Indonesian
J
our
nal
of
Electrical
Engineering
and
Computer
Science
V
ol.
17,
No.
1,
January
2020,
pp.
324
330
ISSN:
2502-4752,
DOI:
10.11591/ijeecs.v17i1.pp324-330
r
324
A
fuzzy
based
v
ertical
hando
v
er
netw
ork
selection
scheme
Meenakshi
Subramani,
V
inoth
Bab
u
K
umara
v
elu
School
of
Electronics
Engineering,
V
ellore
Institute
of
T
echnology
,
V
ellore,
T
amil
Nadu,
India
Article
Inf
o
Article
history:
Recei
v
ed
Apr
1,
2019
Re
vised
Jun
17,
2019
Accepted
Jul
7,
2019
K
eyw
ords:
Analytic
hierarch
y
process
(AHP)
De
vice-to-De
vice
(D2D)
communication
Fuzzy
logic
Hando
v
er
decision
delay
Simple
additi
v
e
weighting
(SA
W)
V
ertical
Hando
v
er
(VHO)
ABSTRA
CT
One
of
the
most
attracti
v
e
and
challenging
areas
in
the
upcoming
ne
xt-generation
5G
wireless
netw
ork
is
the
v
ertical
hando
v
er
(VHO).
Recently
,
man
y
of
the
heterogeneous
wireless
communication
technologies
are
introduced
to
satisfy
the
demands
of
users
in
all
situations.
Due
to
the
deplo
yment
of
heterogeneous
netw
orks,
the
users
can
ac-
cess
the
internet
an
ywhere,
an
ytime
through
dif
ferent
wireless
netw
orks.
T
o
obtain
seamless
service
and
service
continuity
,
the
de
vice
should
be
handed
o
v
er
to
the
best
wireless
netw
orks.
Here,
a
half
hando
v
er
scheme
for
De
vice-to-De
vice
(D2D)
com-
munication
is
implemented
for
the
selection
of
the
best
netw
ork.
The
tar
get
netw
ork
selection
for
v
ertical
hando
v
er
can
be
handled
using
multiple
attrib
ute
decision
mak-
ing
(MADM)
methods.
An
intelligent
and
f
ast
v
ertical
hando
v
er
decision
is
much
needed,
which
should
be
reliable
e
v
en
for
random
and
uncertain
en
vironments.
Fuzzy
logic
is
pro
v
ed
to
be
ef
fecti
v
e
in
handling
imprecise
data.
Hence,
in
this
w
ork,
the
impact
of
combining
fuzzy
with
the
con
v
entional
MADM
scheme,
simple
additi
v
e
weighting
(SA
W)
is
analyzed
and
the
h
ybrid
scheme
is
compared
with
the
con
v
entional
MADM
schemes
lik
e
SA
W
,
T
echniques
for
order
preference
by
similarity
to
ideal
so-
lution
(T
OPSIS),
VlseKriterijumska
optimizacija
I
K
ompromisno
Resenje
(VIK
OR)
in
terms
of
hando
v
er
decision
delay
.
Since,
the
numbers
of
hando
v
ers
e
x
ecuted
are
lo
w
,
the
hando
v
er
decision
delay
performance
of
the
proposed
scheme
is
superior
than
the
considered
classical
MADM
schemes.
Copyright
c
2020
Insitute
of
Advanced
Engineeering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
V
inoth
Bab
u
K
umara
v
elu,
School
of
Electronics
Engineering,
V
ellore
Institute
of
T
echnology
,
V
ellore,
T
amil
Nadu,
India.
Email:
vinothbab@gmail.com
1.
INTR
ODUCTION
Due
to
increased
demands
of
the
users,
5G
will
o
v
ercome
the
shortages
of
4G
communication,
which
is
e
xpected
to
of
fer
higher
data
rate,
incr
eased
v
oice
quality
calls,
impro
v
ed
spectrum
ef
ficienc
y
,
reduced
la-
tenc
y
,
etc.
to
the
end
users
[1].
5G
netw
orks
will
support
the
emer
ging
and
e
xisting
technologies,
which
inte
grates
ne
wer
solutions
to
obtain
the
increasing
demand
for
higher
data
rate.
One
of
the
important
features
of
5G
is
D2D
communication
[2].
In
D2D,
tw
o
de
vices
in
close
proximity
communicate
directly
rather
than
communicating
through
the
e
v
olv
ed
node
base
station
(eNB)
or
the
core
netw
ork.
Due
to
its
direct
link
communication,
it
of
floads
data
traf
fic
and
impro
v
es
spectral
ef
ficienc
y
.
It
also
reduces
po
wer
consumption
and
latenc
y
.
In
5G,
the
concept
of
small
cells
is
v
ery
popular
,
where
the
co
v
erage
range
of
access
points
are
reduced
[3].
Due
to
the
mobility
nature,
the
de
vices
under
going
D2D
communica-
tion
requires
frequent
hando
v
ers
[2].
The
selection
of
the
tar
get
netw
ork
is
to
ensure
proper
communication
and
seamless
service
between
the
de
vices,
which
acts
as
a
basic
requirement
in
man
y
practical
applications.
T
o
achie
v
e
al
w
ays
best
connection
(ABC)
and
to
meet
the
good
quality
of
service
(QoS),
the
de
vice
has
to
J
ournal
homepage:
http://ijeecs.iaescor
e
.com/inde
x.php/IJEECS
f
or
d
e
vice
-
to-de
vi
c
e
comm
unicatio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
325
switch
to
dif
ferent
netw
orks
[4].
The
process
of
switching
to
dif
ferent
netw
orks
is
called
VHO
and
the
process
of
switching
between
the
same
netw
orks
is
called
horizontal
hando
v
er
[2].
The
interesting
and
challenging
research
area
in
the
wireless
en
vironment
is
to
select
the
best
netw
ork
from
se
v
eral
candidate
netw
orks.
When
one
of
the
de
vices
under
going
D2D
communication
mo
v
es
a
w
ay
from
the
other
de
vice,
the
link
quality
be-
tween
them
becomes
v
ery
poor
.
This
leads
to
poor
QoS
and
connection
breakdo
wn.
Hence,
one
of
the
de
vices
is
handed
o
v
er
to
the
other
netw
ork.
After
the
hando
v
er
,
these
de
vices
may
continue
their
communication
through
cellular
links.
This
type
of
hando
v
er
is
termed
as
half
hando
v
er
.
A
sample
D2D
scenario
requiring
hando
v
er
is
sho
wn
in
Figure
1a.
D1
and
D2
mark
ed
in
Figure
1a
represent
the
de
vices
under
going
D2D
com-
munication.
The
process
of
half
hando
v
er
is
illustrated
in
Figure
1b
.
In
some
cases,
both
the
de
vices
under
going
D2D
communication
may
mo
v
e
to
w
ards
the
neighboring
netw
ork.
F
or
seamless
service
continuity
,
both
the
de
vices
are
jointly
handed
o
v
er
to
the
best
neighboring
netw
ork.
This
process
is
termed
as
j
oint
hando
v
er
[2],
which
is
illustrated
in
Figure
1c.
(a)
(b)
(c)
Figure
1.
(a)
A
scenario
for
the
de
vice
in
D2D
communication
requiring
hando
v
er
,
(b)
After
half
hando
v
er
,
(c)
After
joint
hando
v
er
T
ar
get
netw
ork
selection
is
an
important
task
to
achie
v
e
a
seamless
connection
and
QoS
in
VHO
en
vironment.
The
netw
ork
g
athers
the
parameters
from
e
v
ery
candidate
netw
ork
and
ranks
them
to
choose
the
best
tar
get
netw
ork.
Most
of
the
con
v
entional
hando
v
er
schemes
mak
e
use
of
recei
v
ed
signal
strength
(RSS)
to
select
the
tar
get
netw
ork
[4,
5].
This
introduces
a
ping-pong
ef
f
ect,
which
leads
to
decreased
throughput,
increased
latenc
y
and
dropping
rate.
Man
y
of
the
recent
approaches
mak
e
use
of
v
arious
parameters
to
decide
the
hando
v
er
and
the
tar
get
netw
ork.
These
include
a
signal
to
noise
ratio
(SNR),
achie
v
able
bit
rate,
bit
error
rate
(BER),
outage
probability
,
cost,
security
,
po
wer
consumption,
a
v
ailable
bandwidth,
etc.
[4].
Due
to
its
simplicity
in
operation,
MADM
algorithms
are
widely
used
in
VHO
decision
making
and
tar
get
netw
ork
selection.
In
literature,
there
e
xist
man
y
compensatory
and
non-compensatory
MADM
algo-
rithms.
Most
of
the
netw
ork
selection
algorithms
proposed
are
based
on
the
compensatory
method.
The
compensatory
algorithms
combine
multiple
criteria
to
find
the
best
netw
ork,
whereas
non-compensatory
algo-
rithms
combi
ne
multiple
criteria
to
find
the
acceptable
netw
ork,
which
satisfies
the
minimum
requirements.
SA
W
[6],
T
OPSIS
[7],
VIK
OR
[8]
algorithms
come
under
compensatory
cate
gory
.
These
are
popular
for
lo
wer
computational
comple
xity
and
impro
v
ed
accurac
y
in
decision
making.
Fuzzy
logic
models
comple
x
systems
f
airly
without
an
y
bias,
which
is
not
in
the
case
of
the
AHP
process
[9,
10].
Fuzzy
logic
is
capable
of
processing
a
lar
ge
number
of
inputs
and
mak
es
a
soft
decision.
It
ef
ficiently
handles
the
imprecise
data
and
represents
it
in
an
innate
form
[3].
Most
of
the
con
v
entional
VHO
decision-making
algorithms
are
based
on
RSS,
which
fluctuates
based
on
v
arious
parameters
lik
e
distance,
mobility
,
speed
and
shado
wing
f
actor
,
etc.
The
imprecise
input
may
cause
inaccurate
decision
in
deciding
the
hando
v
er
.
This
leads
to
o
v
er
or
under
-utilization
of
netw
ork
resources.
Fuzzy
logic
is
pro
v
ed
to
be
ef
ficient
in
handling
the
data
related
to
radio,
QoS
and
user
preferences
[4].
2.
THE
PR
OPOSED
METHOD
In
this
w
ork,
fuzzy
logic
is
combined
with
the
con
v
entional
AHP
and
SA
W
methods
to
sim
ultaneously
process
a
lar
ge
number
of
inputs
and
to
ef
fecti
v
ely
handle
the
imprecise
data
related
to
hando
v
er
decision
making
and
tar
get
netw
ork
selection.
Based
on
the
input
criteria,
Fuzzy
AHP
is
used
to
calculate
the
weights
of
each
criteria.
These
weights
and
the
a
v
ailable
c
andidate
netw
orks
are
gi
v
en
as
the
input
for
the
Fuzzy
SA
W
scheme.
The
netw
ork
with
the
highest
rank
is
chosen
as
the
tar
get
netw
ork
to
hando
v
er
.
The
proposed
method
for
multi-criteria
netw
ork
selection
block
diagram
is
sho
wn
in
Figure
2.
A
fuzzy
based
vertical
hando
ver
network
selection...
(Meenakshi
Subr
amani)
Evaluation Warning : The document was created with Spire.PDF for Python.
326
r
ISSN:
2502-4752
The
rest
of
the
w
ork
is
arranged
as
follo
ws:
An
introduction
to
fuzzy
theory
,
Fuzzy
AHP
and
Fuzzy
SA
W
are
e
xplained
in
section
3.
The
results
are
discussed
in
section
4
and
the
paper
is
concluded
in
section
5.
Figure
2.
Block
diagram
of
Fuzzy
AHP-Fuzzy
SA
W
MADM
netw
ork
selection
3.
RESEARCH
METHOD
3.1.
Fuzzy
theory
Fuzzy
set
theory
denotes
the
ambiguous
data
in
an
innate
form
[3].
Due
to
the
lo
wer
com
p
ut
ational
comple
xity
and
simpler
mathematical
implementation,
triangular
fuzzy
number
(TFN)
is
widely
preferred
for
the
applications
related
to
wireless
communication.
It
is
also
pro
v
ed
that
the
comple
xities
related
to
handling
imprecise
and
uncertainty
information
used
for
netw
ork
selection
is
minimized
with
TFN.
Hence,
in
this
w
ork,
TFN
is
utilized
to
establ
ish
ambiguity
metrics.
TFN
is
one
of
the
important
classification
of
fuzzy
number
with
its
membership
function
defined
by
(
x;
y
;
z
)
,
three
crisp
numbers
as
(
x
y
z
)
,
where
x
is
the
lo
wer
limit,
y
is
the
modal
v
alue
and
z
is
the
upper
limit.
When
x
=
y
=
z
,
then
the
fuzzy
number
will
become
a
real
number
.
A
fuzzy
set
e
G
in
a
uni
v
erse
of
discourse
A
is
represented
by
a
membership
function
e
G
(
a
)
,
which
is
related
to
each
element
a
in
A
and
the
real
number
with
an
interv
al
[0
;
1]
.
The
e
G
(
a
)
function
v
alue
is
referred
as
the
membership
grade
of
a
in
e
G
.
The
fuzzy
number
e
G
on
R
to
be
TFN,
when
the
membership
function,
e
G
(
a
)
:
R
!
[0
;
1]
.
TFN
can
be
defined
as
e
G
(
a
)
=
8
>
<
>
:
(
a
x
)
(
y
x
)
;
x
a
y
(
z
a
)
(
z
y
)
;
y
a
z
0
;
other
w
ise
(1)
3.2.
Fuzzy
AHP
AHP
is
mainly
used
for
decision-making
in
multi-criteria
problems,
which
is
initiated
with
a
measure-
ment
of
ratio
scales
[11].
AHP
use
comparison
and
pre-set
option
selection,
which
depends
on
the
pairwise
comparisons
between
the
options
and
criteria.
Here,
a
qualitati
v
e
judgment
is
used
for
pairwise
comparison.
Fuzzy
AHP
is
v
ery
easy
and
simple
to
analyze
in
making
decisions.
Fuzzy
AHP
causes
the
significance
of
fuzzy
,
which
occurs
in
the
same
ro
w
with
tw
o
combination
items.
The
fuzzy
e
xtension
is
needed
because
the
basic
AHP
f
ailed
to
address
the
main
issue
of
handling
the
high
de
gree
of
imprecise
in
subjecti
v
e
personal
judgments
and
i
ts
preferences.
Fuzzy
AHP
is
applic
able
to
tackle
the
problem
at
hand
by
considering
the
structure
of
multi-criteria
and
v
ague-
ness
in
a
real-time
en
vironment,
which
impro
v
es
the
consistenc
y
in
the
netw
ork
selection.
Fuzzy
AHP
uses
a
9-point
fundamental
scale,
which
is
e
xpressed
in
terms
of
TFN
to
indicate
the
relati
v
e
performance
between
the
pairwise
decision
f
actors.
The
TFN
v
alues
are
represented
in
T
able
1.
T
able
1.
9-point
fundamental
fuzzy
scale
using
TFN
[12]
Fuzzy
scale
intensity
of
importance
Linguistic
scale
description
TFN
Reciprocal
of
TFN
e
1
Equal
importance
(1,1,1)
(1,1,1)
e
2
Moderately
equal
important
(1,2,3)
(1/3,1/2,1)
e
3
Moderately
important
(1,3,5)
(1/5,1/3,1)
e
4
Moderately
strongly
important
(2,4,6)
(1/6,1/4,1/2)
e
5
Strongly
important
(3,5,7)
(1/7,1/5,1/3)
e
6
Strongly
v
ery
important
(4,6,8)
(1/8,1/6,1/4)
e
7
V
ery
strongly
important
(5,7,9)
(1/9,1/7,1/5)
e
8
V
ery
strongly
e
xtremely
important
(6,8,9)
(1/9,1/8,1/6)
e
9
Extremely
important
(7,9,9)
(1/9,1/9,1/7)
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
17,
No.
1,
January
2020
:
324
–
330
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
327
The
Fuzzy
AHP
has
the
follo
wing
steps:
Step
1:
Based
on
the
objecti
v
es,
select
the
suitable
criteria.
Step
2:
Construct
a
pairwise
comparison
matrix
e
R
based
on
each
criteria.
The
elements
f
r
ij
in
e
R
represents
the
pairwise
comparison
of
criteria
i
with
j
.
e
R
=
[
f
r
ij
]
n
n
=
2
6
6
6
4
f
r
11
f
r
12
f
r
13
:
:
:
f
r
1
n
f
r
21
.
.
.
f
r
22
.
.
.
f
r
23
r
2
n
.
.
.
.
.
.
f
r
n
1
f
r
n
2
f
r
n
3
:
:
:
g
r
nn
3
7
7
7
5
(2)
where
n
denotes
the
number
of
criteria
decision
compared
and
i
=
j
=
1
;
2
;
::::n:
f
r
ij
represents
the
relati
v
e
strength
of
tw
o
elements
based
on
TFN.
f
r
j
i
=
[
f
r
ij
]
1
=
(
x
ij
;
y
ij
;
z
ij
)
1
=
1
z
ij
;
1
y
ij
;
1
x
ij
(3)
Step
3:
The
Fuzzy
AHP
comparison
matrix
is
represented
with
TFN
as
e
R
=
[
f
r
ij
]
n
n
=
2
6
6
6
4
(1
;
1
;
1)
(
x
12
;
y
12
;
z
12
)
(
x
1
n
;
y
1
n
;
z
1
n
)
(
x
21
;
y
21
;
z
21
)
.
.
.
(1
;
1
;
1)
.
.
.
(
x
2
n
;
y
2
n
;
z
2
n
)
.
.
.
(
x
n
1
;
y
n
1
;
z
n
1
)
(
x
n
2
;
y
n
2
;
z
n
2
)
(1
;
1
;
1)
3
7
7
7
5
(4)
Step
4:
The
fuzzy
geometric
mean
and
fuzzy
weight
for
each
criteria
is
calculated
as
[13,
14]
e
l
i
=
(
f
r
i
1
f
r
i
2
:::::
f
r
in
)
1
=n
(5)
f
w
i
=
e
l
i
e
l
1
l
2
l
n
1
(6)
The
sign
indicates
fuzzy
multiplication
and
sign
indicates
fuzzy
addition.
f
r
in
is
the
fuzzy
comparison
v
alue
of
i
th
criteria
to
n
th
criteria,
e
l
i
is
the
geometric
mean
of
fuzzy
comparison
v
alue
of
i
th
criteria
to
each
criteria,
f
w
i
is
the
fuzzy
weight
of
i
th
criteria.
Step
5:
In
order
to
check
the
inconsistenc
y
in
the
pairwise
comparison
matrix,
consistenc
y
inde
x
(CI)
is
introduced
to
obtain
the
consistenc
y
ratio
(CR).
CI
is
gi
v
en
as
C
I
=
max
n
n
1
(7)
where
max
=
e
R
:
e
w
e
w
(8)
where
max
is
the
lar
gest
Eigen
v
alue
of
the
comparison
matrix
e
R
,
n
represents
the
number
of
criteria
and
e
w
is
the
weight
v
alue
calculated
to
obtain
Eigen
v
ector
.
Based
on
the
CI,
the
v
alue
of
CR
is
calculated
as
C
R
=
C
I
R
I
(9)
where
RI
is
the
random
consistenc
y
inde
x
as
sho
wn
in
T
able
2.
V
arious
authors
measured
RI
v
alues
for
a
dif
ferent
number
of
criteria.
This
is
tab
ulated
in
[15].
In
[15],
the
authors
estimated
RI
for
each
number
of
criteria
using
100,000
matrices.
The
y
ha
v
e
generated
random
matrices
with
uniform
distrib
ution.
Then,
CIs
are
calculated
for
each
matrix.
The
RI
for
each
number
of
criteria
is
obtained
by
taking
mean
of
these
CI
v
alues.
It
is
pro
v
ed
that
the
RI
v
alue
calculated
by
[16]
is
ef
ficient
than
t
he
other
methods.
Hence,
in
this
w
ork,
we
ha
v
e
used
the
same
table,
which
w
as
listed
in
[16].
If
the
estimated
CR
v
alue
is
less
than
0.1,
then
the
pairwise
construction
is
acceptable,
otherwise,
the
matrix
has
to
be
re
vised
[17].
T
able
2.
Random
consistenc
y
v
alue
[16]
Criteria
1
2
3
4
5
6
7
8
9
10
RI
0
0
0.58
0.9
1.12
1.24
1.32
1.41
1.45
1.49
A
fuzzy
based
vertical
hando
ver
network
selection...
(Meenakshi
Subr
amani)
Evaluation Warning : The document was created with Spire.PDF for Python.
328
r
ISSN:
2502-4752
3.3.
Fuzzy
SA
W
Because
of
its
simpli
city
,
Fuzzy
SA
W
is
widely
used
in
the
MADM
algorithms[18,
4].
Fuzzy
SA
W
requires
the
normalizing
procedure
for
the
decision
matrix
into
a
scale,
which
is
compared
with
e
v
ery
netw
ork
rating.
The
main
idea
of
Fuzzy
SA
W
method
is
to
detect
the
weighted
sum
of
performance
rating
of
e
v
ery
netw
ork
on
each
criteria.
The
steps
are
as
follo
ws:
Step
1:
W
eight
calculation
Obtain
the
weight
v
alue
for
each
criteria
from
Fuzzy
AHP
method.
Step
2:
Fuzzy
decision
matrix
F
ormulate
the
decision
matrix
e
F
and
select
the
suitable
linguistic
v
ariables
with
respect
to
dif
ferent
netw
orks
and
criteria.
A
q
n
fuzzy
decision
matrix
is
formulated
with
the
ratings
of
each
netw
ork
with
each
criteria.
The
entries
are
TFN
instead
of
crisp
v
alues.
C
1
C
2
C
3
C
n
e
F
=
N
w
1
N
w
2
N
w
3
.
.
.
N
w
q
2
6
6
6
6
6
6
4
f
b
11
f
b
12
f
b
13
f
b
1
n
f
b
21
f
b
22
f
b
23
f
b
2
n
f
b
31
f
b
32
f
b
33
f
b
3
n
.
.
.
.
.
.
.
.
.
.
.
.
f
b
q
1
f
b
q
2
f
b
q
3
f
b
q
n
3
7
7
7
7
7
7
5
(10)
where
N
w
1
;
N
w
2
;
N
w
3
;
:::::::::
::N
w
q
are
the
possible
netw
orks,
C
1
;
C
2
;
C
3
;
:::::::::
::;
C
n
are
the
criteria.
The
element
f
b
ij
is
the
fuzzy
decision
matrix,
which
indicates
the
performance
rating
of
each
netw
ork
N
w
i
with
respect
to
criteria
C
j
,
where
i
=
1
;
2
;
3
;
::::::
q
and
j
=
1
;
2
;
3
;
:::::n
respecti
v
ely
.
Step
3:
Fuzzy
normalization
weight
v
alue
The
fuzzy
decision
matrix
e
F
depends
on
the
l
inguistic
v
ariables
and
the
corresponding
TFN.
Each
entry
of
e
F
is
TFN,
which
corresponds
to
3
v
alues,
i.e,
g
b
ij
x
;
g
b
ij
y
;
g
b
ij
z
:
.The
normalized
fuzzy
decision
matrix
f
g
ij
is
gi
v
en
by
f
g
ij
=
g
b
ij
x
e
b
j
+
;
g
b
ij
y
e
b
j
+
;
g
b
ij
z
e
b
j
+
!
(11)
F
or
benefit
criteria,
the
maximum
of
3rd
v
alue
of
TFN
in
the
fuzzy
decision
matrix
e
F
is
identified
using
e
b
j
+
=
max
i
g
b
ij
z
;
w
her
e
j
2
B
(12)
where
B
is
the
set
of
benefit
criteria,
which
is
al
w
ays
e
xpected
to
be
maximum.
All
the
elements
of
e
F
are
normalized
using
e
b
j
+
:
.
Alternati
v
ely
,
cost
criteria
can
also
be
used.
The
normalized
fuzzy
decision
matrix
f
g
ij
for
cost
criteria
is
gi
v
en
by
f
g
ij
=
e
c
j
g
b
ij
z
;
e
c
j
g
b
ij
y
;
e
c
j
g
b
ij
x
!
(13)
F
or
cost
criteria,
the
minimum
of
the
1st
v
alue
of
TFN
in
the
fuzzy
decision
matrix
e
F
is
identified
using
e
c
j
=
min
i
g
b
ij
x
;
w
her
e
j
2
C
(14)
where
C
is
the
set
of
cost
criteria,
which
is
al
w
ays
e
xpected
to
be
minimum.
All
the
elements
of
e
F
are
normalized
using
e
c
j
.
F
or
benefit
criteria,
the
highly
acceptable
v
alue
is
considered
as
the
best
v
alue.
F
or
e
xample,
RSS
and
data
rate.
F
or
cost
criteria,
the
lo
wer
acceptable
v
alue
is
considered
as
the
best
v
alue.
Step
4:
W
eight
normalization
v
alue
In
this
step,
a
weight
normalized
decision
matrix
g
M
ij
is
formulated
by
multiplying
each
element
f
g
ij
of
normalized
fuzzy
decision
matrix
with
the
weights
e
w
obtained
through
Fuzzy
AHP
in
step
4
of
section
3
as
g
M
ij
=
2
6
6
6
4
f
w
1
f
g
11
f
w
2
f
g
12
f
w
3
f
g
13
f
w
n
f
g
1
n
f
w
1
f
g
21
f
w
2
f
g
22
f
w
3
f
g
23
f
w
n
f
g
2
n
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
f
w
1
f
g
q
1
f
w
2
f
g
q
2
f
w
3
f
g
q
3
f
w
n
f
g
q
n
3
7
7
7
5
(15)
Indonesian
J
Elec
Eng
&
Comp
Sci,
V
ol.
17,
No.
1,
January
2020
:
324
–
330
Evaluation Warning : The document was created with Spire.PDF for Python.
Indonesian
J
Elec
Eng
&
Comp
Sci
ISSN:
2502-4752
r
329
Step
5:
Netw
ork
ranking
Fuzzy
SA
W
algorithm
e
v
aluates
all
the
netw
orks
and
mak
es
a
decision
to
perform
hando
v
er
.
The
o
v
erall
rank
of
i
th
netw
ork
is
e
v
aluated
using
D
S
AW
i
=
1
n
n
X
j
=1
f
g
ij
:
f
w
j
(16)
4.
RESUL
TS
AND
DISCUSSION
MA
TLAB
2017a
tool
is
used
for
simulation.
The
consi
dered
simulation
parameters
are
li
sted
in
T
able
3.
Figure
3
sho
ws
the
hando
v
er
decision
delay
v
ersus
a
number
of
inputs.
The
simulations
are
carried
out
for
streaming
applications.
During
the
v
ertical
hando
v
er
decision,
the
hando
v
er
decision
delay
is
considered
as
one
of
the
important
parameters.
The
delay
in
the
hando
v
er
decision
leads
to
QoS
de
gradation.
If
the
hando
v
er
decision
is
done
too
early
,
it
leads
to
unnecessary
hando
v
er
.
The
goal
is
to
measure
the
hando
v
er
decision
delay
for
the
increased
number
of
inputs.
In
man
y
real-w
orld
problems,
decision-mak
ers
may
not
be
sure
about
their
preferences.
This
leads
to
uncertainty
.
The
inclusion
of
fuzzy
logic
ef
fecti
v
ely
handles
v
arious
decision-making
problems.
In
Fuzzy
AHP
,
the
pairwise
comparison
matrix
is
formulated
with
the
help
of
TFN.
This
a
v
oids
ambiguities
in
finding
the
weights.
These
weights
are
used
in
fuzzy
SA
W
for
ranking
and
selecting
the
tar
get
netw
orks.
The
decision
mak
ers
also
use
the
fuzzy
logic
in
formulating
the
decision
matrix
which
compares
alternati
v
es
with
criteria.
Since,
the
uncertainties
are
handled
ef
fecti
v
ely
and
f
ast,
the
hando
v
er
decision
t
ime
during
the
selection
of
the
tar
get
netw
ork
is
v
ery
lo
w
for
the
Fuzzy
AHP-Fuzzy
SA
W
scheme.
T
able
3.
Simulation
parameters
P
arameters
V
alues
Netw
orks
W
i-Fi
=
W
iMAX
=
L
TE-A
Cell
radius
(km)
W
i-Fi:
0.25;
W
iMAX:
10;
L
TE-A:
3
T
ransmit
Po
wer
(dBm)
W
i-Fi:
13;
W
iMAX:
47;
L
TE-A:
46
Bandwidth
(MHz)
W
i-Fi:
20;
W
iMAX:
40;
L
TE-A:
100
P
ath
loss
model
for
W
i-Fi
P
L
(
dB
)
W
i
F
i
=
34
:
48
+
32
:
79log
10
d
(
m
)
[2]
P
ath
loss
model
for
W
iMAX
P
L
(
dB
)
W
iM
AX
=
130
:
62
+
37
:
6log
10
d
(
k
m
)
[2]
P
ath
loss
model
for
L
TE-A
P
L
(
dB
)
LT
E
A
=
103
:
8
+
20
:
9log
10
d
(
k
m
)
[2]
Figure
3.
Hando
v
er
decision
delay
v
ersus
number
of
inputs
comparison
of
v
arious
MADM
schemes
F
or
three
criteria,
Fuzzy
AHP-Fuz
zy
SA
W
of
fers
29.03
%
,
43.59
%
,
55.55
%
,
20
%
,
reduction
in
hando
v
er
decision
delay
o
v
er
the
con
v
entional
SA
W
[4,
6],
T
OPSIS
[4,
7],
VIK
OR
[4,
8]
and
Fuzzy
SA
W
[4]
schemes.
F
or
an
y
number
of
increased
inputs,
Fuzzy
AHP-Fuzzy
SA
W
hando
v
er
decision
time
will
be
lesser
.
Hence,
Fuzzy
AHP-Fuzzy
SA
W
selects
the
best
tar
get
netw
ork
to
perform
an
ef
ficient,
f
ast
and
seamless
hando
v
er
.
5.
CONCLUSION
Since
D2D
communication
mostly
happens
for
a
smaller
duration,
the
hando
v
er
decision
delay
should
be
much
smaller
for
a
seamless
connection.
M
ost
of
the
con
v
entional
MADM
schemes
are
not
f
ast
and
reliable.
The
y
also
f
ail
when
handling
the
imprecise
data.
Hence,
i
n
t
his
w
ork,
the
concept
of
fuzzy
is
combined
with
A
fuzzy
based
vertical
hando
ver
network
selection...
(Meenakshi
Subr
amani)
Evaluation Warning : The document was created with Spire.PDF for Python.
330
r
ISSN:
2502-4752
the
con
v
entional
SA
W
and
the
h
ybrid
scheme
Fuz
zy
AHP-Fuzzy
SA
W
is
pro
v
ed
to
of
fer
impro
v
ed
hando
v
er
decision
delay
performance
o
v
er
SA
W
,
T
OPSIS,
VIK
OR,
Fuzzy
SA
W
schemes.
Due
to
lo
wer
hando
v
er
deci-
sion
delay
,
the
pack
et
drop
ratio
and
service
interruption
are
greatly
reduced
for
the
proposed
scheme.
This
increases
the
a
v
erage
data
rate
e
xperienced
by
each
de
vice.
This
also
minimizes
the
latenc
y
of
the
proposed
scheme.
This
simple
w
ork
can
be
e
xtended
for
other
criteria,
netw
orks
and
MADM
algorithms.
As
a
future
study
,
these
schemes
can
also
be
combined
and
tested
with
neuro-fuzzy
based
algorithms.
REFERENCES
[1]
S.T
.
Shah,
et
al.,
“De
vice-to-de
vice
communications:
A
contemporary
surv
e
y”,
W
ireless
Personal
Com-
munications
,
98,
No.1,
pp.1247-1284,
2018.
[2]
M.
Subramani,
and
V
.B.
K
umara
v
elu,
“
A
Quality-A
w
are
Fuzzy-Logic-Based
V
ertical
Hando
v
er
Decision
Algorithm
for
De
vice-to-De
vice
Communication”,
Arabian
Journal
for
Science
and
Engineering
,
pp.1-13,
2018.
[3]
A.
Murug
adass,
and
A.
P
achiyappan,
“Fuzzy
Logic
Based
Co
v
erage
and
Cost-Ef
fecti
v
e
Placement
of
Serving
Nodes
for
4G
and
Be
yond
Cellular
Netw
orks”,
W
ireless
Communications
and
Mobile
Comput-
ing
,
2017.
[4]
A.B
Zineb,
et
al.,
“
An
enhanced
v
ertical
hando
v
er
based
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