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tiv
ate
d
th
e
UE
is
au
to
m
atica
ll
y
co
n
n
ec
ted
to
th
is
w
ir
ele
s
s
n
et
wo
r
k
.
B
u
t
w
h
at
i
f
th
i
s
n
et
w
o
r
k
i
s
n
o
t
w
o
r
k
i
n
g
?
Ou
r
s
o
l
u
tio
n
,
G
A
f
VH,
u
s
es
t
h
e
b
est
v
al
u
es
o
f
t
h
e
R
SS
,
o
b
tain
ed
f
r
o
m
th
e
G
A
a
s
a
t
h
r
esh
o
ld
an
d
co
m
p
ar
e
it
w
it
h
th
e
m
ea
s
u
r
ed
v
alu
e.
E
v
e
n
if
t
h
e
R
SS
o
f
a
n
et
w
o
r
k
b
ec
o
m
es
b
etter
th
a
n
an
o
th
er
,
if
it
is
n
o
t
s
u
p
er
io
r
to
t
h
e
t
h
r
es
h
o
ld
v
al
u
e
t
h
er
e
i
s
n
o
n
ee
d
to
m
ak
e
a
h
an
d
o
v
er
d
ec
i
s
io
n
.
No
tin
g
t
h
at,
t
h
e
Ga
f
V
H
alg
o
r
is
m
is
b
ased
o
n
m
u
lt
ip
les
cr
iter
ia,
th
e
h
an
d
o
v
er
d
ec
is
io
n
w
ill b
e
b
ased
o
n
m
u
ltip
le
p
a
r
a
m
eter
s
.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
;
s
ec
tio
n
t
w
o
p
r
ese
n
ts
r
elate
d
w
o
r
k
s
,
s
ec
t
io
n
th
r
ee
p
r
esen
ts
t
h
e
Gen
etic
A
l
g
o
r
it
h
m
a
n
d
t
h
e
p
r
o
p
o
s
ed
v
er
tical
h
a
n
d
o
f
f
alg
o
r
ith
m
(
G
Af
VH)
,
s
ec
t
io
n
f
o
u
r
p
r
esen
ts
t
h
e
s
i
m
u
lat
io
n
r
es
u
lts
.
Fi
n
all
y
w
e
co
n
clu
d
e
in
s
ec
tio
n
f
i
v
e
2.
R
E
L
AT
E
D
WO
RK
S
Var
io
u
s
m
eth
o
d
s
a
n
d
ap
p
r
o
ac
h
es
o
f
n
et
w
o
r
k
s
elec
tio
n
h
av
e
be
e
n
d
is
c
u
s
s
ed
i
n
t
h
e
liter
atu
r
e.
A
n
e
t
w
o
r
k
s
elec
tio
n
s
ch
e
m
e
b
ased
o
n
w
ei
g
h
t
esti
m
atio
n
o
f
Qo
S
p
ar
am
eter
s
in
Hete
r
o
g
en
eo
u
s
w
ir
ele
s
s
m
u
lti
m
ed
ia
n
et
w
o
r
k
i
s
p
r
o
p
o
s
ed
in
[
1
]
.
T
h
is
s
ch
e
m
e
est
i
m
ated
th
e
w
ei
g
h
ts
o
f
Qo
S
p
ar
a
m
eter
s
s
u
ch
a
s
b
an
d
w
id
t
h
,
d
ela
y
,
p
ac
k
e
ts
lo
s
s
a
n
d
d
ata
tr
an
s
f
er
co
s
t,
b
y
u
s
i
n
g
s
i
m
ilar
it
y
to
id
ea
l
s
o
lu
tio
n
.
T
h
e
r
es
u
lt
s
s
h
o
w
ed
t
h
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ca
n
s
elec
t
th
e
b
est
n
et
w
o
r
k
in
a
Het
er
o
g
e
n
eo
u
s
e
n
v
ir
o
n
m
e
n
t.
Au
t
h
o
r
s
in
[
2
]
p
r
o
p
o
s
ed
an
an
al
y
tical
m
o
d
el
to
ca
p
tu
r
e
t
h
e
p
r
ef
er
en
ce
s
o
f
en
d
-
u
s
er
s
,
u
s
in
g
t
h
is
m
o
d
el
th
e
y
d
esi
g
n
ed
an
A
u
to
m
atic
Ne
t
w
o
r
k
Select
i
o
n
(
A
N
S)
m
ec
h
an
is
m
t
h
at
to
o
k
in
to
ac
co
u
n
t a
ll a
s
p
ec
t o
f
t
h
e
tr
ad
e
-
o
f
f
,
b
et
w
ee
n
th
e
q
u
alit
y
o
f
th
e
co
n
n
ec
tio
n
,
th
e
p
r
ef
er
en
ce
s
o
f
th
e
u
s
er
s
,
an
d
th
e
co
s
t.
T
h
is
m
o
d
el
w
a
s
i
m
p
le
m
en
ted
an
d
test
ed
in
a
m
u
lti
tec
h
n
o
lo
g
ies
s
i
m
u
lato
r
.
R
es
u
lt
s
s
h
o
w
ed
th
a
t
th
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
ca
n
b
e
b
en
ef
icial
to
b
o
th
u
s
er
s
a
n
d
o
p
er
ato
r
s
.
A
n
e
w
i
n
telli
g
e
n
t
v
er
tical
h
an
d
o
f
f
d
ec
is
io
n
al
g
o
r
ith
m
u
s
in
g
F
u
zz
y
L
o
g
ic
an
d
Gen
er
i
c
alg
o
r
ith
m
w
as
p
r
o
p
o
s
ed
in
[
3
]
.
I
t
esti
m
ates
t
h
e
h
a
n
d
o
f
f
r
eq
u
ir
e
m
en
t
to
s
elec
t
th
e
o
p
ti
m
al
n
et
w
o
r
k
an
d
m
a
k
e
s
a
h
an
d
o
f
f
d
ec
is
io
n
.
In
[
4
]
,
a
m
u
l
ti
-
cr
i
ter
ia
ac
ce
s
s
n
et
w
o
r
k
s
e
lectio
n
alg
o
r
it
h
m
w
a
s
p
r
o
p
o
s
ed
in
W
I
M
A
X
en
v
ir
o
n
m
en
t,
in
o
r
d
er
to
p
r
o
v
id
e
h
i
g
h
q
u
ali
t
y
o
f
s
er
v
ice
s
a
n
d
s
a
tis
f
y
d
i
f
f
er
en
t
t
y
p
es
o
f
u
s
e
r
s
.
T
h
is
alg
o
r
ith
m
i
s
b
ased
o
n
a
co
m
b
i
n
atio
n
o
f
An
al
y
tical
H
ier
ar
ch
y
P
r
o
ce
s
s
[5
],
an
d
Gr
e
y
R
elatio
n
a
l
An
al
y
s
i
s
[6
].
T
h
e
A
HP
m
et
h
o
d
d
ec
id
ed
o
n
th
e
r
elativ
e
w
e
ig
h
t
s
o
f
th
e
cr
iter
ia
s
et,
ac
co
r
d
in
g
to
n
et
w
o
r
k
s
p
er
f
o
r
m
a
n
ce
s
.
T
h
e
G
A
R
r
a
n
k
ed
t
h
e
n
et
w
o
r
k
alter
n
ati
v
es.
A
n
o
v
el
ap
p
r
o
ac
h
f
o
r
n
et
w
o
r
k
s
elec
t
io
n
w
as p
r
o
p
o
s
ed
in
[7
],
it is
b
as
ed
o
n
A
HP
m
eth
o
d
an
d
B
an
k
r
u
p
tc
y
Ga
m
e.
I
n
th
is
ap
p
r
o
ac
h
th
e
A
HP
m
et
h
o
d
ev
alu
ated
th
e
w
ei
g
h
ts
o
f
m
u
ltip
l
e
d
ec
is
io
n
cr
iter
ia,
th
an
t
h
e
B
an
k
r
u
p
tc
y
Ga
m
e
i
s
u
s
ed
to
a
s
s
es
s
t
h
e
p
o
t
en
tials
o
f
a
v
ailab
le
ca
n
d
id
ate
n
et
w
o
r
k
s
.
A
n
o
t
h
er
n
et
w
o
r
k
s
elec
t
io
n
m
ec
h
a
n
is
m
a
s
p
r
ese
n
ted
i
n
[8
]
,
w
a
s
b
ased
o
n
t
wo
d
ec
is
io
n
m
ak
in
g
m
et
h
o
d
s
,
t
h
e
M
u
lti
-
A
HP
a
n
d
GR
A
.
Mu
l
ti
-
A
HP
is
u
s
ed
to
w
ei
g
h
cr
iter
io
n
s
w
h
ile
G
R
A
r
an
k
ed
t
h
e
alter
n
ati
v
es.
I
n
[
9
]
th
e
au
t
h
o
r
s
p
r
o
p
o
s
ed
a
n
e
w
r
a
n
k
i
n
g
al
g
h
o
r
it
h
m
w
h
i
ch
co
m
b
i
n
e
M
u
lti
-
A
ttrib
u
tes
Dec
is
io
n
Ma
k
i
n
g
[
1
0
]
an
d
Ma
h
alan
o
b
is
d
is
ta
n
ce
.
Firt
th
e
y
ap
p
laied
a
class
i
f
icat
io
n
m
et
h
o
d
to
b
u
ild
e
class
es
w
h
ic
h
h
a
v
e
h
o
m
o
g
e
n
o
u
s
cr
ite
r
iat.
T
h
en
th
e
Fu
zz
y
A
HP
an
d
M
ADM
m
eth
o
d
s
ar
e
ap
p
lied
to
d
eter
m
in
e
b
o
t
h
th
e
w
ei
g
t
h
s
o
f
i
n
ter
class
e
s
a
n
d
in
tr
a
-
cla
s
s
e
s
,
f
in
all
y
Ma
h
ala
n
o
b
is
d
is
tan
ce
i
s
u
s
ed
t
o
r
an
k
th
e
al
ter
n
ati
v
es.
I
n
[
1
1
]
a
b
r
an
d
n
e
w
ap
p
r
o
ac
h
f
o
r
n
et
w
o
r
k
s
elec
tio
n
w
a
s
p
r
o
p
o
s
ed
.
I
t u
s
ed
MA
DM
as a
r
an
k
i
n
g
al
g
o
r
ith
m
f
o
llo
w
ed
b
y
E
n
tr
o
p
y
to
in
i
tial
ized
w
ei
g
h
t
.
3.
T
H
E
A
G
F
VH
AL
G
O
R
I
T
H
M
3
.
1
.
G
enet
ic
Alg
o
rit
h
m
Gen
etic
al
g
o
r
ith
m
(
G
A
)
is
a
m
eta
h
eu
r
i
s
tic
i
n
s
p
ir
ed
b
y
t
h
e
m
ec
h
a
n
i
s
m
o
f
n
at
u
r
al
s
elec
t
io
n
.
I
t
u
s
ed
to
g
en
er
ate
h
i
g
h
q
u
alit
y
s
o
lu
tio
n
s
i
n
o
r
d
er
to
o
p
ti
m
ize
a
s
ea
r
ch
p
r
o
b
le
m
.
T
h
e
m
ai
n
ad
v
a
n
ta
g
e
o
f
t
h
e
G
A
i
s
it
s
f
ast
co
n
v
er
g
e
n
ce
.
I
t
ca
n
co
n
v
er
g
e
q
u
ic
k
l
y
o
n
a
p
r
o
b
le
m
’
s
s
p
ec
if
ic
s
o
lu
tio
n
[1
2
]
.
An
o
th
er
a
d
v
an
ta
g
e
i
s
t
h
at
t
h
e
GA
d
o
es n
o
t p
r
o
v
id
e
a
s
i
n
g
le
s
o
lu
tio
n
b
u
t
a
lis
t o
f
o
p
ti
m
u
m
s
o
lu
tio
n
s
.
T
h
e
G
A
c
h
r
o
m
o
s
o
m
e
i
s
r
ep
r
esen
ted
b
y
a
s
i
m
p
le
d
ata
s
tr
u
ct
u
r
e
v
ec
to
r
.
T
h
is
s
tr
u
ct
u
r
e
h
a
s
d
if
f
er
en
t
d
ata
t
y
p
es
w
h
ic
h
d
ef
i
n
e
t
h
eir
g
en
es.
I
n
t
h
e
ca
s
e
o
f
R
A
T
s
elec
tio
n
th
e
c
h
r
o
m
o
s
o
m
e
ca
n
h
av
e
m
a
n
y
p
ar
a
m
eter
s
as
g
e
n
es,
w
e
ch
o
s
e
th
e
m
o
s
t
i
m
p
o
r
tan
t
o
f
th
e
m
w
h
ic
h
ar
e
th
e
DR
,
R
SS
,
B
E
R
Dela
y
.
T
h
e
Gen
etic
A
lg
o
r
it
h
m
ca
n
b
e
i
m
p
le
m
e
n
ted
u
s
i
n
g
t
h
e
f
o
llo
w
in
g
s
tep
s
:
1.
I
n
itializatio
n
:
w
e
r
an
d
o
m
l
y
g
e
n
er
ated
an
in
itial
p
o
p
u
latio
n
o
f
‘
n
’
ch
r
o
m
o
s
o
m
e
s
.
2.
Fit
n
e
s
s
m
ea
s
u
r
es:
w
e
e
v
al
u
ate
th
e
f
i
tn
e
s
s
o
f
t
h
e
i
n
itial p
o
p
u
l
atio
n
’
s
ch
r
o
m
o
s
o
m
e
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
4
,
A
u
g
u
s
t 2
0
1
9
:
2
5
3
4
-
2540
2536
3.
C
o
n
s
tr
u
ctio
n
o
f
a
n
e
w
p
o
p
u
lat
io
n
: u
s
in
g
t
h
e
f
o
llo
w
i
n
g
s
tep
s
w
e
r
ep
r
o
d
u
ce
th
e
n
e
w
g
e
n
er
ati
o
n
.
a.
Selectio
n
: t
h
e
s
elec
t
io
n
p
r
o
ce
s
s
is
b
ased
o
n
th
e
le
v
el
o
f
t
h
e
c
h
r
o
m
o
s
o
m
es
f
it
n
ess
.
b.
C
r
o
s
s
o
v
er
: T
h
e
o
b
j
ec
tiv
e
o
f
th
e
cr
o
s
s
o
v
er
is
to
m
ak
e
n
e
w
i
n
d
iv
id
u
al
s
f
o
r
t
h
e
in
co
m
i
n
g
g
e
n
er
atio
n
w
i
t
h
a
p
r
o
b
a
b
ilit
y
o
f
cr
o
s
s
o
v
er
.
c.
Mu
tatio
n
: T
h
e
n
e
w
cr
ea
ted
in
d
iv
id
u
al
w
il
l b
e
m
u
tated
at
a
d
ef
i
n
ite
p
o
in
t.
4.
Sto
p
p
in
g
C
r
iter
ia
: th
e
p
r
o
ce
s
s
is
r
ep
ea
ted
u
n
til a
d
esire
d
o
p
tim
u
m
s
o
lu
tio
n
is
r
ea
ch
ed
.
3
.
2
.
I
m
ple
m
ent
a
t
io
n o
f
g
enet
ic
a
l
g
o
rit
h
m
in
net
w
o
rk
s
elec
t
io
n
3
.
2
.
1
.
Co
ns
t
ruct
ing
t
he
chro
m
o
s
o
m
e
s
t
ruct
ure
W
e
co
n
s
id
er
f
o
u
r
attr
ib
u
tes:
T
h
e
Data
-
R
ate
(
DR
)
,
th
e
R
ec
eiv
ed
Sig
n
al
Stre
n
g
t
h
(
R
SS
)
,
B
it
E
r
r
o
r
R
ate
(
B
E
R
)
,
an
d
Dela
y
,
as s
h
o
w
n
i
n
th
e
T
ab
le
1.
T
ab
le
1
.
T
h
e
ch
r
o
m
o
s
o
m
e
s
tr
u
ctu
r
e
G
e
n
e
C
o
d
e
d
b
i
t
s
R
a
n
g
e
L
e
v
e
l
D
a
t
a
r
a
t
e
7
B
i
t
0
M
b
p
s~
1
0
0
M
b
p
s
00
R
S
S
6
B
i
t
-
1
0
0
d
b
m~
-
5
0
d
b
m
50
B
i
t
e
r
r
o
r
r
a
t
e
3
B
i
t
1
0
~
1
0
8
D
e
l
a
y
2
B
i
t
1
0
m
s~
1
0
0
ms
4
Ou
r
ch
r
o
m
o
s
o
m
e
s
tr
u
c
tu
r
e
c
o
n
s
is
ts
o
f
f
o
u
r
g
e
n
e
s
,
w
it
h
d
i
f
f
er
en
t
s
ize.
T
h
e
DR
g
e
n
e
co
n
tai
n
s
1
0
0
v
alu
e
s
f
r
o
m
0
Mb
p
s
to
1
0
0
Mb
p
s
,
w
h
ic
h
m
ea
n
s
1
0
0
d
ec
im
a
l
v
al
u
es
it
ca
n
b
e
co
d
ed
o
n
7
b
its
.
T
h
e
R
SS
g
e
n
e
co
n
tain
s
5
0
d
ec
im
al
v
al
u
es,
i
t’
s
co
d
ed
o
n
6
b
its
.
T
h
e
B
E
R
g
en
e
h
as
8
d
e
cim
al
v
alu
e
s
it
ca
n
b
e
c
o
d
ed
o
n
4
b
its
,
an
d
th
e
Dela
y
g
e
n
e
h
as
o
n
l
y
4
v
al
u
es i
t’
s
co
d
ed
o
n
2
b
its
as s
h
o
w
n
i
n
T
ab
le
1
.
3
.
2
.
2
.
I
nitia
liza
t
io
n a
nd
f
it
nes
s
m
ea
s
ure
W
e
r
an
d
o
m
l
y
g
e
n
er
ated
an
in
itial
p
o
p
u
latio
n
o
f
‘
n
’
ch
r
o
m
o
s
o
m
es.
E
ac
h
c
h
r
o
m
o
s
o
m
e
i
s
a
co
m
b
i
n
atio
n
o
f
t
h
e
f
o
u
r
g
e
n
e
s
a
n
d
it
’
s
co
d
ed
o
n
1
9
b
its
.
W
e
h
a
v
e
to
co
m
p
u
te
n
o
w
t
h
e
F
itn
es
s
m
ea
s
u
r
e
f
o
r
each
g
e
n
e,
f
o
llo
w
ed
b
y
th
e
f
it
n
es
s
o
f
t
h
e
c
h
r
o
m
o
s
o
m
e
W
e
h
av
e
to
co
m
p
u
te
n
o
w
t
h
e
Fi
tn
e
s
s
m
ea
s
u
r
e
f
o
r
ea
c
h
g
en
e,
f
o
llo
w
ed
b
y
t
h
e
f
it
n
ess
o
f
th
e
c
h
r
o
m
o
s
o
m
e.
T
h
e
f
it
n
ess
m
ea
s
u
r
e
o
f
th
e
g
e
n
e
(
i)
is
g
i
v
en
b
y
:
fi
{
w
i
|
g
i
−
g
i
d
|
g
i
d
,
|
g
i
−
g
i
d
|
<
g
i
d
w
i
,
ℎ
W
e
s
et
th
e
p
ar
a
m
eter
s
g
1
,
g
2
,
g
3
an
d
g
4
co
r
r
esp
o
n
d
in
g
r
es
p
ec
tiv
el
y
to
th
e
D
R
,
th
e
R
SS
,
th
e
B
E
R
,
an
d
t
h
e
Dela
y
.
g
i
d
is
th
e
r
eq
u
ir
ed
Qo
S
p
ar
a
m
eter
,
w
h
ile
w
i
r
ep
r
esen
ts
th
e
w
ei
g
h
t
p
ar
a
m
eter
.
T
h
e
f
itn
e
s
s
v
a
lu
e
o
f
t
h
e
ch
r
o
m
o
s
o
m
e
i
s
o
b
tain
ed
b
y
:
F=
∑
f
i
4
i
=
1
C
o
n
s
tr
u
cti
n
g
th
e
n
e
w
g
e
n
er
ati
o
n
u
s
i
n
g
g
e
n
etic
s
s
tep
s
.
W
e
u
s
e
t
h
e
R
o
u
lette
W
h
ee
l
[1
3
]
s
elec
tio
n
m
et
h
o
d
,
f
o
r
ea
ch
ch
r
o
m
o
s
o
m
e,
w
e
ca
lc
u
late
p
r
o
b
ab
ilit
y
o
f
it’
s
s
elec
tio
n
p
i
,
th
is
p
r
o
b
ab
ilit
y
i
s
g
i
v
en
b
y
:
p
i
=
f
i
∑
fi
4
i
=
1
3
.
2
.
3
.
Cro
s
s
o
v
er
a
nd
m
uta
t
io
n
T
h
e
aim
o
f
cr
o
s
s
o
v
er
p
r
o
ce
s
s
is
to
ex
ch
an
g
e
ch
ar
ac
ter
is
ti
cs
o
f
an
y
t
w
o
ch
r
o
m
o
s
o
m
es
w
it
h
ea
ch
o
th
er
to
f
o
r
m
t
w
o
n
e
w
c
h
r
o
m
o
s
o
m
e
s
.
T
h
er
e
ar
e
s
ev
er
al
cr
o
s
s
o
v
er
tech
n
iq
u
e
s
,
i
n
th
is
p
ap
er
w
e
ap
p
lied
th
e
2
-
p
o
i
n
t
cr
o
s
s
o
v
er
p
r
o
ce
s
s
as
s
h
o
w
n
in
F
i
g
u
r
e
1
.
T
h
e
Mu
tatio
n
tec
h
n
iq
u
e
is
ap
p
lied
o
n
th
e
ch
i
ld
g
e
n
es,
by
alter
i
n
g
a
b
in
ar
y
b
it o
f
0
to
1
o
r
v
ice
v
er
s
a.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
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lec
&
C
o
m
p
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g
I
SS
N:
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Gen
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a
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o
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ith
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fo
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ve
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l h
a
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GA
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in
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etw
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ma
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2537
F
ig
u
r
e
1
.
T
h
e
cr
o
s
s
o
v
er
2
-
p
o
i
n
t p
r
o
ce
s
s
3
.
2
.
4
.
T
he
s
t
o
pp
ing
cr
it
er
ia
T
h
e
o
p
tim
u
m
s
to
p
p
in
g
cr
iter
i
a
is
w
h
e
n
a
b
est
s
o
lu
tio
n
i
s
r
e
ac
h
ed
,
in
o
u
r
ca
s
e
t
h
e
s
to
p
p
in
g
cr
iter
ia
i
s
w
h
e
n
w
e
o
b
tain
th
e
b
est c
h
r
o
m
o
s
o
m
e.
3
.
3
.
T
he
pro
po
s
e
d
v
er
t
ica
l ha
nd
o
v
er
a
lg
o
rit
hm
T
h
e
p
r
o
p
o
s
ed
v
er
tical
h
an
d
o
v
er
alg
o
r
it
h
m
is
b
ased
o
n
G
en
etic
A
l
g
o
r
ith
m
a
s
s
h
o
w
n
i
n
F
i
g
u
r
e
2
.
T
h
e
GA
s
elec
ts
t
h
e
b
est
c
h
r
o
m
o
s
o
m
e,
w
h
ic
h
i
s
co
m
p
o
s
ed
f
r
o
m
t
h
e
b
est
g
e
n
es
(
D
R
,
R
S
S
,
B
E
R
,
Dela
y
)
,
th
is
ch
r
o
m
o
s
o
m
e
v
alu
e
s
ar
e
co
m
p
ar
ed
w
it
h
th
e
c
u
r
r
en
ts
n
et
w
o
r
k
s
v
al
u
e
s
w
h
ic
h
ar
e
th
e
cu
r
r
en
t
(
DR
,
R
SS
,
B
E
R
an
d
Dela
y
)
,
f
o
r
ea
ch
n
et
w
o
r
k
.
I
f
th
e
ca
lcu
lated
v
alu
e
i
s
less
s
i
g
n
i
f
ica
n
t
t
h
an
th
e
b
est
v
alu
e,
th
e
alg
o
r
it
h
m
au
to
m
at
icall
y
d
ec
id
es
to
h
an
d
o
f
f
f
r
o
m
t
h
e
cu
r
r
e
n
t
n
et
w
o
r
k
,
if
n
o
t
t
h
e
h
an
d
o
v
er
d
o
es
n
o
t
o
cc
u
r
.
W
ith
t
h
e
R
S
S
b
ased
m
et
h
o
d
th
e
UE
m
a
k
es
a
Han
d
o
v
er
,
ea
c
h
t
i
m
e
t
h
e
R
S
S
o
f
a
n
et
w
o
r
k
is
b
etter
th
a
n
an
o
th
er
,
e
v
en
w
h
e
n
th
is
R
S
S
v
al
u
e
is
n
o
t
w
o
r
s
t.
T
h
e
GA
f
V
H
u
s
es
t
h
e
b
est
v
alu
e
s
o
f
t
h
e
R
SS
,
o
b
tain
ed
f
r
o
m
th
e
G
A
a
s
a
th
r
es
h
o
ld
an
d
co
m
p
ar
e
it
w
i
th
th
e
m
ea
s
u
r
ed
v
al
u
e.
E
v
e
n
i
f
t
h
e
R
SS
o
f
a
n
et
w
o
r
k
b
ec
o
m
es
b
etter
th
an
a
n
o
t
h
er
,
if
it
’
s
n
o
t s
u
p
er
io
r
to
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e
th
r
es
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v
alu
e
t
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e
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n
o
n
ee
d
to
m
ak
e
a
h
an
d
o
v
er
d
ec
is
io
n
.
Fig
u
r
e
2
.
Gr
ap
h
ic
i
llu
s
tr
atio
n
o
f
t
h
e
G
Af
VH
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I
SS
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SI
M
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r
p
r
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o
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ith
m
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o
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g
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d
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w
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r
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o
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ig
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r
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n
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ted
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n
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ig
u
r
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4
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at
t
h
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t
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m
es
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,
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p
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h
at
d
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iv
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th
e
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et
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et
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e
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t
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th
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s
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r
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h
a
n
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h
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et
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ith
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h
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S
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ased
m
et
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th
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m
a
k
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Ha
n
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ch
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e
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h
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et
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h
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o
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n
w
h
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th
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g
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ith
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.
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h
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n
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ted
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in
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ig
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r
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5
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h
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lated
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m
o
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h
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te
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h
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ize
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ith
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r
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m
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to
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atica
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ted
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et
w
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i
f
th
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ir
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ter
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ated
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n
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t
h
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et
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a
s
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ter
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n
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tio
n
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n
t
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s
e
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n
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l
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p
ar
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eter
to
m
a
k
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h
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n
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is
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n
o
t
ef
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icien
t.
No
tin
g
t
h
at,
t
h
e
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Af
VH
alg
o
r
is
m
is
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ased
o
n
m
u
lt
i
p
les cr
iter
ia
w
h
ich
ar
e
m
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n
tio
n
ed
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r
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e
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d
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er
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ec
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w
ill b
e
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as
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n
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l
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ar
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m
eter
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ate
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ased
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lc
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late
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e
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p
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t
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b
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an
d
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e
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et
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atica
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w
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h
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w
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i
n
F
ig
u
r
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6
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ate
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ased
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o
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o
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ased
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ased
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,
Vo
l.
9
,
No
.
4
,
A
u
g
u
s
t 2
0
1
9
:
2
5
3
4
-
2540
2540
5.
CO
NCLU
SI
O
N
Ou
r
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
g
i
v
in
g
b
etter
an
d
m
o
r
e
ac
cu
r
at
e
r
esu
lts
r
ea
d
in
g
s
,
d
u
e
to
th
e
s
i
m
p
li
f
ied
an
d
o
p
tim
ized
s
elec
tio
n
cr
iter
ia
p
r
o
ce
s
s
.
Fu
r
th
er
m
o
r
e
in
t
h
e
ca
s
e
o
f
th
e
ap
p
licatio
n
s
u
s
ag
e
t
h
e
alg
o
r
ith
m
s
h
o
w
s
s
ig
n
i
f
ica
n
t
an
d
p
o
s
itiv
e
r
esu
lt
s
,
w
h
ic
h
w
ill
also
en
h
a
n
ce
th
e
p
er
f
o
r
m
a
n
ce
an
d
r
eliab
ilit
y
s
tats
o
f
th
is
ap
p
licatio
n
.
RE
F
E
R
E
NC
E
S
[1
]
K.
A
h
u
ja,
e
t
a
l.
,
“
Ne
t
w
o
rk
S
e
lec
ti
o
n
Ba
se
d
o
n
W
e
ig
h
t
Esti
m
a
ti
o
n
o
f
Qo
S
P
a
ra
m
e
ters
in
He
tero
g
e
n
e
o
u
s
W
irele
ss
M
u
lt
im
e
d
ia
Ne
t
w
o
rk
s,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
W
ire
les
s
Per
so
n
a
l
Co
mm
u
n
ica
ti
o
n
s
,
v
ol
/i
ss
u
e
:
77
(
4
)
,
p
p
.
3
0
2
7
-
3
0
4
0
,
2
0
1
4
.
[2
]
Q.
N
.
V
u
o
n
g
,
e
t
a
l.
,
“
M
u
lt
i
-
Crit
e
ria
Op
ti
m
iza
ti
o
n
o
f
A
c
c
e
ss
S
e
l
e
c
ti
o
n
t
o
Im
p
ro
v
e
th
e
Qu
a
li
ty
o
f
Ex
p
e
rien
c
e
i
n
He
tero
g
e
n
e
o
u
s
W
irele
s
s
A
c
c
e
s
s
Ne
t
w
o
rk
s,
”
IEE
E
T
ra
n
sa
c
ti
o
n
so
f
Veh
icu
la
r
T
e
c
h
n
o
l
o
g
y
,
v
ol
/i
ss
u
e
:
62
(
4
)
,
p
p
.
1
7
8
5
-
1
8
0
0
,
2
0
1
2
.
[3
]
V
.
A
.
Na
ra
y
a
n
a
n
,
e
t
a
l.
,
“
A
n
In
telli
g
e
n
t
V
e
rti
c
a
l
Ha
n
d
o
v
e
r
De
c
isio
n
A
lg
o
rit
h
m
f
o
r
W
irel
e
ss
He
tero
g
e
n
e
o
u
s
Ne
tw
o
rk
s,”
Ame
ric
a
n
J
o
u
rn
a
l
o
f
Ap
p
li
e
d
S
c
ie
n
c
e
s
,
v
ol
/i
ss
u
e
:
11
(
5
)
,
p
p
.
7
3
2
-
7
3
9
,
2
0
1
4
.
[4
]
R.
V
e
rm
a
a
n
d
N.
P
.
S
i
n
g
h
,
“
G
R
A
Ba
se
d
Ne
t
w
o
rk
S
e
lec
ti
o
n
in
H
e
tero
g
e
n
e
o
u
s
W
irele
ss
Ne
t
w
o
rk
s,”
In
ter
n
a
ti
o
n
a
l
Jo
u
rn
a
l
o
f
W
ire
les
s P
e
rs
o
n
a
l
C
o
mm
u
n
ica
ti
o
n
s
,
v
ol
/i
ss
u
e
:
75
(
2
)
,
p
p
.
1
4
3
7
-
1
4
5
2
,
2
0
1
3
.
[5
]
I
.
Ch
a
m
o
d
ra
k
a
s
a
n
d
D.
M
a
rtak
o
s,
“
A
u
ti
li
ty
-
b
a
s
e
d
f
u
z
z
y
T
OP
S
IS
m
e
th
o
d
f
o
r
e
n
e
rg
y
e
ff
icie
n
t
n
e
two
rk
se
lec
ti
o
n
in
h
e
tero
g
e
n
e
o
u
s w
irele
ss
n
e
tw
o
rk
,
”
Ap
p
li
e
d
S
o
f
t
Co
m
p
u
ti
n
g
,
v
o
l.
1
2
,
p
p
.
1
9
2
9
-
1
9
3
8
.
[6
]
H.
P
e
rv
a
iz
a
n
d
J.
Big
h
a
m
,
“
Ga
m
e
th
e
o
re
ti
c
a
l
f
o
rm
u
latio
n
o
f
n
e
tw
o
rk
se
lec
ti
o
n
in
c
o
m
p
e
ti
n
g
w
irele
ss
n
e
tw
o
rk
s:
A
n
a
n
a
ly
ti
c
a
l
h
iera
rc
h
y
p
ro
c
e
ss
m
o
d
e
l
,
”
T
h
e
T
h
ird
i
n
ter
n
a
t
io
n
a
l
c
o
n
f
e
re
n
c
e
o
n
n
e
x
t
g
e
n
e
ra
ti
o
n
mo
b
i
l
e
a
p
p
li
c
a
ti
o
n
s,
se
rv
ice
s a
n
d
tec
h
n
o
lo
g
ies
.
[7
]
B.
L
iu
,
e
t
a
l.
,
“
A
HP
a
n
d
G
a
m
e
T
h
e
o
r
y
b
a
s
e
d
A
p
p
ro
a
c
h
f
o
r
Ne
tw
o
rk
S
e
lec
ti
o
n
in
H
e
tero
g
e
n
e
o
u
s
W
irele
ss
Ne
tw
o
rk
s,
”
IEE
E
Co
n
su
me
r
Co
mm
u
n
ica
ti
o
n
s
a
n
d
Ne
two
rk
in
g
Co
n
fer
e
n
c
e
(
CCNC)
,
L
a
s
V
e
g
a
s
,
Ne
v
a
d
a
,
US
A
,
pp.
5
0
1
-
506
,
2
0
1
4
.
[8
]
M
.
L
a
h
b
y
a
n
d
A
.
A
d
ib
,
“
Ne
two
rk
se
lec
ti
o
n
m
e
c
h
a
n
is
m
b
y
u
si
n
g
M
A
HP
/
G
R
A
f
o
r
h
e
tero
g
e
n
e
o
u
s
n
e
tw
o
rk
s
,
”
6
th
J
o
in
t
IFI
P
W
ire
les
s a
n
d
M
o
b
i
le Netwo
rk
in
g
C
o
n
fer
e
n
c
e
(
W
M
N
C)
,
Du
b
a
i,
UA
E,
p
p
.
1
-
6
,
2
0
1
3
.
[9
]
M
.
L
a
h
b
y
,
e
t
a
l.
,
“
A
No
v
e
l
Ra
n
k
in
g
A
lg
o
rit
h
m
B
a
se
d
Ne
t
w
o
rk
S
e
lec
ti
o
n
F
o
r
He
ter
o
g
e
n
e
o
u
s
W
i
re
les
s
A
c
c
e
ss
,
”
J
o
u
rn
a
l
o
f
n
e
tw
o
rk
s
,
v
ol
/i
ss
u
e
:
8
(
2
)
,
p
p
.
2
6
3
-
2
7
2
,
2
0
1
3
.
[1
0
]
G
.
W
e
i
,
“
G
re
y
r
e
latio
n
a
l
a
n
a
ly
sis
m
o
d
e
l
f
o
r
d
y
n
a
m
ic
h
y
b
rid
m
u
l
ti
p
le
a
tt
rib
u
te
d
e
c
isio
n
m
a
k
in
g
,
”
El
se
v
ier
,
J
o
u
rn
a
l
o
f
Kn
o
wled
g
e
-
Ba
se
d
S
y
ste
ms
,
v
o
l
.
24
,
p
p
.
6
7
2
–
6
7
9
,
2
0
1
1
.
[1
1
]
A
.
G
h
a
rsa
ll
a
h
,
e
t
a
l.
,
“
Ne
tw
o
rk
S
e
lec
ti
o
n
in
He
tero
g
e
n
e
o
u
s
W
irele
ss
S
y
ste
m
En
v
iro
n
m
e
n
ts
,
”
J
o
u
rn
a
l
o
f
Ne
tw
o
rk
s
,
v
o
l/
issu
e
:
10
(
12
)
,
2
0
1
5
.
[1
2
]
C
.
R
Re
e
v
e
s
a
n
d
J
E
Ro
w
e
,
“
Ge
n
e
ti
c
A
lg
o
rit
h
m
s:
P
rin
c
ip
les
a
n
d
P
e
rsp
e
c
ti
v
e
s
A
G
u
id
e
to
GA
T
h
e
o
r
y
,”
AA
Do
rd
re
c
h
t
,
Klu
w
e
r
A
c
a
d
e
m
ic
P
u
b
li
sh
e
rs,
2
0
0
3
.
[1
3
]
P
.
N.
T
ra
n
a
n
d
N.
Bo
u
k
h
a
tem
,
“
Co
m
p
a
riso
n
o
f
M
AD
M
d
e
c
isio
n
a
lg
o
rit
h
m
s
f
o
r
in
terf
a
c
e
se
lec
ti
o
n
i
n
h
e
tero
g
e
n
e
o
u
s
w
irele
ss
n
e
t
w
o
rk
s
,
”
16
th
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
S
o
ft
wa
re
,
T
e
lec
o
mm
u
n
ica
t
io
n
s
a
n
d
Co
mp
u
ter
Ne
two
rk
s (
S
o
ft
COM
)
,
p
p
.
1
1
9
-
1
2
4
,
2
0
0
8
.
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