challenge due to seamless mobility among different networks. In order to provide wireless access for the end user moving from one network to another network handover between the networks is very much essential. Bandwidth is the one of most important metric used for analyzing the handover. In this paper, the bandwidth based probability of handover (PHO) algorithm has been implemented for Beyond Fourth Generation (B4G) heterogeneous wireless networks. The probability of handover relies on the traffic load, bandwidth and threshold. Probability of handover analysis has been done for different configurations such as the variation of probability of handover with respect to traffic load, threshold and bandwidth. It is noticed from the results that with increase in threshold, the probability of handover is reduced for constant traffic load. It is also observed that even though network traffic load increases, the probability of handover does not vary much for the equal bandwidth network configuration.

Keywords: probability of handover (PHO), beyond fourth generation (B4G), bandwidth, traffic load.

1. INTRODUCTION

The use of wireless communication services is increasing significantly from day by day. Currently, there exist different wireless systems, GSM/GPRS, UMTS, CDMA2000 as cellular networks with 2nd and 3rd generations (2G & 3G), WiMAX and LTE (4G) for wide areas, WLANs for metropolitan area networks, Bluetooth and Ultra Wideband Radio (UWB) for personal area networks and satellite networks [1]. These heterogeneous wireless access technologies can be integrated due to supportive characteristics of different wireless networks [2]. The heterogeneous networks will cooperate with each other with overlapping coverage which allows the user to use the best available access network and device at any point of time. In order to achieve this many research issues, have to be solved. Then, users benefited with lower cost and overall performance of the integrated networks.

In B4G networks, the upcoming mobile devices will initiate the demanding issue of mobility aid among various networks. Users will extend their relation only when there is no disturbance while moving from one network to another which is handover or handoff [3] [4]. In the past, this process has done between mobile nodes of a cellular network based on Received Signal Strength (RSS) when it is less than the predefined threshold value for particular mobile node which gives only the serviceability of the network but not bandwidth which is a powerful parameter for the upper-layer applications [5] [6]. The B4G heterogeneous wireless networks deals with the upper-link layer parameters. Particularly, handover process between networks using various access technologies is defined as vertical handover (VHO). VHO decision is based on the RSS, link layer parameters such as, bandwidth, delay and Quality of Service (QoS) [7] [8] [9].

The objective of this paper is to present a

mathematical model for probability of handover (PHO) for five wireless networks is introduced and the simulation results for traffic load vs. PHO at diverse threshold levels and threshold vs. PHO at diverse traffic loads and bandwidth vs. PHO at diverse traffic loads are presented. The PHO is used to find the Wrong Decision Probability of Handover (WDPHO) it is the combination of probability of missing handover and probability of unnecessary handover.

2. DEVELOPMENT OF HANDOVER MATHEMATICAL MODEL FOR FIVE HETEROGENEOUS WIRELESS NETWORKS

Consider, NWi heterogeneous wireless networks, Wi is maximum bandwidth of the networks, wi is the available bandwidth of the network, where i is an integer ranges between one to five. Handover mathematical model for five wireless networks is developed based on five state Markov design as shown in Figure-1.

Figure-1. Handoff between five heterogeneous wireless Z4

NW1

g

h

d a

e

n

c

b

f

m

p

r

t

v

l

k

y

s

u

q

NW5

NW2 NW3 NW4

Z3

Z2 Z

(2)

Let PNWjNWi_{be the probability of mobile node} shifting from network NWi to NWj at time t.

NW NWi i

P _{be the probability of mobile node remaining at}

network NWi at time t.

M/M/Wi process model is represents a procedure

for change of bandwidth of network i where 1 ≤ i ≤ 5. The

arrival rate (δi, where i is 1 ≤ i ≤ 5.) follows a Poisson

distribution which is the rate at which the requests are

arrived to the network. The service rate (ƞiwhere i is 1 ≤ i

≤ 5) is the rate at which channel is cleared follows an

exponential distribution. Πi,x represents the probability of

occupied channels (x) where, x= 0,1,….. Wi. The

following mathematical equations are hold from queuing theory result:

i i,xx i, xW



₍₁₎ otherwise , 0  x i i,x i,0



i x!,0 x W 

 

₍₂₎ Where,



is traffic load

i W i,0 x i x 0 1

,i 1, 2,3, 4,5 x!   





(3)

In order to find the mobile node moving between different nodes by using five state Markov design and the variables p, a, m and y represent the probability of mobile node switching from NW1 to NW2, NW3, NW4 and NW5 respectively. Similarly, for the remaining nodes with different probabilities. The probabilities (1-p-a-m-y), (1-q-r-e-c), (1-s-b-t-g), (1-n-v-f-u) and (1-l-d-h-k) represent the probability of mobile node stay at NW1, NW2, NW3, NW4 and NW5 and are represented by PNW1, PNW2, PNW3, PNW4 and PNW5_.







PNW NW1 2PNW NW1 3PNW NW1 4PNW NW1 5



PNW1

PNW NW1 2PNW NW1 3PNW NW1 4PNW NW1 5PNW NW2 1PNW NW3 1PNW NW4 1PNW NW5 1

           y) + m + a + x + n + b + q + (p x) + n + b + (q

 ₍₄₎









PNW2

           d) + f + s + c + r + e + q + (p d) + f + s + (p 









PNW3 _P _P _P _P _P _P _P _P

NW NW3 1 NW NW3 2 NW NW3 4 NW NW3 5 NW NW1 3 NW NW2 3 NW NW4 3 NW NW5 3

           h) + o + g + t + b + s + r + (a h) + o + r + (a 









PNW4

           k) + o + f + n + z + t + e + (m z) + t + e + (m 









PNW5

P_{NW NW}₄ ₁P_{NW NW}₄ ₂P_{NW NW}₄ ₄P_{NW NW}₄ ₅P_{NW NW}₁ ₄P_{NW NW}₂ ₄P_{NW NW}₃ ₄P_{NW NW}₅ ₄

           ) z + h + d + x + k + g + c + (y k) + g + c + (y 

3. PHO ALGORITHM BASED ON BANDWIDTH

The mobile node moves from its present network when the bandwidth of the current network is less than the other networks bandwidth at threshold G.

A. Algorithm: PHO algorithm based on bandwidth. Let the available bandwidth of network be wi,

where i is an integer (1 ≤ i ≤ 5) and threshold is

represented as G.

I: Consider the mobile node is at NWi.

If wj - wi ≥ G then the mobile node moves to

NWj, i≠j, 1 ≤ i, j ≤ 5

If wi - wj≥ G, then the mobile node stays at NWi

II: Find the values of PNW NWi i_,PNWjNWi_{using the} following equations:



i j



NWiNWj P w w G ,for(i j)

P    

(5)   





5 1 , /

1

j i i

j NWj NWi NW i

NW i

P

III: Calculate the probability of handover

For each network bandwidth changes dynamically so consider the network stable state such that handover or not does not affected by the time period t.

With independent assumptions of w1, w2, w3, w4, w5 then,





i j

i j _i _j

W W

P w w G

PNW NW_i _j i, W j j, W i

i 0 j 0

i j G

        

 

  ₍₆₎

Assume, Bandwidth=Wi=W, Traffic load= ∂i= ∂

LetPNWi NWjP,ij,i j 1,2,3,4,5 Probability of handover (PHO) is:

( )

NW NW NW NW NW NW NW NW

NW1 2 1 3 1 4 1 5 1

( )

NW2 NW NW1 2 NW3NW2 NW4NW2 NW5NW2

( )

( ) 10 P

PHO P P P P P

P P P P P

                     

4. RESULTS AND DISCUSSIONS

(3)

Figure-2. Probability of Handover (PHO) vs. Traffic load for different thresholds = 11, 12, 13 at bandwidth =20MHz.

Table-1. Probability of Handover (PHO) vs. Traffic for different thresholds G = 11, 12, 13 at bandwidth =20MHz.

S. No Traffic load

()

PHO at G =11MHz

PHO at G=12 MHz

PHO at G =13 MHz

1 1 0.94337868 0.87403989 0.81272178

2 2 0.94557697 0.87742586 0.81830937

3 3 0.94776653 0.88086291 0.82409977

4 4 0.95049393 0.88525365 0.83171005

5 5 0.95418417 0.89135993 0.84262244

6 6 0.95936841 0.90018678 0.85890354

7 7 0.96680723 0.91323038 0.88377219

8 8 0.97764680 0.93282958 0.92249197

Figure-2 and Table-1 represent the variation of probability of handover with respect to traffic load for different threshold values, here the bandwidth is fixed at 20MHz. For the traffic load ()=1, threshold (G)=11MHz the probability of handover is 0.94337868 and for the traffic load ()=8, threshold (G)=11MHz the probability of handover is 0.97764680. It is noticed that the probability of handover increases with increase in traffic load.

(4)

Table-2. Probability of Handover (PHO) vs. Threshold for different Traffic load =6, 7, 8 at bandwidth =20MHz.

S. No Threshold (G) in MHz PHO At =6 PHO At =7 PHO At =8

1 11 0.89015238 0.88293084 0.87652543

2 12 0.82046912 0.81363087 0.80763668

3 13 0.75888209 0.75247922 0.74693461

4 14 0.70368231 0.69778746 0.69274606

5 15 0.65348261 0.64819465 0.64373022

Figure-3 and Table-2 describe the variation of probability of handover with respect to threshold for different traffic load values; here the bandwidth is fixed at 20MHz. For the threshold (G)=11, traffic load() = 6 the probability of handover is 0.89015238 and for the threshold (G)=15, traffic load ()=6 the probability of handover is 0.65348261. It is observed that the probability of handover decreases with increase in threshold.

Figure-4. Probability of Handover (PHO) vs. Bandwidth for different Traffic load =6,7,8 at Threshold = 11MHz.

Table-3. Probability of Handover (PHO) vs. Bandwidth for different Traffic load =6, 7, 8 at Threshold = 11MHz.

S. No Bandwidth PHO At =6 PHO At =7 PHO At =8

1 12 0.76923077 0.76923077 0.76923077

2 13 0.80079681 0.79752379 0.79500828

3 14 0.82281981 0.81785943 0.81395807

4 15 0.83984481 0.83383199 0.82898660

5 16 0.85363580 0.84696073 0.84144932

6 17 0.86510033 0.85803857 0.85207743

7 18 0.87478298 0.86753549 0.86129940

8 19 0.88304671 0.87575969 0.86938645

9 20 0.89015238 0.88293084 0.87652543

Figure-4 and Table-3 represent the variation of probability of handover with respect to bandwidth for different traffic load, here the threshold is fixed at 11. For the bandwidth =12, traffic load ()=6 the probability of handover is 0.76923077 and for the bandwidth =20MHz, traffic load ()=6 the probability of handover is 0.89015238. It is noticed that the probability of handover slightly increases with increase in bandwidth.

CONCLUSIONS

In this paper, a bandwidth based PHO algorithm five heterogeneous wireless networks has been

observed that increase in traffic load with fixed threshold there is very less variation in PHO. It is also observed that as the threshold increases the PHO reduces with constant traffic load. Further, it is noticed that the PHO slightly increases with increase in bandwidth. This PHO analysis is helpful for calculation of WDPHO of the B4G wireless networks for improving the quality of service.

ACKNOWLEDGMENT

(5)

REFERENCES

[1] Eva Gustafsson and Annika Jonsson, Ericsson Research. 2003. ALWAYS BEST CONNECTED. IEEE Wireless Communications. 10(1): 1536-1284.

[2] Chuanxiong Guo, Zihua Guo, Qian Zhang, and Wenwu Zhu. 2004. A Seamless and Proactive End-to-End Mobility Solution for Roaming Across Heterogeneous Wireless Networks. IEEE Journal on Selected Areas in Communications. 22(5): 834-848.

[3] Wei Zhao, Rahim Tafazolli, and Barry G. Evans. 1997. Internetwork Handover Performance Analysis in a GSM-Satellite Integrated Mobile Communication System. IEEE Journal on Selected Areas in Communications. 15(8): 1657-1671.

[4] Sarhan M. Musa and Nader F. Mir. 2010. Handoff Management for Mobile Agents in High Speed Wireless Networks. International Journal of Communication Networks and Information Security (IJCNIS). 2(3): 202-206.

[5] Mikael Gudmundson. 1991. Analysis of Handover Algorithms. Radio Communication Systems, IEEE, CH2944-7/91/0000/0.

[6] Sunisa Kunarak and Raungrong Suleesathira. 2010. Predictive RSS with Fuzzy Logic based Vertical Handoff Algorithm in Heterogeneous Wireless Networks. National Telecommunications Commission Fund under Grant No. PHD/004/2008, IEEE. 978-1-4244-7010-5/10.

[7] C. Chi, X. Cai, R. Hao and F. Liu. Modeling and Analysis of Handover Algorithms. IEEE GLOBECOM 2007, 1930-529X/07.

[8] Xuejun Cai, Caixia Chi. 2007. An Analytical Model for Performance Evaluation of Handover Decision Algorithms. Second International Conference on Communications and Networking in China, IEEE.