AN ALGORITHM TO ENHANCE THE NETWORK SELECTION

PERFORMANCE IN IN AN INTEGRATED UMTS/WLAN

ENVIRONMENT

Abolfazl Nazari, Naser Yazdani, Naser Rezayi

ECE department- University of Tehran, Iran

Keywords: Heterogeneous networks, UMTS, WLAN, Network selection.

Abstract: Future wireless networks will be multi-access networks, and the users can benefit from the advantages of

any radio access technology. Then, network selection can play a critical role in overall network

functionalities. Interworking between UMTS and WLAN is considered as a feasible approach toward next

generation wireless networks. In this paper, we propose a method that uses statistical parameters of a

WLAN cell traffic load to measure its capabilities to ensure QoS for the requested service. A network

selection algorithm can use this measurement and user preference to select the best radio access technology.

We investigate the performance of this algorithm through simulation of transferring video traffic in WLAN.

The results show that the proposed algorithm has an acceptable performance and in about 95 percent of the

times, a user that opt WLAN as his access network will have the requested QoS.

1 INTRODUCTION

Wireless networks have emerged, evolved, and

experienced an extensive deployment. These

networks include cellular networks like GSM,

GPRS, UMTS, and CDMA 2000 for voice and data

transmission and wireless LAN for broadband

wireless Internet access. Each network has its own

merits and most likely the next generation wireless

networks will be multiple access networks

exploiting the advantages of all existing radio access

technologies (RAT).

Interworking between UMTS and WLAN is

considered as a feasible approach toward next

generation wireless networks (Salkintzis, 2005).

UMTS can provide data rates from 384 Kbps up to 2

Mbps for wireless Internet access over a wide

geographical area. In hot-spots, where user’s

mobility is limited, we can use WLAN to provide

users with higher data rates (up to 54 Mbps) and also

benefit from low deployment cost of this network

and its license free bandwidth.

Recently 3GPP has developed two UMTS-

WLAN interworking architectures for different

usage scenarios (3GPP, 2003), (3GPP, 2005a). A

common scenario is when a UMTS subscriber can

connect to a WLAN to use Internet and other

WLAN data services. This architecture, called

“WLAN direct access”, supports transferring

authentication, authorization and accounting (AAA)

signaling between two networks. The other

architecture which is named “WLAN 3GPP IP

access” enables UMTS subscribers to access UMTS

packet switching services through WLAN.

In heterogeneous networks, where the user can

select his access technology, the network selection

algorithm can have an important effect on the

overall network performance and the user’s

experience of received QoS. An efficient algorithm

for network selection should consider the network

condition like received radio signal strength (RSS)

and network traffic load to select the best RAT for

the user.

In this paper, we focus on network selection

issue in an integrated UMTS and WLAN

environment. We offer an algorithm that measures

WLAN cells capabilities in satisfying service

requirements and user preference. In this method

rather than using instant amount of the cell’s traffic

load, we use statistical parameters of the network

traffic load like mean and variance. These

parameters are calculated in Access Point (AP) and

are broadcast in the WLAN cell. We show that while

in some situation instant amount of the traffic load

can not be used, our method have an acceptable

performance even for highly stochastic traffics like

video.

Nazari A., Yazdani N. and Rezayi N. (2006).

AN ALGORITHM TO ENHANCE THE NETWORK SELECTION PERFORMANCE IN IN AN INTEGRATED UMTS/WLAN ENVIRONMENT.

In Proceedings of the International Conference on Wireless Information Networks and Systems, pages 45-50

 SciTePress

The rest of the paper is organizes as follows. In

section II, we discuss the network selection issue in

heterogeneous networks and introduce the network

selection and advertisement scenario that 3GPP has

proposed for UMTS and WLAN interworking. In

section III, we introduce our algorithm. Simulation

results come in section IV. Finally, we conclude in

section V.

2 NETWORK SELECTION IN

HETEROGENEOUS

ENVIRONMENTS

In heterogeneous networks, where user can opt for

several access technologies, network selection

algorithm plays a vital role in insuring the quality of

the received service and efficient usage of the

network resources. An efficient network selection

technique which keeps the user Always Best

Connected (ABC) should consider user preference,

service requirement, and network condition

(Qingyang, 2005). The concept of ABC as defined

in (Gustafsson, 2003) is that the user is not only

connected but also connected through the best

device and access technology. In a network selection

procedure, the first step is collecting necessary

information about network condition, application

requirement, and user preference. Then, this

information is used for making decision.

For interworking between UMTS and WLAN,

3GPP considers the generic network advertisement

and selection scenario as been depicted in figure 1.

In this scenario, a user which is a subscriber of the

3GPP home network is located in an area which is

covered with several WLAN access networks. Using

information which is advertised by WLAN access

networks, the user decides which WLAN and which

3GPP visited network should be used.

The user can use periodic beacon frames sent by

WLAN access networks to gather the necessary

information. This method is called passive scanning.

The user can also use active scanning to collect this

information. In active scanning, by sending probe

request frame, the station asks the AP to send the

necessary information in the probe response frame.

In any WLAN access network, it is possible to

have cells with overlapping areas. In these areas the

user can opt for possible APs.

Therefore in an integrated UMTS and WLAN

environment, the network selection algorithm should

determine whether we must connect to UMTS or

WLAN access network, which WLAN should be

used and which AP in the WLAN should be

selected.

Most simple conventional network selection

algorithms use RSS for RAT selection. In (Yilmaz,

2005) the authors show that a simple ‘WLAN if

coverage’ strategy would lead to satisfactory results.

This is because when the hotspot is not congested,

WLAN with its low service cost and high bandwidth

can satisfy the user’s preference and the service

requirement. This simple strategy can also boost the

UMTS performance. Because by selecting WLAN

for wireless Internet access, UMTS channels become

free for voice traffic.

However, in this paper it is shown that when the

hotspot is congested, UMTS can offer higher

bandwidth. Hence considering only RSS is not

sufficient and the network traffic load should be

considered as an input parameter for network

selection algorithm, as the authors in (Hyun-woo,

2005) have used for AP selection. The authors show

that advertising the amount of traffic processed in

AP can lead to better network performance and

fairness among users.

Authors in (Qingyang, 2005) propose a general

decision method to take into account the other users

requirements like security, cost, and reliability.

In this paper, we propose a method for

evaluating WLAN capabilities in providing QoS for

real-time applications such as video and voice. We

show that for video services, the traffic load is

highly stochastic and instant amount of the traffic

load can not be used for network selection, as it is

used in (Hyun-woo, 2005) for AP selection.

Therefore in our algorithm, the statistical parameters

of the WLAN cell traffic load are estimated in the

AP and broadcasted in WLAN beacon frames. A

UMTS subscriber can use this information to

determine if this AP can ensure transferring data

frames in their due time or not.

Figure 1: Generic scenario for network advertisement and

selection.

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Figure 2: Proposed IE for broadcasting traffic information.

3 THE PROPOSED SOLUTION

WLAN stations within a cell can use distributed

coordination function (DCF) and optional point

coordination function (PCF) for accessing the

medium. The former in which users compete with

each other to send their frames is not a feasible

option to support QoS for real-time application

(Rajavelsamy, 2005). Therefore, we have chosen the

latter method, PCF, to be used in WLAN cells.

In PCF, the AP periodically announces the

inception of a contention free period (CFP) with a

specified maximum duration. The period of the CFP

and its maximum duration is announced in a

standard information element (IE), which is included

in the beacon frame. In the CFP, the AP polls any

station on a polling list. During this period, stations

may transmit only if the AP solicits the transmission

with a CF-Poll frame. After elapsing the maximum

duration time or reaching the end of the polling list,

the CFP ends. After CFP, contention period (CP)

begins, in which users can access the medium using

DCF.

Depending on the traffic load, it is possible that

some station on the polling list can not be polled in

the CFP. Now the purpose of our algorithm is to

determine if a new user is added to the polling list,

can it be polled at least in the x percent of the CFPs

or not. The choice of x depends on the service

requirement and the user preference. If this AP is not

capable of polling the station, we conclude that the

WLAN cell is congested and we search for other

APs. If there is not any suitable AP, UMTS network

is selected. Our algorithm for assessing the WLAN

cell capabilities has the following stages: 1) The AP

measures the traffic transmission time (TTT) for

each CFP. TTT is the time spent for transmitting and

receiving data frames to and from polled stations. 2)

The AP estimates the mean and the variance of the

TTT. 3) These estimated parameters are broadcasted

using a non-standard IE which is included in the

beacon frame for passive scanning or in probe

response frame for active scanning. 4) By assuming

normal probability density function (pdf) for TTT,

and by considering the service requirements and the

user preference, any station is able to estimate the

upper bound so that, TTT in x percent of the CFPs is

bellow it. 5) By considering CFP maximum

duration, the estimated bound, the length of data

packets and the transmission rate, the station can

determine if this AP is capable to ensure the desired

QoS or not.

Before going into detail, we shortly discuss the

rationales behind our algorithm. For reasonable

handoff decision, we must consider cells traffic load.

However in the 802.11 standard, each station can

scale back its transmission rate, according to its

radio link condition. Considering only cell’s traffic

load, will lead to ignoring station’s transmission rate

which has a significant effect on the cell capacity.

For this reason, we have used traffic transmission

time to consider users rates in our WLAN capability

assessment.

Rather than using instant amount of the traffic

load, we use statistical parameters, mean and

variance. Even though VoIP flows usually have

constant bit rate, other real time services like video

generate data packets with vastly different packet

size. The interval between two successive packets

can also be very different. In this condition, the

amount of the traffic load in one CFP has little

information about successive CFPs. In the next

section we will show that the instant amount of the

traffic load is not a viable network selection criterion

for video flows.

When the number of the users associated with

the AP is large enough, the assumption of the

normal pdf for traffic transmission time is

reasonable. This condition usually occurs within

hotspots with high traffic load where the network

selection algorithm can play a pivotal role in overall

network functionalities.

Now we show how the mean and the variance

can be estimated and how the upper bound for TTT

is determined. A simple method for estimating mean

and variance is the moving average, which is shown

in bellow.

][]1[][ iTiavriavr ×

−

. (1)

.])[][(]1var[]var[

iavriTii −×+−×=

βα

(2)

Where:

and

1=+

In these formulas, T[i] is the traffic transmission

time in the i

CFP and α is called forgetting factor

and shows how much we pay attention to the past

data. The choice of α is a trade off between

algorithm precision and its delay. With greater α,

estimation of the mean and the variance would be

more prices, but the effect of the entrance of a new

user to the WLAN cell is detected with more delay.

The mean and the standard deviation of the TTT

can be broadcasted in WLAN cell by using a non-

standard IE. Figure 2 shows this IE. In the 802.11

standard each IE has a variable length and is

identified by ‘Element ID’ field. Many of the

AN ALGORITHM TO ENHANCE THE NETWORK SELECTION PERFORMANCE IN AN INTEGRATED

UMTS/WLAN ENVIRONMENT

Figure 3: CFP duration for all CFPs.

Element IDs are reserved for future uses. One of

them can be used for this non-standard IE.

If we assume the normal pdf for the TTT, we

easily can estimate an upper bound so that TTT in

the x percent of the CFPs is less than it.

() .

100

bound Q avr

−

=×+

(3)

Where

1−

Q is the inverse of the standard normal

cumulative density function,

and av

are the

standard deviation and the mean of the traffic

transmission time. But as a matter of fact, the normal

pdf is only an approximation for probability

distribution of TTT and the estimation of the mean

and variance is not very accurate. Therefore we

should use a little higher bound to ensure that the

estimated bound is equal or greater than actual

bound and our algorithm works well. Our simulation

results show that by using only 25% greater standard

deviation, the estimated bound has satisfactory

performance. So we use (4) to estimate the bound.

When the network has high traffic load, this bound

is not very different from what is estimated by

normal pdf.

1.25* ( ) .

100

bound Q avr

−

=×+

(4)

By considering this bound, maximum packet size

and the transmission rate, if the station selects this

AP, it can be sure that x percent of the data packets

could be sent in their due time.

The choice of the x is a trade-off between service

requirements and user preference on the one hand,

and network performance on the other. Some

applications like VoIP can tolerate more frame loss.

But some applications like video are more sensitive

on frame loss. Users also have different preferences.

For example, rather than using an expensive UMTS

service, a user may prefer to pay less and use a

WLAN access network that guarantees to transfer

only 95% of the data frames. The network

performance is another issue. When transferring of

more data frames is required, fewer users can be

accepted and WLAN performance in most of the

time is bellow its maximum.

4 SIMULATION RESULTS

To study the performance of the proposed algorithm,

we have used a simulation scenario, in which 80

UMTS users get into a WLAN cell. The entrance

time of each user is randomly chosen from 1 second

to 1200 seconds. The entire simulation duration is

3000 seconds.

The AP of the WLAN cell supports 802.11a

standard. All UMTS users which want to connect to

WLAN should support WLAN radio interface as

they do in our simulation scenario. The transmission

rate of the users can be different according to their

radio link condition. We did not simulate the WLAN

physical layer. So we randomly chose 18 Mbps or

24 Mbps as the user’s transmission rate to imitate

the different radio link condition.

WLAN cell supports PCF for medium access.

The CFP maximum duration is 50 msec and CFP

announcement interval is 60 msec. Therefore,

stations can use DCF for remaining 10 msec. CP

was not explicitly simulated. We have used a

constant 10 msec period plus a random variable

chosen from [0, 1 msec] interval for CP to mimic the

real conditions in which CFP inception can be

delayed by CP.

All users have a bidirectional (up-link and down-

link) real-time video session. Video streams are

coded by H.263 standards with target rate of 64

Kbps. H.263, MPEG-4 and H.264 are three codecs

which 3GPP recommends for conversational video

services (3GPP, 2005b). For generating video traffic,

we have used trace files that publicly can be

accessed in http://trace.eas.asu.edu/trace /ltvt.html.

Figure 3 shows the traffic transmission time for

all CFPs. From this figure it is evident that since the

instant values of the TTT are vastly different, it is

not a viable criterion for network selection algorithm

as it is used in (Hyun-woo, 2005) for AP selection.

Figure 4 shows the cumulative density function

of the TTT, when there are 80 users in the WLAN

cell. In this figure 95% and 99% of the CFPs have

traffic transmission time less than 45 and 49.5 msec

respectively. Therefore the actual upper bound for x

= 95 and x = 99 is 45 and 49.5 msec respectively.

The CFP maximum duration is 50 msec. As a result,

if the maximum size of the packets generated by

application is 1500 bytes, for sending 99% of the

frames, more users can not be accepted. But for

sending 95% of the frames in CFP, still more users

can be accepted. So we can see that there is a trade

off between network performance and service

quality.

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Figure 4: Cumulative density function of CFP durations.

Figure 5: Mean of the CFP duration.

Figure 6: Estimated bound for sending 95% of the frames.

For estimating the mean and the variance of the

traffic transmission time we have used (1) and (2)

with α = 0.01. Figure 5 shows the mean of the traffic

transmission time. The mean increases when more

users enter the WLAN cell and finally it reaches to

about 36 msec. As mentioned previously, to send

99% of the frames, no new user can be accepted.

Therefore the performance of the WLAN cell in

many times is below its maximum. The source of

this deficiency is the stochastic nature of video

traffic.

We have used (4) to estimate the upper bound for

traffic transmission time. As stated before, this

bound is a little greater than what is predicted by

normal pdf. In our simulated scenario, the standard

deviation is about 4 msec and the mean is 36 msec.

therefore the bound estimated by (4) is only 3%

greater.

This bound for x = 95 is depicted in figure 6. Our

simulation results show the estimated bound by a

new user in 98.5% of cases will be equal or greater

than its actual value, 45 msec. so in these cases, if

the user selects WLAN as its access technology, he

can transfer at least 95% of his frames. In only 4%

of the cases, the estimated bound is larger than 48.5

msec. In these cases a user with maximum packet

size of 1500 and transmission rate of 24 Mbps will

not choose WLAN as its access network. In these

cases our network selection algorithm has been

wrongly too skeptic. But as one can see in this 4% of

cases, only a small percent of the cell capacity

remains idle. Thus, our network selection algorithm

has an acceptable performance.

Figure 7 shows the estimated bound x = 99. Our

results show that in 6.3% of the cases, new users

wrongly estimate the bound less than its actual

value, which is 49.5 msec. This estimated bound

can be misleading for new users. For example in 2%

of the cases, a new user will estimate that the bound

is less than 49 msec. if the transmission rate of this

user is 24 Mbps and the maximum packet size is

1000 byte, the network selection algorithm wrongly

will choose the AP.

Our results show that the access delay is mainly

bellow the CFP announcement period. Access delay

is a function of network load, user’s place in the

polling list and the characteristics of the traffic load

generated by the user’s application. To demonstrate

this issue, in figure 8 we have shown the access

delay for three users which are placed in first, 50

and 80

row of the polling list.

From this figure we can see that the access delay

of the first user in the polling list is mainly affected

by his traffic load characteristics. Due to fact that

polling frame for this station is sent after CFP

inception. So network load can not affect the access

delay. On the other hand, access delay of the last

user in the polling list (user 80) is mainly affected by

CFP traffic transmission time. By comparing access

delay of this user with access delay of the 50

user

in the polling list, we can infer that being in the end

of the polling list does not mean that the station will

have greater access delay.

As a result of being the last one to be polled, the

last station has more chance to transfer its frame that

has been generated in this CFP interval. Therefore

AN ALGORITHM TO ENHANCE THE NETWORK SELECTION PERFORMANCE IN AN INTEGRATED

UMTS/WLAN ENVIRONMENT

Figure 7: Estimated bound for transferring 99% of frames.

Figure 8: Cumulative density functions of the access

delay.

the access delay for this station is less than CFP

period.

5 CONCLUSION

Next generation wireless networks will be multi-

access networks. In these networks, a subscriber can

use one of the access technologies to become always

best connected (ABC) according to his requested

service and network condition. Integration of UMTS

and WLAN is a viable solution toward the next

generation wireless networks. As we discussed in

this paper, the network selection algorithm has a

significant effect on the overall network

performance. In this paper we proposed a method for

measuring WLAN cell’s capabilities in such a way

that enables the network selection algorithm to

consider network condition, service requirement,

and user preference to keep the user ABC.

We have used video traffic in order to study the

performance of our proposed method. Simulation

results show that the proposed method has

acceptable performance in measuring WLAN cell

capabilities to accept the UMTS users.

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