VI INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS2006),SEPTEMBER 3-6,2006,FORTALEZA-CE,BRAZIL

Impact of Intelligent Access Selection Algorithms

in Cooperative Wireless Networks

F.R.P.Cavalcanti,V.A.de Sousa Jr.,R.A.de O.Neto and F.de S.Chaves

Abstract—Future generation of wireless networks will solicit

the cooperation of heterogeneous Radio Access Networks (RANs)

in order to provide multimedia trafﬁc to different classes of

users with varying Quality of Service (QoS) requirements over

regions and time zones.The use of such RANs as subsystems of

a larger wireless network,i.e,a multi-access network,claims for

an Access Selection (AS) scheme to coordinate the load sharing

so as to meet the optimum combined network performance.

Depending on the individual subsystem capacities,the trafﬁc

load conditions and the subsystem channel quality,an intelligent

AS algorithm can provide capacities beyond the sum of the

subsystems capacities.This paper addresses the issue of the AS

in the context of multi-access networks.We intend to provide

an analytical formulation which indicates the suitable/unsuitable

scenarios for employment of an intelligent AS strategy.The

usability of the proposed qualitative studies are demonstrated

by means of simulations.

Index Terms—Cooperative Beyond 3G Systems,Multi-Access

Scenarios,Access Selection.

I.I

NTRODUCTION

Overall wireless connectivity is an essential aspect of

upcoming wireless systems.Mobile users wish to have access

to voice and data services everywhere (homes,vehicles,

workplaces or public areas).There is a large variety of

radio-enabled equipments at the disposal of the users -

smartphones,PDAs,laptops,and desktop computers - as well

as the choice of RANs (3G cellular networks,wireless local

area networks,and satellite systems).One important question

that arises for network operators is how different RANs may

be compared in terms of coverage,capacity and performance,

and how these differences could be used for increasing the

user perceived QoS.Then,the basic motivation for studying

this problemis to determine scenarios where an intelligent AS

approach yields in performance gains.

The deﬁnition of multi-access scenarios is a task that

consists of situating the social context of the possible

applications,as well as providing technical aspects of the

models and parameters that may have a meaningful impact

over the proposed multi-access solutions.

Social aspects of the interworking between two or more

RANs may be essentially characterized by how the subsystems

are deployed.As an example,a speciﬁc RAN can be

distributed in public areas such as restaurants,shopping centers

and airports,enabling the users to select the desired network

and seamlessly maintaining their connectivity while passing

through the different RANs.

The authors are with the GTEL-UFC:Wireless Telecom Research Group,

Teleinformatics Engineering Department,Federal University of Ceara,Brazil.

URL:www.gtel.ufc.br.E-mail:{rodrigo,vicente,neto,fabiano}@gtel.ufc.br.

Technical aspects include identiﬁcation of parameters that

inﬂuence the multi-access networks performance.For a useful

qualitative result on multi-access scenarios,we have to identify

general parameters and rules that cover a large set of

scenarios.This is addressed in this paper.Several research

projects have been developing a similar investigation on

the multi-access subject [1]–[8].Besides papers and public

deliverables produced in these projects,there are two academic

works directly related to our investigation [9]–[11].Results

show that there is no signiﬁcant cost in adding hot spot

like subsystems (e.g.WLAN) to legacy cellular (e.g.UMTS).

In such a hybrid network,a dual-mode mobile equipment

will access the network via a hot spot like subsystem when

inside the hot spot,or cellular subsystem otherwise.The

ﬁnancial advantages of the multi-access networks have been

investigated in [9].Results show that there is no signiﬁcant

cost in adding hot spot like subsystems (e.g.WLAN) to

legacy cellular (e.g.UMTS).In such a hybrid network,a

dual-mode mobile equipment will access the network via a

hot spot like subsystem when inside the hot spot,or cellular

subsystem otherwise.This AS strategy is also investigated in

[11],[12].For different system-level aspects,the gain of the

“coverage threshold algorithm” criterion is investigated and the

possible gains when taking load trafﬁc into account during the

AS process.However,there is no detailed studies about the

attractiveness of intelligent AS and its analytical formulation.

This paper presents studies of attractiveness of AS in

the multi-access wireless networks.Initially,we intend

to provide an analytical formulation which indicates the

suitable/unsuitable scenarios for employment of an intelligent

AS strategy.After that,the validity of these “rules” is

illustrated in a number of more detailed case studies.

II.A

CCESS

S

ELECTION

P

ROBLEM

In multi-access heterogeneous networks the concept of

Common Radio Resource Management (CRRM) is essential

for assuring an efﬁcient handling of the radio resources.It

extends the traditional RRM techniques and has a much

broader scope,which certainly makes a difference in terms

of resource optimization and performance gains.Nevertheless,

one is not intended to replace the other,but rather work in a

complementary fashion.

Some of the existing RRM techniques may be extended to

the CRRMcontext.For instance,the traditional call admission

procedure determines whether a user may be admitted into the

system.In a multi-access scenario,however,the call admission

control (here called AS algorithm) is additionally responsible

for deciding which is the best suited RAN to accommodate

the incoming connections.

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The upcoming sections will look at the multi-access

scenarios in order to deﬁne a general rule to determine a

suitable combination of RANs where an intelligent AS strategy

provides performance gains.

III.A

CCESS

S

ELECTION

A

TTRACTIVENESS

E

VALUATION

We study the attractiveness of the employment of an

intelligent AS algorithm considering the Coverage Threshold

Algorithm (CTA) as a reference.Such algorithm does not use

any kind of intelligence in the process of allocation.In such

a procedure,a dual-mode mobile equipment will access the

network via a hot spot like subsystem when inside the hot

spot,and use cellular subsystem otherwise.

A.Multi-Access System Model

We use a system model which represents the access of the

shared channel by the link utilization concept.This model

is originally presented in [12] and described below.The

multi-access environment is illustrated in Fig.1.

RAN 1

RAN 2

HS

Fig.1:Multi-Access Environment.

We consider a single-cell including a hot spot (HS) and two

generic RANs.Our main performance indicator is the CSE

(Circuit Switched Equivalent) bit rate which is determined

based on a queue theory formulation.As deﬁned in [13],

user perceived data quality could be represented by CSE bit

rate.Roughly,the CSE bit rate measures the rate at which

transmission buffers of users are emptied and hence,includes

effects of queuing.

Firstly,we deﬁne the desired link utilization factor as the

ratio of the trafﬁc generated per user and the mapped radio

link bit rate,

ρ

Des

i

=

t

i

R

i

(1)

For high loads,the remaining capacity is shared by users

with worst link qualities following a maximumrate scheduling

policy.Then,the highest possible link utilization (ρ

Res

) for

these users is evaluated as:

N

i=1

min

ρ

Des

i

,ρ

Res

= 1 (2)

where p users are connected with their correspondent ρ

Des

and

(N−p) users with ρ

Res

matching the RAN capacity limitation.

According to equation (2),the effective link utilization factor

of the i

th

user can be written as

ρ

i

= min

ρ

Des

i

,ρ

Res

(3)

Then,the CSE bit rate of the i

th

user is calculated

as follows:

CSE

i

= R

i

·

⎛

⎝

1 −

j

=i

ρ

j

⎞

⎠

(4)

Prior to exposing our studies,we present some general

deﬁnitions which specify the above formulations for the

multi-access model in focus:

•

Set of users outside (Ω

Out

) and inside (Ω

In

) the HS;

•

Set of users connected to the RAN

j

(Ω

RAN

j

);

•

Capacity limitation model:

i∈Ω

RAN

j

ρ

i

1,(j = 1,2).

One can note that Ω

RAN1

= Ω

Out

and Ω

RAN2

=

Ω

In

considering the CTA.Next section presents the general

formulation of scenario attractiveness.

B.Theoretical Analysis of the Scenario Attractiveness

The theoretical study of scenario attractiveness starts

determining a criterion that indicates if an intelligent AS

algorithm outperforms the CTA.Equation (5) shows a simple

way to measure the free “resources” considering that all users

inside the HS area are connected to the RAN 2,i.e.,the CTA.

⎛

⎝

1 −

j∈Ω

Out

ρ

j

⎞

⎠

−

1 −

i∈Ω

In

ρ

i

> 0

⇒

i∈Ω

In

ρ

i

>

j∈Ω

Out

ρ

j

(5)

Considering the following approximation:

1kn

ρ

k

≈

n · E[ρ],where E[·] is the expectation operator,we can

rewrite (5) as:

n

In

.E[ρ

In

] > n

Out

.E[ρ

Out

] ⇒

n

In

n

Out

>

E[ρ

Out

]

E[ρ

In

]

(6)

Here,ρ

Out

and ρ

In

are random variables which represent

the link utilization factors of users connected to the RAN 1

(outside HS) and RAN 2 (inside HS),respectively.

One can note that the formulation presented in (5) is a

necessary condition but not sufﬁcient to an AS algorithmbased

on load balancing to provide performance gain over the simple

CTA.Therefore,it is possible that,in some scenarios,the

criterion afﬁrms a low attractiveness,but the utilization of an

algorithm more intelligent than the load balancing one results

in good performance.However,if the criterion indicates high

attractiveness,a more intelligent algorithm will also provide

gain.

Now,the mathematical model of the RANs is stated.

Each RAN is characterized by their radio link quality (SNR

deﬁnition) and link adaptation (speciﬁc link capacity) models.

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VI INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS2006),SEPTEMBER 3-6,2006,FORTALEZA-CE,BRAZIL

The radio link quality is expressed by the SNR

(Signal-to-Noise Ratio).It is modeled as a gaussian distributed

random variable with mean SNR

mean

and standard deviation

σ,resulting from the modeling of path loss and shadowing.

Fig.2 illustrates this SNR model.Herein,we approximate this

gaussian probability density function to a triangular one.This

approach was chosen due to its simplicity for mathematical

manipulation.Then,the input variables related to our radio

link quality are SNR

min

,SNR

max

and SNR

mean

.

Gaussian distributed SNR

Mathematical simplification

SNR

min

∆

∆∆

∆SNR

SNR

mean

SNR

max

Fig.2:PDF of the Radio Link Quality model (f

SNR

).

The triangular probability density function is

mathematically determined by the equation (7):

f

SNR

(s) =

⎧

⎪

⎪

⎪

⎨

⎪

⎪

⎪

⎩

0,if s < SNR

min

4.(s−SNR

min

)

(∆SNR)

2

,if SNR

min

< s ≤ SNR

mean

4.(SNR

max

−s)

(∆SNR)

2

,if SNR

mean

< s ≤ SNR

max

0,if s > SNR

max

(7)

where ∆SNR = SNR

max

−SNR

min

.

The link adaptation is idealized,as seen in Fig.3.Then,

the input variables related to the ideal link adaptation are

SNR

knee

,SNR

sat

,R

min

and R

max

.In order to facilitate the

future notation,we use ∆R = R

max

−R

min

.

SNR

knee

R

max

R

min

SNR

RATE

slope = k

SNR

sat

Fig.3:Ideal Link Adaptation model.

Equation (5) establishes that the AS gains depends on

the relation between the number of users and the resources

required/available of both RANs.Considering users requiring

the same amount of resource (t

i

= t),equation (5) becomes:

n

In

n

Out

>

E[ρ

Out

]

E[ρ

In

]

=

E

1

R

Out

E

1

R

In

(8)

where R

Out

and R

In

are the random variables that represent

the bit rate of users in RAN 1 and RAN 2,respectively.

Now,one can note that the quotient 1/R is a random

variable since,R is a function of a random variable (SNR),

whose probability density function is expressed in (7).From

the stochastic process theory,

E[g(x)] =

+∞

−∞

g(x)f

X

(x)dx (9)

where f

X

(x) is the probability density function of the

random variable X.In our approach,the SNR is the random

variable X and g(x) = 1/R(SNR).

The values of the parameters SNR

sat

,SNR

min

,SNR

max

and SNR

mean

determine different cases regarding the

expression above.Equation (9) shows a case where

SNR

min

< 0,SNR

max

> 0,SNR

knee

= 0 and

SNR

sat

> 0.The other cases can be obtained in an

analogous manner.

Therefore,we can conclude that the degree of attractiveness

depends on three parameters:the proportion of users inside to

outside the hot spot n

In

/n

Out

;the capacity of the systems,

expressed by R

max

and SNR

sat

;and the SNR distribution,

characterized by SNR

min

and SNR

max

.

IV.D

EMONSTRATIVE

P

ERFORMANCE

R

ESULTS AND

G

ENERAL

D

ISCUSSIONS

In this section,we present the performance results aiming

to demonstrate the validity of the proposed attractiveness

formulation.We simulate two AS strategies.The ﬁrst one is

the CTA.The second strategy is the Load Balancing Algorithm

(LBA),which tries to balance the offered load in both RANs,

assigning a new user to RAN1 or RAN2 so that the normalized

load in both RANs becomes closer to each other [14]:

Used Capacity at RAN

1

Total Capacity of RAN

1

Used Capacity at RAN

2

Total Capacity of RAN

2

(10)

For the presented study,we focus on a best-effort service

over shared channels where users are assumed to generate

an average trafﬁc of 128 kbps,regardless of position.The

multi-access scenario is a composition of relevant parameters

in each RAN.These parameters are the expected offered

load (proportion of users inside to outside HS),the link

quality (SNR distribution),the bit rate capacity (SNR to

rate mapping with link-adaptation if any).The last column

of Table I expresses the scenario attractiveness from our

proposed analytical study (see (5),section III-B),which will be

conﬁrmed by the system-level simulations.Column 4 shows

the SNR

min

and SNR

max

which represents the triangular

approximation of the gaussian distributed SNR only for the

veriﬁcation of the attractiveness criterion (see Fig.2).

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if SNR

sat

≤ SNR

mean

E[1/R] =

4

(SNR)

2

·

SNR

2

sat

R

−

R

min

SNR

2

sat

(R)

2

· ln(

R

max

R

min

)+

(SNR

mean

−SNR

sat

) · (SNR

mean

+SNR

sat

−2SNR

min

)

2R

max

+

(SNR

max

−SNR

mean

)

2

2R

max

if SNR

mean

< SNR

sat

≤ SNR

max

E[1/R] =

4SNR

sat

R· (SNR)

2

·

2SNR

mean

−SNR

sat

−

R

min

SNR

sat

R

+SNR

min

·

ln

R· SNR

mean

R

min

SNR

sat

+1

+

R

min

SNR

sat

R

+SNR

max

· ln

R

max

SNR

sat

R· SNR

mean

+R

min

SNR

sat

if SNR

max

< SNR

sat

E[1/R] =

4SNR

sat

R· (SNR)

2

·

2SNR

mean

−SNR

max

−

R

min

SNR

sat

R

+SNR

min

·

ln

R· SNR

mean

R

min

SNR

sat

+1

+

R

min

SNR

sat

R

+SNR

max

· ln

R· SNR

max

+R

min

SNR

sat

R· SNR

mean

+R

min

SNR

sat

(9)

TABLE I:Evaluated scenarios.

Scenario

Expected Offered Load

(n

In

/n

Out

)

Bit Rate

Capacity [Mbps]

Link Quality [dB]

Attractiveness

Scenario 1

4/1

R

max

1

= 6

R

max

2

= 6

SNR

mean

1

= 10,SNR

min

1

= -7,SNR

max

1

= 27

SNR

mean

2

= 11,SNR

min

2

= -7.5,SNR

max

2

= 29.5

high

Scenario 2

1/4

R

max

1

= 6

R

max

2

= 6

SNR

mean

1

= 10,SNR

min

1

= -7,SNR

max

1

= 27

SNR

mean

2

= 11,SNR

min

2

= -7.5,SNR

max

2

= 29.5

low

Scenario 3

1/1

R

max

1

= 54

R

max

2

= 6

SNR

mean

1

= 10,SNR

min

1

= -7,SNR

max

1

= 27

SNR

mean

2

= 26,SNR

min

2

= 8,SNR

max

2

= 44

high

Scenario 4

1/1

R

max

1

= 6

R

max

2

= 54

SNR

mean

1

= 10,SNR

min

1

= -7,SNR

max

1

= 27

SNR

mean

2

= 26,SNR

min

2

= 8,SNR

max

2

= 44

low

In simulations,the SNR of all scenarios is a gaussian

distributed random variable.The mean (SNR

mean

) is given

in the Table I and the standard deviation is 4 dB for users

inside the HS connected to the RAN 1 and 8 dB elsewhere.

Section IV-A demonstrates the inﬂuence of the expected

offered load distribution in the performance of AS strategies.

In section IV-B the impact of the system capacity and

radio link quality in the multi-access scenario deﬁnition is

demonstrated.We present the results in two steps.First,

we show the scenario conﬁguration drawing CDFs of SNR

and rate of both generic RANs.Finally,we show the

results in term of the CSE bit rate averaged over 1000

snapshots.The proportion of users indicates the load difference

between inside and outside HS areas.For instance,considering

n

In

/n

Out

= 1/4 and a load of 20 users per cell,there are 16

users outside the HS and 4 users inside the HS.

A.Scenarios 1 and 2:evaluation of the proportion of users

inside-outside HS

Here,we present two scenarios where the inﬂuence of

the expected offered load distribution (n

In

/n

Out

) in the

performance of AS algorithms can be illustrated.Figure 4

and 5 draw the general conﬁguration of the scenarios.The

maximum rate capacities of the RANs are equivalent and the

10th percentile of their SNR are similar (see Table I).Fig.5

shows that although the RANs have similar 10th percentile of

SNR,there is possibility of rate improvement in the CDF of

the bit rate spatial distribution.

same 10

th

percentile

Fig.4:Scenarios 1 and 2 - CDF of the Radio Link Quality model

(SNR).

Fig.6 shows the CSE bit rate for the CTA and LBA as

a function of the system offered trafﬁc load.We conclude

that an intelligent AS (LBA) will be attractive,if for similar

RANs (SNR distributions and capacities),the number of users

inside the HS is higher than the corresponding one in the

macrocell.This performance enhancement corresponds to a

trunking gain provided by the admission of users inside the HS

in the macrocell site,decreasing the number of connections in

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Possibility of rate

improvement

Fig.5:Scenarios 1 and 2 - CDF of the Bit Rate Spatial Distribution.

the highly loaded RAN 2.One can note that in scenario 2,

the LBA has the same performance of the CTA one (low

attractiveness).

Fig.6:Average CSE bit rate regarding n

In

/n

Out

= 4/1

(Scenario 1:attractiveness is high) and n

In

/n

Out

= 1/4

(Scenario 2:attractiveness is low).

B.Scenarios 3 and 4:Bit Rate Capacity and link Quality

Evaluation

In the previous section,the performance results have been

evaluated considering scenarios with RANs which have the

same maximum capacities and similar radio link quality

characteristics.This time,the behavior of the AS algorithms

is illustrated as a function of the maximum RAN capacity and

radio link quality.

We consider a case where RAN 2 has better reception

properties than RAN 1,i.e.,better SNR (see Fig.7).Figures

8 and 9 show the rate conﬁgurations of scenarios 3 and 4.

In scenario 3,the maximum rate capacities are 54 Mbps and

6 Mbps for RAN 1 and RAN 2,respectively.In scenario 4,the

maximum rate capacities are 6 Mbps and 54 Mbps for RAN 1

and RAN 2,respectively.

As expected,the possibility of rate improvement is strongly

dependent on the maximum system capacities.This can be

conﬁrmed in Fig.10:although link quality is favorable,there

is no gain with the LBA,when the maximum rate capacity of

RAN 2 is much higher than that of the RAN 1 (scenario 4).

RAN 2 has

better SNR

than RAN 1

Fig.7:Scenario 3 - CDF of the Radio Link Quality model (SNR).

Possibility of rate

improvement

Fig.8:Scenario 3 - CDF of the Bit Rate Spatial Distribution

(R

max

1

= 54 Mbps and R

max

2

= 6 Mbps).

Low possibility of

rate improvement

Fig.9:Scenario 4 - CDF of the Bit Rate Spatial Distribution

(R

max

1

= 6 Mbps and R

max

2

= 54 Mbps).

V.C

ONCLUSIONS

In a multi-access network composed of two RANs,one of

them with a “hot spot” like coverage,the simplest admission

strategy is the CTA.Such a strategy does not require more

than the signal level measure for deciding in which RAN the

new user will be admitted.

The attractiveness of intelligent AS strategies in a

multi-access network depends on three parameters:the

proportion of users inside to outside hot spot;the maximum

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VI INTERNATIONAL TELECOMMUNICATIONS SYMPOSIUM (ITS2006),SEPTEMBER 3-6,2006,FORTALEZA-CE,BRAZIL

Fig.10:Performance comparison regarding the inﬂuence of the bit

rate capacity.Scenario 3:R

max

1

= 54 Mbps and R

max

2

= 6 Mbps

(Attractiveness is high).Scenario 4:R

max

1

= 6 Mbps and R

max

2

= 54 Mbps (attractiveness is low).

capacity of both systems,represented by the link adaptation

model;and their radio link qualities,expressed by the SNR

distributions.

An intelligent AS algorithm will not be attractive when

the expected offered load inside the HS is lower than the

corresponding one in the macrocell.Another unattractive

scenario is the one where the capacity of the RAN serving

the HS only,is much higher than the correspondent one of the

RAN covering the whole macrocell.Furthermore,the scenario

characterized by a RAN covering only the HS with a much

better bit rate distribution than the other RAN,also indicates

that the intelligent AS algorithms will achieve performances

similar to that of the CTA.

Further studies include the generalization of the

attractiveness criterion considering other user-related features

like mobility,user service classes,trafﬁc model and access

selection criteria.

A

CKNOWLEDGEMENT

This work is supported by a grant from Ericsson of Brazil -

Research Branch under ERBB/UFC.10 Technical Cooperation

Contract.The authors V.A.de Sousa Jr.and R.A.de O.

Neto are scholarships supported by FUNCAP and Francisco

R.P.Cavalcanti was partly funded by CNPq - Conselho

Nacional de Desenvolvimento Cient´ıﬁco e Tecnol´ogico,grant

no.304477/2002-8.

R

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