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t
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NU
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to
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lse
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lar
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ra
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p
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a
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lse
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a
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li
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p
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y
ste
m
s,
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o
n
v
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n
ti
o
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a
l
ED
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v
u
l
n
e
ra
b
le
t
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n
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l
c
li
p
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S
R
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a
id
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d
ED
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m
a
in
s u
n
a
f
fe
c
ted
.
K
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w
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r
d
s
:
C
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c
c
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CC B
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C
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p
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A
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Hen
r
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O
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Dep
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T
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cr
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u
m
b
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w
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co
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m
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te
m
s
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is
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m
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v
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p
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th
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p
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k
n
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n
as
r
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eq
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(
R
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p
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co
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n
itiv
e
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ad
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n
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w
o
r
k
s
(
C
R
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h
a
s
em
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g
ed
as
h
i
g
h
l
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v
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le
s
o
lu
t
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T
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ttin
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g
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tech
n
o
lo
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y
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d
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ess
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th
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g
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k
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C
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t
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allo
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g
t
h
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s
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ar
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(
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Us)
to
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m
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f
r
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m
s
u
r
r
o
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d
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g
R
F
s
ig
n
al
[
1
]
.
I
n
R
F
-
p
o
w
er
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C
R
N
s
,
s
p
ec
tr
u
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(
u
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p
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s
to
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p
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m
w
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t
ca
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in
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in
ter
f
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n
ce
to
p
r
im
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lice
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u
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s
.
Ho
w
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v
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,
s
p
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tr
u
m
s
en
s
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n
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f
ac
es
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er
io
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ch
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s
.
I
n
lo
w
s
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g
n
al
-
to
-
n
o
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s
e
r
atio
(
SNR
)
en
v
ir
o
n
m
e
n
t,
th
e
d
etec
tio
n
p
er
f
o
r
m
a
n
ce
d
eg
r
ad
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n
o
tab
l
y
w
h
en
i
m
p
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m
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n
ti
n
g
co
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v
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n
tio
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al
en
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g
y
d
etec
tio
n
(
E
D)
m
e
th
o
d
s
[
2
]
.
No
is
e
u
n
ce
r
tai
n
t
y
(
NU)
,
r
es
u
lti
n
g
f
r
o
m
e
n
v
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m
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n
tal
f
l
u
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a
tio
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,
h
ar
d
w
ar
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i
m
p
er
f
ec
tio
n
an
d
in
ter
f
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en
ce
,
i
m
p
ac
t
s
n
eg
at
iv
el
y
o
n
s
p
ec
tr
u
m
d
etec
ti
o
n
ac
cu
r
ac
y
,
h
e
n
ce
in
cr
ea
s
i
n
g
t
h
e
f
alse a
lar
m
p
r
o
b
ab
ilit
y
[
3
]
,
[
4
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
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t E
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C
o
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tr
o
l
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to
ch
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s
tic
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eso
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a
n
ce
-
a
id
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e
n
erg
y
d
etec
tio
n
fo
r
R
F
-
p
o
w
ered
co
g
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itive
…
(
Hen
r
y
On
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ma
u
ch
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Osu
a
g
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)
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co
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ativ
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m
s
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h
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n
cr
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i
n
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p
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tatio
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a
l
an
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y
d
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a
n
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[
5
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.
I
n
f
u
ll
-
d
u
p
lex
n
et
w
o
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k
s
,
s
el
f
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ter
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s
ig
n
al
s
i
n
to
r
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eiv
in
g
d
ev
ice
s
[
6
]
,
co
n
ce
ali
n
g
th
e
p
r
i
m
ar
y
u
s
er
(
PU
)
s
ig
n
al
s
in
C
R
Ns.
T
h
ese
ch
al
len
g
e
s
p
r
ev
en
t
ac
c
u
r
ate
d
etec
tio
n
o
f
s
p
ec
tr
u
m
h
o
les
a
n
d
ex
p
o
s
e
th
e
P
Us
to
in
ter
f
er
e
n
ce
.
I
m
p
r
o
v
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n
g
t
h
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s
it
iv
i
t
y
o
f
s
p
ec
tr
u
m
d
etec
t
in
g
d
ev
ice
s
t
h
at
ca
n
co
n
s
is
te
n
tl
y
id
en
t
if
y
s
p
ec
tr
u
m
o
p
p
o
r
tu
n
iti
e
s
at
v
er
y
lo
w
SN
R
is
cr
u
cial
f
o
r
s
ea
m
le
s
s
o
p
er
atio
n
o
f
C
R
N
s
[
7
]
.
Fu
tu
r
e
m
et
h
o
d
o
lo
g
ies
f
o
r
s
p
ec
tr
u
m
d
etec
tio
n
s
h
o
u
ld
in
c
o
r
p
o
r
ate
d
iv
er
s
e
s
tr
ateg
ies to
a
ch
iev
e
r
o
b
u
s
t p
er
f
o
r
m
an
ce
a
g
ain
s
t
NU
a
n
d
s
i
g
n
al
clip
p
in
g
w
h
ile
g
u
ar
a
n
teein
g
f
a
ls
e
alar
m
p
r
o
b
ab
ilit
y
t
h
at
is
eq
u
al
to
o
r
less
th
an
1
0
%.
T
h
is
s
tu
d
y
ai
m
s
to
tack
le
s
o
m
e
o
f
t
h
e
s
e
c
h
alle
n
g
e
s
t
h
r
o
u
g
h
s
to
ch
as
tic
r
eso
n
a
n
ce
(
S
R
)
-
aid
ed
E
D
s
e
n
s
i
n
g
m
eth
o
d
t
h
a
t
e
m
p
lo
y
s
p
o
w
er
f
u
l
s
ig
n
al
p
r
o
ce
s
s
in
g
tech
n
iq
u
e
s
th
at
ar
e
ef
f
icie
n
t
an
d
ad
ap
tiv
e
to
m
o
d
er
n
w
ir
eless
co
m
m
u
n
ica
tio
n
s
tech
n
o
lo
g
ie
s
.
Ou
tli
n
ed
ar
e
th
e
r
esear
ch
co
n
t
r
ib
u
tio
n
s
:
−
T
h
is
s
tu
d
y
d
er
iv
ed
m
at
h
e
m
a
tical
ex
p
r
ess
io
n
o
f
t
h
e
SN
R
g
ain
g
e
n
er
ated
th
r
o
u
g
h
b
is
t
ab
le
n
o
n
li
n
ea
r
s
y
s
te
m
s
,
th
a
t s
h
o
w
ed
h
o
w
t
h
e
SR
s
y
s
te
m
ad
ap
ts
to
v
ar
y
in
g
n
o
is
e
co
n
d
itio
n
s
.
−
A
n
o
v
el
a
n
al
y
tical
f
o
r
m
u
latio
n
is
o
b
tain
ed
f
o
r
t
h
e
d
etec
tio
n
p
r
o
b
a
b
ilit
y
o
f
S
R
-
a
id
ed
ED
−
I
t
in
v
e
s
ti
g
ate
s
t
h
e
i
m
p
ac
t
o
f
NU
an
d
s
ig
n
al
cl
ip
p
in
g
o
n
t
h
e
d
etec
tio
n
ac
cu
r
ac
y
o
f
S
R
-
ai
d
ed
ED
in
lo
w
SNR
e
n
v
ir
o
n
m
e
n
t.
As
f
o
llo
w
s
ar
e
th
e
s
u
b
s
eq
u
e
n
t
s
ec
tio
n
s
o
f
th
e
p
ap
er
:
s
ec
tio
n
2
p
r
esen
ts
th
e
r
elate
d
w
o
r
k
s
.
T
h
e
co
n
v
e
n
tio
n
al
ED
th
a
t
i
n
co
r
p
o
r
ates
NU
,
s
i
g
n
al
c
lip
p
in
g
,
SR
s
y
s
te
m
,
m
ec
h
a
n
is
m
o
f
m
u
lti
-
tap
er
s
p
ec
tr
al
esti
m
atio
n
,
S
R
p
ar
a
m
eter
s
esti
m
atio
n
,
t
h
e
f
o
r
m
u
lat
io
n
o
f
th
e
SNR
g
a
in
f
o
r
SR
S
y
s
te
m
,
an
d
th
e
an
al
y
tical
m
o
d
e
l
f
o
r
th
e
d
etec
tio
n
p
r
o
b
ab
ilit
y
o
f
SR
-
aid
ed
E
D
ar
e
p
r
esen
te
d
in
s
ec
tio
n
3
.
T
h
e
r
esu
lt
s
o
f
s
i
m
u
lati
o
n
s
,
t
h
e
d
is
cu
s
s
io
n
a
n
d
th
e
d
ev
elo
p
ed
m
o
d
el
co
m
p
ar
i
s
o
n
w
it
h
ex
is
ti
n
g
p
u
b
lis
h
ed
m
o
d
els
in
l
iter
at
u
r
e
ar
e
p
r
o
v
id
ed
in
s
ec
tio
n
4
.
Fin
a
ll
y
,
s
ec
tio
n
5
p
r
esen
t
s
co
n
cl
u
s
io
n
r
ef
lectio
n
s
.
2.
RE
L
AT
E
D
WO
RK
S
I
n
r
ec
en
t
ti
m
es,
S
R
-
aid
ed
ED
tech
n
iq
u
e
h
a
s
m
o
s
tl
y
f
o
c
u
s
e
d
o
n
i
m
p
r
o
v
in
g
t
h
e
s
p
ec
tr
u
m
d
etec
tio
n
ac
cu
r
ac
y
i
n
lo
w
SN
R
en
v
ir
o
n
m
en
ts
,
w
h
ich
is
a
m
aj
o
r
ch
allen
g
e
f
o
r
t
y
p
ical
ED
m
et
h
o
d
.
T
h
e
in
v
es
tig
a
tio
n
i
n
[
8
]
pr
esen
ts
co
o
p
er
ativ
e
p
er
c
ep
tio
n
alg
o
r
ith
m
f
o
r
a
f
e
w
n
o
d
es
th
at
ad
o
p
t
a
s
in
g
le
-
n
o
d
e
s
en
s
i
n
g
s
ch
e
m
e
in
ac
co
r
d
an
ce
w
ith
SR
w
it
h
an
i
m
p
r
o
v
ed
Qu
an
t
u
m
Ma
n
ta
-
R
a
y
Op
ti
m
i
s
atio
n
.
T
h
e
m
et
h
o
d
im
p
r
o
v
es
d
etec
tio
n
ac
cu
r
ac
y
i
n
lo
w
S
NR
s
ce
n
ar
io
s
.
T
h
e
alg
o
r
ith
m
d
ev
e
lo
p
ed
in
[
9
]
lev
er
ag
e
o
n
cu
s
to
m
is
ed
n
o
n
lin
ea
r
s
y
s
te
m
s
to
ex
tr
ac
t
w
ea
k
f
au
l
t
s
ig
n
al
co
n
ce
aled
in
n
o
is
e,
ex
te
n
d
in
g
b
ey
o
n
d
co
n
v
e
n
tio
n
al
SR
b
y
ad
a
p
tin
g
p
o
ten
tia
l
w
e
ll
s
tr
u
ct
u
r
e
f
o
r
ef
f
ec
ti
v
e
n
o
is
e
-
to
-
s
i
g
n
al
tr
a
n
s
it
io
n
.
He
u
r
is
tic
te
ch
n
iq
u
es
n
o
tab
l
y
t
h
e
f
ir
ef
l
y
al
g
o
r
ith
m
(
F
F
A
)
w
a
s
d
ev
elo
p
ed
in
[
10
]
to
d
eter
m
i
n
e
t
h
e
ap
p
r
o
p
r
iate
a
m
o
u
n
t
o
f
n
o
is
e
a
n
d
S
R
p
ar
a
m
e
ter
s
,
l
ea
d
in
g
to
i
m
p
r
o
v
e
p
r
o
b
a
b
ilit
y
o
f
d
etec
tio
n
(
P
d
)
o
f
th
e
s
y
s
te
m
’
s
w
ea
k
s
i
g
n
a
l.
An
in
n
o
v
ativ
e
s
p
ec
tr
u
m
d
etec
tio
n
m
et
h
o
d
th
at
e
m
p
lo
y
s
S
R
to
s
tr
en
g
t
h
en
p
r
i
m
ar
y
u
s
er
’
s
s
p
ec
tr
u
m
d
etec
tio
n
ac
cu
r
ac
y
w
as
p
r
esen
ted
in
[1
1
]
.
W
ith
a
co
n
s
tan
t
f
alse
alar
m
p
r
o
b
ab
ilit
y
,
th
e
t
ec
h
n
iq
u
e
d
etec
tio
n
p
r
o
b
a
b
ilit
y
o
u
tp
er
f
o
r
m
s
t
y
p
ic
al
en
er
g
y
d
etec
to
r
s
in
lo
w
S
NR
en
v
ir
o
n
m
en
ts
,
w
h
ile
m
ai
n
tai
n
in
g
t
h
e
s
a
m
e
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
.
J
ia
n
g
et
a
l.
[1
2
]
s
u
g
g
e
s
ted
an
SR
-
b
ased
alg
o
r
ith
m
f
o
r
co
o
p
er
ativ
e
s
p
ec
tr
u
m
s
e
n
s
in
g
in
lo
w
S
NR
e
n
v
ir
o
n
m
e
n
t
s
.
T
h
e
tech
n
iq
u
e
i
n
co
r
p
o
r
ates
tech
n
o
lo
g
ies
t
h
at
r
esu
lted
i
n
i
m
p
r
o
v
ed
P
d
an
d
m
i
n
i
m
is
ed
er
r
o
r
r
ate,
s
p
ec
if
icall
y
f
o
r
lice
n
s
ed
u
s
er
s
in
co
g
n
itiv
e
r
ad
io
.
T
h
e
s
tu
d
y
in
[1
3
]
p
u
t
f
o
r
w
ar
d
a
n
o
v
el
ap
p
r
o
ac
h
lev
er
ag
i
n
g
ad
ap
ti
v
e
SR
n
et
w
o
r
k
s
,
d
esig
n
ed
to
en
h
an
ce
p
ar
a
m
eter
es
ti
m
atio
n
o
f
w
ir
ele
s
s
c
h
a
n
n
el
in
lo
w
SN
R
en
v
ir
o
n
m
en
t
s
w
h
er
e
ty
p
ical
ap
p
r
o
ac
h
es
u
n
d
er
p
er
f
o
r
m
.
T
h
e
SR
n
et
w
o
r
k
’
s
ca
p
ab
ilit
y
to
b
o
o
s
t
th
e
s
ig
n
al
s
tr
e
n
g
th
t
h
r
o
u
g
h
t
h
e
i
n
co
r
p
o
r
atio
n
o
f
n
o
is
e
is
e
x
p
lo
i
ted
in
th
i
s
ap
p
r
o
ac
h
,
th
u
s
r
es
u
lti
n
g
i
n
i
m
p
r
o
v
ed
d
etec
tio
n
s
en
s
iti
v
it
y
.
T
h
e
ch
allen
g
e
o
f
i
m
p
r
o
v
in
g
s
p
ec
tr
u
m
d
etec
tio
n
i
n
lo
w
SN
R
h
as
b
ee
n
tack
led
ac
r
o
s
s
s
t
u
d
ies
o
n
SR
-
aid
ed
ED
.
Ho
w
e
v
er
,
s
e
v
er
al
s
t
u
d
y
g
ap
s
an
d
u
n
s
o
lv
ed
c
h
alle
n
g
e
s
ex
is
t i
n
s
o
m
e
o
f
th
e
p
u
b
li
s
h
ed
lite
r
at
u
r
e.
T
h
e
ex
is
t
in
g
SR
m
o
d
el
s
o
f
ten
co
n
s
id
er
co
n
ce
p
tu
al
ad
d
itiv
e
w
h
i
te
Gau
s
s
ia
n
n
o
is
e
(
A
W
GN)
s
ce
n
ar
io
s
.
R
esear
ch
er
s
ar
e
y
e
t
to
ex
p
lo
r
e
h
o
w
co
m
p
le
x
,
u
n
p
r
ed
ictab
le
NU,
licen
s
ed
u
s
er
i
n
ter
f
er
e
n
ce
,
an
d
s
ig
n
al
cl
ip
p
in
g
i
m
p
ac
ts
SR
-
aid
ed
ED
ac
cu
r
ac
y
an
d
r
eliab
il
it
y
i
n
f
l
u
ct
u
ati
n
g
R
F
s
u
r
r
o
u
n
d
i
n
g
s
.
T
h
e
co
n
v
en
t
io
n
al
S
R
s
y
s
t
e
m
s
w
er
e
d
esi
g
n
ed
f
o
r
s
tr
aig
h
tf
o
r
w
ar
d
p
er
io
d
ic
s
i
g
n
al
s
;
to
d
ea
l
w
it
h
n
o
n
-
p
er
io
d
ic,
m
u
lti
-
ca
r
r
ier
s
ig
n
al
s
,
SR
s
y
s
te
m
s
r
eq
u
ir
e
r
o
b
u
s
t
ap
p
r
o
ac
h
es.
T
h
e
SR
p
ar
am
eter
s
elec
tio
n
an
d
th
e
ac
cu
r
ate
esti
m
at
io
n
o
f
n
o
is
e
p
o
w
er
ar
e
v
er
y
i
m
p
o
r
ta
n
t
f
o
r
SR
-
aid
ed
E
D
alg
o
r
ith
m
e
f
f
ic
ien
c
y
.
I
n
r
ea
l
-
w
o
r
ld
s
it
u
atio
n
s
,
n
o
is
e
in
te
n
s
it
y
f
l
u
ct
u
ates tr
e
m
e
n
d
o
u
s
l
y
in
r
esp
o
n
s
e
to
th
e
s
u
r
r
o
u
n
d
in
g
co
n
d
itio
n
s
a
n
d
in
ter
f
er
e
n
ce
.
D
u
e
to
t
h
e
i
n
tr
i
n
s
ic
NU
,
it
b
ec
o
m
e
s
c
h
alle
n
g
i
n
g
to
d
ev
elo
p
r
o
b
u
s
t
SR
-
aid
ed
en
er
g
y
d
etec
to
r
s
t
h
a
t
co
u
ld
p
er
f
o
r
m
e
f
f
ec
ti
v
el
y
an
d
ap
p
r
o
p
r
iately
.
I
n
t
h
is
s
t
u
d
y
,
w
e
co
m
b
in
e
m
u
lt
i
-
tap
er
s
p
ec
tr
al
esti
m
a
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I
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eter
s
an
d
o
f
t
h
e
SR
s
y
s
te
m
ar
e
r
esp
o
n
s
ib
le
f
o
r
d
eter
m
i
n
i
n
g
t
h
e
d
i
m
e
n
s
io
n
s
an
d
d
ep
th
o
f
th
e
p
o
ten
tial
w
ell
[2
3
]
,
w
h
er
e
,
>
0
.
W
h
en
a
n
in
p
u
t
p
er
io
d
ic
w
ea
k
s
i
g
n
al
at
ti
m
e
,
(
)
=
(
2
0
)
,
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
3
,
J
u
n
e
20
26
:
7
8
6
-
800
790
Gau
s
s
ia
n
n
o
is
e
(
)
ar
e
ap
p
lied
to
th
is
b
i
s
tab
le
s
y
s
te
m
,
t
h
e
co
n
v
e
n
tio
n
al
o
v
er
d
a
m
p
ed
b
is
tab
le
S
R
h
a
s
th
e
f
o
r
m
th
u
s
[2
2
]
,
[2
4
]
:
(
)
=
−
(
,
)
+
(
)
+
(
)
(
8
)
T
h
e
am
p
lit
u
d
e
o
f
t
h
e
w
ea
k
p
er
io
d
ic
s
ig
n
al
i
s
d
en
o
ted
as
,
an
d
0
is
th
e
d
r
iv
in
g
f
r
eq
u
en
c
y
.
T
h
e
t
w
o
s
tab
le
p
o
s
itio
n
s
o
f
b
is
tab
le
s
y
s
te
m
ar
e
±
=
±
√
⁄
=
±
,
an
d
w
h
e
n
th
e
p
o
ten
tia
l
h
ei
g
h
t
ce
n
ter
ed
at
th
e
o
r
ig
i
n
,
0
=
0
,
th
e
b
ar
r
ier
h
ei
g
h
t
0
=
∆
=
2
4
⁄
.
T
h
e
co
n
d
itio
n
=
2
0
ac
co
r
d
in
g
to
t
h
e
t
w
o
-
s
ta
t
e
th
eo
r
y
SR
,
r
e
f
er
s
t
o
“
s
to
c
h
asti
c
m
atc
h
in
g
,
”
w
h
e
r
e
th
e
m
ea
n
w
aiti
n
g
ti
m
e
(
1
⁄
)
f
o
r
n
o
is
e
-
d
r
i
v
en
tr
a
n
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it
io
n
s
m
atc
h
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h
alf
t
h
e
p
er
io
d
(
1
0
⁄
)
o
f
th
e
a
m
b
ien
t
s
i
g
n
al
[2
5
]
.
T
h
is
s
y
n
ch
r
o
n
is
atio
n
o
p
ti
m
is
e
s
th
e
s
y
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te
m
’
s
r
esp
o
n
s
e
to
a
w
ea
k
p
er
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ic
o
r
ap
er
i
o
d
ic
s
ig
n
al
i
n
th
e
p
r
esen
ce
o
f
n
o
is
e.
W
h
er
e
is
th
e
Kr
a
m
er
s
r
ate
an
d
is
d
e
f
i
n
ed
as
:
=
√
2
e
xp
(
−
∆
)
(
9
)
is
th
e
Gau
s
s
ia
n
w
h
ite
n
o
is
e
i
n
ten
s
i
t
y
.
A
t
y
p
ical
s
i
m
p
li
f
ied
eq
u
atio
n
d
er
iv
ed
f
r
o
m
t
w
o
-
s
tat
e
th
eo
r
y
r
e
latin
g
SNR
to
is
:
=
2
4
2
(
1
0
)
In
(
1
0
)
s
tates
th
at
i
n
a
b
is
tab
le
SR
s
y
s
te
m
,
SN
R
is
d
ir
ec
tl
y
p
r
o
p
o
r
tio
n
al
to
o
f
s
tate
tr
a
n
s
i
tio
n
,
it
p
r
ed
icts
a
m
ax
i
m
u
m
S
NR
o
u
tp
u
t
at
a
s
p
e
cif
ic
n
o
is
e
i
n
te
n
s
it
y
.
I
n
th
is
s
tu
d
y
,
t
h
e
f
o
u
r
t
h
-
o
r
d
er
R
u
n
g
e
-
K
u
tta
(
R
K4
)
tech
n
iq
u
e
[2
6
]
is
em
p
lo
y
ed
to
m
o
d
el
th
e
b
is
tab
le
SR
s
y
s
te
m
.
T
h
e
SDE
u
n
d
er
in
v
e
s
tig
a
tio
n
i
s
:
(
)
=
=
(
,
)
+
(
)
+
(
)
=
−
3
+
(
)
+
(
)
(
1
1
)
W
h
er
e
(
)
,
(
)
ar
e
th
e
i
n
p
u
t
an
d
o
u
t
p
u
t
s
i
g
n
al
at
t
h
e
SR
s
y
s
te
m
r
e
s
p
ec
tiv
el
y
.
,
ar
e
p
o
ten
tial
b
ar
r
ier
SR
p
ar
am
eter
,
a
n
d
(
)
=
√
(
)
is
t
h
e
n
o
is
e
i
n
p
u
t,
(
)
r
ep
r
esen
ts
Gau
s
s
ia
n
w
h
ite
n
o
is
e
w
it
h
ze
r
o
m
ea
n
a
n
d
u
n
i
t
v
ar
ia
n
ce
.
T
o
esti
m
ate
th
e
s
ig
n
al
o
u
tp
u
t
at
t
h
e
SR
s
y
s
te
m
q
u
a
n
titati
v
el
y
,
w
e
ap
p
l
y
R
K4
ap
p
r
o
ac
h
w
i
th
a
s
tep
s
ize
ℎ
.
Fi
n
d
in
g
t
h
e
f
o
u
r
s
u
cc
ess
i
v
e
s
lo
p
es
(
1
,
2
,
3
,
4
)
r
esu
lts
i
n
t
h
e
r
ec
u
r
s
iv
e
d
is
cr
ete
iter
atio
n
o
f
th
e
p
r
esen
t v
al
u
e
to
th
e
s
u
b
s
eq
u
e
n
t v
al
u
e
+
1
.
{
1
=
ℎ
[
−
3
+
+
]
2
=
ℎ
[
(
+
1
2
)
−
(
+
1
2
)
3
+
+
]
3
=
ℎ
[
(
+
2
2
)
−
(
+
2
2
)
3
+
+
1
+
]
4
=
ℎ
[
(
+
3
)
−
(
+
3
)
3
+
+
1
+
]
+
1
=
+
1
6
(
1
+
2
2
+
2
3
+
4
)
(
1
2
)
3
.
4
.
M
ec
ha
nis
m
o
f
t
he
m
u
lt
i
-
t
a
per
s
pect
ra
l e
s
t
i
m
a
t
io
n
P
r
io
r
to
SR
p
r
o
ce
s
s
in
g
,
a
MT
SE
tech
n
iq
u
e
w
as
u
t
ilis
ed
to
i
m
p
r
o
v
e
s
p
ec
tr
u
m
e
s
ti
m
atio
n
p
r
ec
is
io
n
,
ai
m
ed
at
r
ed
u
c
in
g
t
h
e
h
i
g
h
v
ar
i
an
ce
a
n
d
s
p
ec
tr
al
lea
k
ag
e
p
r
o
b
le
m
s
in
h
er
e
n
t
i
n
s
in
g
le
-
tap
er
m
eth
o
d
s
.
T
h
e
MT
SE
tech
n
iq
u
e
esti
m
ate
s
th
e
p
o
w
er
s
p
ec
tr
al
d
en
s
it
y
(
P
SD)
o
f
a
s
ig
n
al
[
2
7
]
b
y
av
er
ag
in
g
m
a
n
y
m
o
d
if
ie
d
p
er
io
d
o
g
r
am
s
ca
lc
u
lated
,
ea
c
h
w
i
th
a
d
i
s
ti
n
ct
tap
er
[
2
8
]
,
[
2
9
]
.
T
h
e
o
p
tim
i
s
ed
s
p
ec
tr
u
m
i
s
s
u
b
s
eq
u
en
tl
y
u
s
ed
f
o
r
th
e
ad
ap
tiv
e
s
elec
tio
n
o
f
S
R
p
ar
a
m
eter
s
u
s
i
n
g
th
e
Gau
s
s
-
Sei
d
el
-
li
k
e
iter
atio
n
m
et
h
o
d
.
T
h
e
MT
SE
p
r
o
ce
s
s
es
o
f
th
e
in
p
u
t si
g
n
al
u
s
ed
in
t
h
is
s
t
u
d
y
ar
e
as f
o
llo
w
s
:
a.
Fo
r
=
1
,
…
,
4
,
r
ec
eiv
ed
s
ig
n
al
[
]
th
r
o
u
g
h
th
e
n
o
is
e
c
h
an
n
el
o
f
t
h
e
to
tal
len
g
th
is
d
iv
id
ed
in
to
ad
j
ac
en
t seg
m
e
n
t
s
[
]
:
=
[
+
(
−
1
)
4
]
,
0
≤
<
4
(1
3
)
b.
T
h
e
P
SD o
f
ea
ch
s
e
g
m
en
t
[
]
is
e
s
ti
m
ated
b
y
ap
p
l
y
i
n
g
th
e
m
u
lti
-
tap
er
m
et
h
o
d
.
T
h
e
esti
m
ate
(
)
f
o
r
s
eg
m
e
n
t
w
ith
a
ti
m
e
-
b
an
d
w
id
th
p
r
o
d
u
ct
ca
n
b
e
d
escr
ib
ed
as
th
e
m
ea
n
o
f
≈
[
2
−
1
]
d
is
tin
ct
“
ei
g
en
tap
er
”
esti
m
ate
s
:
(
)
=
1
∑
⌈
∑
,
(
)
∗
[
]
−
2
4
−
1
⁄
=
0
⌉
2
−
1
=
0
(
1
4
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
S
to
ch
a
s
tic
r
eso
n
a
n
ce
-
a
id
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n
erg
y
d
etec
tio
n
fo
r
R
F
-
p
o
w
ered
co
g
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itive
…
(
Hen
r
y
On
ye
ma
u
ch
e
Osu
a
g
w
u
)
791
W
h
er
e
,
(
)
d
en
o
tes th
e
Slep
ia
n
ta
p
er
s
[2
7
]
,
[
30
]
,
[
31
]
.
T
h
e
d
ec
is
io
n
to
ch
o
o
s
e
=
2
−
1
tap
er
s
g
u
ar
an
tee
o
n
l
y
s
eq
u
e
n
ce
s
w
i
th
h
i
g
h
e
n
er
g
y
d
e
n
s
it
y
ac
r
o
s
s
th
e
b
an
d
o
f
f
r
eq
u
e
n
c
ies
(
−
,
)
.
T
h
e
v
ar
iab
le
r
ep
r
esen
ts
t
h
e
e
s
ti
m
ated
f
r
eq
u
e
n
c
y
d
en
s
it
y
,
d
en
o
tes
t
h
e
n
u
m
b
er
o
f
s
a
m
p
les,
d
en
o
tes
t
h
e
n
ee
d
ed
a
m
o
u
n
t
o
f
b
an
d
w
id
t
h
.
T
o
r
ed
u
ce
s
p
ec
tr
a
l
leak
ag
e,
ad
ap
tiv
e
m
u
lti
-
tap
er
esti
m
atio
n
w
it
h
s
p
ec
i
f
ic
w
ei
g
h
ts
,
(
)
ac
co
r
d
in
g
to
th
e
ei
g
e
n
v
a
lu
e
ac
r
o
s
s
th
e
b
an
d
o
f
f
r
eq
u
e
n
ci
es
(
−
,
)
o
f
ea
c
h
Slep
ia
n
tap
er
o
r
d
is
cr
ete
p
r
o
late
s
p
h
er
o
id
al
s
eq
u
en
ce
(
DP
SS
)
is
d
e
f
in
ed
t
h
u
s
:
,
(
)
=
⌈
̃
(
)
+
(
1
−
)
2
⌉
2
(
1
5
)
w
h
er
e
̃
(
)
is
t
h
e
tap
er
ed
p
er
io
d
o
g
r
a
m
s
.
c.
W
e
s
u
m
t
h
e
est
i
m
a
ted
P
SD to
o
b
tain
av
er
ag
ed
s
p
ec
tr
u
m
(
)
.
(
)
=
∑
(
)
=
1
4
+
2
+
3
+
4
(
1
6
)
d.
T
h
e
d
esti
n
atio
n
v
ar
iab
le
is
d
eter
m
i
n
ed
f
r
o
m
t
h
e
p
o
r
tio
n
o
f
t
h
e
s
u
m
m
ed
s
p
ec
tr
u
m
an
d
s
to
r
ed
f
o
r
s
i
m
u
lat
io
n
o
r
an
al
y
s
i
s
.
=
{
(
)
|
∈
}
in
d
icate
s
th
at
th
e
m
o
d
el
’
s
in
p
u
t
is
g
e
n
er
ated
b
y
ap
p
l
y
i
n
g
an
a
v
er
ag
ed
s
p
ec
tr
u
m
f
u
n
c
tio
n
o
n
ea
ch
d
is
ti
n
ct
s
a
m
p
le
i
n
t
h
e
tr
ain
i
n
g
s
et
.
W
h
er
e
is
a
s
p
ec
if
ic
n
u
m
b
er
o
f
ti
m
e
-
d
o
m
ai
n
tr
ain
in
g
s
a
m
p
les
,
co
r
r
esp
o
n
d
in
g
to
t
h
e
s
y
s
te
m
’
s
f
ast
f
o
u
r
ier
tr
an
s
f
o
r
m
atio
n
(
F
FT
)
s
ize,
th
at
u
s
es
co
n
tr
o
lled
r
an
d
o
m
n
e
s
s
to
p
u
s
h
a
n
S
R
s
y
s
te
m
i
n
to
a
s
tate
o
f
o
p
ti
m
al
p
er
f
o
r
m
a
n
ce
an
d
f
o
r
d
eter
m
i
n
g
id
ea
l
v
alu
e
s
o
f
,
an
d
.
T
h
e
FF
T
s
izes a
r
e
g
en
er
all
y
a
p
o
w
er
o
f
2
(
e,
g
.
,
2
5
6
,
5
1
2
,
1
0
2
4
,
2
0
4
8
)
.
T
h
e
MT
SE
p
r
o
ce
s
s
ab
o
v
e
in
te
g
r
ates
s
p
atial
s
m
o
o
th
i
n
g
t
h
r
o
u
g
h
s
e
g
m
e
n
tat
io
n
a
n
d
s
p
ec
tr
al
s
m
o
o
th
in
g
b
y
ca
r
r
y
in
g
o
u
t
v
ar
iab
le
m
u
lti
-
tap
er
in
g
.
T
h
e
m
u
lti
-
tap
er
m
e
th
o
d
o
p
ti
m
is
e
s
SN
R
esti
m
atio
n
b
y
o
f
f
er
i
n
g
h
i
g
h
q
u
alit
y
P
SD,
ad
ap
tab
le
w
ei
g
h
t
in
g
f
o
r
o
p
ti
m
al
r
es
u
lt i
n
f
l
u
ct
u
atin
g
n
o
is
e
e
n
v
ir
o
n
m
en
t
s
.
3
.
5
.
E
s
t
i
m
a
t
io
n o
f
SR
p
a
ra
m
et
er
s
T
h
e
esti
m
atio
n
o
f
o
p
ti
m
al
S
R
p
ar
am
eter
s
is
o
f
te
n
ex
p
r
es
s
e
d
as
an
o
p
er
atio
n
in
r
elatio
n
to
SNR
g
ain
.
W
e
p
er
f
o
r
m
t
h
e
o
p
ti
m
izatio
n
p
r
o
ce
s
s
as f
o
llo
w
s
:
=
a
r
gma
x
θ
(
)
=
a
r
gma
x
θ
(
1
7
)
w
h
er
e
is
th
e
s
et
o
f
SR
p
ar
a
m
e
ter
s
,
,
an
d
to
b
e
esti
m
ated
,
is
th
e
o
b
j
ec
tiv
e
f
u
n
ct
io
n
f
o
r
th
e
o
u
tp
u
t SN
R
o
r
SNR
g
ai
n
.
T
h
e
SR
p
ar
a
m
e
ter
s
ar
e
u
p
d
ated
s
u
cc
ess
i
v
el
y
u
s
i
n
g
a
Gau
s
s
-
Seid
el
-
li
k
e
iter
atio
n
m
et
h
o
d
[
32
]
,
w
h
er
ein
th
e
v
al
u
e
o
f
-
th
p
ar
a
m
eter
r
eu
s
i
n
g
th
e
m
o
s
t
r
ec
en
tl
y
est
i
m
a
ted
v
al
u
es
o
f
t
h
e
r
e
m
ain
i
n
g
p
ar
a
m
eter
s
.
T
h
e
iter
ativ
e
esti
m
a
tio
n
is
p
r
ed
icate
d
o
n
th
e
e
m
p
ir
ical
d
etec
tio
n
p
r
o
b
ab
ilit
y
,
.
Usi
n
g
M
A
T
L
A
B
to
ca
lcu
lat
e
th
e
d
etec
tio
n
th
r
es
h
o
ld
,
f
o
r
th
e
s
y
s
te
m
p
ar
a
m
eter
s
,
we
co
n
s
id
er
th
e
n
u
m
b
er
o
f
tr
ain
i
n
g
s
a
m
p
le
s
,
an
d
th
e
f
al
s
e
alar
m
p
r
o
b
ab
ilit
y
,
,
b
y
ap
p
l
y
i
n
g
a
C
h
i
-
s
q
u
ar
e
d
is
tr
ib
u
tio
n
,
=
ℎ
2
(
1
−
,
)
(
1
8
)
Fo
r
a
s
et
o
f
p
r
o
b
ab
le
v
alu
e
s
∈
(
ℎ
=
{
0
.
01
,
…
,
1
}
)
,
w
e
co
m
p
u
ted
th
e
n
u
m
er
ical
b
y
tak
i
n
g
t
h
e
av
er
a
g
e
o
f
test
r
esu
lt.
(
)
=
1
∑
|
(
∑
|
|
2
≥
)
=
1
(
1
9
)
w
h
er
e
|
(
∙
)
d
en
o
tes
th
e
i
n
d
icato
r
f
u
n
ctio
n
,
=
_
is
th
e
s
ig
n
al
o
u
tp
u
t
o
f
th
e
R
u
n
g
e
-
K
u
tta
4
t
h
-
o
r
d
er
s
o
lv
er
.
T
h
e
f
ir
s
t
r
a
w
esti
m
ated
v
al
u
e
o
f
is
d
eter
m
i
n
ed
ac
r
o
s
s
th
e
e
v
alu
a
tio
n
s
p
ac
e
b
ased
o
n
co
m
b
i
n
ed
to
tal
d
etec
tio
n
p
r
o
b
ab
ilit
ies:
=
(
⌈
∑
(
)
⌉
)
(
2
0
)
Fin
all
y
,
i
f
th
e
v
ar
iatio
n
i
n
(
∆
(
)
>
0
.
2
)
,
a
s
m
al
l c
o
r
r
ec
tio
n
is
ad
d
ed
to
.
+
1
=
+
0
.
05
T
h
e
v
alu
e
o
f
th
e
p
ar
am
eter
+
1
is
f
ir
s
t
u
p
d
ated
u
s
i
n
g
th
e
in
it
ial
v
alu
e
s
an
d
.
T
h
e
p
ar
am
e
ter
+
1
s
u
b
s
eq
u
en
t
l
y
g
et
s
esti
m
ated
,
u
s
i
n
g
th
e
m
o
s
t
r
ec
en
t
esti
m
at
ed
+
1
an
d
.
T
h
e
p
ar
am
eter
+
1
is
es
ti
m
ate
d
last
l
y
,
u
s
i
n
g
th
e
n
e
w
l
y
ca
lc
u
la
ted
v
alu
e
s
+
1
an
d
+
1
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
3
,
J
u
n
e
20
26
:
7
8
6
-
800
792
3
.
6
.
F
o
r
m
ula
t
io
n o
f
t
he
SNR
g
a
in f
o
r
SR
sy
s
t
e
m
T
h
e
av
er
ag
e
SNR
at
t
h
e
i
n
p
u
t
o
f
S
R
s
y
s
te
m
is
,
=
2
2
2
(
2
1
)
T
h
e
o
p
tim
u
m
SNR
g
ai
n
ca
n
b
e
attain
ed
th
r
o
u
g
h
th
e
d
y
n
a
m
i
c
ad
j
u
s
t
m
en
ts
o
f
t
h
e
ad
d
ed
v
ar
ian
ce
o
f
th
e
n
o
is
e
p
o
w
er
0
2
w
it
h
t
h
e
v
ar
iab
le
0
.
T
h
e
SNR
o
u
tp
u
t a
t S
R
s
y
s
te
m
is
ca
lcu
lated
th
u
s
[
33
]
:
=
4
√
2
2
0
(
2
+
0
(
)
2
)
2
−
2
0
(
2
+
0
(
)
2
)
⁄
(
2
2
)
T
h
e
o
p
tim
u
m
v
al
u
e
o
f
0
(
)
2
ca
n
p
o
ten
tiall
y
b
e
d
er
iv
ed
b
y
s
et
tin
g
t
h
e
d
er
iv
ati
v
e
o
f
SN
R
o
u
tp
u
t
wit
h
r
esp
ec
t
to
0
2
to
ze
r
o
,
(
0
2
)
=
0
⁄
.
W
e
ac
h
iev
ed
th
is
o
p
ti
m
i
s
atio
n
b
y
an
an
al
y
tical
m
e
th
o
d
th
at
d
eter
m
in
e
s
th
e
id
ea
l setti
n
g
s
t
h
at
i
m
p
r
o
v
e
SN
R
g
ai
n
.
T
h
e
p
r
o
d
u
ct
r
u
le
in
ca
lcu
l
u
s
i
s
ap
p
lied
f
o
r
(
2
2
)
t
o
g
et
th
e
d
if
f
er
e
n
tial e
q
u
at
io
n
b
et
w
ee
n
t
w
o
f
u
n
ctio
n
s
t
h
at
ar
e
m
u
ltip
lie
d
to
g
eth
er
.
L
et
,
=
4
√
2
2
0
(
2
+
0
(
)
2
)
2
,
=
−
2
0
(
2
+
0
(
)
2
)
⁄
an
d
=
0
2
=
4
√
2
2
0
(
2
+
0
(
)
2
)
2
×
2
0
(
2
+
0
(
)
2
)
2
−
2
0
(
2
+
0
(
)
2
)
⁄
+
−
2
0
(
2
+
0
(
)
2
)
⁄
×
8
√
2
2
0
(
2
+
0
(
)
2
)
(
2
+
0
(
)
2
)
4
=
0
(
2
3
)
8
√
2
2
0
2
(
2
+
0
(
)
2
)
4
−
2
0
(
2
+
0
(
)
2
)
⁄
=
8
√
2
2
0
(
2
+
0
(
)
2
)
(
2
+
0
(
)
2
)
4
−
2
0
(
2
+
0
(
)
2
)
⁄
(
2
4
a)
0
=
2
+
0
(
)
2
(
2
4
b
)
Fro
m
(
2
4
b
)
,
th
e
o
p
tim
u
m
t
h
eo
r
etica
l v
al
u
e
o
f
0
(
)
2
is
d
eter
m
i
n
ed
,
g
iv
i
n
g
u
s
th
e
r
es
u
lt.
0
(
)
2
=
0
−
2
(
2
5
)
I
t is p
o
s
s
ib
le
to
ev
alu
ate
t
h
e
S
NR
g
ai
n
t
h
r
o
u
g
h
i
n
co
r
p
o
r
atin
g
th
e
f
o
llo
w
i
n
g
(
2
1
)
,
(
2
2
)
,
an
d
(
2
4
b
)
.
=
=
8
√
2
2
2
0
(
2
6
)
T
h
e
ef
f
ec
ti
v
e
m
an
ip
u
latio
n
o
f
th
e
S
R
v
ar
iab
le
0
ca
n
g
u
ar
a
n
t
ee
a
h
ig
h
er
SN
R
o
u
tp
u
t
th
a
n
t
h
e
SN
R
in
p
u
t
(
)
>
1
⁄
,
if
0
>
8
√
2
2
2
(
2
7
)
w
h
er
e
th
e
p
o
ten
t
ial
b
ar
r
ier
h
eig
h
t,
0
=
2
4
⁄
.
3
.
7
.
A
no
v
el
a
na
ly
t
ic
a
l
m
o
de
l f
o
r
t
he
det
ec
t
io
n pro
ba
bil
it
y
o
f
SR
-
a
ided
ED
I
n
an
an
al
y
tical
ap
p
r
o
ac
h
o
f
SR
-
aid
ed
E
D,
a
n
o
n
lin
ea
r
s
y
s
te
m
is
u
s
ed
to
im
p
r
o
v
e
th
e
d
etec
tio
n
p
r
o
b
a
b
ilit
y
o
f
w
ea
k
s
i
g
n
a
ls
.
T
h
is
is
a
cc
o
m
p
l
is
h
ed
b
y
tr
an
s
itio
n
i
n
g
t
h
e
e
n
er
g
y
o
f
n
o
is
e
in
to
t
h
e
e
n
er
g
y
o
f
v
alu
ab
le
s
i
g
n
als.
T
h
e
m
at
h
e
m
atica
l
eq
u
atio
n
s
o
f
SR
-
aid
ed
ED
o
f
f
er
i
m
p
o
r
tan
t
u
n
d
er
s
ta
n
d
in
g
o
f
t
h
e
i
n
ter
n
al
p
r
o
ce
s
s
es
an
d
f
ac
to
r
s
n
ec
ess
a
r
y
f
o
r
o
p
ti
m
is
i
n
g
th
e
d
etec
tio
n
ac
cu
r
ac
y
o
f
a
p
o
o
r
s
i
g
n
al.
I
n
(
2
)
r
ep
r
esen
ts
th
e
p
r
o
b
a
b
ilit
y
o
f
d
etec
tin
g
t
h
e
co
n
v
e
n
tio
n
al
E
D
f
o
r
an
ev
e
n
t t
h
at
in
co
r
p
o
r
ates N
U.
T
h
e
SNR
in
(
2
)
is
th
e
av
er
ag
e
SNR
at
t
h
e
in
p
u
t
(
)
o
f
SR
s
y
s
te
m
.
Fro
m
(
2
6
)
,
th
e
SNR
at
th
e
o
u
tp
u
t
o
f
t
h
e
SR
s
y
s
te
m
is
ca
l
cu
lated
as
f
o
llo
w
s
.
=
8
√
2
2
2
0
×
(
2
8
)
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
S
to
ch
a
s
tic
r
eso
n
a
n
ce
-
a
id
ed
e
n
erg
y
d
etec
tio
n
fo
r
R
F
-
p
o
w
ered
co
g
n
itive
…
(
Hen
r
y
On
ye
ma
u
ch
e
Osu
a
g
w
u
)
793
B
y
i
n
co
r
p
o
r
atin
g
(
2
8
)
in
to
(
2
)
,
a
n
o
v
el
an
al
y
tica
l e
x
p
r
ess
io
n
f
o
r
SR
-
aid
ed
ED
is
d
ev
elo
p
ed
.
_
=
(
−
(
2
⁄
+
8
√
2
4
2
0
)
√
2
(
2
⁄
+
8
√
2
4
2
0
)
2
)
(
2
9
)
w
h
er
e
_
is
th
e
d
etec
tio
n
p
r
o
b
ab
ilit
y
o
f
SR
-
aid
ed
ED
.
Usi
n
g
a
s
tr
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t
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e
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y
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m
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o
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as f
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=
(
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(
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.
F
l
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rt
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o
r
s
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m
ula
t
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dev
elo
ped
SR
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ided
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ce
s
s
Fig
u
r
e
3
ill
u
s
tr
ates
t
h
e
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w
c
h
ar
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o
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S
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g
y
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to
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th
at
i
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r
p
o
r
ates
MT
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ap
p
r
o
ac
h
w
it
h
Gau
s
s
-
Seid
el
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li
k
e
iter
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n
m
e
th
o
d
.
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h
e
d
iag
r
a
m
p
r
o
v
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es
a
s
y
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te
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ir
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p
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s
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u
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lo
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ls
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3
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m
ula
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ra
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I
n
th
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t
u
d
y
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m
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ar
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d
o
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h
M
A
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B
v
e
r
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2
0
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4
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im
u
lato
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d
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o
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te
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ar
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i
m
u
lat
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s
w
er
e
r
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n
to
ac
h
iev
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th
e
d
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o
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tco
m
e.
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r
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i
m
u
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ar
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m
eter
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ee
T
ab
le
1
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h
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ar
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ar
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r
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ll
y
s
elec
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if
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o
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te
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h
e
u
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o
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h
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m
b
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iter
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u
r
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h
e
s
tatis
tical
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b
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h
e
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lt
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d
m
i
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m
izes t
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r
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1
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u
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eter
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P
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me
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r
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T
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795
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
P
l
o
t
o
f
ou
t
pu
t
SNR
a
g
a
i
ns
t
no
is
e
inte
n
s
it
y
T
h
e
o
u
tp
u
t
SN
R
ag
a
in
s
t
n
o
i
s
e
in
te
n
s
it
y
t
y
p
icall
y
ex
h
ib
it
s
a
b
ell
-
li
k
e
s
h
ap
e
cu
r
v
e
r
ea
ch
i
n
g
its
p
ea
k
at
o
p
tim
al
n
o
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s
e
in
te
n
s
it
y
.
As
s
h
o
w
n
in
F
i
g
u
r
e
4
,
th
e
v
ar
i
atio
n
in
SR
p
ar
a
m
eter
‘
b
’
is
o
f
ten
co
n
n
ec
ted
to
p
o
ten
tial b
ar
r
ier
s
lo
p
e.
A
h
ig
h
er
‘
b
’
p
r
o
m
o
tes o
p
ti
m
iza
tio
n
o
f
SR
s
y
s
te
m
ab
ilit
y
to
d
etec
t
w
ea
k
s
ig
n
al,
lead
in
g
to
im
p
r
o
v
ed
m
a
x
i
m
u
m
attai
n
a
b
le
SNR
an
d
p
er
f
o
r
m
an
ce
en
h
an
ce
m
e
n
t
i
n
lo
w
n
o
is
e
en
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ir
o
n
m
en
ts
.
A
d
ec
r
ea
s
in
g
‘
b
’
r
eq
u
ir
es
m
o
r
e
b
ac
k
g
r
o
u
n
d
n
o
is
e
to
in
d
u
ce
tr
an
s
itio
n
,
le
ad
in
g
to
d
ec
r
ea
s
ed
p
ea
k
SNR
o
u
tp
u
t
an
d
f
aster
p
er
f
o
r
m
a
n
ce
d
eg
r
ad
atio
n
.
4
.
2
.
Rec
eiv
er
o
pera
t
ing
cha
r
a
ct
er
is
t
ics (
RO
C)
c
urv
e
T
h
e
v
ar
iatio
n
in
t
h
e
p
r
o
b
ab
ilit
ies
o
f
d
etec
tio
n
i
n
r
elatio
n
t
o
th
e
f
al
s
e
alar
m
p
r
o
b
ab
ilit
y
at
SNR
=
-
15
d
B
an
d
-
10
d
B
is
illu
s
tr
ate
d
in
F
i
g
u
r
e
5
.
T
h
e
p
er
f
o
r
m
an
c
e
co
m
p
ar
is
o
n
o
f
t
h
e
d
ev
e
lo
p
ed
SR
-
a
id
ed
E
D
an
d
th
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ided
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atin
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ata
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a
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o
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th
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t
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l
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id
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is
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m
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ic
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lc
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lated
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u
e
g
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er
at
ed
f
r
o
m
s
t
atis
tical
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o
r
ith
m
s
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n
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te
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ce
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er
id
ea
l
co
n
d
itio
n
s
.
I
n
co
n
tr
ast,
th
e
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