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n
el.
I
t
co
n
s
i
s
ts
o
f
a
s
o
u
r
ce
n
o
d
e,
a
r
ela
y
n
o
d
e
an
d
a
d
esti
n
atio
n
n
o
d
e.
Van
d
er
Mu
ele
n
in
tr
o
d
u
ce
d
th
is
m
o
d
el
an
d
later
it
w
a
s
ex
te
n
s
i
v
el
y
in
v
e
s
ti
g
ated
b
y
C
o
v
er
an
d
E
l
-
Ga
m
al
[
8
]
,
[
9
]
.
B
r
o
ad
ly
s
p
ea
k
in
g
,
t
h
er
e
ar
e
th
r
ee
tec
h
n
iq
u
es
u
s
ed
b
y
t
h
e
r
ela
y
n
o
d
e
to
aid
th
e
tr
an
s
m
is
s
io
n
f
r
o
m
th
e
s
o
u
r
ce
n
o
d
e
to
th
e
d
esti
n
atio
n
n
o
d
e.
T
h
ese
ar
e
Am
p
li
f
y
-
a
n
d
-
Fo
r
w
a
r
d
(
A
&
F)
r
ela
y
i
n
g
,
Dec
o
d
e
-
an
d
-
Fo
r
w
ar
d
(
D
&
F)
r
ela
y
in
g
a
n
d
co
d
ed
r
ela
y
i
n
g
[
8
]
.
E
ac
h
tech
n
iq
u
e
d
escr
ib
es
a
d
i
f
f
er
e
n
t
r
ela
y
b
eh
av
io
r
an
d
th
e
y
d
i
f
f
er
in
ter
m
s
o
f
p
er
f
o
r
m
a
n
ce
an
d
t
h
e
d
eg
r
ee
o
f
co
m
p
le
x
it
y
.
I
n
p
r
ac
tice,
D&
F
h
as
r
ec
eiv
ed
m
o
r
e
atte
n
tio
n
d
u
e
t
o
its
g
o
o
d
p
er
f
o
r
m
a
n
ce
an
d
r
ea
s
o
n
ab
le
i
m
p
le
m
e
n
tatio
n
co
m
p
lex
i
t
y
co
m
p
ar
ed
w
it
h
t
h
e
o
th
er
t
w
o
tech
n
iq
u
es.
I
n
th
i
s
w
o
r
k
w
e
co
n
s
id
er
D
&
F r
ela
y
i
n
g
.
On
e
o
f
t
h
e
c
h
alle
n
g
e
s
f
ac
ed
b
y
r
ela
y
tr
a
n
s
m
i
s
s
io
n
is
w
h
e
n
th
e
r
ela
y
n
o
d
e
is
a
h
al
f
-
d
u
p
lex
w
ir
eles
s
tr
an
s
ce
i
v
er
.
A
h
al
f
-
d
u
p
lex
r
el
a
y
n
o
d
e
ca
n
n
o
t
r
ec
eiv
e
a
n
d
tr
an
s
m
it
at
t
h
e
s
a
m
e
ti
m
e
i
n
t
h
e
s
a
m
e
f
r
eq
u
en
c
y
b
an
d
.
A
cc
o
r
d
in
g
l
y
,
t
h
e
s
o
u
r
c
e
n
o
d
e
an
d
r
ela
y
n
o
d
e
h
a
v
e
t
o
s
h
ar
e
th
e
a
v
ailab
le
d
eg
r
ee
o
f
f
r
ee
d
o
m
[
6
]
.
Fo
r
in
s
ta
n
ce
,
w
h
e
n
a
v
ailab
le
tr
an
s
m
is
s
io
n
ti
m
e
i
s
s
h
ar
ed
,
th
e
s
o
u
r
ce
n
o
d
e
tr
an
s
m
it
f
o
r
a
f
r
ac
ti
o
n
o
f
t
h
e
a
v
ailab
le
ti
m
e
a
n
d
th
e
n
g
o
es i
n
to
id
le
s
t
ate.
Fo
llo
w
i
n
g
,
t
h
e
r
ela
y
n
o
d
e
tr
an
s
m
it
s
f
o
r
th
e
r
e
m
ain
in
g
ti
m
e.
Hav
i
n
g
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
th
e
r
ela
y
n
o
d
e
to
tak
e
tu
r
n
s
to
tr
an
s
m
it
w
a
s
tes
v
al
u
ab
le
r
e
s
o
u
r
ce
s
.
T
h
er
ef
o
r
e,
r
elay
ch
a
n
n
e
ls
w
it
h
h
al
f
-
d
u
p
le
x
co
n
s
tr
ai
n
t
m
a
y
lead
to
d
eg
r
ad
atio
n
in
ch
an
n
e
l
p
er
f
o
r
m
a
n
ce
[
1
0
]
.
Fo
r
a
p
ar
ticu
lar
ca
s
e
,
r
ela
y
i
n
g
i
s
d
ee
m
ed
u
s
ef
u
l
if
u
s
i
n
g
r
ela
y
i
n
g
is
ad
v
a
n
tag
eo
u
s
o
v
er
d
ir
ec
t
tr
an
s
m
is
s
io
n
.
Sp
ec
if
icall
y
,
r
ela
y
i
n
g
is
u
s
e
f
u
l
i
f
th
e
tr
a
n
s
m
i
s
s
io
n
r
ate
ac
h
iev
ab
le
t
h
r
o
u
g
h
r
ela
y
i
n
g
is
h
i
g
h
er
th
at
t
h
e
tr
an
s
m
is
s
io
n
r
ate
ac
h
iev
ab
le
v
ia
d
ir
ec
t
tr
an
s
m
is
s
io
n
.
O
n
e
o
b
j
ec
tiv
e
o
f
th
i
s
s
t
u
d
y
i
s
to
in
v
es
tig
a
te
th
e
co
n
d
itio
n
s
t
h
at
lead
s
to
u
s
e
f
u
l
r
ela
y
i
n
g
.
Use
f
u
l
r
ela
y
i
n
g
r
eq
u
ir
es
ca
r
ef
u
l
allo
ca
tio
n
o
f
r
eso
u
r
ce
s
.
Op
ti
m
a
l
r
eso
u
r
ce
allo
ca
tio
n
h
a
s
b
ee
n
u
n
d
er
in
v
es
tig
a
tio
n
b
y
m
a
n
y
r
esear
ch
er
s
,
f
o
r
ex
a
m
p
le
[
1
]
,
[
1
0
]
–
[
1
3
]
.
T
h
e
p
r
o
b
lem
o
f
r
eso
u
r
ce
allo
ca
tio
n
is
also
r
elate
d
to
r
o
u
tin
g
i
n
m
u
lti
-
h
o
p
r
elay
i
n
g
[
1
4
]
.
T
h
is
p
ap
er
ex
ten
d
s
t
h
e
w
o
r
k
in
[
1
3
]
.
T
h
e
aim
is
to
in
v
esti
g
ate
th
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
t
h
r
ee
-
n
o
d
e
D&
F
r
ela
y
c
h
an
n
el
w
i
th
h
al
f
-
d
u
p
lex
co
n
s
tr
ain
t
u
n
d
er
d
if
f
er
en
t
ti
m
e
a
n
d
p
o
w
er
allo
ca
tio
n
p
o
licies.
C
h
a
n
n
el
p
er
f
o
r
m
a
n
ce
is
m
ea
s
u
r
ed
in
t
er
m
s
o
f
m
u
tu
al
i
n
f
o
r
m
atio
n
.
I
n
in
f
o
r
m
atio
n
th
eo
r
y
,
m
u
tu
a
l
in
f
o
r
m
atio
n
i
s
a
m
ea
s
u
r
e
o
f
th
e
m
u
t
u
al
in
d
ep
e
n
d
en
ce
b
et
w
ee
n
t
w
o
v
ar
iab
les
[
1
5
]
,
[
1
6
]
.
I
n
[
1
3
]
,
f
o
r
th
e
co
m
m
u
n
icatio
n
lin
k
s
b
et
w
ee
n
c
h
an
n
el
n
o
d
es,
i
t
i
s
ass
u
m
ed
t
h
at
t
h
e
c
h
a
n
n
el
i
s
g
o
v
er
n
ed
b
y
A
d
d
iti
v
e
W
h
i
te
Gau
s
s
ia
n
No
is
e
(
A
W
GN)
r
eg
i
m
e.
T
h
is
p
ap
er
ex
ten
d
s
th
e
w
o
r
k
to
in
cl
u
d
e
R
a
y
lei
g
h
f
ad
in
g
as
w
ell.
I
n
t
h
e
f
ad
in
g
ca
s
e,
av
er
a
g
e
m
u
tu
al
in
f
o
r
m
a
tio
n
i
s
u
s
ed
as
th
e
p
er
f
o
r
m
an
ce
m
ea
s
u
r
e.
A
l
ter
n
ati
v
el
y
,
o
u
tag
e
p
r
o
b
ab
ilit
y
ca
n
al
s
o
b
e
u
s
e
d
f
o
r
p
er
f
o
r
m
a
n
ce
e
v
alu
a
tio
n
as
d
o
n
e,
f
o
r
ex
a
m
p
le,
in
[
1
2
]
.
T
o
ac
h
iev
e
o
u
r
o
b
j
ec
tiv
es,
a
p
r
o
p
er
m
ath
e
m
atica
l
m
o
d
el
i
s
estab
lis
h
ed
.
De
r
iv
ed
eq
u
a
tio
n
s
ar
e
t
h
e
n
u
s
ed
to
p
lo
t
p
er
f
o
r
m
a
n
ce
g
r
a
p
h
s
w
h
ic
h
ar
e
an
a
l
y
ze
d
to
c
o
m
e
w
it
h
co
n
c
lu
s
io
n
s
.
T
h
e
r
est
o
f
th
i
s
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
I
n
Sect
i
o
n
2
,
m
u
t
u
al
i
n
f
o
r
m
atio
n
f
o
r
m
u
la
ar
e
d
er
iv
ed
as
a
f
u
n
ct
io
n
o
f
ti
m
e
a
n
d
p
o
w
er
allo
ca
tio
n
.
Si
m
u
latio
n
r
es
u
lt
s
an
d
d
is
cu
s
s
io
n
o
n
r
es
u
lt
s
is
i
n
Sectio
n
3
.
Fi
n
all
y
,
co
n
cl
u
d
in
g
r
e
m
ar
k
s
ar
e
g
iv
e
n
in
Sectio
n
4
.
2.
CH
ANNE
L
M
O
DE
L
I
n
t
h
is
s
ec
tio
n
,
m
o
d
el
o
f
t
h
e
w
ir
ele
s
s
D&
F
r
ela
y
c
h
an
n
el
is
d
e
m
o
n
s
tr
ated
.
A
s
ill
u
s
tr
ated
i
n
F
ig
u
r
e
1
,
th
e
3
-
n
o
d
e
r
ela
y
c
h
a
n
n
el
s
co
n
s
i
s
t
s
o
f
th
r
ee
n
o
d
es:
t
h
e
s
o
u
r
ce
n
o
d
e
(
S)
th
a
t
h
as
a
m
e
s
s
a
g
e
to
s
en
d
,
t
h
e
d
esti
n
atio
n
n
o
d
e
(
D)
in
te
n
d
ed
to
r
ec
eiv
e
th
e
s
o
u
r
ce
’
s
m
e
s
s
a
g
e;
an
d
th
e
r
ela
y
n
o
d
e
(
R
)
to
aid
th
e
tr
an
s
m
i
s
s
io
n
f
r
o
m
t
h
e
s
o
u
r
ce
to
th
e
d
est
in
at
io
n
.
Fig
u
r
e
1
.
R
e
p
r
esen
tatio
n
o
f
t
h
e
th
r
ee
-
n
o
d
e
r
ela
y
ch
a
n
n
el
Evaluation Warning : The document was created with Spire.PDF for Python.
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T
h
e
r
elay
n
o
d
e
is
h
al
f
-
d
u
p
lex
co
n
s
tr
ain
t,
m
ea
n
in
g
t
h
at
it
ca
n
eith
er
tr
an
s
m
it
o
r
r
ec
eiv
e
at
o
n
e
ti
m
e.
I
t
ca
n
n
o
t
d
o
b
o
th
tr
an
s
m
it
an
d
r
ec
eiv
e
at
th
e
s
a
m
e
ti
m
e
i
n
th
e
s
a
m
e
f
r
eq
u
e
n
c
y
b
a
n
d
.
As
a
co
n
s
eq
u
en
ce
,
tr
an
s
m
i
s
s
io
n
f
r
o
m
th
e
s
o
u
r
ce
to
th
e
d
est
in
at
io
n
ta
k
es
p
lac
e
in
t
w
o
p
h
ase
s
.
First,
th
e
s
o
u
r
ce
tr
an
s
m
it
s
t
h
e
m
es
s
ag
e
to
b
o
th
t
h
e
r
ela
y
n
o
d
e
an
d
th
e
d
esti
n
atio
n
n
o
d
e.
T
h
en
,
in
th
e
s
ec
o
n
d
p
h
ase,
af
ter
s
u
cc
e
s
s
f
u
ll
y
d
ec
o
d
in
g
th
e
s
o
u
r
ce
’
s
m
ess
a
g
e
s
en
t
in
t
h
e
f
ir
s
t
p
h
ase
,
th
e
r
ela
y
n
o
d
e
r
e
-
s
e
n
d
s
th
at
m
es
s
ag
e
to
th
e
d
esti
n
atio
n
n
o
d
e.
I
n
th
e
s
ec
o
n
d
p
h
ase
th
e
s
o
u
r
ce
n
o
d
e
r
em
ai
n
s
id
le.
As
a
r
esu
lt
o
f
th
i
s
t
w
o
-
p
h
as
e
tr
an
s
m
is
s
io
n
,
th
e
d
esti
n
atio
n
n
o
d
e
r
ec
eiv
es
t
w
o
co
p
ies
o
f
th
e
s
o
u
r
ce
m
e
s
s
a
g
e
;
o
n
e
r
ec
eiv
ed
d
ir
ec
tly
f
r
o
m
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
an
o
th
er
t
h
r
o
u
g
h
th
e
r
ela
y
n
o
d
e.
T
h
e
d
esti
n
atio
n
u
s
es
b
o
th
s
ig
n
a
ls
to
d
ec
o
d
e
th
e
s
o
u
r
ce
’
s
m
es
s
ag
e.
T
h
er
e
ar
e
th
r
ee
m
ai
n
tech
n
iq
u
e
s
th
at
c
an
b
e
u
s
ed
b
y
th
e
d
esti
n
atio
n
n
o
d
e
to
co
m
b
in
e
th
e
s
e
t
wo
co
p
ies
o
f
th
e
s
e
n
t
m
es
s
ag
e;
n
a
m
el
y
Se
lectio
n
C
o
m
b
i
n
i
n
g
(
SC
)
,
E
q
u
al
-
Ga
in
C
o
m
b
i
n
i
n
g
(
E
GC
)
a
n
d
Ma
x
i
m
a
l
-
R
a
tio
C
o
m
b
in
i
n
g
(
MRC
)
.
M
R
C
p
er
f
o
r
m
a
n
ce
i
s
b
etter
th
a
n
t
h
e
o
t
h
er
t
w
o
tec
h
n
iq
u
es.
W
e
a
s
s
u
m
e
t
h
at
th
e
d
esti
n
atio
n
ap
p
lies
MRC
to
d
ec
o
d
e
th
e
s
o
u
r
ce
’
s
m
es
s
ag
e.
T
h
e
ab
o
v
e
d
escr
ib
ed
tr
an
s
m
i
s
s
io
n
m
eth
o
d
i
s
k
n
o
w
n
a
s
c
o
o
p
er
ativ
e
tr
an
s
m
i
s
s
io
n
o
r
c
o
o
p
er
ativ
e
r
ela
y
in
g
.
I
n
co
n
tr
ast,
i
n
n
o
n
-
c
o
o
p
er
ativ
e
r
ela
y
in
g
,
th
e
d
esti
n
atio
n
i
g
n
o
r
es t
h
e
s
ig
n
al
r
ec
ei
v
ed
d
ir
ec
tl
y
f
r
o
m
th
e
s
o
u
r
ce
n
o
d
e
an
d
r
el
y
s
o
lel
y
o
n
th
at
r
ec
eiv
ed
f
r
o
m
th
e
r
ela
y
n
o
d
e
to
d
ec
o
d
e
th
e
m
e
s
s
a
g
e.
As
ex
p
lain
ed
i
n
Fig
u
r
e
2
,
th
e
ch
an
n
el
ca
n
b
e
v
ie
w
f
r
o
m
t
wo
p
er
s
p
ec
tiv
es:
B
r
o
ad
c
ast
C
h
a
n
n
el
(
B
C
)
w
h
er
e
th
e
s
o
u
r
ce
n
o
d
e
tr
an
s
m
i
ts
to
t
w
o
d
esti
n
atio
n
s
,
t
h
e
r
elay
n
o
d
e
an
d
th
e
d
es
tin
at
io
n
n
o
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es; an
d
a
Mu
l
tip
le
-
A
cc
e
s
s
C
h
a
n
n
e
l (
MC)
w
h
er
e
t
h
e
d
esti
n
atio
n
r
ec
ei
v
es
f
r
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m
t
w
o
n
o
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es,
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
th
e
r
ela
y
n
o
d
e.
Fig
u
r
e
2
.
T
h
e
3
-
n
o
d
e
r
elay
ch
an
n
el
ca
n
b
e
v
ie
w
ed
as a
co
m
b
in
atio
n
o
f
t
w
o
c
h
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n
el
s
,
a
b
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ad
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s
t c
h
an
n
el
w
it
h
t
h
e
s
o
u
r
ce
tr
a
n
s
m
itti
n
g
to
th
e
r
ela
y
a
n
d
th
e
d
esti
n
atio
n
; a
n
d
a
m
u
ltip
le
-
ac
ess
c
h
a
n
n
el
w
it
h
s
o
u
r
ce
an
d
th
e
r
ela
y
tr
an
s
m
i
tti
n
g
to
th
e
d
es
tin
atio
n
2
.
1
.
Allo
ca
t
io
n o
f
Av
a
ila
ble Ti
m
e
C
o
n
v
en
t
io
n
all
y
,
av
a
ilab
le
d
eg
r
ee
-
of
-
f
r
ee
d
o
m
i
s
allo
ca
ted
e
q
u
all
y
b
et
w
ee
n
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
th
e
r
ela
y
n
o
d
e.
Fo
r
ex
a
m
p
le,
av
ai
l
ab
le
tr
an
s
m
i
s
s
io
n
ti
m
e
ca
n
b
e
d
iv
id
ed
in
to
t
w
o
h
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v
e
s
w
h
er
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s
t
h
e
s
o
u
r
ce
n
o
d
e
tr
an
s
m
it
s
d
u
r
in
g
t
h
e
f
ir
s
t
h
al
f
an
d
th
e
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ela
y
n
o
d
e
tr
an
s
m
its
d
u
r
in
g
th
e
s
ec
o
n
d
h
a
lf
.
Fo
r
o
p
tim
izin
g
c
h
an
n
el
o
p
er
atio
n
w
e
as
s
u
m
e
ar
b
itra
r
y
ti
m
e
allo
ca
tio
n
in
s
tead
.
Fra
ct
io
n
(
n
o
t
n
ec
e
s
s
ar
y
h
al
f
)
o
f
t
h
e
av
ailab
le
ti
m
e
(
o
r
s
p
ec
tr
u
m
)
i
s
allo
ca
ted
to
th
e
s
o
u
r
ce
an
d
th
e
r
e
m
ai
n
i
n
g
ti
m
e
i
s
u
s
ed
b
y
t
h
e
r
ela
y
tr
a
n
s
m
is
s
io
n
.
L
et
[
]
b
e
th
e
ti
m
e
u
s
ed
b
y
th
e
r
ela
y
f
o
r
r
ep
ea
tin
g
th
e
s
o
u
r
ce
m
e
s
s
a
g
e.
T
h
en
t
h
e
s
o
u
r
ce
h
a
s
(
)
f
r
ac
tio
n
o
f
th
e
ti
m
e
to
tr
an
s
m
it.
T
h
e
r
elay
m
u
s
t
b
e
ab
le
t
o
f
u
ll
y
d
ec
o
d
e
s
o
u
r
ce
’
s
s
i
g
n
a
l
in
th
e
f
ir
s
t
p
h
ase
b
ef
o
r
e
it c
an
as
s
is
t i
n
t
h
e
s
ec
o
n
d
p
h
ase.
ca
n
b
e
s
ee
n
a
s
a
m
ea
s
u
r
e
o
f
co
o
p
er
atio
n
.
Gr
ea
ter
in
d
ic
ates
m
o
r
e
co
o
p
er
atio
n
f
r
o
m
t
h
e
r
ela
y
n
o
d
e.
is
th
e
ca
s
e
o
f
n
o
co
o
p
er
atio
n
w
h
ile
is
th
e
f
u
ll
co
o
p
er
atio
n
s
ce
n
ar
io
.
⁄
is
th
e
co
n
v
e
n
tio
n
al
eq
u
al
-
ti
m
e
allo
ca
tio
n
s
et
u
p
.
No
te
m
o
r
e
c
o
o
p
er
atio
n
d
o
es
n
o
t
n
ec
ess
a
r
y
m
ea
n
i
m
p
r
o
v
ed
p
er
f
o
r
m
a
n
ce
.
Fo
r
ex
a
m
p
le,
th
e
r
ate
f
o
r
th
e
f
u
ll
co
o
p
er
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ca
s
e
is
ze
r
o
,
s
in
ce
th
e
s
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e
is
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r
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it.
T
h
e
m
u
t
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al
i
n
f
o
r
m
atio
n
b
et
w
ee
n
t
h
e
s
o
u
r
c
e
an
d
th
e
d
esti
n
atio
n
as a
f
u
n
c
tio
n
o
f
is
g
i
v
e
n
b
y
[
1
0
]
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1
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r
ate
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r
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esti
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.
ass
u
m
es
th
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r
ela
y
h
as
n
o
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o
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th
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tr
an
s
m
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s
s
io
n
.
is
g
i
v
e
n
b
y
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(
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(
2
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N:
2502
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4752
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251
w
h
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I
S
th
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ec
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Si
g
n
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-
to
-
No
is
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(
SNR
)
at
th
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d
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tio
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(
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is
g
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(
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(
3
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w
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SNR
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h
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.
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[
(
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(
4
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w
h
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t
h
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SN
R
s
f
r
o
m
t
h
e
r
ela
y
to
th
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d
esti
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.
No
te
th
at
in
(
2
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is
n
o
t
f
u
n
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o
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s
in
ce
al
l
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m
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i
s
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ca
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i
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th
is
ca
s
e.
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h
e
s
ce
n
ar
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b
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b
y
(
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f
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r
an
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ap
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v
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r
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y
ch
a
n
n
el
w
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to
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n
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it is
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2
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2
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Allo
ca
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f
T
ra
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m
i
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P
o
w
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́
in
(
3
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is
th
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SNR
at
th
e
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y
g
iv
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n
tr
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s
m
is
s
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f
r
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m
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h
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s
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ce
n
o
d
e.
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t
is
a
r
e
s
u
lt
o
f
s
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al
f
ac
to
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s
in
c
lu
d
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g
th
e
s
o
u
r
ce
n
o
d
e
tr
an
s
m
i
s
s
io
n
p
o
w
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,
t
h
e
d
is
ta
n
ce
b
et
w
ee
n
t
h
e
t
w
o
n
o
d
es,
n
o
is
e,
s
ig
n
al
p
o
w
er
d
eg
r
ad
atio
n
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ate
o
v
er
d
is
tan
ce
an
d
c
h
an
n
el
f
ad
i
n
g
.
́
ca
n
b
e
ex
p
r
ess
ed
as,
́
(
5
)
w
h
e
r
e,
is
th
e
s
o
u
r
ce
n
o
d
e
t
r
an
s
m
is
s
io
n
p
o
w
er
,
is
th
e
d
is
tan
ce
b
et
w
ee
n
t
h
e
s
o
u
r
ce
n
o
d
e
an
d
th
e
d
esti
n
atio
n
n
o
d
e,
is
th
e
p
o
w
er
lo
s
s
f
ac
to
r
,
is
th
e
n
o
is
e
p
o
w
er
a
n
d
is
a
f
ac
to
r
to
ca
p
tu
r
e
o
th
er
ch
an
n
el
e
f
f
ec
ts
s
u
c
h
as
lo
n
g
-
t
er
m
an
d
s
h
o
r
t
-
ter
m
f
ad
in
g
.
I
n
an
A
W
GN
r
e
g
i
m
e
w
e
ca
n
ass
u
m
e
.
Si
m
i
lar
l
y
,
an
d
ar
e
g
iv
en
,
r
esp
ec
tiv
el
y
,
b
y
,
́
(
6
)
a
nd
́
(
7
)
in
(
7
)
is
r
ela
y
tr
a
n
s
m
is
s
io
n
p
o
w
er
.
T
h
is
w
o
r
k
co
n
s
id
er
s
a
co
n
s
tr
ain
t
o
n
t
h
e
ch
a
n
n
el
t
o
tal
tr
an
s
m
is
s
io
n
p
o
w
er
,
.
T
h
at
im
p
lie
s
,
(
8
)
L
et
b
e
th
e
p
o
w
er
allo
ca
tio
n
f
ac
to
r
.
I
n
t
h
is
ca
s
e,
[
]
is
t
h
e
f
r
ac
tio
n
o
f
p
o
w
e
r
allo
ca
ted
to
t
h
e
r
el
a
y
n
o
d
e.
A
cc
o
r
d
in
g
l
y
,
(
)
́
(
9
)
w
h
er
e,
́
(
1
0
)
Si
m
i
lar
l
y
,
an
d
ca
n
b
e
w
r
itte
n
as,
(
)
́
(
1
1
)
a
nd
́
(
1
2
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
:
2
48
–
2
57
252
R
esp
ec
ti
v
el
y
.
S
i
m
ilar
to
,
also
m
ea
s
u
r
es
t
h
e
d
eg
r
ee
o
f
co
o
p
er
atio
n
w
h
er
e
an
d
in
d
icate
n
o
co
o
p
er
atio
n
an
d
f
u
ll c
o
o
p
er
atio
n
,
r
esp
ec
tiv
el
y
.
Acc
o
r
d
in
g
l
y
,
w
e
m
a
y
r
e
-
w
r
ite
(
1
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as b
elo
w
,
(
)
{
{
(
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(
)
}
(
1
3
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w
h
er
e,
(
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(
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(
(
)
́
)
(
1
4
)
(
)
(
)
[
(
)
́
(
́
)
]
(
1
5
)
2
.
3
.
F
a
din
g
Cha
nn
els
I
n
a
A
W
GN
r
eg
i
m
e,
,
,
an
d
in
(
5
)
,
(
6
)
an
d
(
7
)
,
r
esp
ec
tiv
ely
;
ar
e
f
ix
ed
.
T
h
er
ef
o
r
e,
́
,
́
,
an
d
́
ar
e
f
ix
ed
to
o
.
I
n
th
e
co
n
tr
ar
y
,
i
n
a
f
ad
in
g
s
ce
n
ar
io
,
,
,
an
d
;
an
d
co
n
s
eq
u
en
tl
y
,
́
,
́
,
an
d
́
; a
r
e
all
ch
an
g
in
g
r
an
d
o
m
l
y
.
R
a
y
le
ig
h
f
ad
i
n
g
m
o
d
el
is
co
m
m
o
n
l
y
u
s
ed
to
m
o
d
el
th
e
f
ad
i
n
g
e
f
f
ec
t.
I
n
R
a
y
leig
h
f
ad
in
g
,
,
w
h
er
e
(
)
(
)
(
)
o
r
(
)
,
is
an
ex
p
o
n
en
tial
r
an
d
o
m
v
a
r
iab
le.
Fo
r
a
r
an
d
o
m
v
ar
iab
le
,
th
e
p
r
o
b
a
b
ilit
y
d
is
tr
ib
u
tio
n
f
u
n
ct
io
n
is
g
i
v
en
b
y
,
(
)
{
(
1
6
)
w
h
er
e,
[
]
(
1
7
)
a
n
d
[
]
is
th
e
ex
p
ec
ted
v
al
u
e
o
f
th
e
r
an
d
o
m
v
ar
iab
le
.
Fo
r
th
e
s
o
u
r
ce
-
to
-
r
ela
y
,
s
o
u
r
ce
-
to
-
d
es
tin
atio
n
an
d
r
ela
y
-
to
-
d
esti
n
atio
n
lin
k
s
th
e
d
is
tr
ib
u
tio
n
is
g
iv
e
n
,
r
esp
ec
ti
v
e
l
y
,
b
y
,
(
)
{
(
1
8
)
(
)
{
(
1
9
)
(
)
{
(
2
0
)
w
h
er
e,
[
́
]
[
]
(
2
1
)
[
́
]
[
]
(
2
2
)
[
́
]
[
]
(
2
3
)
T
h
e
d
is
tr
ib
u
tio
n
o
f
is
g
i
v
e
n
i
n
[
1
0
]
.
On
e
w
a
y
to
ev
a
lu
ate
ch
an
n
el
p
er
f
o
r
m
a
n
ce
i
n
a
f
ad
i
n
g
is
to
co
n
s
id
er
th
e
av
er
a
g
e
m
u
t
u
al
i
n
f
o
r
m
atio
n
,
[
]
.
I
t
is
w
o
r
t
h
n
o
tin
g
th
at
m
ath
e
m
atica
ll
y
,
c
h
a
n
n
el
p
er
f
o
r
m
a
n
ce
in
th
e
f
ad
i
n
g
ca
s
e
is
w
o
r
s
e
th
a
n
t
h
e
A
W
GN
ca
s
e
i
f
a
v
ea
g
e
SNR
i
n
th
e
f
ad
in
g
ca
s
e
i
s
s
a
m
e
as
t
h
e
SN
R
in
A
W
GN.
T
h
is
T
h
is
p
r
ed
ictio
n
is
m
ad
e
b
ased
o
n
J
en
s
en
’
s
i
n
eq
u
ali
t
y
w
h
ic
h
s
tat
e
s
th
at
f
o
r
a
r
an
d
o
m
v
ar
iab
le
an
d
an
y
f
u
n
ct
io
n
(
)
,
[
(
)
]
(
[
]
)
(
2
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
A
WGN
a
n
d
R
a
yleig
h
F
a
d
in
g
B
eh
a
vio
r
o
f th
e
W
ir
eles
s
Dec
o
d
e
.
.
.
.
(
Mu
h
a
mma
d
Za
r
o
l F
itr
i Kh
o
ir
o
l F
a
u
z
i)
253
3.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
AND
DIS
CUSS
I
O
N
I
n
th
is
s
ec
tio
n
s
i
m
u
la
tio
n
r
esu
lts
ar
e
p
r
esen
ted
.
P
lo
ttin
g
o
f
m
u
tu
al
i
n
f
o
r
m
atio
n
o
f
th
e
c
h
a
n
n
el
a
g
ain
s
t
an
d
h
elp
u
n
d
er
s
ta
n
d
th
e
ef
f
e
ct
o
f
ch
an
g
in
g
ti
m
e
allo
ca
tio
n
an
d
p
o
w
er
allo
ca
tio
n
o
n
ch
an
n
el
p
er
f
o
r
m
an
ce
.
C
o
n
s
eq
u
en
tl
y
,
o
p
ti
m
a
l o
p
er
atio
n
o
f
th
e
c
h
an
n
el
m
a
y
b
e
ac
h
i
ev
ed
.
3
.
1
.
Cha
nn
el
B
eha
v
io
ur
in AW
G
N
E
nv
iro
n
m
e
nt
T
o
u
n
d
er
s
tan
d
th
e
b
eh
a
v
io
u
r
o
f
th
e
ch
a
n
n
e
l
is
f
ir
s
t
p
lo
tte
d
ve
r
s
u
s
u
s
in
g
(
1
3
)
as
s
h
o
w
n
i
n
Fig
u
r
e
3.
,
,
an
d
ar
e
s
et
as
1
3
,
5
,
an
d
1
1
,
r
esp
ec
tiv
ely
.
T
h
is
is
a
t
y
p
ical
ca
s
e
w
h
er
e
th
e
lin
k
b
et
w
ee
n
th
e
s
o
u
r
ce
n
o
d
e
an
d
th
e
r
ela
y
n
o
d
e
is
p
ar
ticu
lar
l
y
wea
k
d
u
e
to
f
o
r
ex
a
m
p
le,
s
h
ad
o
w
i
n
g
ef
f
ec
t
.
I
n
th
is
ca
s
e
r
ela
y
in
g
ca
n
b
e
co
n
s
id
er
ed
as
an
ef
f
ec
ti
v
e
m
e
th
o
d
to
i
m
p
r
o
v
e
tr
an
s
m
i
s
s
io
n
p
er
f
o
r
m
an
ce
.
I
n
a
A
W
GN
r
eg
i
m
e,
,
,
an
d
ar
e
f
ix
ed
th
r
o
u
g
h
o
u
t
t
h
e
s
i
m
u
latio
n
.
T
h
e
A
W
GN
a
s
s
u
m
p
tio
n
ch
a
n
g
i
n
g
s
lo
w
l
y
(
s
lo
w
f
ad
in
g
ch
a
n
n
el)
an
d
th
er
ef
o
r
e
r
e
m
ain
co
n
s
ta
n
t t
h
r
o
u
g
h
o
u
t th
e
s
i
m
u
lat
io
n
ti
m
e.
Fig
u
r
e
3
.
Mu
tu
a
l in
f
o
r
m
atio
n
v
er
s
u
s
f
o
r
d
if
f
er
en
t p
o
w
er
all
o
ca
tio
n
I
t
ca
n
b
e
s
ee
n
f
r
o
m
Fi
g
u
r
e
3
t
h
at
f
o
r
a
g
iv
e
n
p
o
w
er
allo
ca
ti
o
n
,
ch
a
n
n
e
l
p
er
f
o
r
m
a
n
ce
c
h
a
n
g
es
a
s
ti
m
e
allo
ca
ted
to
th
e
r
ela
y
i
s
ch
a
n
g
ed
.
T
h
e
b
est
ti
m
e
al
lo
ca
tio
n
p
o
licy
ca
n
b
e
r
ea
d
il
y
s
elec
te
d
f
r
o
m
Fi
g
.
3
.
W
e
n
o
tice
t
h
at
f
o
r
th
e
s
ce
n
ar
io
co
n
s
id
er
ed
h
er
e,
o
u
t
o
f
t
h
e
6
d
if
f
er
en
t
v
al
u
es
f
o
r
,
d
ir
ec
t
tr
an
s
m
is
s
io
n
is
clea
r
l
y
p
r
ef
er
r
ed
in
5
o
f
th
e
m
,
n
a
m
el
y
an
d
.
T
h
e
o
n
l
y
ti
m
e
w
h
e
n
r
ela
y
i
n
g
w
a
s
u
s
e
f
u
l
is
w
h
en
ac
h
iev
i
n
g
a
m
ax
i
m
u
m
r
ate
b
/s
/Hz
f
o
r
.
in
o
th
er
w
o
r
d
s
,
allo
ca
tin
g
m
o
r
e
p
o
w
er
to
t
h
e
r
ela
y
n
o
d
e
m
a
k
es
i
t
m
o
r
e
u
s
e
f
u
l
to
t
h
e
ch
an
n
el.
T
h
is
is
p
ar
tic
u
lar
l
y
t
r
u
e
f
o
r
h
al
f
-
d
u
p
lex
ch
an
n
el
s
.
W
e
also
n
o
tice
th
at
in
th
e
ca
s
e
w
h
e
n
all
p
o
w
er
is
allo
ca
ted
to
th
e
r
elay
,
in
f
o
r
m
atio
n
r
ate
is
n
u
l
l
r
eg
ar
d
less
o
f
t
h
e
ti
m
e
allo
ca
ti
o
n
.
I
n
F
ig
u
r
e
4
,
(
1
3
)
is
also
u
s
ed
to
p
lo
t
ag
ai
n
s
t
f
o
r
d
i
f
f
er
e
n
t
v
al
u
es
o
f
.
,
,
an
d
r
em
ai
n
u
n
c
h
an
g
ed
.
Si
m
ilar
b
eh
a
v
io
r
is
n
o
ted
in
Fi
g
u
r
e
4
.
A
llo
ca
ti
n
g
m
o
r
e
ti
m
e
to
th
e
r
ela
y
n
o
d
e
m
a
k
es it
u
s
e
f
u
l
f
o
r
tr
an
s
m
is
s
io
n
.
I
n
t
h
e
s
ce
n
ar
io
c
o
n
s
id
er
ed
,
d
ir
ec
t tr
an
s
m
i
s
s
io
n
is
o
p
ti
m
u
m
f
o
r
th
e
f
ir
s
t
t
h
r
ee
ca
s
es
o
f
an
d
.
Ho
w
e
v
er
,
r
ela
y
in
g
is
p
r
ef
er
ab
le
w
h
e
n
m
o
r
e
ti
m
e
is
allo
ca
ted
to
th
e
r
ela
y
n
o
d
e.
I
n
t
h
i
s
p
ar
ti
cu
lar
ca
s
e
t
h
e
o
p
ti
m
u
m
p
o
w
er
allo
ca
tio
n
i
s
n
o
n
e
-
ze
r
o
f
o
r
an
d
.
W
e
also
n
o
tice
th
at
m
u
t
u
al
i
n
f
o
r
m
at
io
n
is
ze
r
o
w
h
e
n
r
eg
ar
d
less
o
f
t
h
e
p
o
w
er
allo
ca
tio
n
.
Fig
u
r
e
4
.
Mu
tu
a
l in
f
o
r
m
atio
n
v
er
s
u
s
f
o
r
d
if
f
er
en
t ti
m
e
al
lo
ca
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
:
2
48
–
2
57
254
T
o
s
ee
th
e
co
m
b
in
ed
ef
f
ec
t
o
f
ti
m
e
allo
ca
tio
n
a
n
d
p
o
w
er
allo
ca
tio
n
,
v
er
s
u
s
an
d
is
p
lo
tted
in
Fig
u
r
e
5
.
T
h
e
o
b
s
er
v
at
io
n
m
ad
e
i
n
Fi
g
u
r
e
3
an
d
Fig
u
r
e
4
is
d
e
m
o
n
s
tr
ated
i
n
Fi
g
u
r
e
5
in
th
e
f
o
r
m
o
f
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h
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m
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t
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al
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lex
co
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if
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e
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o
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ch
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et
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s
o
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ce
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o
d
e
an
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e
r
ela
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d
e.
P
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ticu
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ly
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i
m
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i
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er
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eg
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m
i
s
s
io
n
r
ate
f
o
r
th
is
p
ar
tic
u
lar
ca
s
e.
Fig
u
r
e
5
.
Mu
tu
a
l in
f
o
r
m
atio
n
v
er
s
u
s
f
o
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d
if
f
er
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a
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n
t
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e
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er
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o
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n
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ch
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n
el
b
et
w
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t
h
e
s
o
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r
ce
n
o
d
e
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th
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d
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n
atio
n
n
o
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th
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r
ela
y
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o
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e
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la
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a
p
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r
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le
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io
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n
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e
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n
d
p
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er
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th
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n
o
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e
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lt
s
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i
m
p
r
o
v
ed
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an
s
m
is
s
io
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ate.
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h
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a
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n
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clea
r
l
y
d
e
m
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n
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tr
ated
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Fi
g
u
r
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6
w
h
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e
w
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ca
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ee
th
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t
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s
m
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io
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r
ate
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t
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ir
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t
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io
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ate
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ac
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iev
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le
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o
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s
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m
e
an
d
.
Fig
u
r
e
6
.
P
lo
t o
f
m
u
t
u
al
in
f
o
r
m
atio
n
v
er
s
u
s
an
d
.
W
ea
k
s
o
u
r
ce
to
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esti
n
atio
n
ch
a
n
n
e
l
T
o
f
u
r
th
er
h
i
g
h
l
ig
h
t
t
h
e
ef
f
ec
t
o
f
s
o
u
r
ce
to
d
esti
n
a
tio
n
c
h
an
n
el,
T
a
b
le
1
s
h
o
w
s
t
h
e
o
p
ti
m
u
m
r
eso
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r
ce
allo
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tio
n
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lic
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f
o
r
d
if
f
er
en
t
s
o
u
r
ce
to
d
esti
n
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n
c
h
an
n
el
co
n
d
itio
n
s
.
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e
o
b
s
er
v
e
t
h
at
in
t
h
is
s
ce
n
ar
io
,
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g
is
u
s
e
f
u
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n
l
y
w
h
en
t
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e
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o
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r
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to
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is
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ea
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,
i.e
.
,
an
d
w
h
e
n
.
I
n
co
n
tr
ar
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,
i
m
p
r
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v
ed
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o
u
r
ce
to
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elay
a
n
d
r
ela
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to
d
esti
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tio
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ch
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n
n
el
co
n
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itio
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s
m
ak
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s
r
ela
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i
n
g
m
o
r
e
f
av
o
r
ab
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T
h
is
clea
r
l
y
illu
s
tr
ated
in
T
ab
le
2
an
d
T
a
b
le
3
b
el
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w
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W
h
en
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o
u
r
ce
to
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h
an
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el
SN
R
in
cr
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es i
n
T
ab
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2
,
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e
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m
o
r
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c
h
an
ce
s
t
h
at
th
e
r
ela
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b
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o
m
e
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u
s
e
f
u
l.
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m
ilar
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i
n
T
ab
le
3
,
im
p
r
o
v
i
n
g
t
h
e
r
ela
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to
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esti
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atio
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ch
a
n
n
el
m
ad
e
th
e
r
ela
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n
o
d
e
m
o
r
e
u
s
ef
u
l f
o
r
in
f
o
r
m
atio
n
tr
a
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s
m
i
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n
.
T
ab
le
1
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llu
s
tr
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o
f
t
h
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f
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ct
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o
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ch
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o
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l
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ess
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atio
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1
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1
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8
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1
5
9
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1
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6
0
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5
3
8
5
0
.
5
8
9
7
1
.
2
2
0
2
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
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esia
n
J
E
lec
E
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g
&
C
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m
p
Sci
I
SS
N:
2502
-
4752
A
WGN
a
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a
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255
1
13
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5
3
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1
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2
6
3
2
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ab
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2
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I
llu
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a
tio
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ela
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n
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y
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ess
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ch
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el
m
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m
o
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f
u
l f
o
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s
m
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n
max
1
13
0
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1
1
13
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1
1
3
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atio
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f
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(
1
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)
o
v
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m
a
n
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ch
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n
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el
o
b
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er
v
atio
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s
.
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icall
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,
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n
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i
s
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is
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er
ag
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v
er
1
0
0
0
0
o
b
s
er
v
atio
n
s
.
I
n
ad
d
itio
n
,
[
]
,
[
]
,
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d
[
]
ar
e
s
et
as 1
3
,
5
,
an
d
1
1
,
r
esp
ec
tiv
el
y
.
Fig
u
r
e
7
s
h
o
w
s
t
h
e
a
v
er
ag
e
m
u
tu
al
in
f
o
r
m
a
tio
n
v
er
s
u
s
.
A
g
e
n
er
al
o
b
s
er
v
ati
o
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ca
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b
e
m
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b
y
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ar
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F
ig
u
r
e
7
w
it
h
Fi
g
u
r
e
3
.
C
lear
ly
,
p
er
f
o
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m
a
n
ce
o
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th
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ch
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n
n
el
in
f
ad
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g
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eg
i
m
es
is
less
t
h
an
t
h
at
i
n
th
e
A
W
GN
ca
s
e,
as
p
r
ed
icted
b
y
(
2
3
)
.
W
e
also
n
o
tice
f
r
o
m
Fig
u
r
e
7
t
h
at,
s
i
m
ilar
to
A
W
G
N,
d
if
f
er
en
t
p
o
w
er
allo
ca
tio
n
p
o
licies
a
f
f
ec
t
c
h
a
n
n
el
b
e
h
av
io
r
.
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s
t
o
f
t
h
e
ti
m
e,
d
ir
ec
t
tr
an
s
m
is
s
io
n
i
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ad
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o
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er
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g
.
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h
e
o
n
l
y
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e
w
h
e
n
r
ela
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i
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p
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a
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tp
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f
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m
ed
d
ir
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a
n
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io
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ain
w
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en
8
0
%
o
f
th
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tr
an
s
m
is
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io
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p
o
w
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i
s
allo
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to
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e
r
ela
y
n
o
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e.
I
n
ter
e
s
tin
g
l
y
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th
e
o
p
ti
m
u
m
ti
m
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allo
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to
m
a
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m
ize
m
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atio
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ap
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m
atel
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i
m
ilar
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e
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Fig
u
r
e
7
.
Av
er
ag
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m
u
t
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al
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n
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r
m
atio
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s
f
o
r
d
if
f
er
en
t p
o
w
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allo
ca
tio
n
I
n
Fig
u
r
e
8
,
th
e
[
]
is
p
lo
tted
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ain
s
t
f
o
r
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if
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e
n
t
ti
m
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.
R
e
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in
co
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f
ir
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eg
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ad
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ly
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es it
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s
e
f
u
l f
o
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an
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is
s
io
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
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J
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ase
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n
th
e
p
r
ese
n
ce
o
f
a
f
i
x
ed
r
ela
y
n
o
d
es
r
es
u
lt
s
i
n
h
ig
h
er
tr
a
n
s
m
i
s
s
io
n
r
ate.
Ho
w
e
v
er
,
th
e
o
p
p
o
s
ite
is
n
o
t
n
ec
ess
ar
y
tr
u
e.
B
ase
s
tatio
n
s
h
a
v
e
h
ig
h
er
tr
an
s
m
is
s
io
n
p
o
w
er
an
d
t
h
er
ef
o
r
e,
d
ir
ec
t tr
an
s
m
is
s
io
n
t
h
e
p
r
ef
er
r
ed
m
o
d
e
o
f
tr
an
s
m
i
s
s
io
n
in
th
is
ca
s
e.
4.
CO
NCLU
SI
O
N
T
h
is
p
ap
er
s
tu
d
ied
th
e
ef
f
ec
t
o
f
ti
m
e
al
lo
ca
tio
n
an
d
p
o
w
er
allo
ca
tio
n
o
n
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
3
-
n
o
d
e
d
ec
o
d
e
-
an
d
-
f
o
r
w
ar
d
c
h
an
n
el.
Ma
th
e
m
atica
l
m
o
d
els
w
er
e
d
er
iv
ed
f
o
r
th
e
c
h
a
n
n
el
w
h
er
e
m
u
t
u
al
in
f
o
r
m
ati
o
n
w
as
u
s
ed
f
o
r
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
e
m
en
t.
T
h
e
r
ela
y
n
o
d
e
is
h
al
f
-
d
u
p
lex
co
n
s
tr
ain
t.
T
h
er
e
is
also
a
to
tal
tr
an
s
m
i
s
s
io
n
p
o
w
er
co
n
s
tr
ain
t
o
n
t
h
e
s
o
u
r
ce
a
n
d
r
ela
y
n
o
d
es.
Der
iv
ed
m
o
d
el
co
n
s
id
er
ed
b
o
th
t
h
e
A
W
GN
a
n
d
th
e
R
a
y
lei
g
h
f
ad
i
n
g
s
ce
n
ar
io
s
.
N
u
m
er
ical
r
es
u
l
t
s
s
h
o
w
ed
th
at
r
ela
y
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n
g
b
ec
o
m
es
m
o
r
e
u
s
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l
a
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m
o
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e
r
eso
u
r
ce
ar
e
allo
ca
ted
to
th
e
r
ela
y
n
o
d
e.
I
t is co
n
cl
u
d
ed
th
at,
w
i
th
th
e
h
al
f
-
d
u
p
le
x
co
n
s
tr
ai
n
o
n
t
h
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ela
y
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d
ir
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t
tr
an
s
m
i
s
s
io
n
is
p
r
ef
er
r
ed
u
n
les
s
s
o
u
r
ce
to
d
esti
n
atio
n
ch
an
n
el
is
m
u
ch
w
o
r
s
e
th
a
n
th
e
s
o
u
r
ce
to
r
elay
an
d
r
ela
y
to
d
esti
n
atio
n
c
h
a
n
n
els.
A
p
p
lied
to
t
h
e
ce
ll
u
lar
s
y
s
te
m
s
,
r
ela
y
i
n
g
is
m
o
r
e
b
e
n
e
f
icial
to
th
e
b
atter
y
o
p
er
ated
m
o
b
ile
n
o
d
es si
tti
n
g
at
ce
ll e
d
g
es o
r
w
it
h
o
u
t lin
e
-
of
-
s
ite
t
h
a
n
to
b
ase
s
tatio
n
s
.
ACK
NO
WL
E
D
G
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M
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h
is
w
o
r
k
is
s
u
p
p
o
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te
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b
y
th
e
R
esear
ch
I
n
itia
tiv
e
Gr
a
n
t
Sch
e
m
e
(
R
I
GS)
o
f
f
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ed
by
th
e
I
n
t
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n
atio
n
a
l
I
s
la
m
ic
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n
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v
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s
it
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Ma
la
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ia
(
I
I
UM
)
u
n
d
er
p
r
o
j
ec
t n
u
m
b
er
R
I
GS1
5
-
154
-
0154.
RE
F
E
R
E
NC
E
S
[1
]
Z.
Din
g
,
e
t
a
l
.
,
“
P
o
w
e
r
A
ll
o
c
a
ti
o
n
S
trate
g
ies
in
En
e
rg
y
Ha
r
v
e
stin
g
W
irele
s
s
Co
o
p
e
ra
ti
v
e
Ne
tw
o
rk
s,”
IEE
E
T
r
a
n
s
.
W
ire
l.
Co
mm
u
n
.
,
v
o
l
/i
ss
u
e
:
13
(
2
)
,
p
p
.
8
4
6
–
8
6
0
,
2
0
1
4
.
[2
]
Y
.
G
u
a
n
d
S
.
A
ïs
sa
,
“
RF
-
Ba
s
e
d
En
e
rg
y
Ha
r
v
e
stin
g
in
De
c
o
d
e
-
a
n
d
-
F
o
rw
a
rd
Re
la
y
in
g
S
y
ste
m
s:
Er
g
o
d
ic
a
n
d
Ou
tag
e
Ca
p
a
c
it
ies
,
”
IEE
E
T
ra
n
s.
W
ire
l.
Co
mm
u
n
.
,
v
o
l
/i
ss
u
e
:
14
(
11
)
,
p
p
.
6
4
2
5
–
6
4
3
4
,
2
0
1
5
.
[3
]
O.
Oz
e
l,
e
t
a
l.
,
“
F
u
n
d
a
m
e
n
tal
li
m
it
s
o
f
e
n
e
rg
y
h
a
rv
e
stin
g
c
o
m
m
u
n
ica
ti
o
n
s,”
IEE
E
Co
mm
u
n
.
M
a
g
.
,
v
o
l
/i
ss
u
e
:
53
(
4
)
,
p
p
.
1
2
6
–
1
3
2
,
2
0
1
5
.
[4
]
N.
M
a
rc
h
e
n
k
o
,
e
t
a
l.
,
“
A
n
Ex
p
e
rim
e
n
tal
S
tu
d
y
o
f
S
e
le
c
ti
v
e
Co
o
p
e
ra
ti
v
e
Re
la
y
in
g
in
In
d
u
strial
W
irele
ss
S
e
n
so
r
Ne
tw
o
rk
s,”
I
EE
E
T
ra
n
s
.
In
d
.
I
n
fo
rm
.
,
v
o
l
/i
ss
u
e
:
10
(
3
)
,
p
p
.
1
8
0
6
–
1
8
1
6
,
2
0
1
4
.
[5
]
K.
K.
P
a
n
d
e
y
,
e
t
a
l.
,
“
P
e
rf
o
rm
a
n
c
e
a
n
a
l
y
sis
o
f
c
o
o
p
e
ra
ti
v
e
c
o
m
m
u
n
ica
ti
o
n
in
w
irele
ss
se
n
so
r
n
e
tw
o
rk
,
”
in
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
A
d
v
a
n
c
e
s in
C
o
mp
u
ti
n
g
,
C
o
mm
u
n
ica
t
io
n
s
a
n
d
In
fo
rm
a
t
ics
,
p
p
.
2
0
2
1
–
2
0
2
6
,
2
0
1
6
.
[6
]
“
T
ra
n
s
m
is
sio
n
o
f
In
f
o
rm
a
ti
o
n
in
a
T
-
ter
m
in
a
l
Disc
re
te M
e
m
o
r
y
l
e
s
s Ch
a
n
n
e
l
,”
U
n
iv
e
rsity
o
f
Ca
li
f
o
r
n
ia,
1
9
6
8
.
[7
]
M
.
Iw
a
n
o
w
,
e
t
a
l.
,
“
A
S
tu
d
y
o
n
S
o
u
rc
e
-
Re
la
y
Co
o
p
e
ra
ti
o
n
f
o
r
th
e
Ou
tag
e
-
c
o
n
stra
in
e
d
Re
la
y
Ch
a
n
n
e
l,
”
in
W
S
A
2
0
1
6
,
2
0
th
I
n
ter
n
a
ti
o
n
a
l
I
T
G W
o
r
k
sh
o
p
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n
S
ma
rt
An
te
n
n
a
s
,
p
p
.
1
–
7
,
2
0
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6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
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J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
A
WGN
a
n
d
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a
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h
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a
d
in
g
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eh
a
vio
r
o
f th
e
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s
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o
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.
.
.
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Mu
h
a
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d
Za
r
o
l F
itr
i Kh
o
ir
o
l F
a
u
z
i)
257
[8
]
Z.
Ch
e
n
,
e
t
a
l
.
,
“
Co
o
p
e
ra
ti
o
n
i
n
5
G
He
tero
g
e
n
e
o
u
s
Ne
tw
o
rk
in
g
:
Re
la
y
S
c
h
e
m
e
Co
m
b
in
a
ti
o
n
a
n
d
Re
so
u
rc
e
A
ll
o
c
a
ti
o
n
,
”
IEE
E
T
ra
n
s.
Co
mm
u
n
.
,
v
o
l
/i
ss
u
e
:
64
(
8
)
,
p
p
.
3
4
3
0
–
3
4
4
3
,
2
0
1
6
.
[9
]
S
.
Ka
h
v
e
c
i
,
“
S
o
m
e
c
o
o
p
e
ra
ti
v
e
re
la
y
in
g
tec
h
n
iq
u
e
s
f
o
r
w
irele
ss
c
o
m
m
u
n
ica
ti
o
n
s
y
ste
m
s,”
in
2
2
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
a
n
d
Co
mm
u
n
ica
ti
o
n
s
Ap
p
li
c
a
ti
o
n
s C
o
n
fer
e
n
c
e
(
S
IU)
,
p
p
.
1
6
9
0
–
1
6
9
3
,
2
0
1
4
.
[1
0
]
E.
M
.
A
.
El
sh
e
ik
h
,
“
W
ir
e
les
s
D&
F
re
la
y
c
h
a
n
n
e
ls:
ti
m
e
a
ll
o
c
a
ti
o
n
stra
teg
ies
f
o
r
c
o
o
p
e
ra
ti
o
n
a
n
d
o
p
ti
m
u
m
o
p
e
ra
ti
o
n
,
”
Un
iv
e
rsity
Co
ll
e
g
e
Lo
n
d
o
n
,
L
o
n
d
o
n
-
UK
,
2
0
1
0
.
[1
1
]
E.
M
.
El
s
h
e
ik
h
a
n
d
K
.
K.
W
o
n
g
,
“
Op
ti
m
izin
g
T
i
m
e
a
n
d
P
o
w
e
r
A
l
lo
c
a
ti
o
n
f
o
r
Co
o
p
e
ra
ti
o
n
Div
e
rsity
in
a
De
c
o
d
e
-
a
n
d
-
F
o
rw
a
rd
T
h
re
e
-
No
d
e
Re
la
y
Ch
a
n
n
e
l,
”
J
CM
,
v
o
l/
iss
u
e
:
3
(
2
)
,
p
p
.
4
3
–
5
2
,
2
0
0
8
.
[1
2
]
T
.
P
.
Do
a
n
d
Y.
H.
Kim
,
“
Ou
tag
e
-
Op
ti
m
a
l
P
o
w
e
r
a
n
d
T
i
m
e
A
ll
o
c
a
ti
o
n
f
o
r
Ra
te
-
Aw
a
r
e
Tw
o
-
W
a
y
R
e
la
y
in
g
W
it
h
a
De
c
o
d
e
-
a
n
d
-
F
o
rw
a
rd
P
r
o
to
c
o
l,
”
I
EE
E
T
ra
n
s.
Ve
h
.
T
e
c
h
n
o
l
.
,
v
o
l
/i
ss
u
e
:
65
(
12
)
,
p
p
.
9
6
7
3
–
9
6
8
6
,
2
0
1
6
.
[1
3
]
M
u
h
a
m
m
a
d
Z
.
F
.
K
.
F
.
a
n
d
El
sh
e
ik
h
M
.
A
.
E
.
,
“
T
h
e
I
m
p
a
c
t
o
f
T
i
m
e
a
n
d
P
o
w
e
r
A
ll
o
c
a
ti
o
n
o
n
t
h
e
P
e
rf
o
rm
a
n
c
e
o
f
th
e
T
h
re
e
-
No
d
e
De
c
o
d
e
a
n
d
-
F
o
r
w
a
rd
R
e
la
y
Ch
a
n
n
e
l,
”
p
re
se
n
ted
a
t
th
e
2
0
1
7
IEE
E
4
th
I
n
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
S
ma
rt I
n
stru
me
n
t
a
ti
o
n
,
M
e
a
su
re
me
n
t
a
n
d
A
p
p
li
c
a
ti
o
n
s (
ICS
IM
A
2
0
1
7
),
P
u
tra
j
a
y
a
,
M
a
la
y
sia
,
2
0
1
7
.
[1
4
]
Z.
Ya
n
g
a
n
d
A
.
H
.
M
a
d
se
n
,
“
Ro
u
ti
n
g
a
n
d
P
o
w
e
r
A
ll
o
c
a
ti
o
n
in
A
s
y
n
c
h
ro
n
o
u
s
G
a
u
ss
ian
M
u
lt
ip
le
-
R
e
la
y
Ch
a
n
n
e
ls,
”
EURA
S
IP
J
.
W
ire
l.
Co
mm
u
n
.
Ne
t
w.
,
v
o
l
/i
ss
u
e
:
2
0
0
6
(
1
)
,
p
p
.
0
5
6
9
1
4
,
2
0
0
6
.
[1
5
]
A
.
M
.
Wy
g
li
n
sk
i,
e
t
a
l
.,
“
Co
g
n
it
i
v
e
ra
d
io
c
o
m
m
u
n
ica
ti
o
n
s
a
n
d
n
e
t
w
o
rk
s:
p
rin
c
ip
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