I
n
t
e
r
n
at
ion
al
Jou
r
n
al
of
E
lec
t
r
ical
an
d
Com
p
u
t
e
r
E
n
gin
e
e
r
in
g
(
I
JE
CE
)
Vol.
1
4
,
No.
5
,
Oc
tober
20
2
4
,
pp
.
5308
~
5318
I
S
S
N:
2088
-
8708
,
DO
I
:
10
.
11591/
ij
e
c
e
.
v
1
4
i
5
.
pp
5
308
-
5318
5308
Jou
r
n
al
h
omepage
:
ht
tp:
//
ij
e
c
e
.
iaes
c
or
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.
c
om
Pe
r
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ia
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s
ar
Her
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1
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Die
go
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al
1
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ia
Vac
a
2
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r
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nt
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c
tr
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l
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ngi
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in
g, F
a
c
ul
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T
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c
hnol
ogy, Un
iv
e
r
s
id
a
d D
is
tr
it
a
l
F
r
a
nc
is
c
o J
o
s
é
de
C
a
ld
a
s
,
B
ogot
á
D
. C
., C
ol
ombi
a
2
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pa
r
tm
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nt
of
M
a
s
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, F
a
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ul
ty
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ngi
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in
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ni
ve
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s
id
a
d D
is
tr
it
a
l
F
r
a
nc
is
c
o J
os
é
de
C
a
ld
a
s
,
B
ogot
á
D
.
C
., C
ol
ombi
a
Ar
t
icle
I
n
f
o
AB
S
T
RA
CT
A
r
ti
c
le
h
is
tor
y
:
R
e
c
e
ived
Apr
12,
2024
R
e
vis
e
d
J
ul
11,
2024
Ac
c
e
pted
J
ul
17,
2024
Co
g
n
i
t
i
v
e
rad
i
o
n
et
w
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k
s
o
ffer
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al
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f
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freq
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.
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h
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s
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c
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s
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m
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v
e
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t
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of
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ce
me
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ri
c
s
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s
e
d
:
n
u
mb
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f
h
an
d
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ff
s
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n
u
mb
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f
fa
i
l
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Fro
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a
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(CO
D
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S)
p
res
e
n
t
ed
t
h
e
b
e
s
t
res
u
l
t
fo
r
t
h
e
co
s
t
met
ri
c
s
w
i
t
h
t
h
e
l
o
w
es
t
l
ev
e
l
s
,
an
d
fo
r
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h
e
b
e
n
efi
t
met
ri
cs
,
t
h
e
h
i
g
h
e
s
t
l
ev
el
s
w
ere
o
b
t
ai
n
ed
.
K
e
y
w
o
r
d
s
:
C
ognit
ive
r
a
dio
ne
twor
ks
C
ombi
na
ti
ve
dis
tanc
e
-
ba
s
e
d
a
s
s
e
s
s
ment
De
c
is
ion
-
making
models
M
ult
icr
it
e
r
ia
s
tr
a
tegie
s
S
pe
c
tr
a
l
mobi
li
ty
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
C
e
s
a
r
He
r
na
nde
z
De
pa
r
tm
e
nt
of
E
lec
tr
ica
l
E
nginee
r
ing,
F
a
c
ult
y
T
e
c
hnology,
Unive
r
s
idad
Dis
tr
it
a
l
F
r
a
nc
is
c
o
J
os
é
de
C
a
ldas
B
ogotá
D.
C
.
,
C
olom
bia
E
mail:
c
a
he
r
na
nde
z
s
@udis
tr
it
a
l.
e
du.
c
o
1.
I
NT
RODU
C
T
I
ON
Ove
r
the
las
t
de
c
a
de
,
t
he
nu
mbe
r
o
f
de
vice
s
c
o
nne
c
ted
t
o
t
he
I
nte
r
ne
t
ha
s
g
r
ow
n
e
xp
one
n
ti
a
l
ly
.
T
he
us
e
o
f
f
r
e
e
s
pe
c
tr
um
f
or
va
r
ious
a
pp
li
c
a
ti
ons
h
a
s
a
ls
o
inc
r
e
a
s
e
d
[
1]
.
S
tud
ies
of
the
us
e
o
f
th
e
s
pe
c
t
r
u
m
s
h
ow
the
ine
f
f
icie
nc
y
wi
th
wh
ich
th
e
maj
or
i
ty
o
f
the
r
a
d
i
o
s
pe
c
tr
um
is
us
e
d
[
1
]
.
T
he
inves
t
igat
ions
ha
ve
s
h
own
tha
t
gove
r
nmen
t
a
ll
oc
a
ti
on
po
li
c
ies
ha
ve
f
a
il
e
d,
a
nd
the
a
s
s
igne
d
ba
nds
a
r
e
ove
r
us
e
d
or
u
nde
r
us
e
d
,
a
c
ha
r
a
c
ter
is
ti
c
that
p
r
e
ve
n
ts
the
e
l
e
c
t
r
omag
ne
ti
c
s
pe
c
t
r
um
f
r
om
o
pe
r
a
ti
n
g
e
f
f
ic
ient
ly
[
2]
.
Due
to
th
is
,
dif
f
e
r
e
nt
C
omm
un
ica
t
ions
C
omm
is
s
ions
ha
ve
ge
ne
r
a
ted
p
r
opos
a
ls
to
im
pr
ove
the
a
l
loca
t
ion
m
ode
ls
.
C
omm
u
nica
ti
ons
the
ine
f
f
icie
nt
dis
tr
i
but
ion
o
f
e
xpe
c
tat
ions
[
2]
.
A
s
olut
i
on
to
im
p
r
ove
the
ine
f
f
ici
e
nt
us
e
o
f
the
s
pe
c
tr
u
m
is
c
ogni
ti
ve
r
a
dio
(
C
R
)
[
3
]
–
[
5
]
the
ope
r
a
ti
on
of
a
ne
t
wor
k
th
r
o
ugh
the
C
R
r
e
qu
ir
e
s
us
in
g
a
c
og
nit
ive
c
yc
le,
whi
c
h
is
p
r
e
s
e
nted
in
F
igu
r
e
1
,
t
his
c
yc
le
a
ll
ows
f
or
i
nt
e
ll
ige
nt
a
da
p
tati
ons
,
th
r
o
ugh
lea
r
ni
ng
a
nd
the
e
xc
ha
nge
of
inf
o
r
ma
ti
o
n
[
6
]
.
Unlike
tr
a
dit
iona
l
ne
tw
or
ks
,
in
the
c
ogn
it
i
ve
r
a
di
o
ne
two
r
ks
(
C
R
N)
,
th
e
r
e
a
r
e
tw
o
ty
pe
s
of
us
e
r
s
:
the
p
r
i
mar
y
us
e
r
(
P
U
)
a
nd
the
s
e
c
onda
r
y
us
e
r
(
S
U)
.
T
he
S
U
is
the
us
e
r
who
a
c
c
e
s
s
e
s
t
he
s
pe
c
tr
u
m
oppo
r
tun
is
ti
c
a
ll
y
;
the
P
U
is
th
e
us
e
r
who
a
c
c
e
s
s
e
s
the
s
pe
c
tr
u
m
in
a
li
c
e
ns
e
d
manne
r
[
7
]
.
T
he
o
bjec
ti
ve
o
f
a
C
R
N
is
to
p
r
ov
ide
a
c
c
e
s
s
to
a
n
S
U
t
o
a
n
a
va
i
lable
f
r
e
que
n
c
y
c
h
a
nne
l
in
the
l
ice
ns
e
d
ba
nd
wit
hou
t
a
f
f
e
c
ti
ng
the
pe
r
f
or
manc
e
or
c
omm
unica
ti
on
of
the
P
U
[
7
]
.
T
h
is
p
r
oc
e
s
s
wh
e
r
e
the
S
U
c
ha
n
ge
s
f
r
e
qu
e
nc
y
is
c
a
ll
e
d
s
pe
c
tr
a
l
mobi
li
t
y
[
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
P
e
r
for
manc
e
e
v
aluat
ion
of
a
pr
opos
al
for
s
pe
c
tr
u
m
as
s
ignme
nt
bas
e
d
on
c
ombinati
v
e
…
(
C
e
s
ar
He
r
nande
z
)
5309
F
igur
e
1
.
C
ognit
ive
c
yc
le
s
tr
uc
tu
r
e
(
M
a
r
ti
ne
z
e
t
al
.
[
8]
)
De
c
is
ion
-
ma
king
is
r
e
leva
n
t
i
n
C
R
Ns
;
a
g
ood
met
hodol
ogy
a
ll
ows
f
o
r
i
mpr
ovi
ng
qu
a
li
ty
-
of
-
s
e
r
vice
(
QoS
)
ind
ica
to
r
s
.
I
n
C
R
Ns
,
it
is
e
s
s
e
nt
ial
that
the
S
Us
c
a
n
a
c
c
e
s
s
the
s
pe
c
t
r
um
a
c
c
or
din
g
to
the
r
e
qu
ir
e
d
QoS
c
ha
r
a
c
te
r
is
ti
c
s
[
8]
.
An
i
nc
or
r
e
c
t
c
ha
n
ne
l
s
e
le
c
ti
on
ge
ne
r
a
tes
di
f
f
e
r
e
nt
p
r
oble
ms
a
s
s
oc
iat
e
d
wi
th
s
pe
c
tr
a
l
mobi
li
t
y
[
9
]
.
T
o
s
e
lec
t
s
pe
c
tr
a
l
op
por
tun
it
ies
,
de
c
is
ion
-
mak
ing
tec
hn
iques
mus
t
a
na
l
yz
e
va
r
ious
v
a
r
iab
les
.
M
ult
i
-
c
r
it
e
r
ia
de
c
is
ion
mak
ing
(
M
C
DM
)
-
ba
s
e
d
a
lg
or
i
thm
s
a
r
e
wi
de
ly
us
e
d
in
t
his
type
o
f
pr
o
blem
d
u
e
to
th
e
ir
e
f
f
icie
n
t
r
e
s
u
lt
s
a
nd
low
c
o
mput
a
ti
ona
l
l
oa
d
.
W
it
h
the
M
C
DM
it
is
pos
s
i
ble
t
o
e
s
tabl
is
h
whi
c
h
c
ha
n
n
e
ls
ha
ve
the
gr
e
a
tes
t
n
umbe
r
o
f
s
pe
c
t
r
a
l
oppo
r
tu
nit
ies
.
F
o
r
t
he
M
C
DM
,
the
r
e
la
ti
o
ns
hip
b
e
twe
e
n
the
de
c
is
ion
c
r
it
e
r
ia
is
mea
s
ur
e
d
th
r
oug
h
we
ig
hts
,
w
hich
a
r
e
a
d
jus
ted
a
c
c
or
di
ng
to
the
de
s
i
gne
r
's
r
e
q
ui
r
e
men
ts
[
1
0]
.
T
he
na
mes
a
n
d
r
e
s
pe
c
t
ive
a
c
r
ony
ms
of
s
o
me
mu
lt
ic
r
i
ter
ia
tec
hniq
u
e
s
a
r
e
de
s
c
r
ibed
:
i
)
T
O
P
S
I
S
:
tec
h
niqu
e
f
o
r
o
r
de
r
p
r
e
f
e
r
e
nc
e
by
s
i
mi
l
a
r
i
ty
to
idea
l
s
ol
uti
on
[
10]
,
i
i)
V
I
KO
R
:
mul
t
i
-
c
r
i
ter
ia
opt
im
iz
a
ti
on
a
nd
c
om
pr
omi
s
e
s
ol
uti
on
[
10
]
,
ii
i
)
P
R
OM
E
T
H
E
:
pr
e
f
e
r
e
nc
e
r
a
n
king
o
r
ga
n
iza
ti
on
meth
ods
f
o
r
e
n
r
ic
hmen
t
e
va
luat
ions
[
1
1]
,
iv
)
W
ASP
A
S
:
metho
d
u
ti
l
ize
s
the
c
onc
e
pt
of
r
a
nk
ing
a
c
c
u
r
a
c
y
[
12
]
,
v
)
DE
M
A
T
E
L
:
de
c
is
i
on
-
maki
ng
t
r
ial
a
nd
e
va
luat
ion
labo
r
a
to
r
y
[
13]
,
[
14
]
,
v
i)
M
OO
R
A:
M
u
lt
i
-
ob
je
c
ti
ve
op
ti
m
iza
t
ion
ba
s
e
d
on
r
a
di
us
a
na
lys
is
[
1
5]
,
[
16
]
,
vii
)
B
W
M
:
B
e
s
t
–
wo
r
s
t
meth
od
[
17]
,
a
nd
v
ii
i)
F
UC
OM
:
f
ul
l
c
ons
is
te
nc
y
met
hod
[
15
]
.
Ac
c
or
d
ing
to
t
he
r
e
view
o
f
t
he
l
it
e
r
a
tu
r
e
,
di
f
f
e
r
e
nt
a
pp
li
c
a
t
ions
o
f
t
he
a
lg
or
it
h
ms
we
r
e
f
ou
nd;
f
o
r
e
xa
mpl
e
,
D
E
M
A
T
E
L
is
us
e
d
in
the
in
ve
s
ti
ga
ti
on
to
a
na
l
yz
e
the
f
a
c
t
or
s
t
ha
t
i
nf
luenc
e
the
de
c
is
ion
to
a
dop
t
vir
tual
r
e
a
li
t
y
tec
hnol
ogy
by
the
r
e
a
l
e
s
tate
c
om
pa
nies
.
T
h
e
r
e
s
ult
s
s
h
owe
d
that
s
e
ve
r
a
l
a
s
pe
c
ts
in
f
l
ue
nc
e
d
t
he
int
e
n
ti
on
o
f
the
r
e
a
l
e
s
t
a
te
c
ompa
nies
;
howe
ve
r
,
the
mos
t
i
mpo
r
tan
t
o
ne
[
14
]
wa
s
t
he
pr
i
c
e
r
a
t
io
,
e
s
pe
c
i
a
ll
y
a
s
a
c
ont
r
ibu
ti
o
n
to
the
mana
ge
men
t
o
f
C
OV
I
D
-
19
[
1
4
]
.
M
OO
R
A
is
the
mul
ti
p
le
o
bjec
t
ives
o
pti
mi
z
a
ti
on
meth
od
by
r
a
ti
o
a
na
lys
is
.
A
mong
the
c
r
it
e
r
ia
us
e
d
f
o
r
de
c
is
ion
-
ma
kin
g,
we
ha
ve
a
ve
r
a
ge
s
c
o
r
e
s
,
ps
yc
holog
ica
l
e
va
luat
ions
,
mat
he
mat
ica
l
e
va
luat
ions
,
a
nd
i
nte
r
vi
e
ws
.
T
he
e
xpos
e
d
m
a
tr
ix
is
a
pp
li
e
d
b
e
c
a
us
e
e
a
c
h
c
r
it
e
r
i
on
ha
s
a
n
e
va
l
ua
ti
on
va
lue
.
M
OO
R
A
is
a
s
im
ple
s
tr
a
te
gy
t
o
im
p
leme
n
t,
b
ut
it
is
c
ha
r
a
c
te
r
ize
d
by
t
he
r
ob
u
s
tnes
s
of
the
de
c
is
io
ns
[
16]
.
c
om
bina
ti
ve
dis
tanc
e
-
ba
s
e
d
a
s
s
e
s
s
ment
(
C
OD
AS
)
is
a
n
M
C
DM
a
l
gor
it
h
m
that
us
e
s
the
c
ombi
ne
d
e
va
lua
ti
o
n
ba
s
e
d
on
dis
tan
c
e
,
whe
r
e
the
E
uc
li
de
a
n
dis
ta
nc
e
a
nd
the
T
a
xica
b
a
r
e
c
a
lcu
late
d;
it
ha
s
be
e
n
us
e
d
by
c
o
mpan
ies
de
d
ica
ted
to
the
s
te
e
l
i
ndu
s
tr
y
to
he
lp
e
va
lua
te
a
nd
s
e
lec
t
the
be
s
t
s
upp
li
e
r
a
mong
s
i
x
pos
s
ibl
e
a
l
ter
na
ti
ve
s
[
18
]
,
[
19
]
.
T
his
wo
r
k
a
im
s
to
i
mpl
e
ment
th
e
C
OD
A
S
a
lgo
r
i
th
m
[
7]
,
[
20]
i
n
a
de
c
is
ion
-
ma
king
p
r
oc
e
s
s
in
C
R
Ns
.
C
OD
AS
ha
s
s
h
own
go
od
r
e
s
u
lt
s
in
a
pp
li
c
a
ti
ons
a
s
s
oc
iate
d
wi
th
de
c
is
i
on
-
mak
ing
,
mak
ing
the
p
r
oc
e
s
s
mo
r
e
e
quit
a
ble
,
c
le
a
r
,
a
n
d
e
f
f
icie
n
t
[
19
]
.
T
o
e
s
tab
li
s
h
the
good
pe
r
f
o
r
ma
nc
e
o
f
t
he
a
l
gor
it
h
m
,
f
ive
met
r
ics
a
r
e
us
e
d
:
numbe
r
of
ha
n
dof
f
s
,
n
umbe
r
of
f
a
il
e
d
ha
ndo
f
f
s
,
a
ve
r
a
ge
ba
ndw
idt
h,
a
ve
r
a
ge
t
hr
o
ughp
ut
,
a
nd
c
u
mul
a
t
ive
a
ve
r
a
ge
de
la
y
.
F
o
r
the
e
va
l
ua
ti
on
t
o
be
f
a
i
r
,
the
m
e
t
r
ics
ob
taine
d
in
C
OD
AS
a
r
e
c
ompa
r
e
d
w
it
h
s
im
p
le
a
ddit
ive
we
i
ght
ing
(
S
AW
)
,
a
n
M
C
DM
tec
hn
iqu
e
t
ha
t
ha
s
s
how
n
e
f
f
ic
ient
r
e
s
ul
ts
in
s
pe
c
t
r
a
l
a
s
s
ignm
e
nt
.
Addit
iona
ll
y
,
a
s
e
c
ond
c
o
mpa
r
is
on
is
ma
de
;
in
t
hi
s
s
c
e
na
r
io
,
a
R
AN
DO
M
s
e
lec
ti
on
of
the
c
ha
n
ne
ls
is
ma
de
,
a
nd
the
me
tr
ics
obta
ined
in
C
OD
A
S
a
r
e
c
o
mpa
r
e
d,
a
s
in
put
i
nf
or
mati
on
s
pe
c
t
r
a
l
oc
c
upa
nc
y
da
ta
is
us
e
d
,
whic
h
c
a
n
be
r
a
nd
oml
y
ge
ne
r
a
ted
o
r
ob
taine
d
th
r
o
ugh
mea
s
u
r
e
men
ts
,
f
o
r
thi
s
wo
r
k
,
a
nd
in
or
de
r
to
e
va
luate
C
OD
AS
in
r
e
a
li
s
t
ic
s
c
e
na
r
ios
,
s
pe
c
tr
a
l
powe
r
mea
s
ur
e
me
nts
a
r
e
us
e
d
.
T
his
a
r
t
icle
is
s
tr
uc
tur
e
d
in
f
ou
r
s
e
c
ti
ons
with
the
I
ntr
oduc
ti
on.
I
n
s
e
c
ti
on
2,
the
methodology
is
pr
e
s
e
nted.
I
n
s
e
c
ti
on
3
,
the
r
e
s
ult
s
a
r
e
pr
e
s
e
nted.
F
i
na
ll
y,
in
s
e
c
ti
on
5,
the
c
onc
lus
ions
a
r
e
pr
e
s
e
nted.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
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8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5308
-
5318
5310
2.
CODA
S
M
UL
T
I
CR
I
T
E
R
I
A
S
T
RA
T
E
GY
T
his
wor
k
im
pleme
nts
the
M
C
DM
C
OD
AS
f
or
s
pe
c
tr
um
s
e
lec
ti
on
in
c
ognit
ive
wir
e
les
s
ne
twor
ks
.
F
or
the
c
ompar
a
ti
ve
a
na
lys
is
,
S
AW
a
nd
a
R
AN
DO
M
c
ha
nne
l
s
e
lec
ti
on
methodology
a
r
e
us
e
d;
s
pe
c
tr
a
l
mobi
li
ty
metr
ics
a
r
e
us
e
d
f
o
r
the
pe
r
f
or
manc
e
a
na
lys
is
.
F
igur
e
2
p
r
e
s
e
nts
the
pr
opos
e
d
methodology
f
or
the
s
pe
c
tr
um
s
e
lec
ti
on
us
ing
C
OD
AS
thr
ough
blocks
.
T
he
mo
de
l
in
it
ia
ll
y
s
ta
r
ts
f
r
o
m
the
s
pe
c
t
r
a
l
in
f
o
r
matio
n
da
ta
p
r
ovi
de
d
by
th
e
b
e
ha
vi
or
o
f
the
us
e
r
s
,
e
s
pe
c
iall
y
the
P
U
,
s
in
c
e
thi
s
is
t
he
in
f
or
mat
ion
f
r
om
whic
h
the
p
r
opos
e
d
a
lgo
r
i
thm
ma
ke
s
de
c
is
io
n
s
.
T
he
s
e
s
pe
c
tr
a
l
inf
or
mati
on
da
ta
we
r
e
ob
taine
d
thr
ough
a
p
r
e
vi
ous
ly
c
a
r
r
ied
out
s
pe
c
tr
a
l
mea
s
u
r
e
men
t
c
a
mpaig
n,
whos
e
in
f
o
r
ma
ti
on
wa
s
p
r
oc
e
s
s
e
d
a
nd
o
r
ga
nize
d
in
a
s
pe
c
t
r
a
l
a
va
il
a
bi
li
ty
ma
tr
i
x
ma
de
up
o
f
one
s
(
a
va
il
a
bi
li
t
y)
a
nd
z
e
r
os
(
oc
c
upa
nc
y
)
.
S
ubs
e
q
ue
nt
ly
,
the
in
f
o
r
mat
i
on
f
r
o
m
the
a
va
i
labi
li
t
y
mat
r
ix
is
de
li
ve
r
e
d
to
the
p
r
opos
e
d
F
e
e
dba
c
k
C
OD
AS
a
l
gor
it
h
m
,
whos
e
be
ha
vio
r
is
d
e
s
c
r
ibed
late
r
.
T
he
p
r
o
pos
e
d
a
lg
or
it
h
m
de
l
iver
s
a
s
a
r
e
s
ult
a
r
a
nk
ing
o
f
s
pe
c
tr
a
l
opp
or
tuni
ti
e
s
th
r
ou
gh
whi
c
h
the
c
om
mun
ica
ti
on
o
f
the
S
U
is
c
a
r
r
ied
ou
t.
Dur
ing
the
S
U
c
omm
unica
ti
on,
it
is
pos
s
ibl
e
that
the
s
e
lec
ted
s
pe
c
tr
a
l
oppor
tuni
ty
is
r
e
quir
e
d
by
a
P
U,
in
whic
h
c
a
s
e
it
is
ne
c
e
s
s
a
r
y
to
r
e
lea
s
e
it
a
nd
s
e
lec
t
a
ne
w
s
pe
c
tr
a
l
oppo
r
tuni
ty
,
a
c
c
or
ding
to
the
r
a
nking
pr
ovided
by
the
pr
opos
e
d
a
lgor
it
h
m.
T
he
p
r
e
vious
pr
oc
e
s
s
is
c
a
ll
e
d
s
pe
c
tr
a
l
mobi
li
ty
.
F
inally
,
f
r
om
the
da
ta
obtaine
d
dur
ing
the
s
pe
c
tr
a
l
mobi
li
ty
pr
oc
e
s
s
,
f
iv
e
e
va
luation
metr
ics
a
r
e
c
ons
tr
uc
ted:
N
umber
o
f
ha
ndof
f
s
,
number
of
f
a
il
e
d
ha
ndof
f
s
,
a
ve
r
a
ge
ba
ndwidth
,
a
ve
r
a
ge
thr
oughput
,
a
nd
c
umul
a
ti
ve
a
ve
r
a
ge
de
lay
.
F
igur
e
2.
P
r
opos
e
d
methodolog
ies
f
or
the
s
pe
c
tr
u
m
s
e
lec
ti
on
us
ing
C
OD
AS
2.
1.
Us
e
r
s
T
he
e
va
luation
of
the
s
tr
a
tegy
is
c
a
r
r
ied
ou
t
b
y
im
pleme
nti
ng
a
r
a
dio
e
nvir
onment
with
r
e
a
l
inf
or
mation
on
the
be
ha
vior
of
the
P
Us
.
T
his
inf
or
mation
c
or
r
e
s
ponds
to
a
s
pe
c
tr
a
l
powe
r
matr
ix
in
t
he
W
i
-
F
i
f
r
e
que
nc
y
ba
nd,
obtaine
d
th
r
ough
a
mea
s
ur
e
men
t
pr
oc
e
s
s
us
ing
the
e
ne
r
gy
de
tec
ti
on
tec
hnique.
T
a
ble
1
de
s
c
r
ibes
the
s
iz
e
of
the
mea
s
ur
e
d
powe
r
s
pe
c
tr
a
l
matr
ix.
T
he
c
ha
nne
ls
a
r
e
c
ha
r
a
c
ter
ize
d
by
the
c
olum
ns
a
nd
the
ti
me
by
the
r
ows
.
T
a
ble
1.
M
e
a
s
ur
e
d
s
pe
c
tr
a
l
powe
r
matr
ix
F
r
e
que
nc
y
B
a
nd
R
ow
s
(
ti
m
e
)
C
ol
um
ns
(
c
h
a
nn
e
l
s
)
T
ot
a
l
d
a
ta
Wi
-
Fi
2.490
.000
550
1.369
.500
.000
2.
2.
P
r
op
os
e
d
m
od
e
l
b
as
e
d
on
CODA
S
T
he
de
c
is
ion
matr
ix
mus
t
be
ge
ne
r
a
ted
.
T
he
(
1)
p
r
e
s
e
nts
thi
s
matr
ix.
,
is
the
s
e
lec
ti
on
(
de
c
is
ion)
c
r
it
e
r
ion
f
or
c
ha
nne
l
a
nd
r
e
pr
e
s
e
nt
the
we
ight
(
)
t
o
the
s
e
lec
ti
on
c
r
it
e
r
ion
.
̄
=
(
1
1
,
1
…
1
,
⋮
⋱
⋮
1
,
1
⋯
,
)
(
1)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
P
e
r
for
manc
e
e
v
aluat
ion
of
a
pr
opos
al
for
s
pe
c
tr
u
m
as
s
ignme
nt
bas
e
d
on
c
ombinati
v
e
…
(
C
e
s
ar
He
r
nande
z
)
5311
S
ubs
e
que
ntl
y,
the
s
e
lec
ti
on
c
r
it
e
r
ia
a
r
e
e
s
tablis
he
d,
whic
h
mus
t
be
obtaine
d
f
o
r
e
a
c
h
m;
the
mea
ning,
de
s
c
r
ipt
ion,
a
nd
it
s
r
e
s
pe
c
ti
ve
a
c
r
onym
a
r
e
p
r
e
s
e
nted
in
F
igu
r
e
3.
I
n
or
de
r
to
c
a
r
r
y
out
a
c
ompar
a
ti
ve
a
na
lys
is
,
S
A
W
a
nd
R
AN
DO
M
a
r
e
im
pleme
nted
in
a
ddit
ion
to
C
OD
AS.
T
he
s
e
lec
ti
on
wa
s
made
f
r
om
the
pr
e
vi
ous
r
e
vis
ion
[
21]
.
C
ombi
na
ti
ve
dis
tanc
e
-
ba
s
e
d
a
s
s
e
s
s
ment
(
C
OD
AS)
:
e
s
tablis
h
the
a
lt
e
r
na
ti
ve
s
f
r
om
the
E
uc
l
idea
n
dis
tanc
e
s
a
nd
the
dis
tur
ba
nc
e
of
T
a
xica
b.
W
he
r
e
the
E
uc
li
dian
dis
tanc
e
is
the
main
mea
s
ur
e
a
nd
the
dis
t
a
nc
e
of
T
a
xica
b,
the
mos
t
de
s
ir
a
ble
idea
l
ne
ga
ti
ve
s
olut
ion
is
whe
n
the
va
lue
of
the
dis
tanc
e
is
the
lar
ge
s
t
[
18]
,
[
20]
.
T
he
methodology
to
im
pleme
nt
C
O
DA
S
is
de
s
c
r
ibed
be
low
.
F
igur
e
4
p
r
e
s
e
nts
the
f
lowc
ha
r
t.
F
igur
e
3.
De
s
c
r
ipt
ion
o
f
de
c
is
ion
c
r
it
e
r
ia
F
igur
e
4.
C
OD
AS
f
low
c
ha
r
t
−
E
s
tablis
h
the
s
e
lec
ti
on
mat
r
ix
or
de
c
is
ion
-
making
matr
ix
th
r
ough
(
1)
,
whe
r
e
(
0)
de
f
ines
the
e
f
f
icie
nc
y
va
lue
of
the
th
a
lt
e
r
na
ti
ve
in
the
th
c
r
it
e
r
i
on
{
1
,
2
,
…
,
}
{
1
,
2
,
…
,
}
.
Evaluation Warning : The document was created with Spire.PDF for Python.
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S
S
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:
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I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5308
-
5318
5312
−
De
ter
mi
ne
the
nor
malize
d
s
e
lec
ti
on
(
de
c
is
ion)
mat
r
ix
thr
ough
(
2
)
.
W
he
r
e
a
nd
r
e
pr
e
s
e
nt
the
be
ne
f
it
<
c
os
t
c
r
it
e
r
ia
s
e
t.
=
{
m
a
x
∈
m
i
n
∈
(
2)
−
De
ter
mi
ne
the
nor
malize
d
we
ight
thr
ough
(
3
)
.
=
∑
=
1
=
1
(
3)
−
S
e
t
the
ne
ga
ti
ve
idea
l
s
olut
ion
a
c
c
or
ding
to
(
4
)
.
=
[
]
1
w
h
e
r
e
=
(
4)
−
De
ter
mi
ne
the
E
uc
li
de
a
n
a
nd
taxi
dis
tanc
e
f
o
r
e
a
c
h
ne
ga
ti
ve
idea
l
s
olut
ion
a
c
c
or
ding
to
(
5)
a
nd
(
6
)
.
=
√
∑
(
−
)
=
1
(
5)
=
∑
|
−
|
=
1
(
6)
−
B
uil
d
the
r
e
lative
e
va
luation
matr
ix
a
c
c
or
ding
to
(
7)
,
(
8
)
,
a
nd
(
9)
.
{
1
,
2
,
…
,
}
,
e
s
tablis
he
s
the
thr
e
s
hold
f
or
the
E
uc
li
de
a
n
e
qua
li
ty.
in
is
the
th
r
e
s
hold
pa
r
a
mete
r
r
e
s
pons
ibl
e
f
or
e
s
tablis
hing
the
de
c
is
ion.
As
a
r
e
c
omm
e
nda
ti
on,
thi
s
va
lue
is
a
djus
ted
in
the
in
ter
va
l
[
0.
01
-
0.
05]
.
I
n
thi
s
wor
k,
it
is
a
s
s
umed
that
=
0.
05
wi
th
the
va
r
iable
u
f
or
the
c
a
lcula
ti
ons
.
=
[
ℎ
]
×
(
7)
ℎ
=
(
−
)
+
(
(
−
)
(
−
)
)
(
8)
(
)
=
{
1
|
|
≥
0
|
|
<
(
9)
−
De
ter
mi
ne
the
r
e
s
pe
c
ti
ve
e
va
luation
s
c
or
e
f
o
r
e
a
c
h
of
the
a
lt
e
r
na
ti
ve
s
.
T
his
s
c
or
e
is
obtaine
d
thr
ough
(
10)
.
=
∑
=
1
(
10)
−
F
inally,
the
e
va
luation
s
c
or
e
(
)
is
or
de
r
e
d
in
de
s
c
e
nding
or
de
r
.
T
he
highes
t
gives
the
be
s
t
opti
ons
;
th
e
wor
s
t
opti
ons
a
r
e
given
by
the
lowe
s
t
.
S
im
ple
a
ddit
ive
we
ight
ing
(
S
AW
)
e
s
tablis
he
s
a
r
a
nking
f
o
r
e
a
c
h
a
lt
e
r
na
ti
ve
a
c
c
or
ding
to
the
de
c
is
ion
c
r
it
e
r
ia.
T
he
s
pe
c
tr
a
l
oppo
r
tuni
ty
with
th
e
highes
t
s
c
or
e
will
be
s
e
lec
ted.
I
n
(
11
)
,
the
math
e
matica
l
model
f
or
S
AW
is
p
r
e
s
e
nted.
T
he
S
AW
index
is
d
e
ter
mi
ne
d
f
r
om
a
nd
,
, (
).
=
∑
,
=
1
∑
=
1
=
1
(
11)
2.
3.
S
p
e
c
t
r
al
m
ob
i
li
t
y
as
s
e
s
s
m
e
n
t
s
t
r
at
e
gy
T
he
s
pe
c
tr
a
l
mobi
li
ty
is
qua
nti
f
ied
th
r
ough
the
c
h
a
nge
s
e
s
tablis
he
d
by
the
de
c
is
ion
ve
c
tor
.
One
o
r
s
e
ve
r
a
l
S
Us
mus
t
c
ha
nge
c
olum
n
(
c
ha
nne
l)
whe
n
bus
y
a
nd
c
ha
nge
r
ow
whe
n
the
inf
or
mation
is
t
r
a
ns
mi
tt
e
d
[
22]
,
[
23]
.
C
ons
ider
ing
that
the
r
ows
r
e
pr
e
s
e
nt
the
ins
tants
of
ti
me,
the
s
pe
c
tr
a
l
mobi
li
ty
pr
oc
e
s
s
is
c
a
r
r
ied
out
unti
l
the
ti
me
of
int
e
r
e
s
t
a
nd/o
r
s
im
ulation
is
c
om
plete
d
[
24]
,
[
25]
.
T
he
a
va
il
a
bil
i
ty,
the
c
ha
nge
s
of
c
ha
nne
ls
,
a
nd
the
c
ha
r
a
c
ter
is
ti
c
s
a
r
e
s
tor
e
d
to
qua
nti
f
y
the
in
dica
tor
s
of
QoS
[
26]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
P
e
r
for
manc
e
e
v
aluat
ion
of
a
pr
opos
al
for
s
pe
c
tr
u
m
as
s
ignme
nt
bas
e
d
on
c
ombinati
v
e
…
(
C
e
s
ar
He
r
nande
z
)
5313
2.
4.
Valid
a
t
ion
o
f
t
h
e
p
r
op
os
e
d
m
od
e
l
F
or
the
e
va
luat
ion
to
be
f
a
ir
,
the
met
r
ics
ob
taine
d
in
C
OD
AS
a
r
e
c
o
mpa
r
e
d
wit
h
S
AW
,
a
n
M
C
DM
tec
hni
que
th
a
t
ha
s
s
how
n
e
f
f
ic
ient
r
e
s
ul
ts
in
s
pe
c
tr
a
l
a
s
s
i
gnme
nt
.
Add
it
i
ona
l
ly
,
a
s
e
c
ond
c
ompa
r
is
o
n
is
m
a
de
;
in
t
his
s
c
e
na
r
io
,
a
R
AN
DO
M
s
e
lec
t
ion
of
the
c
h
a
nne
ls
is
made
,
a
nd
the
met
r
ics
o
btai
ne
d
in
C
O
DA
S
a
r
e
c
ompa
r
e
d
,
a
s
inp
ut
in
f
o
r
ma
ti
on
s
pe
c
t
r
a
l
oc
c
upa
nc
y
da
ta
is
us
e
d
,
w
hich
c
a
n
be
r
a
ndo
ml
y
ge
ne
r
a
te
d
o
r
obtai
ne
d
thr
o
ugh
mea
s
u
r
e
me
nts
,
f
o
r
thi
s
wo
r
k
,
a
nd
to
e
va
luate
C
OD
A
S
in
r
e
a
li
s
t
ic
s
c
e
n
a
r
i
os
,
s
pe
c
t
r
a
l
po
we
r
mea
s
ur
e
ments
a
r
e
us
e
d
.
Va
li
d
a
ti
o
n
is
c
a
r
r
ie
d
out
a
c
c
o
r
d
ing
to
f
i
ve
e
va
lu
a
ti
o
n
met
r
ics
:
ba
ndwidt
h,
de
lay,
thr
oughput,
f
a
il
e
d
ha
ndof
f
s
,
a
nd
tot
a
l
ha
ndof
f
s
.
3.
RE
S
UL
T
S
A
s
e
t
of
f
igur
e
s
obtaine
d
by
pe
r
f
or
mi
ng
the
va
l
idation
of
the
pe
r
f
or
manc
e
of
the
im
pleme
nted
C
OD
AS
methodology
is
pr
e
s
e
nted.
F
igur
e
5
pr
e
s
e
nts
the
pe
r
f
or
manc
e
metr
ics
:
ba
ndwidth,
de
lay,
thr
oughput,
f
a
il
e
d
ha
ndof
f
s
,
a
nd
tot
a
l
ha
ndof
f
s
c
ompar
e
d
to
R
AN
DO
M
a
nd
S
AW
.
T
he
c
r
it
e
r
ia
us
e
d
a
r
e
matr
ix
a
ve
r
a
ge
a
va
il
a
bil
it
y
(
P
D)
,
mea
n
ti
me
to
a
va
il
a
bil
it
y
(
T
E
D)
,
matr
ix
a
ve
r
a
ge
S
I
NR
(
P
S
I
NR
)
a
nd
ba
ndwidt
h
matr
ix
a
ve
r
a
ge
(
P
W
A)
.
3.
1.
CODA
S
vs
RA
ND
OM
F
igur
e
5
pr
e
s
e
nts
the
number
of
ha
ndof
f
s
.
F
igu
r
e
6
pr
e
s
e
nts
the
number
of
f
a
il
e
d
ha
ndof
f
s
.
F
igur
e
7
pr
e
s
e
nts
the
c
umul
a
ti
ve
a
ve
r
a
ge
de
lay
(
s
)
.
F
igu
r
e
8
pr
e
s
e
nts
the
a
ve
r
a
ge
ba
ndwidth
(
kHz
)
.
F
inally,
F
igur
e
9
pr
e
s
e
nts
the
a
ve
r
a
ge
thr
oughput
(
kbps
)
.
3.
2.
CODA
S
vs
.
S
AW
F
igur
e
10
pr
e
s
e
nts
the
number
o
f
ha
ndof
f
s
.
F
ig
ur
e
11
p
r
e
s
e
nts
the
number
of
f
a
il
e
d
ha
ndof
f
s
.
F
igur
e
12
pr
e
s
e
nts
the
c
umul
a
ti
ve
a
ve
r
a
ge
de
lay
(
s
)
.
F
igur
e
13
p
r
e
s
e
nts
the
a
ve
r
a
ge
ba
ndwidt
h
(
kHz
)
.
F
inally,
F
igur
e
14
pr
e
s
e
nts
the
a
ve
r
a
ge
thr
oughpu
t
(
kbps
)
.
F
igur
e
5.
Numbe
r
of
ha
ndo
f
f
s
F
igur
e
6.
Numbe
r
of
f
a
il
e
d
ha
ndof
f
s
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
N
H
ff
s
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
N
F
H
ff
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5308
-
5318
5314
F
igur
e
7.
C
umul
a
ti
ve
a
ve
r
a
ge
de
lay
(
s
)
F
igur
e
8.
Ave
r
a
ge
ba
ndwidth
(
kHz
)
F
igur
e
9.
Ave
r
a
ge
th
r
oughpu
t
(
kbps
)
F
igur
e
10.
Numbe
r
of
ha
ndo
f
f
s
D
a
ta
(
k
B
)
C
u
m
u
l
a
t
i
v
e
A
v
e
r
a
g
e
D
e
l
a
y
(
s
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
A
v
e
r
a
g
e
B
a
n
d
w
i
d
t
h
(
k
H
z
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
A
v
e
r
a
g
e
T
h
r
o
u
g
h
p
u
t
(
k
b
p
s
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
N
H
ff
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
P
e
r
for
manc
e
e
v
aluat
ion
of
a
pr
opos
al
for
s
pe
c
tr
u
m
as
s
ignme
nt
bas
e
d
on
c
ombinati
v
e
…
(
C
e
s
ar
He
r
nande
z
)
5315
F
igur
e
11.
Numbe
r
of
f
a
il
e
d
ha
ndof
f
s
F
igur
e
12.
C
umul
a
ti
ve
a
ve
r
a
ge
de
lay
(
s
)
F
igur
e
13.
Ave
r
a
ge
ba
ndwidt
h
(
kHz
)
F
igur
e
14.
Ave
r
a
ge
th
r
oughput
(
kbps
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
N
F
H
ff
s
D
a
ta
(
k
B
)
C
u
m
u
l
a
t
i
v
e
A
v
e
r
a
g
e
D
e
l
a
y
(
s
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
A
v
e
r
a
g
e
B
a
n
d
w
i
d
t
h
(
k
H
z
)
S
U
t
ra
n
s
m
is
s
io
n
t
im
e
(
m
in
)
A
v
e
r
a
g
e
T
h
r
o
u
g
h
p
u
t
(
k
b
p
s
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2088
-
8708
I
nt
J
E
lec
&
C
omp
E
ng
,
Vol
.
1
4
,
No.
5
,
Oc
tober
2
02
4
:
5308
-
5318
5316
3.
3.
Dis
c
u
s
s
ion
T
a
ble
2
a
nd
T
a
ble
3
de
s
c
r
ibe
the
c
ompar
a
ti
ve
e
va
luation
of
the
thr
e
e
s
pe
c
tr
a
l
ha
ndof
f
models
f
o
r
C
R
N
r
e
ga
r
ding
the
f
ive
-
e
va
luation
metr
ics
r
un.
A
c
c
or
ding
to
the
c
os
t
met
r
ics
:
number
o
f
ha
ndo
f
f
s
,
number
of
f
a
i
led
ha
ndof
f
s
,
a
nd
c
umul
a
ti
ve
a
ve
r
a
ge
de
lay,
C
OD
AS
obtains
the
be
s
t
pe
r
f
o
r
manc
e
with
the
lea
s
t
va
lue.
C
OD
AS
obtains
the
be
s
t
r
e
s
ult
s
a
c
c
or
ding
to
the
b
e
ne
f
it
metr
ics
,
ba
nk
width,
a
nd
th
r
oughput.
T
he
s
e
lec
ti
on
of
S
AW
to
c
ompar
a
ti
ve
ly
e
va
lu
a
te
the
pe
r
f
or
manc
e
o
f
the
f
e
e
dba
c
k
C
OD
AS
a
lgor
it
hm
wa
s
made
ba
s
e
d
on
the
r
e
s
ult
s
that
S
AW
ha
s
obtaine
d
in
pr
e
vious
r
e
s
e
a
r
c
h
whe
r
e
mos
t
of
the
ti
me
it
take
s
f
ir
s
t
plac
e
f
or
ha
ving
not
only
the
be
s
t
r
e
s
ult
s
but
a
ls
o
a
low
c
omput
a
ti
ona
l
c
os
t.
I
n
t
his
c
a
s
e
F
e
e
dba
c
k
C
OD
AS
mana
ge
s
to
im
pr
ove
the
pe
r
f
or
manc
e
o
f
S
AW
;
howe
ve
r
,
it
only
a
c
hieve
s
thi
s
by
a
ppr
oxim
a
tely
3%
.
On
the
other
ha
nd,
the
R
AN
D
OM
s
e
lec
ti
on
is
c
a
r
r
ied
ou
t
to
be
a
ble
to
mea
s
ur
e
by
wha
t
pe
r
c
e
ntage
the
pe
r
f
or
manc
e
of
the
C
R
N
im
pr
o
ve
s
whe
n
it
ha
s
a
s
tr
a
tegy
f
or
the
s
e
lec
ti
on
of
s
pe
c
tr
a
l
oppor
tuni
ti
e
s
c
ompar
e
d
to
not
ha
ving
one
.
I
n
thi
s
c
a
s
e
the
dif
f
e
r
e
nc
e
is
370
%
,
that
is
,
a
lm
os
t
4
t
im
e
s
be
tt
e
r
.
T
he
pr
e
vious
r
e
s
ult
s
a
c
hieve
d
by
F
e
e
dba
c
k
C
OD
AS
a
r
e
obtaine
d
thanks
to
the
f
a
c
t
that
it
s
a
lgor
it
hm
is
much
mor
e
r
obus
t
than
S
AW
,
howe
ve
r
,
it
would
be
int
e
r
e
s
ti
ng
to
a
na
lyze
how
much
c
omput
ing
pr
oc
e
s
s
ing
f
e
e
dba
c
k
C
OD
AS
r
e
quir
e
s
c
ompar
e
d
to
S
AW
.
Although
the
p
r
opos
e
d
a
lgor
it
hm
ha
s
a
good
c
ompar
a
ti
ve
pe
r
f
o
r
manc
e
in
ge
ne
r
a
l
ter
ms
,
it
is
im
por
tant
to
high
li
ght
the
l
im
it
a
ti
ons
that
it
may
h
a
ve
in
the
r
e
a
l
wor
ld
on
a
lar
ge
r
s
c
a
le.
T
he
main
li
mi
tation
li
e
s
in
the
a
va
il
a
bil
it
y
matr
ix
s
ince
a
gr
e
a
ter
a
m
ount
of
inf
o
r
mation
r
e
qui
r
e
s
,
on
the
one
ha
nd,
a
gr
e
a
ter
a
mount
of
memo
r
y
a
nd,
on
the
othe
r
,
a
gr
e
a
ter
a
mount
of
inf
or
mat
ion
pr
oc
e
s
s
ing,
whic
h
t
r
a
ns
late
s
int
o
gr
e
a
ter
de
lays
a
nd
g
r
e
a
ter
e
ne
r
gy
e
xpe
ndit
ur
e
.
T
he
a
bove
is
pos
s
ibl
e
to
s
olve
th
r
ough
a
c
oll
a
bor
a
ti
ve
s
tr
a
tegy
in
whic
h
va
r
ious
S
Us
ha
ve
dive
r
s
e
inf
or
mation
tha
t
c
a
n
be
s
ha
r
e
d
ba
s
e
d
on
mor
e
r
e
leva
nt
in
f
or
matio
n
ve
c
tor
s
s
uc
h
a
s
r
a
nkings
of
s
pe
c
tr
a
l
oppor
tuni
ti
e
s
.
T
a
ble
2.
C
os
t
m
e
tr
ics
A
lg
or
it
h
ms
M
a
x
im
um
va
lu
e
s
N
umbe
r
ha
ndof
f
s
N
umbe
r
f
a
il
e
d
ha
n
dof
f
s
C
umul
a
ti
v
e
a
ve
r
a
g
e
d
e
la
y
C
O
D
A
S
2516
998
447.5
4
S
A
W
2545
1019
451.7
2
R
A
N
D
O
M
11822
4788
1956.
20
T
a
ble
3.
B
e
ne
f
it
m
e
tr
ics
A
lg
or
it
h
ms
A
ve
r
a
ge
va
l
ue
s
A
ve
r
a
ge
ba
ndw
i
dt
h
A
ve
r
a
ge
th
r
ou
ghput
C
O
D
A
S
334.4
5
417.8
8
S
A
W
329.8
6
408.4
4
R
A
N
D
O
M
203.2
9
0
4.
CONC
L
USI
ON
One
s
olut
ion
to
im
pr
ove
the
inef
f
icie
nt
us
e
of
s
pe
c
tr
um
is
C
R
.
T
he
objec
ti
ve
of
the
C
R
is
to
p
r
ovide
a
c
c
e
s
s
to
a
n
a
va
il
a
ble
c
ha
nne
l
without
a
f
f
e
c
ti
ng
pe
r
f
or
manc
e
.
S
pe
c
tr
a
l
de
c
is
ion
is
a
ke
y
a
s
pe
c
t
in
C
R
Ns
to
im
pr
ove
QoS
indi
c
a
tor
s
.
M
C
DM
-
ba
s
e
d
a
lgor
it
hms
a
r
e
wide
ly
us
e
d
in
th
is
type
of
pr
oblem
du
e
to
their
e
f
f
icie
nt
r
e
s
ult
s
a
nd
low
c
omput
a
ti
ona
l
load
.
W
it
h
the
M
C
DM
,
it
is
pos
s
ibl
e
to
e
s
tablis
h
the
c
ha
nne
ls
a
c
c
or
ding
to
the
a
na
lys
is
of
s
pe
c
tr
a
l
oppor
tuni
ti
e
s
.
T
his
wor
k
us
e
s
r
e
a
l
s
pe
c
tr
a
l
oc
c
upa
nc
y
da
ta
to
im
pleme
nt
the
C
OD
AS
a
lgor
it
hm
f
or
the
s
pe
c
tr
um
s
e
lec
ti
on
pr
oc
e
s
s
in
a
C
R
N.
T
he
f
oll
owing
metr
ics
we
r
e
us
e
d:
number
of
ha
ndof
f
s
,
number
o
f
f
a
il
e
d
ha
ndof
f
s
,
c
umul
a
ti
ve
a
ve
r
a
ge
de
lay,
a
ve
r
a
ge
ba
ndwidth
,
a
nd
a
ve
r
a
ge
thr
oughput
(
kbps
)
.
T
he
r
e
s
ult
s
we
r
e
c
ompar
e
d
with
the
metr
ics
obtaine
d
f
or
the
S
AW
tec
hnique
a
nd
f
or
a
R
AN
DO
M
s
e
lec
ti
on
of
the
c
ha
nne
ls
.
Ac
c
or
ding
to
the
r
e
s
ult
s
obtaine
d,
C
OD
AS
pr
e
s
e
nts
the
be
s
t
r
e
s
ult
;
f
or
the
c
os
t
metr
ic,
the
lowe
s
t
leve
ls
we
r
e
obtaine
d,
a
nd
f
or
the
be
ne
f
i
t
metr
ic
,
the
highes
t
leve
ls
we
r
e
obtaine
d.
T
he
number
o
f
f
a
il
e
d
ha
ndo
f
f
s
is
a
ppr
oxim
a
tely
4
0%
of
the
tot
a
l
ha
ndof
f
s
,
s
o
the
number
of
to
tal
ha
ndof
f
s
is
of
gr
e
a
ter
im
por
tanc
e
whe
n
c
ompar
a
ti
ve
ly
a
na
lyzing
the
pe
r
f
or
manc
e
of
two
-
c
ha
nne
l
s
e
lec
ti
on
a
l
gor
it
hms
s
ince
the
number
o
f
incr
e
a
s
e
d
ha
ndof
f
de
lays
dur
i
ng
S
U
c
omm
unica
ti
on
is
gr
e
a
ter
.
How
e
ve
r
,
the
nu
mber
o
f
f
a
il
e
d
ha
ndof
f
s
a
ls
o
pr
ov
ides
a
mea
s
ur
e
of
a
c
c
ur
a
c
y
in
the
a
lgo
r
it
hm
s
ince
they
a
r
e
c
ha
nne
ls
that
the
a
lgor
it
hm
de
ter
mi
ne
s
a
s
a
va
il
a
ble
but
a
r
e
bus
y
wh
e
n
the
c
ha
nge
is
made
.
An
im
por
tant
a
na
lys
is
is
th
e
im
pa
c
t
that
im
pleme
nti
ng
s
pe
c
tr
a
l
de
c
is
ion
tec
hniques
s
u
c
h
a
s
the
p
r
opos
e
d
F
e
e
dba
c
k
C
OD
AS
a
lgor
i
thm
c
a
n
ha
ve
on
the
qua
li
ty
of
s
e
r
vice
of
mob
il
e
c
omm
unica
ti
on
s
,
whe
r
e
f
r
e
que
nc
y
ba
nds
a
r
e
ge
ne
r
a
ll
y
s
a
tur
a
ted.
R
e
leva
nt
f
utur
e
wor
k
would
be
i
mpl
e
menting
a
c
oll
a
bo
r
a
ti
ve
s
tr
a
tegy
that
incr
e
a
s
e
s
the
pr
opos
e
d
a
lg
or
it
hm's
e
f
f
e
c
ti
ve
ne
s
s
a
nd
e
f
f
icie
nc
y.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
E
lec
&
C
omp
E
ng
I
S
S
N:
2088
-
8708
P
e
r
for
manc
e
e
v
aluat
ion
of
a
pr
opos
al
for
s
pe
c
tr
u
m
as
s
ignme
nt
bas
e
d
on
c
ombinati
v
e
…
(
C
e
s
ar
He
r
nande
z
)
5317
AC
KNOWL
E
DGE
M
E
NT
S
T
his
wor
k
wa
s
s
uppor
ted
by
Of
icina
de
I
nve
s
ti
ga
c
iones
of
the
Unive
r
s
idad
Dis
tr
i
tal
F
r
a
nc
is
c
o
J
os
e
de
C
a
ldas
.
RE
F
E
RE
NC
E
S
[
1]
E
r
ne
s
to
C
a
de
na
M
u
ñ
oz
,
“
P
r
im
a
r
y
us
e
r
e
mul
a
ti
on
de
te
c
ti
on
w
it
h
dyna
mi
c
lo
c
a
ti
on
in
th
e
mobi
le
c
ogni
ti
ve
r
a
di
o
ne
twor
k
u
s
in
g
c
r
os
s
-
la
ye
r
de
s
ig
n,
”
U
ni
v
e
r
s
id
a
d N
a
c
io
n
a
l
de
C
ol
ombi
a
, 2020.
[
2]
R
.
M
a
r
ti
ne
z
A
lo
n
s
o,
D
.
P
le
ts
,
M
.
D
e
r
uyc
k,
L
.
M
a
r
te
ns
,
G
.
G
ui
ll
e
n
N
ie
to
,
a
nd
W
.
J
os
e
ph,
“
M
ul
ti
-
obj
e
c
ti
ve
opt
im
iz
a
ti
on
of
c
ogni
ti
ve
r
a
di
o ne
twor
ks
,
”
C
om
put
e
r
N
e
tw
o
r
k
s
, vol
. 184, p. 10
7651, J
a
n. 2021, doi:
10.1016/j
.c
omne
t.
2020.107651.
[
3]
N
.
A
bba
s
,
Y
.
N
a
s
s
e
r
,
a
nd
K
.
E
l
A
hma
d,
“
R
e
c
e
nt
a
dva
nc
e
s
o
n
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
a
nd
le
a
r
ni
ng
te
c
hni
que
s
in
c
ogni
ti
ve
r
a
di
o
ne
twor
ks
,
”
E
U
R
A
SI
P
J
our
nal
on
W
ir
e
le
s
s
C
om
m
uni
c
at
io
ns
a
nd
N
e
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th
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ha
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F
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if
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ti
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n
a
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e
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le
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ve
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ul
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a
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E
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ti
ve
s
pe
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c
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ti
ve
r
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“
T
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(
T
S
F
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ht
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