# Access Network Selection Based on Fuzzy Logic and Genetic Algorithms

Authors: Mohammed Alkhawlani; Aladdin Ayesh
Journal: Advances in Artificial Intelligence
 (2008)

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## Abstract

In the next generation of heterogeneous wireless networks (HWNs), a large number of different radio access technologies (RATs) will be integrated into a common network. In this type of networks, selecting the most optimal and promising access network (AN) is an important consideration for overall networks stability, resource utilization, user satisfaction, and quality of service (QoS) provisioning. This paper proposes a general scheme to solve the access network selection (ANS) problem in the HWN. The proposed scheme has been used to present and design a general multicriteria software assistant (SA) that can consider the user, operator, and/or the QoS view points. Combined fuzzy logic (FL) and genetic algorithms (GAs) have been used to give the proposed scheme the required scalability, flexibility, and simplicity. The simulation results show that the proposed scheme and SA have better and more robust performance over the random-based selection.


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## Body

## 1. Introduction

The current radio resource management (RRM)
[1–3] solutions and mechanisms for
the wireless networks consider only the case of a single RAT where mobile users
can only access that RAT and coexisting subnetworks can only be operated
independently. Common RRM (CRRM) [4–7] and joint RRM (JRRM) [8–11] are two types of the
processes that enable the management within a single and between the different
RATs of the HWN. CRRM is a cooperative RRM architecture proposed by 3GPP
[4, 5] to make UMTS and GSM/GPRS
networks cooperate. It is responsible for coordinating the individual RRM
entities of each RAT. JRRM is another cooperative  RRM architecture and mechanism
proposed by IST SCOUT [10] and TRUST [11] projects and is similar to CRRM, but it is not
restricted to GSM and UMTS and complements the CRRM with additional features
and algorithms. Both JRRM and CRRM imply the use of some new RRM mechanisms
such as access network selection (ANS), joint admission control (JAC), joint
scheduling control (JSC), and vertical handover (VHO). Our work concentrates on
the first mechanism, that is, ANS, which decides how to select the most
suitable access network (AN) based on the discovered accesses, QoS constraints,
operator policies, user preferences and available system capacity, and
utilization.Not many algorithms to the ANS problem can be
encountered in the literature so far. In [12], there are different signal thresholds for each AN.
Mobile node compares the RSS with the signal thresholds and decides vertical
handoff procedure. In [13], mobile nodes keep connection with the higher
throughput network until RSS goes below a threshold. In [14], service types are considered and differentiating
service is achieved by different levels of RSS. A network selection algorithm
that is based on the distinct features of voice and data traffic is proposed in
[15]. In this
algorithm, the new calls prefer the cellular network for voice service and
WLANs for the data service. In general, the current conventional single
criteria-based algorithms such as the proposed [12–14] are rigid and take only one criterion and cannot
react easily to the changing environment conditions and accumulated human
knowledge. Usually they cannot cope with the different view points and goals of
the operators, users, and QoS requirements which make them inefficient for a
multicriteria problem such as ANS problem.Several policy-based RAT selection solutions are
proposed in [16–18]. General frameworks for a policy-based RAT selection
with some specific examples are proposed by Gelabert et al. [16]. A network capacity  surfaces policy-based admission controller is presented by Murray and Pesch
[17]. A policy-based
handover mechanism for mobile multihomed hosts is presented in [18] by Ylitalo et al. The
handover decision is based on explicit user defined policies, and considers
different criteria in the order of user defined priority. These solutions do
not consider the tradeoff between criteria of the ANS problem and does not
specify the importance and sensitivity of each criterion to the selection
problem.Several multicriteria decision making-based algorithms
are proposed [19–23]. These conventional
multicriteria-based algorithms do not take into account the complexities and
uncertainties that arise from the different characteristics and natures of the
different RATs. For these algorithms, it is not an easy task to incorporate the
accumulated human knowledge about the problem and the only method to adapt the
algorithms is to change the criteria weights randomly to get better results. In
addition, these algorithms do not have a proper method to address the
importance of the different criteria to the ANS.Several multicriteria-based algorithms with the aid of
artificial intelligence tools such as fuzzy logic, neural networks, and genetic
algorithms [24–26] are suffering from scalability and modularity
problems. They cannot cope easily with the increased numbers of RATs and
criteria in the HWN. These algorithms take all inputs from the different RATs
at once to one fuzzy logic block, so they suffer from scalability and
complexity problems when more RATs or membership functions added due to the
exponential increase on the number of inference rules.It is worth to mention that most of the above
algorithms do not addressed clearly the roles of the users and the operators in
the selection process. Furthermore, some of the current algorithms could not
differentiate between the initial selection process for the new service request
and the selection process during the VHO process.The main contribution of this paper is the development
of a new class of ANS algorithms that is based on hybrid parallel fuzzy logic
(FL)-based decision, multiple-criteria decision making (MCDM) systems, and
genetic algorithms (GAs). This class of algorithms represents the first attempt
to develop adaptive, flexible, and scalable ANS algorithms that are utilize the
advantages of hybrid parallel FL decision-making systems and MCDM systems. Up
to our knowledge, no utilization for GA or parallel FL-based decision abilities
for ANS has been carried out so far.Our proposed scheme to solve the ANS problem is
described in Section2. Software assistant (SA) based on
the proposed scheme in
coexisted CDMA- and TDMA-based systems is presented in Section 3. The
simulation models and performance evaluation are carried out in Section 4. Conclusions
are presented in Section 5.


## 2. ANS Problem Solving Scheme

More
intelligent and robust solutions than the existed ones are needed. In general,
any ANS solution has to take into its account the following general
requirements.(i)
The solution has to solve the ANS problem in a
simple way to give a reasonable and acceptable delay before the decision
appears.(ii)
The solution has to cope with the different
view points and goals of the operators and the users and to give both parties
the right and fair role in the selection process.(iii)
The solution has to react to the changing
environment conditions and accumulated human knowledge about the problem.(iv)
The solution has to allow any type of inputs
and to be applicable to any type of ANs.(v)
The solution has to be able to handle the
increasing number of RATs and it also has to be able to handle a large number
of criteria. The ANS
procedure can be divided into three phases, initiation phase, decision phase,
and execution phase. The objective of the initiation phase is to recognize the
need for ANS and subsequently initiate it and find out the required information
and measurements for the decision phase. In the decision phase, a comparison of
the information and the measurements calculated from a variety of sources
including networks measurements, QoS requirements, user preferences, and
operator policies. This comparison lead to the identification of the best
available AN according to the defined performance evaluation metrics. In the
execution phase, the user is assigned to the selected network in the decision
phase.A generic scheme to solve the ANS problem and any
other similar selection problem is shown in Figure1. The scheme
decision phase consists of three main components, the first component contains
a set of small parallel fuzzy logic (FL)-based subsystems, the second component
is a multiple-criteria decision making (MCDM) system, and the third component
is a genetic algorithm (GA)-based component to assign a suitable weight for the
criteria in the second component. The scheme decision phase can be described in
more detail as follows.(i)
The heterogeneous wireless environment
contains up ton ANs (RAT1,RAT2,…,RATn) and the framework has to select the most
promising one or to rank the RATs according to their suitability.(ii)
The selection depends on multiple criteria up
toi (c1,c2,…,ci). Different type of criteria can be measured
from different sources to cover the different view points of the users, the
operators, the applications, and the network conditions. Each criterion is
measured then passed to its FL-based control subsystem in the first component.(iii)
Every FL-based subsystem gives an initial
score for each RAT that reflects the suitability of that RAT according the FL
subsystem criterion. The different sets of scores (d1,d2,…,di) are sent to the MCDM in the second
component.(iv)
The GA component assigns a suitable weight (w1,w2,…,wi) for each initial decision according to the
objective function that is specified by the operator according to the
importance and sensitivities of ANS criteria to the different characteristics
of a wireless heterogeneous environment.(v)
Using the initial scores coming from the first
component and the weights that are assigned manually or using the third
component, the MCDM will select the most promising AN or will rank the
available RATs according to their suitability.Figure 1
The
proposed solving scheme for access network selection problem based on FL, MCDM,
and GA.Our proposed algorithm utilizes the advantages of
parallel FL control and MCDM, and GA.  The following subsections summarize the
main advantages and strengths of our proposed ANS.


## 2.1. The Advantages of the Application of FL to the ANS Problem

The framework
utilizes the advantages of the application of FL to the ANS problem that can be
summarized as follows.(i)
Because of the HWN complex nature, any
proposed ANS has to allow the solution of the ANS problem in a simple way and
to avoid the complex analytical and mathematical equations used to in the
conventional solutions. FL is conceptually easy to understand and easy to
apply. The mathematical concepts behind fuzzy reasoning are very simple. The
simplicity of the FL helps in providing the required simple ANS solution.(ii)
The data, information, and measurements that
have to be taken into account in the ANS are in general very dissimilar,
imprecise, contradictory, and coming from different sources. For example, some
input parameters such as the received signal strength, the cell loads, and the
SNR from different RATs are not directly comparable. FL is tolerant of
imprecise data and it allows imprecise and contradictory inputs. As a result of
that, an FL-based solution has been thought to be a good candidate for reaching
suitable ANS decisions from such imprecise and dissimilar information.(iii)
FLC reconciles the conflicting objectives of
the operators, users, and applications about the ANS.(iv)
ANS solution has to be able to response to the
changing conditions of the HWN environments and the accumulated experience of
the operators and users. FL-based solution is easy to modify by tuning and
adjusting the inference rules and membership functions.(v)
The use of linguistic variables and inference
rules make the FL-based solution similar to the way humans think, which makes
it more intelligent and powerful than the conventional algorithm. This also can
help in easy incorporation for the accumulated operator and user knowledge
about the ANS problem.


## 2.2. Advantages of Using Parallel FLC to the ANS Problem

Applying the
parallel FLC rather than the traditional FLC has some advantages when solving
the ANS problem. The advantages of the application of parallel FL to the ANS
problem can be summarized as follows.(i)
The expected huge complexity involved in the
heterogeneous environment is greatly reduced using the idea of parallel FLC
that reduce the number of needed inference rules and the complexity of each
rule. We have avoided the exponential increase of the rule base, and thus the
increase in the complexity of the decision, when the number of input variables
increases. To illustrate this point assume that we have two RATs only, five
inputs per RAT to one FL system and every input has 3 membership functions,
then we will have up to310 inference rules. In our scheme, if we use five
parallel FLC with the same inputs and number
of networks, we will have 5∗32 rules only.(ii)
The idea of the parallel FLC reduces the
complexity of the inference rules used in the fuzzy-based solutions. To
illustrate this point assume that we have five inputs per RAT to one fuzzy
control system in HWN that have two RATs. The most complex inference rule will
be as follows ifc1/n1 AND c1/n2 AND c1/n3 AND c1/n4 AND c1/n5 AND c2/n1 AND
c2/n2 AND c2/n3 AND c2/n4 AND c2/n5 AND c3/n1 AND c3/n2 AND c3/n3 AND c3/n4 AND
c3/n5 AND c4/n1 AND c4/n2 AND c4/n3 AND c4/n4 AND c4/n5 AND c5/n1 AND c5/n2 AND
c5/n3 AND c5/n4 AND c5/n5 Thend1 AND d2 AND d3 AND d4 AND d5, where ci/nj means network, j the rating value for criteria i, and dj means initial score for network j.
However, using our parallel FLC, the most complex inference rule will be as
follows If c1/n1 AND c1/n2 AND c1/n3 AND c1/n4 AND c1/n5 Thend1 AND d2 AND d3
AND d4 AND d5.(iii)
The output of the parallel FLC are in the
range [0, 1] provide already normalize inputs (i.e., RATs rating against
criteria) for the MCDM. This provides great advantage over the current
MCDM-based solution that need to normalize their input data with some
inappropriate methods.


## 2.3. The Advantages of the Application of MCDM to the ANS Problem

The framework
utilizes the advantages of the application of MCDM to the ANS problem that can
be summarized as follows.(i)
In a very complex and uncertain environments
such as HWN, MCDM can sufficiently reduce the uncertainty and doubt about the
alternatives and allows a reasonable choice to be made from among them.(ii)
ANS problem is a multicriteria problem in
nature and the flexibility and complementary ANS multicriteria have to be
utilized to provide a solution that can cope with the different view points and
goals. If suitable weights have been assigned for each criterion using proper
optimization methods such as GA, the user satisfaction average and the operator
benefit can be maximized at the same time. This makes the proposed solution
more efficient than the traditional algorithms that are either user centric or
operator centric.


## 2.4. The Advantages of the Application of GA to the ANS Problem

In our study,
the GA is used to overcome one of the MCDM drawbacks, which is the finding of
suitable weights for the different criteria. The following points summarize the
main advantages of applying the GA to the ANS problem.(i)
GA can deal with the large number of variables
and the complex search space included on the ANS criteria weights with high
probability of success in finding optimal, near optimal, or at least good
solution.(ii)
GA can handle the different constraints and
objectives of the ANS criteria weights.(iii)
GA does not require
derivative information, which can help out in the user-centric algorithm where
not enough data is existed due to the limitation of storage in the mobile
terminal.(iv)
GA is less likely to be trapped by local
optimal minima or maxima.(v)
GA works with numerically generated data,
experimental data, or analytical functions. This can give different options
when designing the ANS algorithm.


## 3. ANS SA for Coexisted CDMA-and TDMA-Based Systems

User software
assistant (USA) based on our scheme with suitable input criteria can be used in
the user equipment. Operator software assistant (OSA) based on this scheme with
suitable input criteria can be used in the operator CRRM or JRRM entity. Based
on the scheme proposed on the last section, ANS software assistants for users
and operators can be developed. Since both OSA and USA are based on the same
scheme and the main difference will be in the used input criteria, a general SA
is presented in this section. The proposed SA deals with a wireless environment
that contains a CDMA-based and a TDMA-based wireless networks. The details of
the general SA is shown in Figure2. The components of the SA can be
summarized in Sections 3.1, 3.2, 3.3.Figure 2
ANS SA for coexisted CDMA and TDMA systems.


## 3.1. The FL-Based Control Component

Our SA contains
four FL-based subsystems. Each subsystem considers one of the ANS important
criteria. The RSS subsystem considers the received signal strength criteria.
The MSS subsystem considers the mobile station speed criteria. The ST subsystem
considers the service-type criteria. The UP subsystem considers the user
preference and price criteria. RSS subsystem has two input variables  RSS1 to describe the
received signal strength from CDMA-based network and  RSS2 to describe the
received signal strength from TDMA-based network. MSS subsystem has only one
input variable MSS to describe the mobile station speed. ST subsystem has two
input variables, the first is “DelayReqc” to describe the one-way delay
needed for the required service and the second is “RateReqc” to describe
the bit rate needed for the required service. UP subsystem has only one input
variable “Price” to describe the user preferred price. Every input
variable has three membership functions{low, medium, high}. Figure 3
shows the membership functions of the input variables.Membership functions of the input variables.
(a)(b)(c)(d)(e)(f)Every subsystem has two output variables, one variable
to describe the probability of acceptance for the new user in the CDMA network
and the other variable to describe the probability of acceptance for the new
user in the TDMA network. Each output variable has four membership functions{TR
(totally reject), PR (probability reject), PA (probability accept), and TA
(totally accept)}. The subsystems output  variables are “RSSc1” and
“RSSc2” for RSS subsystem, “MSSc1” and “MSSc2” for MSS
subsystem, “STc1” and “STc2” for ST subsystem, and “UPc1” and
“UPc2” for UP subsystem. Figure 4 shows “STc1” variable
with its membership functions as a sample for the output variables.Figure 4
Membership functions for “STc1” output variable.


## 3.2. The MCDM Component

Enhanced
version of simple multiattribute rating technique (SMART) has been used. SMART
is one of the simplest MCDM and most efficient methods. The ranking valuexj of alternative Aj is obtained simply as the weighted algebraic
mean of the utility values associated with it, that is, aij according to (1):(1)xj=∑i=1mwiaij∑i=1mwi,j=1,2,…,n.SMART employs relatively uncomplicated and
straightforward manipulation method, which makes it stronger and easier to use
in a hybrid and more complex models such as the proposed one in this paper.
With the aid of both FL and GA, SMART has all the capabilities required to
address the specific considerations that are involved in the ANS decision
making process. SMART can be quickly and easily understood by the inexperienced
decision makers. However, to get better understanding and deeper insights into
the selection decision making, other advanced MCDM methods can be used in the
future work and compared with SMART method.In our algorithm, there are two alternatives for the
MCDM, one is a CDMA-based network and the other is a TDMA-based network. The
input criteria of the MCDM are the outputs of the FL-based control subsystems
in the first component. The GA component assigns the weightwi for criteria i to reflect its relative importance. The
criteria with more importance to the operator and user can be assigned higher
weight using the objective function of the GA specified by the operator. Since
all the outputs of FL subsystems
are in the range [0, 1], there is not be any
need to scale the criteria performance against alternatives.The ranking value
of CDMA networkxc and the ranking value of TDMA network xt can be calculated as follows:(2)xc=MSSc1∗Wv+RSSc1∗Ws+STc1∗Wt+UPc1∗WuTW,xt=MSSc2∗Wv+RSSc2∗Ws+STc2∗Wt+UPc2∗WuTW,where Wv is the assigned weight for the mobile station
speed criteria. Ws is the assigned weight for the received signal
strength criteria. Wt is the assigned weight for the service type
criteria. Wu is the assigned weight for the user preferred
price criteria. TW is the total weight and is calculated using
(3):(3)TW=Wv+Ws+Wt+Wu.


## 3.3. The GA Component

The GA has been
used in our scheme to help the users or network operator to find suitable
values for the weights{Wv,Ws,Wt,Wu} in the offline mode. Beside the GA advantages
that are summarized on Section 2.4, our decision to use GA was based on the
nature of our objective functions that have several dynamic and stochastic
components, where any other derivative-based optimization method cannot perform
well. Another important issue that encouraged the selection of GA to our
problem is the high interaction between different variables. At the end,
usually the GA is not used to find an optimal solution and most of the time it
is considered as a very promising option to find good and acceptable solution.
It is worthy to mention that GA is not used on every selection decision. It is
optional component on the scheme where the user can use it or not in offline
mode prior to the selection process.Since the ANS is a multicriteria problem in nature,
different objectives need to be optimized. In this paper, three different
objective functions are proposed to cover the different and opposite objectives
and requirements of the users, QoS and operators. The proposed objective
functions are outlined in the following points.(i)
The first objective is to maximize the
percentage of satisfied users, that is, users who are assigned to a network of
their preference (Pu): since the algorithm considers multiple
criteria, it may assign a user to a network different from his/her original
preference. Pu is considered appropriate for performance
evaluation from the user point of  view. To maximize Pu, the objective function ObjFun1 shown
in Algorithm 1 is used.(ii)
The second objective is to maximize the
percentage of users assigned to the networks with stronger signal strength (Pq): since the algorithm considers multiple
criteria, it may assign a user to a network with weaker signal strength. Pq is considered as a simple indicator for
performance evaluation from the QoS point of view. To maximize Pq, the objective function ObjFun2 shown in Algorithm 2 is used.(iii)
The third objective is to achieve the load balancing
between networks:Po is the percentage between the number of users
in both networks. It is used as a basic indicator for load balancing. If the
resources of both networks have the same importance and cost for network
operator, this objective is considered appropriate for performance evaluation
from network operator point of view. To achieve load balancing, the objective
function ObjFun3 shown in Algorithm 3 is used.Algorithm 1:Pesudeocode for objective function ObjFun1.
fori←1toNo. of UsersdoNet1=MSSc1∗Wu+RSSc1∗Ws+STc1∗Wt+UPc1∗WuNet2=MSSc2∗Wu+RSSc2∗Ws+STc2∗Wt+UPc2∗WuifRSSc1>RSSc2thenz=net1/net2;elsez=net2/net1;endifZ>1thenss=ss+1;endendObjFun1 = ssAlgorithm 2:Pesudeocode for objective function ObjFun2.
fori←1toNo. of UsersdoNet1=MSSc1∗Wu+RSSc1∗Ws+STc1∗Wt+UPc1∗WuNet2=MSSc2∗Wu+RSSc2∗Ws+STc2∗Wt+UPc2∗WuifUPc1>UPc2thenz=net1/net2;elsez=net2/net1;endifZ>1thenss=ss+1;endendObjFun2 = ssAlgorithm 3:Pesudeocode for objective function ObjFun3.
fori←1toNo. of UsersdoNet1=MSSc1∗Wu+RSSc1∗Ws+STc1∗Wt+UPc1∗WuNet2=MSSc2∗Wu+RSSc2∗Ws+STc2∗Wt+UPc2∗WuifNet1>Net2thennet1sel=net1sel+1;elsenet2sel=net2sel+1;endifnet1sel>net2selthenObjFun3 =net1sel+1;elseObjFun3 =net2sel+1;endendThe weights of the input criteria{Wv,Ws,Wt,Wu} have been encoded using real encoding method.
The length of the real-valued encoding is 4 real floating numbers. The length
of each floating number is depending on the internal precision and roundoff
used by the computer to define the precision of the floating numbers.The results achieved by extensive comparisons of GA
performance as a function of the different GA parameters (i.e., the population
size, the mutation rate, and the crossover fraction) and the different GA
operators (i.e., the selection and crossover operators) have been summarized by
R. L. Haupt and S. E. Haupt [27]. They have concluded that crossover fraction,
selection operator, and crossover operator are not of much importance. On the
other side, the population size and the mutation rate have the most significant
impact on the ability of the GA to find better minimum value for objective function.Based on that, several sets of experiments to
determine good population size and mutation rate for our proposed objective
functions have been carried out. Each experiment has been repeated several
iterations while the performance is recorded. The results from all the
iterations are then combined by calculating the average (and standard
deviation) for each experiment. Usually, testing GAs includes mainly two
issues; how far the result obtained by GA is from the benchmark results, which
can be measured by average fitness value. The second issue is how fast GA is in
finding the best solution, which can be measured by the total number of
function evaluations. Since our GA is working offline, only the average best
fitness value achieved by the GA is used. The average best fitness and the
standard deviation are plotted against the tested parameters. When studying a
parameter all the other parameters and operators are kept constant.Only a sample for the experiments results achieved is
given in this paper as shown in Figure5. The
lower plot in Figure 5 shows the means and
standard deviations of the ObjFun1 function best fitness values over 20 runs,
for each of the values of the population sizes between 10 and 100 with step
size equals to 10 individuals. The upper plot shows a color-coded display of
the best fitness values in each run where dark color indicates better results.
For ObjFun1 fitness function, setting the population size to 20 individuals or
more yields better result. Based on these results and the other experiments
results for other objective functions, the population size chosen in our GAs
has been set to 20. Other sets of experiments have been carried out to determine a suitable
mutation rate. Based on the experiments, the mutation rate has been set to 0.1.The effect of
population size on objective function ObjFun1.
(a)(b)In addition, our GA uses a roulette selection
function. To ensure elitism, the number of  individuals that are guaranteed to
survive to the next generation has been set to 4 individuals. It uses two-point
crossover function and a uniform mutation function. The GA terminates if the
maximum number of the GA iterations  reaches to 300 iterations, if there is no
improvement in the best fitness value for number of consecutive generations
equals to 100 generation, or if there is no improvement in the best fitness
value for an interval of time equals to 300 seconds.


## 4. The Performance Evaluation

The used simulation models and some simulation results
are presented in this section.


## 4.1. The Simulation Models

Our proposed
scheme and SA are evaluated using the simulation approach. MATLAB mathematical
software and a set of functions called RUNE [1] have been used for the simulation. The system model
considers the coexistence of a CDMA-based WWAN network with seven macrocells
with omnidirectional antenna and cell radius = 1000 m and a TDMA-based WLAN
network with twelve microcells with omnidirectional antenna and cell radius =
500 m. In the system environment, each mobile has a velocity and is moved with
a random distance and a random direction at defined time steps. The velocity is
a vector quantity with magnitude and direction. The velocity of the ith mobile is updated according to
(4);(4)Ui=Ui∗P+1−P2∗Vm∗X,where Ui is the complex speed [m/s]. P is the correlation of the velocity between
time steps. X is Rayleigh distributed magnitude with mean 1
and a random direction. Vm is the mean speed of mobiles. Figure
6 shows the users, in blue, over the simulated
environment.  Four types of
services are considered in our simulation, the voice calls, the low bit rate
real-time video telephony, the high bit rate streaming video, and the
nonreal-time data traffic. The traffic is modeled according to Poisson process.
The main call holding time is assumed to be 50 seconds.Figure 6
The users over the
simulated environment.The propagation model used in this paper can be
described in logarithmic scale as in (5)(5)G=GD+GF+GR+GA.Equation (5) contains four
components. The first component is the distance attenuation GD,
which is given by the Okumura-Hata formula (6):(6)GD=−{β+10α∗log(d)},where d is the distance to the transmitter, β is a constant used to model the effect of
carrier frequency, the antenna size and other physical parameters. The
parameter α is the distance attenuation coefficient. β has been set to −28 dB and α has been set to 3.5. The second component is
the shadow fading GF,
which is modeled as a log-normal distribution with standard deviation of 6 dB
and 0 dB mean. The third component is the Rayleigh fading GR,
which is a Rayleigh distributed. The fourth component is the antenna gain GA.


## 4.2. The Simulation Results

This subsection
shows some simulation results and compares the performance of our proposed
solution to three different reference ANS algorithms. The first algorithm is a
service-type-based selection algorithm where high bit services with low
propagation delay requirements are sent to the WLAN and the low bit rate
services with the high propagation delay requirements are sent to the WWAN. The
second algorithm is a terminal speed-based selection algorithm where high-speed
users are sent to the large-coverage network (i.e., WWAN) and the low-speed
users are sent to the small-coverage network (i.e., WLAN). The third algorithm
is a random-based selection algorithm where the users are assigned randomly to
the two networks. All solutions have been simulated, evaluated, and compared
for the three different objectives addressed in Section3.3. Several runs of
simulation have been carried out. Each run uses a different number of users.Figure7 shows Pu values in all solutions. The horizontal axis
shows the number of users while the vertical axis shows the Pu values.
From both Figure
7 and the numerical samples for Pu values shown in Table 1, the great improvement
in the number of the satisfied users in our solution can be seen. For example,
with 840 users in the environment, the percentage of satisfied users with all
different reference solutions is around 50% while the same percentage in our
proposed solution is around 80.7%. In general, our proposed solution achieves
around 31% enhancement over the different reference algorithms. 	In Figure
8, the lower plot shows the weights assigned by the GA for the
different criteria using the first objective function. The upper plot shows the
best function value in each generation versus iteration number.Table 1
Pu values in all solutions.
UsersThe proposedRandom-basedService type-basedTerminal speed-basedNo.solutionPuselectionPuselectionPuselectionPu1250.7440.5600.6000.5202230.8390.5200.5070.4893180.8330.5220.4870.4814310.7890.5100.5270.5015390.7850.4840.5010.4868400.8070.5020.5070.49810700.7980.4670.5170.47112690.7900.5190.5020.50113930.8250.5030.4900.49316340.8600.4880.5110.52317350.8670.4930.4850.502Figure 7
The percentage of satisfied users (Pu) in the proposed algorithm against the three
references algorithms.The weights assigned by GA for the 1st
objective.
(a)(b)Figure9 shows Pq values in all solutions. The horizontal axis
shows the number of users while the vertical axis shows the Pq values.
From both Figure
9 and the  numerical samples
for Pq values shown in Table 2, the great
improvement in the percentage of the users assigned to networks with stronger
received signal can be seen. For example, with 840 users in the environment Pq with the different reference algorithms is
around 50% while the same percentage in our proposed solution is around 64%. In
average, our proposed solution achieves around 26.5% enhancement over the
random-based selection. 				Figure
10 shows the weights assigned by the GA for the second objective.Table 2
Pq values in all solutions.
UsersThe proposedRandom-basedService-type-basedTerminal speed-basedNo.solutionPqselectionPqselectionPqselectionPq1250.7200.4560.5040.4402230.7980.5650.4840.5203180.8300.5030.5030.4724310.8310.5060.5380.4905390.8440.4860.5420.4648400.6400.5100.5070.50510700.7500.4730.4940.45612690.7970.5020.4900.45313930.8190.4970.4980.47316340.5980.5050.4920.47417350.6220.5190.4820.476Figure 9
The percentagePq values in the proposed algorithm against the
three references.The
weights assigned by GA for the 2nd objective.
(a)(b)The results ofPo produced by all solutions for the third
objective are shown in Table 3. Our proposed solution achieves good and
comparable results against the results achieved by other solutions. Figure 11
shows the weights assigned by the GA for the third objective.Table 3
Po values in all solutions.
UsersThe proposedRandom-basedService type-basedTerminal speed-basedNo.solutionPoselectionPoselectionPoselectionPo1251.3411.1550.9840.9442231.1591.1390.8480.7413181.1960.9890.9350.8664311.0780.9271.0250.8825391.3550.9680.9000.8558401.1691.0531.1750.93910701.2321.0070.8560.88212691.1781.1301.0460.85613931.0631.0480.9110.87016341.2120.9371.0300.88817351.1710.9241.1750.877The weights assigned by GA for the 3rd
objective.
(a)(b)


## 5. Conclusions

A general scheme to solve the multicriteria ANS
problem has been presented in this paper. The proposed scheme is scalable and
is able to handle any number of RATs with a large set of criteria. The scheme
can cope with the different and contrast view points and goals of the operator
and users and it can react to the accumulated human knowledge about the
problem. The proposed scheme has been used to present and design a
multicriteria ANS solution that considered the user, the QoS, and the operator
view points. The simulation results show that the proposed solution has a
better and more robust performance over the reference solutions.Different aspects of our work have to be developed.
More criteria can be included such as resources availability and
signal-to-noise ratio (SNR). Multiobjective optimization method can be used to
find an optimum weights jointly rather than using the single-objective GA that
optimize each objective weight independently. The used rules and membership
functions of the fuzzy subsystems are still subjective, so they can be built or
tuned using a suitable learning method such as the genetic algorithms or the neural
networks. Our algorithm can be compared with more reference algorithms.



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*Source: 793058-2008-05-25.xml*