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Sp
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s
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2684
3.
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Descr
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x
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ed
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tatio
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id
Sp
ec
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m
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R
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lt
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c
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i
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cl
u
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io
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7
r
ef
er
e
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ce
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.
Fig
u
r
e
2
.
C
o
g
n
itiv
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R
ad
io
c
y
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le
2.
L
I
T
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AT
U
RE
R
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VI
E
W
Sp
ec
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ilit
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llo
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te
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est
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ai
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ed
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ec
tr
u
m
to
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ec
o
n
d
ar
y
u
s
er
s
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SU
s
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k
ee
p
in
g
i
n
m
i
n
d
th
eir
Q
u
alit
y
o
f
s
er
v
ices
(
Qo
S)
b
u
t
w
it
h
o
u
t
d
is
t
u
r
b
in
g
th
e
p
r
i
m
ar
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Hyb
r
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S
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ter
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etec
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p
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a
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n
s
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r
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etter
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ar
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P
Us)
p
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n
d
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w
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alse
alar
m
p
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b
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r
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e
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ar
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r
s
(
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.
A
f
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m
p
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f
1
0
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an
d
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d
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tio
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p
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0
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e
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as th
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o
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ith
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s
.
Fig
u
r
e
3
.
H
y
b
r
id
Sp
ec
tr
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m
s
e
n
s
i
n
g
s
tr
u
c
tu
r
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n
a
co
g
n
i
tiv
e
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ad
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n
et
w
o
r
k
2
.
1
.
Ana
ly
t
ica
l
M
o
de
l (
T
w
o
hy
po
t
hes
is
)
Sp
ec
tr
u
m
s
e
n
s
in
g
is
o
n
e
o
f
th
e
m
o
s
t
i
m
p
o
r
tan
t
tas
k
in
t
h
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co
g
n
iti
v
e
r
ad
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n
et
w
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k
.
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t
is
th
e
f
ir
s
t
s
te
p
f
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co
m
m
u
n
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n
to
ta
k
e
p
l
ac
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th
at
n
ee
d
s
to
b
e
p
er
f
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r
m
ed
.
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tr
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m
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en
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i
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s
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p
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lar
l
y
k
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as
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i
s
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y
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es:
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0
:
x
(
t
)=
n
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1
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x
(
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)=
s
(
t
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x
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t)
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Us.
n
(
t)
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n
o
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e
A
W
GN
(
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d
itiv
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ite
Ga
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s
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ian
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.
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at
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ar
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n
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n
d
o
n
ly
n
o
is
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is
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ese
n
t.
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d
h
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ce
it
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tted
to
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ar
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er
s
.
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s
u
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ed
to
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ll
t
h
at
p
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m
ar
y
s
i
g
n
al
s
ar
e
p
r
es
e
n
t
in
t
h
e
s
p
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tr
u
m
a
lo
n
g
w
it
h
t
h
e
n
o
is
e.
An
d
h
e
n
ce
it
i
s
n
o
t
a
llo
tted
to
th
e
s
ec
o
n
d
ar
y
u
s
er
s
else
t
h
er
e
w
ill
b
e
h
ar
m
f
u
l in
ter
f
er
e
n
ce
to
th
e
p
r
im
ar
y
u
s
er
s
.
2
.
2
.
Sy
s
t
em
P
a
ra
m
et
er
s
Ou
r
f
u
n
d
a
m
en
ta
l
o
b
j
ec
tiv
e
o
f
s
p
ec
tr
u
m
s
e
n
s
in
g
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s
to
u
s
e
t
h
e
s
y
s
te
m
f
o
r
d
ec
is
io
n
m
ak
in
g
p
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o
ce
s
s
to
ch
ec
k
th
e
av
a
ilab
ilit
y
o
f
t
h
e
s
p
ec
tr
u
m
h
o
le
s
,
r
ec
o
r
d
th
e
d
ata
an
d
t
h
e
n
ca
r
r
y
o
u
t
s
i
m
u
la
tio
n
to
a
n
al
y
ze
t
h
e
s
to
r
ed
d
ata:
2
.
2
.
1
.
P
ro
ba
bil
it
y
o
f
f
a
ls
e
a
la
r
m
(
P
fa
):
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r
o
b
ab
ilit
y
o
f
f
alse
alar
m
o
cc
u
r
s
w
h
e
n
n
o
p
r
i
m
ar
y
s
ig
n
al
s
a
r
e
p
r
esen
t
i
n
t
h
e
s
p
ec
tr
u
m
b
u
t
w
e
g
et
t
h
e
id
ea
th
at
th
e
y
ar
e
p
r
esen
t
an
d
h
en
ce
d
o
n
o
t
allo
ca
te
b
an
d
s
to
th
e
S
Us.
I
t
o
cc
u
r
s
w
h
e
n
o
n
l
y
n
o
is
e
is
p
r
esen
t
i
n
th
e
ch
a
n
n
el
an
d
en
er
g
y
o
f
n
o
is
e
lev
el
e
x
ce
ed
th
e
p
r
ed
ef
i
n
ed
th
r
es
h
o
ld
v
al
u
e
an
d
h
e
n
ce
th
e
p
r
esen
ce
o
f
p
r
im
ar
y
u
s
er
is
d
etec
ted
b
y
th
e
d
ec
is
io
n
m
a
k
i
n
g
d
e
v
ice.
T
h
is
is
f
a
ls
e
r
ep
r
esen
tat
io
n
an
d
s
h
o
u
ld
b
e
m
i
n
i
m
ized
.
(
1
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
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5
,
Octo
b
er
2
0
1
7
:
2
6
8
3
–
2
6
9
5
2686
2
.
2
.
2
.
P
ro
ba
bil
it
y
o
f
m
is
s
ed
det
ec
t
io
n
(P
m
):
P
r
o
b
ab
ilit
y
o
f
m
i
s
s
ed
d
etec
tio
n
o
cc
u
r
s
w
h
en
p
r
i
m
ar
y
s
i
g
n
al
s
ar
e
p
r
esen
t
in
t
h
e
s
p
ec
tr
u
m
b
u
t
w
e
g
et
th
e
id
ea
t
h
at
t
h
e
y
ar
e
n
o
t p
r
ese
n
t a
n
d
h
e
n
ce
s
p
ec
tr
u
m
is
allo
c
ated
to
o
th
er
SUs
.
T
h
is
ca
u
s
es
in
ter
f
er
en
ce
to
t
h
e
p
r
im
ar
y
u
s
er
s
.
I
t
h
ap
p
en
s
w
h
e
n
a
s
ig
n
al
i
s
p
r
esen
t
in
t
h
e
ch
an
n
el
a
n
d
en
er
g
y
o
f
s
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g
n
al
p
r
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t
d
o
n
o
t
ex
ce
ed
th
e
p
r
ed
ef
i
n
ed
t
h
r
es
h
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ld
v
al
u
e
a
n
d
h
e
n
ce
th
e
p
r
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ce
o
f
p
r
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m
ar
y
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er
s
i
s
n
o
t
d
etec
t
ed
b
y
t
h
e
d
ec
is
io
n
m
ak
in
g
d
ev
ice.
T
h
is
is
m
is
s
co
n
d
itio
n
a
n
d
h
en
ce
s
h
o
u
ld
b
e
m
in
i
m
ized
.
Fig
u
r
e
4
s
h
o
w
s
t
h
e
tr
ad
e
-
o
f
f
b
et
w
ee
n
P
m
an
d
P
fa
w
ith
r
esp
e
ct
to
th
e
th
r
es
h
o
ld
v
alu
e
:
PM
=
1
–
P
D
(
2
)
(
3
)
Fig
u
r
e
4
.
P
DFs
f
o
r
H
y
p
o
th
es
is
T
est M
o
d
el
I
n
t
h
e
ab
o
v
e
Fig
u
r
e
4
,
w
e
o
b
s
er
v
e
t
h
at
t
h
r
es
h
o
ld
v
al
u
e
d
ec
r
ea
s
e
w
it
h
t
h
e
d
ec
r
ea
s
e
p
r
o
b
ab
ilit
y
o
f
m
i
s
s
ed
d
etec
tio
n
w
o
u
ld
i
n
cr
ea
s
e
t
h
e
p
r
o
b
a
b
ilit
y
o
f
f
al
s
e
alar
m
an
d
in
cr
ea
s
i
n
g
th
e
th
r
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h
o
ld
v
a
lu
e
to
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ec
r
ea
s
e
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h
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a
b
ilit
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f
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ld
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n
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s
e
t
h
e
p
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b
ab
ilit
y
o
f
m
is
s
ed
d
etec
tio
n
.
Si
n
ce
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o
th
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e
u
n
w
a
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ted
a
n
d
b
o
th
ca
n
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t
b
e
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ec
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i
m
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lta
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l
y
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e
n
ce
tr
ad
e
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o
f
f
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et
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n
t
h
ese
t
w
o
p
ar
a
m
ete
r
s
is
d
o
n
e
an
d
t
h
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r
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ld
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ac
co
r
d
in
g
l
y
.
3
.
P
RO
P
O
SE
D
SYS
T
E
M
T
o
ef
f
icien
t
l
y
s
en
s
e
t
h
e
s
p
ec
tr
u
m
,
a
u
t
h
o
r
s
u
s
es
f
iv
e
s
p
ec
t
r
u
m
s
e
n
s
i
n
g
tech
n
iq
u
es
a
n
d
clu
b
th
e
m
to
g
eth
er
to
cr
ea
te
a
h
y
b
r
id
s
p
ec
tr
u
m
s
en
s
i
n
g
m
et
h
o
d
.
T
h
is
h
y
b
r
id
m
et
h
o
d
is
s
u
m
m
ar
ized
as
f
o
llo
w
s
:
H
y
b
r
id
s
p
ec
tr
u
m
s
e
n
s
in
g
m
et
h
o
d
is
b
ased
u
p
o
n
C
e
n
tr
alize
d
C
o
o
r
d
in
atio
n
co
n
ce
p
t
in
w
h
ich
,
an
i
n
f
r
astr
u
ctu
r
e
d
ep
lo
y
m
en
t i
s
t
h
o
u
g
h
t
f
o
r
th
e
C
R
u
s
er
s
.
O
n
ce
C
R
d
etec
ts
t
h
e
p
r
esen
ce
o
f
a
p
r
i
m
ar
y
tr
a
n
s
m
itter
,
it i
n
f
o
r
m
s
C
R
co
n
tr
o
ller
w
h
ich
ca
n
b
e
a
w
ir
ed
im
m
o
b
ile
d
ev
ice.
T
h
e
C
R
co
n
tr
o
ller
in
f
o
r
m
s
all
th
e
C
R
u
s
er
s
in
it
s
r
an
g
e
u
s
i
n
g
b
r
o
ad
ca
s
t c
o
n
tr
o
l m
e
s
s
a
g
e.
3
.
1
.
Cent
ra
lized
s
che
m
e
s
ca
n
be
f
urt
her
cla
s
s
if
ied a
cc
o
rdi
ng
t
o
t
heir
lev
el
o
f
co
o
pera
t
i
o
n a
s
:
a.
P
ar
tially
C
o
o
p
er
ativ
e
Sc
h
e
m
e
w
h
er
e
n
et
w
o
r
k
n
o
d
es
co
o
p
er
ate
o
n
l
y
i
n
s
en
s
i
n
g
t
h
e
c
h
an
n
el.
C
R
u
s
er
s
in
d
ep
en
d
en
tl
y
d
etec
t t
h
e
ch
a
n
n
el
an
d
i
n
f
o
r
m
th
e
C
R
co
n
tr
o
ller
ab
o
u
t it.
b.
T
o
tally
C
o
o
p
er
ativ
e
Sc
h
e
m
e
s
w
h
er
e
n
o
d
es
co
o
p
er
ate
in
r
elay
in
g
ea
ch
o
t
h
er
’
s
in
f
o
r
m
atio
n
i
n
ad
d
itio
n
to
co
o
p
e
r
ativ
e
s
en
s
i
n
g
ch
a
n
n
e
l.
T
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
co
n
s
is
ts
o
f
f
i
v
e
s
p
ec
tr
u
m
s
e
n
s
in
g
m
eth
o
d
s
:
a.
Ma
tch
Fil
ter
d
etec
to
r
,
b.
E
n
er
g
y
d
etec
to
r
,
c.
GL
R
T
,
d.
R
o
b
u
s
t E
s
ti
m
a
to
r
C
o
r
r
elato
r
a
n
d
e.
T
em
p
er
atu
r
e
b
ased
d
etec
to
r
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
E
C
E
I
SS
N:
2
0
8
8
-
8708
Hyb
r
id
S
p
ec
tr
u
m
S
en
s
in
g
Met
h
o
d
fo
r
C
o
g
n
itive
R
a
d
io
(
A
w
a
n
i S
.
K
h
o
b
r
a
g
a
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e
)
2687
T
h
ese
m
et
h
o
d
s
ar
e
e
x
p
lai
n
ed
in
b
r
ie
f
b
elo
w
t
h
e
h
y
b
r
id
s
en
s
in
g
al
g
o
r
it
h
m
.
A
ll
th
e
s
e
m
et
h
o
d
s
h
a
v
e
th
eir
s
p
ec
ial
f
u
n
ctio
n
s
to
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etec
t
th
e
s
p
ec
tr
u
m
w
h
e
th
er
it
is
f
r
ee
o
r
o
cc
u
p
ied
.
T
o
p
er
f
o
r
m
th
e
h
y
b
r
id
s
p
ec
tr
u
m
s
en
s
in
g
m
et
h
o
d
th
e
s
y
s
te
m
will
tak
e
t
h
e
i
n
p
u
t
a
s
t
h
e
r
ec
eiv
ed
s
ig
n
al
an
d
w
i
ll
tes
t
th
is
r
ec
eiv
ed
s
i
g
n
a
l
o
n
all
th
e
f
i
v
e
d
etec
to
r
m
et
h
o
d
s
,
th
en
co
m
p
ar
ed
th
e
o
u
tp
u
ts
w
it
h
th
e
th
r
es
h
o
ld
v
al
u
e
an
d
th
e
n
f
i
n
all
y
o
u
tp
u
t
i
s
d
ec
lar
ed
w
h
et
h
er
th
e
b
an
d
is
f
r
ee
o
r
o
cc
u
p
ied
.
T
h
e
A
lg
o
r
it
h
m
is
as
f
o
llo
w
s
:
Step
1
: T
ak
e
in
p
u
t sig
n
al.
Step
2
: G
iv
e
in
p
u
t to
th
e
Ma
tc
h
Fil
ter
m
et
h
o
d
.
Step
3
: r
ec
o
r
d
th
e
o
u
tp
u
t.
Step
4
: Rep
ea
t step
2
an
d
s
tep
3
f
o
r
all
th
e
o
th
er
4
m
et
h
o
d
s
.
Step
5
: if
n
u
m
b
er
o
f
tr
u
e
r
esu
l
ts
r
ec
o
r
d
ed
d
iv
id
e
b
y
5
is
g
r
ea
ter
th
an
0
.
6
6
.
T
h
en
r
etu
r
n
tr
u
e
E
ls
e
r
etu
r
n
Fa
ls
e.
3
.
2
.
T
he
i
m
ple
m
ent
ed
s
ub
-
m
et
ho
ds
a
re
ex
pla
i
ned a
s
f
o
llo
w
s
:
3
.
2
.
1
.
M
a
t
ch
F
ilte
r
T
h
e
MFB
(
Ma
tch
ed
Fil
ter
B
o
u
n
d
)
is
a
v
er
s
at
ile
m
et
h
o
d
to
an
al
y
s
i
s
t
h
e
t
h
eo
r
etica
ll
y
o
p
ti
m
al
p
er
f
o
r
m
a
n
ce
o
f
a
w
ir
eles
s
tr
a
n
s
m
i
s
s
io
n
s
y
s
te
m
in
a
ti
m
e
-
f
r
eq
u
en
c
y
v
ar
ia
n
t
f
ad
i
n
g
c
h
a
n
n
el.
T
h
e
ef
f
ec
ti
v
en
e
s
s
o
f
a
d
ig
ital
co
m
m
u
n
icat
io
n
s
y
s
te
m
s
in
t
h
e
p
r
esen
ce
o
f
i
n
ter
f
er
en
ce
is
m
ea
s
u
r
ed
b
y
th
e
r
ate
at
w
h
ich
er
r
o
r
s
in
th
e
r
ec
ep
ti
o
n
o
f
t
h
e
i
n
f
o
r
m
a
tio
n
b
it
s
ar
e
m
ad
e.
T
h
is
is
r
ef
er
r
ed
to
as
t
h
e
b
it
er
r
o
r
r
ate
(
B
E
R
)
.
I
f
t
h
e
in
ter
f
er
e
n
ce
is
a
s
s
u
m
ed
to
b
e
Gau
s
s
ia
n
n
o
is
e,
t
h
en
B
E
R
i
s
a
m
in
i
m
u
m
i
n
an
y
s
y
s
te
m
i
n
w
h
ic
h
th
e
s
ig
n
al
-
to
-
n
o
is
e
r
atio
o
f
t
h
e
in
d
i
v
id
u
a
l
b
its
is
a
m
a
x
i
m
u
m
.
T
h
eo
r
etic
al
an
al
y
s
is
h
a
s
s
h
o
w
n
t
h
at
if
th
e
s
ig
n
al
-
to
-
n
o
i
s
e
r
atio
is
o
p
ti
m
ized
f
o
r
w
h
ite
Gau
s
s
ia
n
n
o
is
e
i
n
ter
f
er
en
ce
,
th
en
th
e
r
ec
ei
v
er
is
i
m
p
le
m
e
n
ted
as
a
“m
atc
h
ed
f
ilter
”
[
7
]
.
T
h
e
ch
ar
ac
ter
is
tics
o
f
m
atc
h
ed
f
ilter
s
ca
n
b
e
d
esig
n
ated
b
y
eith
er
a
f
r
eq
u
e
n
c
y
r
esp
o
n
s
e
f
u
n
c
tio
n
o
r
a
ti
m
e
r
esp
o
n
s
e
f
u
n
ct
io
n
,
b
o
th
ar
e
r
elate
d
to
ea
ch
o
th
er
b
y
a
Fo
u
r
ier
tr
an
s
f
o
r
m
o
p
er
atio
n
.
I
n
t
h
e
f
r
eq
u
e
n
c
y
d
o
m
ai
n
,
t
h
e
m
atc
h
ed
-
f
ilter
tr
an
s
f
er
f
u
n
ctio
n
H(
f
)
is
th
e
co
m
p
le
x
co
n
j
u
g
ate
f
u
n
ctio
n
o
f
t
h
e
s
p
ec
tr
u
m
o
f
t
h
e
s
ig
n
al
T
d
b
e
p
r
o
ce
s
s
ed
in
an
o
p
tim
u
m
f
a
s
h
io
n
.
T
h
u
s
,
i
n
g
e
n
er
al
ter
m
s
:
H(
f
)
=2
K/No
S
*
(
f
)
e
-
jwTb
(
4
)
w
h
er
e
S
*
(
f
)
is
th
e
s
p
ec
tr
u
m
o
f
th
e
i
n
p
u
t
s
i
g
n
al
s
(
t)
,
an
d
T
d
is
a
d
elay
co
n
s
tan
t
r
eq
u
ir
ed
to
m
atc
h
ed
f
ilter
p
h
y
s
icall
y
r
ea
l
izab
le.
T
h
e
n
o
r
m
alizi
n
g
f
ac
to
r
,
‘
K
’
a
n
d
th
e
d
el
a
y
co
n
s
tan
t
ar
e
g
en
er
all
y
i
g
n
o
r
ed
in
f
o
r
m
u
lati
n
g
th
e
u
n
d
er
l
y
i
n
g
s
i
g
n
if
ican
t r
ela
tio
n
s
h
ip
,
u
s
u
all
y
ex
p
r
es
s
ed
as
:
H(
f
)
=
S
*
(
f
)
.
(
5
)
T
h
e
co
r
r
esp
o
n
d
in
g
ti
m
e
d
o
m
a
in
r
elatio
n
s
h
ip
b
et
w
ee
n
th
e
s
i
g
n
al
to
b
e
o
p
er
ated
u
p
o
n
an
d
th
e
m
a
tch
ed
f
ilter
i
s
o
b
tain
ed
f
r
o
m
th
e
in
v
er
s
e
F
o
u
r
ier
tr
an
s
f
o
r
m
o
f
H(
f
)
.
T
h
is
l
ea
d
s
to
th
e
r
es
u
lt
th
at
th
e
i
m
p
u
ls
e
r
e
s
p
o
n
s
e
o
f
t
h
e
m
atc
h
ed
f
ilter
i
s
a
r
ep
lica
o
f
th
e
ti
m
e
i
n
v
er
s
e
o
f
th
e
k
n
o
w
n
s
i
g
n
al
f
u
n
c
tio
n
.
h
(
t)
=K
S(T
b
-
T
)
(
6
)
Fig
u
r
e
5
.
Ma
tch
ed
Fil
ter
Dete
cto
r
A
g
e
n
er
al
r
ep
r
esen
tatio
n
f
o
r
a
m
atc
h
ed
f
i
lter
is
i
llu
s
tr
ated
i
n
F
ig
u
r
e
5
T
h
e
f
ilter
in
p
u
t
x
(
t)
co
n
s
is
ts
o
f
a
p
u
ls
e
s
i
g
n
al
s
(
t)
co
r
r
u
p
ted
b
y
ad
d
itiv
e
c
h
a
n
n
el
n
o
is
e
w
(
t)
,
as
s
h
o
w
n
i
n
F
ig
u
r
e
.
w
h
er
e
T
b
is
an
ar
b
itra
r
y
o
b
s
er
v
atio
n
in
ter
v
al.
T
h
e
p
u
ls
e
s
ig
n
al
s
(
t)
m
a
y
r
ep
r
esen
t
a
b
in
ar
y
s
y
m
b
o
l
1
o
r
0
in
a
d
ig
ital
co
m
m
u
n
icatio
n
s
y
s
te
m
.
T
h
e
w
(
t)
i
s
t
h
e
s
a
m
p
l
e
f
u
n
ct
io
n
o
f
a
w
h
ite
n
o
is
e
o
f
ze
r
o
m
ea
n
a
n
d
p
o
w
er
s
p
ec
tr
al
d
en
s
it
y
No
/2
.
T
h
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
E
Vo
l.
7
,
No
.
5
,
Octo
b
er
2
0
1
7
:
2
6
8
3
–
2
6
9
5
2688
s
o
u
r
ce
o
f
u
n
ce
r
tai
n
t
y
lie
s
i
n
t
h
e
n
o
is
e
w
(
t)
.
T
h
e
f
u
n
ctio
n
o
f
t
h
e
r
ec
eiv
er
i
s
to
d
etec
t
t
h
e
p
u
l
s
e
s
i
g
n
a
l
s
(
t)
i
n
an
opt
im
u
m
m
an
n
er
,
g
iv
e
s
th
e
r
ec
eiv
ed
s
i
g
n
a
l x
(
t)
.
T
o
s
atis
f
y
t
h
is
r
eq
u
ir
e
m
e
n
t,
w
e
h
a
v
e
to
o
p
ti
m
ize
t
h
e
d
esi
g
n
o
f
th
e
f
ilter
s
o
as
to
m
i
n
i
m
ize
t
h
e
ef
f
ec
t
s
o
f
n
o
is
e
at
t
h
e
f
ilt
er
o
u
tp
u
t
in
s
o
m
e
s
tati
s
tical
s
en
s
e,
an
d
th
er
eb
y
en
h
a
n
ce
t
h
e
d
etec
tio
n
o
f
t
h
e
p
u
ls
e
s
i
g
n
al
s
(
t)
.
Si
n
ce
th
e
f
ilter
is
li
n
ea
r
,
th
e
r
es
u
lti
n
g
o
u
tp
u
t
y
(
t
)
m
a
y
b
e
ex
p
r
ess
ed
as:
y
(
t)
=s(
t)
+
w
(
t)
(
7
)
w
h
er
e
s
(
t)
an
d
w
(
t)
ar
e
p
r
o
d
u
c
ed
b
y
th
e
s
i
g
n
al
a
n
d
n
o
is
e
co
m
p
o
n
en
ts
o
f
t
h
e
i
n
p
u
t
x
(
t)
,
r
esp
ec
tiv
el
y
.
A
s
i
m
p
le
w
a
y
o
f
d
escr
ib
in
g
t
h
e
r
eq
u
ir
e
m
en
t
t
h
at
t
h
e
o
u
tp
u
t
s
i
g
n
al
co
m
p
o
n
e
n
t
s
(
t)
b
e
co
n
s
id
er
ab
l
y
g
r
ea
ter
t
h
an
th
e
o
u
tp
u
t
n
o
is
e
c
o
m
p
o
n
en
t
w
(t
)
i
s
to
h
a
v
e
t
h
e
f
ilter
m
a
k
e
th
e
i
n
s
ta
n
ta
n
eo
u
s
p
o
w
er
i
n
t
h
e
o
u
tp
u
t
s
ig
n
al
s
(
t)
,
m
ea
s
u
r
ed
at
ti
m
e
t
=
Tb
,
as
lar
g
e
as
p
o
s
s
ib
le
co
m
p
ar
ed
w
it
h
th
e
av
er
a
g
e
p
o
w
er
o
f
th
e
o
u
tp
u
t
n
o
i
s
e
n
(
t)
.
T
h
is
is
eq
u
i
v
alen
t to
m
a
x
i
m
izi
n
g
th
e
p
ea
k
p
u
ls
e
s
ig
n
al
-
to
-
n
o
is
e
r
atio
,
d
ef
i
n
ed
as
η
=
|
s
(
t)
|
2
/E
|
w
(
t)
|
(
8
)
Fin
all
y
,
t
h
e
s
tr
u
ct
u
r
e
o
f
th
e
r
ec
eiv
er
u
s
ed
to
p
er
f
o
r
m
d
ec
is
io
n
-
m
ak
i
n
g
p
r
o
ce
s
s
i
s
s
h
o
w
n
in
Fi
g
u
r
e
5
.
I
t
co
n
s
i
s
ts
o
f
a
m
atc
h
f
ilter
f
o
l
lo
w
ed
b
y
a
s
a
m
p
ler
,
an
d
t
h
e
n
f
i
n
all
y
a
d
ec
is
io
n
d
ev
ice.
T
h
e
f
ilter
i
s
m
atc
h
ed
to
a
T
r
ian
g
u
lar
p
u
ls
e
o
f
a
m
p
lit
u
d
e
A
an
d
d
u
r
atio
n
T
b
,
ex
p
lo
itin
g
th
e
b
it
-
ti
m
in
g
in
f
o
r
m
a
tio
n
av
ailab
le
to
th
e
r
ec
eiv
er
.
T
h
e
r
esu
ltin
g
m
atc
h
e
d
f
ilter
o
u
tp
u
t
is
s
a
m
p
led
at
th
e
en
d
o
f
ea
ch
s
ig
n
ali
n
g
in
ter
v
al.
T
h
e
p
r
esen
ce
o
f
ch
an
n
el
n
o
is
e
w
(
t)
ad
d
s
r
an
d
o
m
n
e
s
s
to
t
h
e
m
atc
h
ed
f
i
lter
o
u
tp
u
t.
T
h
e
s
a
m
p
le
v
al
u
e
y
is
co
m
p
ar
ed
to
a
p
r
eset
th
r
es
h
o
ld
A
i
n
th
e
d
ec
is
io
n
d
ev
ice.
I
f
t
h
e
t
h
r
es
h
o
ld
is
ex
c
ee
d
ed
,
th
e
r
ec
eiv
er
m
a
k
es
a
d
ec
is
io
n
i
n
f
a
v
o
r
o
f
s
y
m
b
o
l 1
; if
n
o
t,
a
d
ec
is
io
n
i
s
m
ad
e
in
f
av
o
r
o
f
s
y
m
b
o
l 0
.
3
.
2
.
2
.
E
nerg
y
Det
ec
t
o
r
C
o
n
v
en
t
io
n
al
E
n
er
g
y
d
etec
to
r
is
a
s
i
m
p
le
d
etec
to
r
.
T
h
e
d
etec
to
r
co
m
p
u
tes
t
h
e
e
n
er
g
y
o
f
t
h
e
r
ec
eiv
ed
s
ig
n
al
a
n
d
co
m
p
ar
es
it
to
ce
r
t
ain
th
r
e
s
h
o
ld
v
al
u
e
to
d
ec
id
e
w
h
et
h
er
th
e
d
esire
d
s
i
g
n
al
i
s
p
r
esen
t
o
r
n
o
t.
W
h
en
th
e
p
r
i
m
ar
y
u
s
er
s
ig
n
al
i
s
u
n
k
n
o
w
n
o
r
th
e
r
ec
eiv
er
ca
n
n
o
t
g
ath
er
s
u
f
f
icie
n
t
i
n
f
o
r
m
atio
n
ab
o
u
t
th
e
p
r
i
m
ar
y
u
s
er
s
i
g
n
al,
th
e
e
n
er
g
y
d
etec
t
io
n
m
et
h
o
d
is
u
s
ed
.
T
h
is
m
et
h
o
d
is
o
p
ti
m
al
f
o
r
d
etec
ti
n
g
an
y
u
n
k
n
o
w
n
ze
r
o
-
m
ea
n
co
n
s
tel
latio
n
s
ig
n
al
s
an
d
ca
n
b
e
ap
p
lied
to
C
R
(
C
o
g
n
iti
v
e
R
ad
io
)
is
th
e
n
e
w
i
n
telli
g
e
n
t
w
ir
eles
s
co
m
m
u
n
icatio
n
tec
h
n
o
lo
g
y
to
s
o
lv
e
t
h
e
in
e
f
f
icie
n
c
y
o
f
a
f
i
x
e
d
s
p
ec
tr
u
m
as
s
i
g
n
m
e
n
t p
o
lic
y
[
6
]
.
Gen
er
all
y
,
th
e
p
er
f
o
r
m
an
ce
o
f
s
p
ec
tr
u
m
s
en
s
i
n
g
d
ep
en
d
s
m
aj
o
r
ly
o
n
th
e
s
etti
n
g
s
o
f
th
e
d
etec
tio
n
th
r
es
h
o
ld
.
A
d
o
p
tin
g
a
f
i
x
ed
th
r
esh
o
ld
to
d
is
tin
g
u
is
h
t
h
e
p
r
im
ar
y
u
s
er
f
r
o
m
n
o
is
e
i
s
o
n
e
o
f
th
e
m
o
s
t
co
n
v
e
n
tio
n
al
s
p
ec
tr
u
m
s
e
n
s
in
g
m
eth
o
d
s
[
9
]
.
B
u
t
w
it
h
t
h
e
f
i
x
ed
th
r
es
h
o
ld
s
etti
n
g
m
eth
o
d
,
it
is
d
if
f
ic
u
lt
to
g
u
ar
a
n
tee
t
h
e
d
etec
tio
n
p
r
o
b
ab
ilit
y
a
n
d
f
alse
alar
m
p
r
o
b
ab
ilit
y
,
e
s
p
ec
iall
y
w
it
h
th
e
Fl
u
ct
u
ati
n
g
n
o
i
s
e
p
o
w
er
.
Un
li
k
e
t
h
e
co
n
v
e
n
tio
n
al
m
et
h
o
d
o
f
f
ix
ed
t
h
r
es
h
o
ld
s
en
s
i
n
g
a
lg
o
r
it
h
m
,
r
ec
e
n
t
w
o
r
k
co
n
s
id
er
s
a
n
ad
ap
tiv
e
th
r
es
h
o
ld
.
A
cc
o
r
d
in
g
to
th
e
S
NR
,
s
e
n
s
i
n
g
t
i
m
e
o
r
tr
an
s
m
i
t
p
o
w
er
th
e
ad
ap
tiv
e
t
h
r
esh
o
ld
d
y
n
a
m
icall
y
ad
j
u
s
t
th
e
SU.
St
u
d
y
s
h
o
w
s
th
e
d
e
s
ig
n
o
f
s
e
n
s
i
n
g
d
u
r
atio
n
to
m
ax
i
m
ize
t
h
e
en
er
g
y
ef
f
icie
n
c
y
f
o
r
SU
s
w
it
h
co
o
p
er
ativ
e
s
en
s
i
n
g
in
co
g
n
it
i
v
e
r
ad
io
n
et
w
o
r
k
s
.
Fig
u
r
e
6
.
Fre
q
u
en
c
y
d
o
m
ain
r
e
p
r
esen
tatio
n
o
f
E
n
er
g
y
Dete
ct
o
r
T
h
e
en
er
g
y
d
etec
tio
n
m
eth
o
d
ca
lcu
lates
th
e
e
n
er
g
y
o
f
t
h
e
in
p
u
t
s
i
g
n
al
a
n
d
co
m
p
ar
es
it
w
i
th
a
T
h
r
esh
o
ld
en
er
g
y
v
al
u
e.
T
h
e
s
ig
n
al
i
s
s
aid
to
b
e
p
r
esen
t
at
a
p
ar
ticu
lar
f
r
eq
u
e
n
c
y
if
t
h
e
e
n
er
g
y
o
f
t
h
e
s
ig
n
al
ex
ce
ed
s
t
h
e
E
n
er
g
y
le
v
el
o
f
t
h
e
th
r
es
h
o
ld
.
T
h
e
th
r
e
s
h
o
ld
v
al
u
e
i
s
c
h
o
s
en
s
o
as
to
co
n
tr
o
l
t
h
e
p
ar
a
m
eter
s
s
u
c
h
as p
r
o
b
ab
ilit
y
o
f
f
alse a
lar
m
a
n
d
p
r
o
b
ab
ilit
y
o
f
d
etec
tio
n
.
T
h
e
th
r
esh
o
ld
v
o
lta
g
e
ca
n
b
e
ca
lcu
lated
f
r
o
m
:
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ased
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if
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ce
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o
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am
eter
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id
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ti
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e
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es
s
o
f
co
g
n
iti
v
e
s
y
s
te
m
s
b
esid
es
t
h
eir
o
p
ti
m
izatio
n
tec
h
n
i
q
u
es
f
o
r
en
er
g
y
ef
f
icie
n
c
y
.
Fo
r
m
u
l
tip
ath
f
ad
in
g
t
h
e
G
L
R
T
is
co
n
s
id
er
ed
as
th
e
b
est
m
e
th
o
d
in
d
e
m
o
d
u
lato
r
d
o
m
ai
n
f
o
r
ch
an
n
el
esti
m
atio
n
a
n
d
d
ata
d
etec
tio
n
.
I
t
is
r
e
g
ar
d
ed
as
b
lin
d
b
ec
au
s
e
th
e
c
h
a
n
n
el
f
ad
i
n
g
co
ef
f
icie
n
ts
ar
e
u
n
k
n
o
w
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to
b
o
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th
e
r
ec
eiv
er
an
d
tr
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s
m
i
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T
h
e
GL
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th
at
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t
h
e
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e
n
e
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alize
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lik
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o
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r
atio
test
is
co
n
s
id
er
ed
th
e
b
es
t
d
ec
is
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n
m
a
k
er
f
o
r
t
h
is
w
o
r
k
.
T
h
e
GL
R
T
C
r
iter
io
n
is
as
f
o
llo
w
s
:
F
ir
s
tl
y
w
e
g
i
v
e
t
w
o
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u
m
p
tio
n
s
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o
th
tr
an
s
m
itter
a
n
d
r
ec
ei
v
er
k
n
o
ws t
h
e
m
e
m
o
r
y
o
r
d
er
o
f
t
h
e
c
h
a
n
n
el
ν
.
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n
d
l
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n
o
n
e
o
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e
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h
as
t
h
e
k
n
o
w
led
g
e
ab
o
u
t c
h
an
n
el
f
ad
in
g
λ
.
Fig
u
r
e
7
.
P
r
in
cip
le
o
f
th
e
GL
R
T
Dete
cto
r
T
h
e
r
ec
eiv
ed
s
ig
n
a
l c
an
b
e
ex
p
r
ess
ed
in
th
e
f
o
r
m
y
(
n
)
=θ
A
co
s
(
2
π
f
c
n
+φ
)
+
w
(
n
)
;
n
=
0
,
1
,
.
.
.
,
N
−
1
,
θ
=
0
o
r
1
w
h
er
e
A
is
t
h
e
a
m
p
lit
u
d
e,
φ
is
th
e
p
h
ase,
an
d
f
c
is
th
e
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p
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P
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4
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Ro
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tain
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eter
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ties
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er
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s
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e
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n
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e
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f
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ar
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D
v
s
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o
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ld
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e
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o
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n
s
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at
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o
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eter
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ized
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y
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s
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tis
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t
h
e
m
atica
ll
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in
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t.
D
v
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1
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1
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m
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ad
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s
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v
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ates
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itab
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ated
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p
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ased
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n
s
h
a
v
e
ta
k
en
p
lac
e
[
1
0
]
-
[
1
2
]
.
L
et
th
e
MI
MO
b
ase
-
b
an
d
s
y
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te
m
m
o
d
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ase
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ar
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er
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co
r
r
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n
d
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g
to
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k
t
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s
y
m
b
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s
m
is
s
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b
e
r
ep
r
esen
ted
as,
y
(
k
)
=
H
x
(
k
)
+η(
k
)
(
1
4
)
w
h
er
e
y
(
k
)
Ɛ
C
Nr*1
,
x
(
k
)
Ɛ
C
Nr*1
ar
e
th
e
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ec
eiv
ed
an
d
tr
a
n
s
m
itted
te
m
p
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r
all
y
i
n
d
ep
en
d
en
t
an
d
id
en
ticall
y
d
is
tr
ib
u
ted
(
I
I
D)
ze
r
o
-
m
ea
n
G
au
s
s
ian
s
i
g
n
al
v
ec
to
r
s
r
esp
ec
ti
v
el
y
,
w
it
h
t
h
e
Gau
s
s
ia
n
s
i
g
n
a
l
co
v
ar
ian
ce
m
atr
ix
d
ef
in
ed
as
:
R
S
Ɛ
C
Nr*Nr
(
1
5
)
R
s
=E
{s(k
)
s
H
(
k
)
}=
H.
E
{
x
(
k
)
x
H
(
k
)
}H
H
(
1
6
)
3
.
3
.
Ro
bu
s
t
T
est
Sta
t
ic
Det
ec
t
o
r
T
h
e
test
s
tatis
tic
co
r
r
esp
o
n
d
in
g
to
R
T
SD
f
o
r
s
p
ec
tr
u
m
s
e
n
s
in
g
in
MI
MO
co
g
n
it
iv
e
r
ad
io
s
ce
n
ar
io
s
ca
n
b
e
eq
u
iv
ale
n
tl
y
g
iv
e
n
as,
T
RTS
D
=Σ
k=
1
y
H
(
k
)
R
-
1
η
y
(
k
)
–
f
RT
S
D
*
(
1
7
)
W
h
er
e
d
en
o
te
th
e
o
p
ti
m
al
v
al
u
e
o
f
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
f
o
r
th
e
o
p
ti
m
izatio
n
f
r
a
m
e
w
o
r
k
.
3
.
4
.
Ro
bu
s
t
E
s
t
i
m
a
t
o
r
Co
rr
e
la
t
o
r
Det
ec
t
o
r:
T
h
e
o
p
tim
izatio
n
f
r
a
m
e
w
o
r
k
i
n
,
th
e
te
s
t stati
s
tic
i
n
ca
n
b
e
eq
u
iv
a
len
t
l
y
f
o
r
m
u
lated
as,
T
RECD
=Σ
K
k=
1
y
H
(
k
)
R
-
1
η
y
(
k
)
-
f*
R
ECD
(
1
8
)
W
h
er
e
f
*
RECD
d
en
o
te
t
h
e
o
p
tim
a
l
v
alu
e
o
f
t
h
e
o
b
j
ec
tiv
e
Fu
n
ctio
n
T
h
e
ab
o
v
e
test
s
tat
i
s
tic
y
ield
s
a
r
o
b
u
s
t
d
ec
is
io
n
r
u
le
f
o
r
p
r
im
ar
y
u
s
er
d
etec
tio
n
in
MI
MO
co
g
n
iti
v
e
r
ad
io
n
et
w
o
r
k
s
.
3
.
5
.
I
nte
rf
er
ence
B
a
s
ed
Det
e
ct
io
n:
Un
li
k
e
t
h
e
p
r
i
m
ar
y
r
ec
eiv
e
r
d
etec
tio
n
,
th
e
b
asic
id
ea
b
eh
in
d
t
h
e
i
n
ter
f
er
e
n
ce
te
m
p
er
at
u
r
e
m
an
a
g
e
m
e
n
t
is
to
s
et
u
p
an
u
p
p
er
in
ter
f
er
en
ce
li
m
it
f
o
r
g
iv
en
f
r
eq
u
e
n
c
y
b
an
d
in
s
p
ec
i
f
ic
g
eo
g
r
ap
h
ic
lo
ca
tio
n
s
u
c
h
t
h
at
th
e
C
R
u
s
er
s
ar
e
n
o
t
allo
w
ed
to
ca
u
s
e
h
ar
m
f
u
l
in
te
r
f
er
en
ce
w
h
ile
u
s
i
n
g
t
h
e
s
p
ec
if
ic
b
a
n
d
i
n
s
p
ec
i
f
ic
ar
ea
.
T
y
p
icall
y
,
C
R
u
s
er
tr
an
s
m
itter
s
co
n
tr
o
l
th
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in
ter
f
er
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ce
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y
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eg
u
lati
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g
t
h
eir
tr
an
s
m
is
s
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n
p
o
w
er
(
th
eir
o
u
t
o
f
b
an
d
e
m
i
s
s
io
n
s
)
b
ase
d
o
n
th
eir
lo
ca
tio
n
s
w
it
h
r
esp
ec
t
to
p
r
im
ar
y
u
s
er
s
.
T
h
is
m
eth
o
d
b
asicall
y
co
n
ce
n
tr
ates
o
n
m
ea
s
u
r
in
g
i
n
ter
f
er
en
ce
at
th
e
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er
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h
e
o
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atin
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p
r
in
cip
le
o
f
t
h
is
m
et
h
o
d
is
li
k
e
a
n
UW
B
tech
n
o
lo
g
y
w
h
er
e
th
e
C
R
u
s
er
s
ar
e
allo
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ed
to
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ex
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s
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ter
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
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C
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I
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N:
2
0
8
8
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Hyb
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2691
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u
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.
[
1
3
]
-
[
1
4
]
I
t
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ted
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at
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R
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.
I
M
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g
tec
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m
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ted
[
1
5
]
-
[
1
6
]
.
I
n
th
is
p
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o
p
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s
ed
tech
n
iq
u
e
all
f
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d
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s
ar
e
ar
r
an
g
ed
in
a
co
o
p
er
ativ
e
m
a
n
n
er
.
a.
Ma
tch
Fil
ter
Dete
cto
r
.
b.
E
n
er
g
y
Dete
cto
r
c.
GL
R
T
.
d.
R
o
b
u
s
t E
s
ti
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a
to
r
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o
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.
e.
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ased
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u
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9
.
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[
1
7
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-
[
1
8
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.
T
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t t
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.
T
h
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s
a
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
J
E
C
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Vo
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7
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5
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2
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to
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s
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2
is
to
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f
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d
is
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ed
[
1
9
]
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2
4
]
.
Fo
r
ex
a
m
p
le:
L
et
u
s
ass
u
m
e
th
at
t
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b
b
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as
f
o
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s
:
M=
1
,
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=1
,
G=
1
,
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=0
,
T
=1
.
Fro
m
th
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o
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4
d
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is
f
r
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t
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5
d
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h
is
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lt
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s
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s
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t
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H
y
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.
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n
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m
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y
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te
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tp
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s
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s
t
h
at
b
a
n
d
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v
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a
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t.
I
n
th
is
w
a
y
m
u
ltip
l
e
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d
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u
r
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th
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n
e
x
t
s
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tio
n
r
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s
o
f
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p
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p
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s
ed
tech
n
iq
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e
s
h
o
w
n
.
5
.
SI
M
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AT
I
O
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SU
L
T
S
I
n
o
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er
to
co
m
p
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t
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e
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ce
o
f
d
if
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er
en
t
s
p
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tr
u
m
s
e
n
s
in
g
m
et
h
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d
s
t
w
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a
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k
s
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co
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s
id
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a.
T
o
d
eter
m
i
n
e
w
h
ich
b
a
n
d
s
ar
e
u
n
o
cc
u
p
ied
an
d
b
.
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est
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eth
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d
ca
n
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e
d
eter
m
i
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f
o
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ar
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er
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o
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s
p
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tr
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m
w
it
h
o
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t
i
n
ter
f
er
in
g
w
it
h
t
h
e
p
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m
ar
y
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s
[
2
1
]
-
[
2
2
]
.
Usi
n
g
MA
T
L
A
B
s
i
m
u
latio
n
o
f
ab
o
v
e
d
etec
to
r
s
w
e
h
av
e
e
x
p
er
i
m
e
n
tall
y
c
h
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k
t
h
e
u
n
o
cc
u
p
ied
b
an
d
.
T
h
e
p
u
r
p
o
s
e
o
f
th
is
p
ap
er
is
to
a
g
g
r
e
g
ate
d
i
f
f
e
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en
t
s
p
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tr
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m
s
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s
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g
m
et
h
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s
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d
co
m
p
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t
h
ese
m
et
h
o
d
s
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ased
o
n
a
v
ar
iet
y
o
f
p
er
f
o
r
m
a
n
ce
m
etr
ics.
Su
c
h
p
er
f
o
r
m
an
ce
m
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ics
i
n
cl
u
d
e
d
etec
tio
n
ac
cu
r
ac
y
,
co
m
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lex
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y
,
r
o
b
u
s
tn
e
s
s
,
f
le
x
ib
ilit
y
o
f
d
esig
n
c
h
o
ices,
R
F
s
p
ec
tr
u
m
u
s
ed
a
n
d
ex
e
cu
tio
n
ti
m
e
o
f
ea
ch
s
y
s
te
m
.
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n
th
e
b
elo
w
tab
le
s
p
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tr
u
m
s
en
s
i
n
g
al
g
o
r
ith
m
s
s
u
c
h
as
en
er
g
y
d
etec
to
r
,
m
atc
h
ed
f
ilter
i
n
g
,
GL
R
T
,
r
o
b
u
s
t
E
s
ti
m
ato
r
co
r
r
elato
r
an
d
a
h
y
b
r
id
tech
n
iq
u
e
ar
e
an
a
l
y
ze
d
an
d
co
m
p
ar
ed
.
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w
ith
e
x
p
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i
m
e
n
tal
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r
ac
tices,
th
e
b
est
s
p
ec
tr
u
m
d
ec
is
io
n
m
et
h
o
d
d
ep
en
d
s
o
n
th
e
ap
p
licatio
n
.
So
m
e
v
ar
iab
les to
b
e
co
n
s
id
er
ed
ar
e:
a.
E
x
p
ec
ted
SNR
v
alu
e
s
.
b.
C
o
m
p
u
tatio
n
a
l a
n
d
i
m
p
le
m
e
n
t
atio
n
co
m
p
lex
it
y
.
c.
R
eq
u
ir
ed
r
eliab
ilit
y
(
e
x
p
r
ess
i
n
ter
m
s
o
f
p
r
o
b
ab
ilit
y
o
f
d
etec
tio
n
,
p
r
o
b
ab
ilit
y
o
f
f
alse a
lar
m
)
.
d.
Am
o
u
n
t o
f
i
n
f
o
r
m
atio
n
th
a
t th
e
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u
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Fig
u
r
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a
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.
Ou
tp
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t o
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H
y
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r
id
S
y
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te
m
i
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ter
m
s
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f
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lo
t s
tatu
s
.
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