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K
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C
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Facu
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B
.
P.
1
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Fès
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co
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[
1
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d
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co
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m
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ca
p
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o
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Am
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iq
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m
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MR)
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wid
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tech
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d
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ly
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m
an
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n
t
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co
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ad
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.
Ho
wev
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th
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ly
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I
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e
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th
ey
a
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f
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p
r
im
a
r
y
s
y
s
tem
s
s
u
ch
as
telev
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io
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(
TV
)
[
2
]
an
d
co
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ld
b
e
u
s
ed
b
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o
n
d
a
r
y
wir
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3
GPP/
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I
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J
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Sci,
Vo
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23
,
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2
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Au
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a
g
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p
tim
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to
u
s
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tr
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c
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ce
p
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r
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C
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)
[
3
]
,
[
4
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is
co
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s
id
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ed
as
a
n
a
p
p
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ac
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t
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s
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tatio
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d
wh
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te
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tio
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f
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f
u
tu
r
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o
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m
m
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y
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a
v
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ab
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th
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d
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ly
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an
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is
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s
o
as
n
o
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to
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ter
f
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with
th
e
m
ain
u
s
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s
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Dif
f
er
en
t
Sp
ec
tr
u
m
Sen
s
in
g
d
etec
tio
n
tech
n
iq
u
es
[
5
]
-
[
10
]
h
a
v
e
b
ee
n
d
e
v
elo
p
e
d
an
d
u
s
ed
to
ex
tr
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n
p
r
ac
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it
m
ea
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th
at
it
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k
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en
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m
m
u
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with
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h
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ased
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h
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h
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a,
im
m
u
n
ity
to
m
u
ltip
ath
f
ad
in
g
a
n
d
s
im
p
licity
o
f
eq
u
aliza
tio
n
[
11
]
,
[
12
]
.
T
h
e
OFDM
tech
n
iq
u
e
h
as
b
ee
n
i
n
t
eg
r
ated
i
n
a
v
a
r
iety
o
f
a
p
p
licatio
n
s
an
d
s
tan
d
ar
d
s
,
s
u
ch
as
I
E
E
E
8
0
2
.
1
1
a
[
13
]
an
d
I
E
E
E
8
0
2
.
1
6
a
[
14
]
.
I
n
ad
d
itio
n
,
th
e
b
u
r
g
e
o
n
in
g
OFDM
wir
eless
co
m
m
u
n
icatio
n
tech
n
o
lo
g
y
p
r
esen
ts
a
n
ew
ch
allen
g
e
to
s
m
ar
t
r
ad
io
d
esig
n
er
s
,
wh
ich
is
th
e
r
ec
o
g
n
itio
n
o
f
d
ig
ital
s
y
s
tem
s
b
ased
o
n
OFDM
m
u
lti
-
ca
r
r
ie
r
m
o
d
u
latio
n
.
T
h
e
m
eth
o
d
s
s
tu
d
ied
in
[
15
]
-
[
18
]
h
av
e
m
ad
e
it
p
o
s
s
ib
le
to
clas
s
if
y
au
to
m
atica
lly
th
e
d
ig
ital
m
o
d
u
latio
n
s
with
o
u
t
a
p
r
io
r
i
k
n
o
wled
g
e
o
f
th
e
p
ar
am
eter
s
o
f
th
e
r
ec
eiv
ed
s
i
g
n
al
b
y
f
o
r
ce
d
esti
m
atio
n
o
f
th
e
s
ig
n
al
a
n
d
n
o
is
e
p
o
wer
,
ca
r
r
ier
f
r
e
q
u
en
c
y
r
ec
o
v
er
y
an
d
th
e
r
ec
o
v
er
y
o
f
s
y
m
b
o
l
tim
in
g
an
d
ca
r
r
ier
i
n
f
o
r
m
atio
n
,
r
esp
ec
tiv
ely
.
Mo
s
t
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
s
ar
e
b
ased
o
n
s
ig
n
a
l
m
o
d
els
cy
clo
s
tatio
n
ar
ity
[
1
9
]
-
[
26
]
.
So
m
e
o
f
th
em
u
s
ed
th
e
cy
clic
p
r
ef
ix
(
C
P)
as
a
p
ar
am
e
ter
i
n
d
u
cin
g
cy
cli
c
s
tatis
tic
s
o
b
tain
ed
b
y
th
e
p
r
o
p
er
ties
o
f
t
h
e
a
u
to
c
o
r
r
elatio
n
f
u
n
ctio
n
[
1
9
]
-
[
27
]
.
T
h
er
e
ar
e
also
o
th
er
s
th
at
r
eq
u
ir
e
th
e
d
etec
tio
n
o
f
c
y
clo
s
tatio
n
ar
y
s
ig
n
atu
r
es
as
a
f
ea
tu
r
e
co
n
s
cio
u
s
ly
em
b
ed
d
e
d
in
d
ig
ital c
o
m
m
u
n
i
ca
tio
n
s
ig
n
als b
y
f
o
cu
s
i
n
g
o
n
OFDM
an
d
r
ep
r
esen
ted
as a
u
n
iq
u
e
id
en
tifie
r
[
22
]
b
y
s
en
d
in
g
r
e
d
u
n
d
an
t
m
ess
ag
e
s
y
m
b
o
ls
o
n
m
u
ltip
le
s
u
b
ca
r
r
ier
s
,
o
n
t
h
e
o
t
h
er
h
a
n
d
th
e
p
ilo
t
-
in
d
u
ce
d
cy
clic
s
tatis
t
ics h
av
e
b
ee
n
r
ep
o
r
ted
i
n
[
22
]
.
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
n
ew
m
eth
o
d
b
ased
o
n
ly
o
n
p
ar
ticu
la
r
p
r
o
p
er
ties
o
f
th
e
s
ec
o
n
d
-
o
r
d
e
r
s
tatis
t
ics
th
at
ch
ar
ac
ter
izes
th
e
p
r
o
p
er
ti
es
o
f
th
e
r
ec
eiv
e
d
s
ig
n
al
as
a
m
ea
s
u
r
in
g
in
s
tr
u
m
e
n
t
.
I
n
o
th
er
wo
r
d
s
,
it
is
to
d
ef
in
e
a
n
ew
d
ec
is
io
n
cr
iter
io
n
th
at
g
iv
es
u
s
an
o
p
tim
al
s
o
lu
tio
n
b
y
wh
ich
s
y
s
tem
s
b
ased
o
n
OFDM
m
o
d
u
latio
n
ca
n
b
e
d
is
tin
g
u
is
h
ed
f
r
o
m
o
t
h
er
m
o
d
u
latio
n
ty
p
es
.
T
h
e
r
ec
o
g
n
itio
n
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
a
lg
o
r
ith
m
is
im
p
lem
en
te
d
th
r
o
u
g
h
an
a
d
d
itiv
e
wh
ite
Ga
u
s
s
ian
n
o
is
e
(
AW
GN)
ch
an
n
el
in
to
n
u
m
er
ical
co
m
p
u
tatio
n
s
u
s
in
g
MA
T
L
A
B
an
d
ex
p
er
im
en
tal
m
ea
s
u
r
e
m
en
ts
u
s
in
g
NI
USR
P
h
ar
d
war
e
d
ev
ices
a
n
d
NI
L
ab
VI
E
W
p
latf
o
r
m
.
T
h
e
m
ai
n
o
b
jectiv
e
o
f
th
is
wo
r
k
is
th
en
to
ev
alu
ate
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
in
a
co
n
te
x
t c
lo
s
e
to
r
e
ality
.
T
h
e
r
est
o
f
th
is
p
ap
er
is
o
r
g
a
n
ized
as
f
o
llo
ws.
OFDM
s
ig
n
al
m
o
d
el
an
d
its
r
ec
o
g
n
itio
n
ap
p
r
o
ac
h
b
ased
o
n
th
e
co
r
r
elatio
n
f
u
n
c
tio
n
ar
e
in
tr
o
d
u
ce
d
in
s
ec
tio
n
2
.
I
m
p
lem
en
tatio
n
d
etails
u
s
in
g
NI
USR
P
-
2930
ar
e
p
r
esen
ted
in
s
ec
tio
n
3
.
Si
m
u
latio
n
an
d
ex
p
e
r
im
en
tal
r
e
s
u
lts
o
f
th
e
im
p
lem
en
tatio
n
p
r
o
ce
s
s
ar
e
g
iv
en
an
d
ex
p
lain
ed
in
s
ec
tio
n
4
,
a
n
d
co
n
clu
s
io
n
s
ar
e
d
r
awn
in
s
ec
tio
n
5
.
2.
SI
G
NA
L
M
O
D
E
L
AN
D
SE
CO
ND
-
O
RDER S
T
A
T
I
S
T
I
CS
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
b
r
ief
ly
th
e
d
ef
in
itio
n
o
f
th
e
tr
a
n
s
m
itted
an
d
r
ec
eiv
ed
s
ig
n
al
f
o
r
m
u
lti
-
ca
r
r
ier
m
o
d
u
latio
n
(
OFDM)
,
in
ad
d
itio
n
to
th
e
a
p
p
r
o
ac
h
u
s
e
d
u
n
d
e
r
th
e
s
ec
o
n
d
-
o
r
d
er
s
tatis
tics
p
r
o
p
er
ties
.
2
.
1
.
O
F
DM
s
ig
na
l m
o
del
T
h
e
tr
an
s
m
itted
co
n
ti
n
u
o
u
s
-
ti
m
e
OFDM
s
ig
n
al
is
wr
itten
as
f
o
llo
ws [
1
9
]:
(
)
=
1
√
∑
∑
,
−
2
(
−
−
)
(
−
)
,
−
1
=
0
−
1
=
0
(
1
)
wh
er
e
{
a
k,
n
}
r
e
p
r
esen
ts
th
e
d
ata
s
y
m
b
o
ls
o
f
th
e
u
n
k
n
o
w
n
in
f
o
r
m
atio
n
d
ata
o
f
s
u
b
ca
r
r
ier
n
an
d
k
OFDM
b
lo
ck
an
d
wh
ich
a
r
e
ass
u
m
ed
to
b
e
ze
r
o
-
m
ea
n
an
d
b
e
in
d
e
p
en
d
e
n
t
an
d
id
en
tically
d
is
tr
ib
u
ted
r
a
n
d
o
m
v
ar
iab
les.
K
is
th
e
n
u
m
b
er
o
f
OFDM
s
y
m
b
o
ls
,
N
is
th
e
n
u
m
b
er
o
f
s
u
b
ca
r
r
ier
s
an
d
T
c
is
th
e
ch
ip
d
u
r
atio
n
wh
er
e
1
/T
c
r
ep
r
esen
ts
th
e
in
f
o
r
m
atio
n
s
y
m
b
o
l
r
ate
in
th
e
ab
s
en
ce
o
f
g
u
ar
d
in
ter
v
al.
NT
c
is
th
e
in
ter
ca
r
r
ier
s
p
ac
in
g
(
u
s
ef
u
l
p
ar
t
o
f
OFDM
s
ig
n
al)
.
DT
c
r
ep
r
esen
ts
th
e
len
g
th
o
f
th
e
c
y
clic
p
r
ef
ix
,
T
s
=(
N
+D)
T
c
is
t
h
e
OFDM
s
y
m
b
o
l
in
ter
v
al
(
to
tal
d
u
r
atio
n
)
,
a
n
d
g
tr
(
t)
is
th
e
o
v
er
all
im
p
u
ls
e
r
es
p
o
n
s
e
o
f
th
e
tr
an
s
m
it
f
il
ter
th
at
is
ass
u
m
ed
to
b
e
eq
u
al
to
1
if
∈
[
0
,
]
an
d
0
o
t
h
er
wis
e.
At
th
e
r
ec
eiv
e
-
s
id
e,
th
e
c
o
n
tin
u
o
u
s
-
tim
e
b
aseb
an
d
OFDM
s
ig
n
al
eq
u
i
v
alen
t c
an
b
e
r
ep
r
esen
ted
as:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f n
e
w
b
lin
d
OF
DM sig
n
a
l reco
g
n
i
tio
n
…
(
Mo
h
a
me
d
F
ir
d
a
o
u
s
s
i
)
1229
(
)
=
2
√
∑
∑
∑
2
−
1
=
0
−
1
=
0
=
1
,
−
2
(
−
−
)
(
−
−
)
(
2
)
+
(
)
,
wh
er
e
L
is
th
e
n
u
m
b
e
r
o
f
p
a
th
s
p
er
f
o
r
m
e
d
wh
en
th
e
tr
an
s
m
itted
s
ig
n
al
p
ass
es
th
r
o
u
g
h
a
m
u
ltip
ath
f
ad
i
n
g
ch
an
n
el.
T
h
e
am
p
litu
d
e
an
d
th
e
d
elay
o
f
th
e
l
th
p
ath
ar
e
r
esp
ec
tiv
ely
d
en
o
ted
b
y
l
an
d
.
ω
(
t)
is
a
ze
r
o
-
m
ea
n
co
m
p
lex
Gau
s
s
ian
n
o
is
e
with
v
ar
ian
ce
2
an
d
wh
er
e
is
th
e
o
f
f
s
et
f
r
eq
u
en
cy
d
u
e
to
lo
ca
l
o
s
ci
llato
r
d
r
if
t
o
r
Do
p
p
ler
ef
f
ec
t
[
1
9
]
.
T
h
e
r
e
ce
iv
ed
co
n
tin
u
o
u
s
tim
e
OF
DM
s
ig
n
al
(
)
is
s
am
p
led
at
th
e
s
am
p
lin
g
f
r
eq
u
e
n
cy
F
e
=1
/T
e
a
n
d
T
e
is
th
e
s
am
p
lin
g
p
er
io
d
.
L
et
=
⌊
0
/
⌋
th
e
n
u
m
b
er
o
f
s
am
p
les
r
ec
eiv
ed
w
h
er
e
T
0
is
th
e
d
u
r
atio
n
o
f
th
e
o
b
s
er
v
atio
n
win
d
o
w
an
d
⌊
.
⌋
r
ep
r
esen
t
s
th
e
in
teg
er
p
a
r
t
o
p
er
ato
r
.
T
h
e
d
is
cr
ete
-
tim
e
OFDM
r
ec
eiv
ed
s
ig
n
al
is
d
en
o
ted
b
y
[
]
=
(
)
an
d
i
s
wr
itten
as:
[
]
=
1
√
∑
∑
∑
2
−
1
=
0
−
1
=
0
=
1
,
−
2
2
(
+
1
)
(
−
−
(
+
)
)
(
3
)
×
2
∆
+
[
]
,
with
W[
m]
=ω
(
mT
e
),
an
d
Δ
f=δ
fT
e
th
e
n
o
r
m
alize
d
ca
r
r
ier
f
r
eq
u
en
cy
o
f
f
s
et.
2
.
2
.
Rec
o
g
nitio
n o
f
O
F
DM
s
y
s
t
em
s
a
pp
ro
a
ch
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
a
p
r
o
p
o
s
ed
b
lin
d
m
eth
o
d
f
o
r
r
e
co
g
n
itio
n
o
f
OFDM
s
ig
n
al
u
s
in
g
o
n
ly
a
p
r
o
p
r
ietie
o
f
th
e
s
e
co
n
d
-
o
r
d
er
s
tatis
tic
s
as
s
h
o
wn
in
Fig
u
r
e
1
i
n
o
r
d
er
to
d
ef
in
e
th
e
d
ec
i
s
io
n
cr
iter
ia
wh
ic
h
lead
u
s
to
q
u
ick
ly
r
ec
o
g
n
ize
t
h
e
OFDM
s
ig
n
al
ag
ain
s
t
o
th
e
r
s
ig
n
al
ty
p
es,
i
n
th
e
c
o
n
tex
t
o
f
Gau
s
s
ian
ch
an
n
el
an
d
p
er
f
ec
t
tim
e
an
d
f
r
eq
u
en
c
y
s
y
n
ch
r
o
n
izatio
n
(
L=1
;
l
=1
;
=
0
;
=
0
)
.
T
h
e
co
r
r
elatio
n
f
u
n
ctio
n
o
f
th
e
d
is
cr
ete
-
tim
e
OFDM
r
ec
eiv
ed
s
ig
n
al
d
escr
ib
ed
in
(
3
)
ca
n
b
e
ex
p
r
ess
ed
as
(
4
)
:
[
,
]
=
⟦
[
]
∗
[
−
]
⟧
=
2
∑
∑
−
2
−
1
=
0
−
1
=
0
(
−
,
)
+
2
[
]
,
(
4
)
wh
er
e
th
e
s
u
p
er
s
cr
ip
t
(
.
)
*
m
e
an
s
th
e
co
n
ju
g
ate
o
p
e
r
ato
r
,
2
=
|
,
|
2
is
th
e
v
ar
ia
n
ce
o
f
th
e
s
y
m
b
o
l
a
k,
n
,
(
−
,
)
=
|
(
−
)
∗
(
−
−
)
|
,
[
]
=
{
1
=
0
;
0
ℎ
.
,
an
d
2
th
e
v
ar
ian
ce
o
f
th
e
Gau
s
s
ian
n
o
is
e
wh
ich
is
wr
itten
as
(
5
)
:
2
=
1
∑
|
∑
(
−
)
=
1
|
2
10
−
10
−
1
=
0
(
5
)
T
h
e
m
ea
n
c
o
r
r
elatio
n
f
u
n
ctio
n
is
wr
itten
as
(
6
)
:
[
]
=
∑
[
,
]
=
∑
[
]
[
−
]
−
1
=
0
+
[
(
+
1
)
]
[
−
(
+
1
)
]
+
[
−
(
+
1
)
]
[
+
(
+
1
)
]
(
6
)
wh
er
e
=
d
en
o
tes th
e
n
u
m
b
er
o
f
s
am
p
les in
th
e
u
s
ef
u
l p
ar
t
o
f
t
h
e
OFDM
s
y
m
b
o
l.
Fo
r
k
b
lo
ck
o
f
a
n
OFDM
s
ig
n
al,
th
e
elem
en
tar
y
co
r
r
elatio
n
f
u
n
ctio
n
ca
n
b
e
e
x
p
r
e
s
s
ed
as
(
7
)
:
(
)
[
]
=
[
]
[
−
]
+
[
(
+
1
)
]
[
−
(
+
1
)
]
+
[
−
(
+
1
)
]
[
+
(
+
1
)
]
(
7
)
I
n
Fig
u
r
e
1
,
th
e
m
ea
n
c
o
r
r
elatio
n
f
u
n
ctio
n
[
]
o
f
all
c
o
n
s
id
er
ed
s
y
s
tem
s
(
3
GPP(L
T
E
)
,
W
iMAX
(
I
E
E
E
8
0
2
.
1
6
)
,
DVB
-
T
2
K,
I
E
E
E
8
0
2
.
2
2
-
1
K,
I
E
E
E
8
0
2
.
2
2
-
2
K,
I
E
E
E
8
0
2
.
2
2
-
4
K)
is
d
is
p
lay
ed
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
23
,
No
.
2
,
Au
g
u
s
t 2
0
2
1
:
1
1
2
7
-
1
1
3
6
1230
Fig
u
r
e
1
.
T
h
e
m
o
d
u
lu
s
o
f
m
ea
n
co
r
r
elatio
n
f
u
n
ctio
n
o
f
3
GP
P (
L
T
E
)
,
W
iMAX
(
I
E
E
E
8
0
2
.
1
6
)
,
DVB
-
T
2
K,
I
E
E
E
8
0
2
.
2
2
-
1
K,
I
E
E
E
8
0
2
.
2
2
-
2
K,
an
d
I
E
E
E
8
0
2
.
2
2
-
4K
I
t
is
clea
r
f
r
o
m
th
is
f
ig
u
r
e,
th
e
co
r
r
elatio
n
f
u
n
ctio
n
p
o
s
s
ess
es
a
p
ea
k
at
τ
=
T
u
=
NT
c
(
th
e
u
s
ef
u
l
p
ar
t
o
f
OFDM
s
ig
n
al)
:
W
e
f
o
u
n
d
6
.
6
6
6
.
1
0
-
5
(
r
esp
.
8
.
9
6
.
1
0
-
5
,
2
.
2
4
.
1
0
-
4
,
1
.
2
8
.
1
0
-
4
,
2
.
5
6
.
1
0
-
4
a
n
d
5
.
1
2
.
1
0
-
4
)
f
o
r
3
GPP
(
r
esp
.
W
iM
AX,
DV
B
T
-
2
K,
I
E
E
E
8
0
2
.
2
2
-
1
K,
I
E
E
E
8
0
2
.
2
2
-
2
K,
an
d
I
E
E
E
8
0
2
.
2
2
-
4
K)
.
W
e
ex
p
lo
it
th
is
f
ea
tu
r
e
to
p
r
o
p
o
s
e
a
n
ew
m
et
h
o
d
o
f
a
n
OFDM
-
b
ased
s
y
s
te
m
r
ec
o
g
n
itio
n
.
T
h
e
r
ec
o
g
n
itio
n
o
f
OFDM
r
elativ
e
to
o
th
er
m
o
d
u
lated
d
i
g
ital
s
ig
n
als
ca
n
b
e
f
o
r
m
u
lated
by
(
d
e
f
in
ed
as
th
e
g
e
o
m
etr
ic
o
v
er
th
e
ar
ith
m
etic
m
ea
n
o
f
th
e
s
eq
u
e
n
ce
|
(
)
|
):
=
(
∏
|
(
)
|
−
1
=
0
)
1
1
∑
|
(
)
|
−
1
=
0
,
=
∑
−
1
=
0
=
1
−
1
−
,
(
8
)
with
(
)
=
ma
x
>
0
|
(
)
[
]
|
2
,
0
≤
≤
1
,
an
d
ξ
ca
n
b
e
s
h
o
wn
th
at:
{
0
≤
≤
1
=
1
⇔
|
(
)
|
=
|
(
′
)
|
∀
,
′
T
h
e
Dec
is
io
n
cr
iter
ia
ca
n
b
e
t
h
en
ex
p
r
ess
ed
as
(
9
)
:
=
|
1
−
|
≶
ℎ
,
(
9
)
wh
er
e
η
is
th
e
p
r
ed
ef
i
n
ed
d
ec
i
s
io
n
th
r
esh
o
ld
f
o
r
th
e
OF
DM
an
d
o
t
h
er
m
o
d
u
lated
d
i
g
ital
s
ig
n
als,
an
d
is
g
iv
en
b
y
[
1
0
]
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f n
e
w
b
lin
d
OF
DM sig
n
a
l reco
g
n
i
tio
n
…
(
Mo
h
a
me
d
F
ir
d
a
o
u
s
s
i
)
1231
=
2
(
−
1
(
)
√
+
1
)
,
(
1
0
)
with
2
is
th
e
v
ar
ian
ce
o
f
A
WGN
ch
an
n
el,
is
th
e
f
alse
alar
m
p
r
o
b
a
b
ilit
y
tar
g
eted
b
y
,
S
c
o
r
r
esp
o
n
d
s
to
th
e
len
g
th
o
f
t
h
e
o
b
s
er
v
atio
n
s
eq
u
en
ce
o
f
s
ig
n
al,
a
n
d
−
1
(
.
)
is
th
e
in
v
er
s
e
Gau
s
s
ian
Q
-
f
u
n
ctio
n
.
T
h
e
n
t
h
e
b
lo
ck
-
d
ia
g
r
am
f
o
r
th
e
m
o
d
u
latio
n
r
ec
o
g
n
itio
n
o
f
OFDM
s
ig
n
al
is
s
h
o
wn
in
Fig
u
r
e
2
.
Fig
u
r
e
2
.
B
lo
ck
-
d
iag
r
am
o
f
p
r
o
p
o
s
ed
r
ec
o
g
n
itio
n
s
y
s
tem
3.
I
M
P
L
E
M
E
NT
A
T
I
O
N
E
NV
I
RO
NM
E
N
T
I
n
th
is
s
ec
tio
n
,
we
u
s
ed
as
an
im
p
lem
en
tatio
n
en
v
ir
o
n
m
e
n
t
a
s
o
f
twar
e
d
ef
in
ed
r
a
d
io
(
SDR
)
p
latf
o
r
m
in
f
o
r
m
o
f
a
NI
USR
P
-
2
9
3
0
p
r
o
to
ty
p
e
ab
le
to
tr
a
n
s
m
it
an
d
r
ec
eiv
e
r
ad
io
f
r
eq
u
en
cy
(
R
F)
s
i
g
n
als
ac
r
o
s
s
a
r
ea
l
tim
e,
p
air
ed
with
NI
lab
VI
E
W
2
0
1
7
an
d
MA
T
L
AB
R
2
0
1
6
b
s
o
f
twar
e
wh
ich
ar
e
in
s
t
alled
o
n
a
d
esk
to
p
co
m
p
u
ter
,
an
d
c
o
n
n
ec
te
d
th
r
o
u
g
h
a
R
J
4
5
Gig
ab
it E
t
h
er
n
et
c
ab
le
as sh
o
wn
in
Fig
u
r
e
3
.
Fig
u
r
e
3
.
Simp
lifie
d
Ov
er
v
iew
o
f
a
SDR
Setu
p
B
u
ilt Ar
o
u
n
d
an
NI
USR
P
-
2930
3
.
1
.
NI
USRP
-
2
9
3
0
T
h
e
USR
P
-
2
9
3
0
is
a
tu
n
ab
le
r
ad
io
f
r
e
q
u
en
c
y
(
R
F)
tr
an
s
ce
i
v
er
with
a
h
ig
h
-
s
p
ee
d
a
n
alo
g
-
to
-
d
ig
ital
co
n
v
er
ter
an
d
d
i
g
ital
-
to
-
an
alo
g
co
n
v
er
ter
f
o
r
s
tr
ea
m
in
g
b
as
eb
an
d
I
a
n
d
Q
s
ig
n
als
to
a
h
o
s
t
PC
o
v
er
1
Gig
a
b
it
E
th
er
n
et.
T
h
e
NI
USR
P
-
2
9
3
0
m
o
d
el
en
ab
les
to
tr
an
s
m
it
a
n
d
r
ec
eiv
e
R
F
s
ig
n
als
ac
r
o
s
s
a
f
r
eq
u
en
cy
r
a
n
g
e
f
r
o
m
5
0
MH
z
u
p
to
2
.
2
GHz
with
an
in
s
tan
tan
eo
u
s
R
ea
l
-
T
im
e
b
an
d
wid
th
o
f
2
0
MH
z
(
with
16
-
b
it
s
am
p
les
wid
th
)
o
r
4
0
MH
z
(
with
8
-
b
it
s
am
p
les
wid
th
)
,
an
d
it
g
iv
es
u
s
th
e
ab
ilit
y
to
u
s
e
it
in
th
e
f
o
llo
wi
n
g
co
m
m
u
n
icatio
n
s
ap
p
licatio
n
s
s
u
ch
as:
b
r
o
ad
ca
s
t
FM;
lo
w
-
p
o
wer
u
n
licen
s
ed
d
ev
ices
o
n
i
n
d
u
s
tr
ial,
s
cien
tific
,
an
d
m
e
d
ical
(
I
SM)
b
an
d
s
;
ce
l
l
p
h
o
n
e;
GPS.
I
t
also
in
clu
d
e
d
GPS
-
Dis
cip
lin
ed
o
s
cillato
r
(
GPSDO)
with
PP
S
ac
cu
r
ac
y
o
f
±
5
0
n
s
[
2
8
]
.
3
.
2
.
T
ra
ns
m
it
t
er
/
re
ce
iv
er
T
h
e
p
r
o
g
r
am
m
in
g
an
d
d
esig
n
ar
e
r
ea
lized
in
L
ab
VI
E
W
2
0
1
7
in
o
r
d
er
to
co
n
tr
o
l
th
e
NI
U
SR
P
-
2930
h
ar
d
war
e.
I
n
Fig
u
r
e
4
,
th
e
f
r
o
n
t
p
an
el
o
f
th
e
tr
an
s
m
itter
VI
co
n
tain
s
two
p
ar
ts
,
th
e
lef
t
p
ar
t
is
co
m
p
o
s
ed
o
f
two
tab
s
,
o
n
e
f
o
r
th
e
p
ar
a
m
ete
r
s
o
f
th
e
USR
P,
in
p
u
t
p
ar
am
et
er
s
o
f
th
e
g
e
n
er
ated
s
ig
n
al
to
b
e
tr
an
s
m
itted
,
a
n
d
th
e
s
ec
o
n
d
tab
“De
b
u
g
”
f
o
r
t
h
e
er
r
o
r
s
d
etec
ted
d
u
r
in
g
th
e
s
en
d
in
g
o
p
er
atio
n
,
th
e
s
ec
o
n
d
p
ar
t
d
is
p
lay
s
th
e
p
o
wer
s
p
ec
tr
u
m
m
o
d
el
o
f
O
FDM
s
ig
n
al
tr
an
s
m
itted
.
All
b
aseb
an
d
I
/Q
tr
an
s
m
itted
s
ig
n
a
ls
ex
p
r
ess
ed
in
s
am
p
les
p
er
s
ec
o
n
d
(
S/s
)
ar
e
s
y
n
th
esized
b
y
th
e
h
o
s
t
co
m
p
u
ter
an
d
f
ed
to
th
e
USR
P
-
2
9
3
0
at
u
p
to
4
0
0
KS/s
o
v
er
Gig
ab
it
E
th
er
n
et
wh
e
n
r
e
p
r
esen
ted
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als ar
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ix
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u
p
to
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z
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r
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r
e
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
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n
d
o
n
esian
J
E
lec
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n
g
&
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m
p
Sci,
Vo
l.
23
,
No
.
2
,
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g
u
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t 2
0
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1
:
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3
6
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At
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e
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r
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el
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as
s
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in
Fig
u
r
e
5
,
h
as
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ar
ts
,
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e
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t
p
ar
t
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tain
s
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s
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e
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ir
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t
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is
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ec
ial
ly
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o
r
th
e
USR
P
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e
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ich
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e
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e
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itter
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ar
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ter
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t th
at
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e
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is
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et
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ad
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itio
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e
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tain
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y
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r
m
eth
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u
ch
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e
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alu
e
o
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th
e
d
ec
is
io
n
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iter
i
a
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c
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,
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d
th
e
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etec
tio
n
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r
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b
ab
ilit
y
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D
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ep
e
n
d
in
g
o
n
th
e
Fals
e
-
alar
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Pro
b
ab
ilit
y
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P
fa
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th
e
v
alu
e
o
f
th
e
Sig
n
al
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to
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n
o
i
s
e
r
atio
(
S
N
R
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an
d
n
u
m
b
e
r
o
f
r
ea
lizati
o
n
s
,
th
e
s
ec
o
n
d
ta
b
“De
b
u
g
”
d
is
p
lay
s
th
e
er
r
o
r
s
d
etec
ted
d
u
r
i
n
g
r
ec
ep
tio
n
.
T
h
e
r
ig
h
t
p
ar
t
o
f
t
h
e
f
r
o
n
t
p
an
el
d
is
p
lay
s
th
e
p
o
wer
s
p
ec
tr
u
m
m
o
d
el
o
f
th
e
r
ec
eiv
ed
OFDM
s
ig
n
al
with
a
ce
r
tain
n
o
is
e
lev
el
ad
d
e
d
at
th
e
to
p
(
r
e
d
c
o
lo
r
,
SNR
=
-
1
0
d
B
)
a
n
d
with
o
u
t n
o
is
e
at
t
h
e
b
o
tto
m
(
wh
ite
co
lo
r
)
.
Fig
u
r
e
4
.
T
r
an
s
m
itter
VI
f
r
o
n
t
p
an
el
f
o
r
OFDM
s
ig
n
al
Fig
u
r
e
5
.
R
ec
eiv
er
VI
f
r
o
n
t p
a
n
el
f
o
r
OFDM
s
ig
n
al
3
.
3
.
P
a
ra
m
et
er
s
T
h
e
f
o
llo
win
g
T
ab
le
s
1
a
n
d
2
p
r
o
v
id
e
th
e
ch
a
r
ac
ter
is
tics
o
f
th
e
USR
P So
f
twar
e
-
d
ef
in
ed
r
a
d
io
an
d
th
e
p
ar
am
eter
s
u
s
ed
i
n
th
e
M
AT
L
AB
s
im
u
latio
n
an
d
th
e
U
SR
P im
p
lem
en
tatio
n
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
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SS
N:
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-
4
7
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n
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l reco
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h
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me
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ir
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1233
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eter
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s
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1
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u
mb
e
r
o
f
S
y
m
b
o
l
s
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mb
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o
f
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b
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r
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s
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t
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d
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4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
I
n
th
is
s
ec
tio
n
,
we
d
is
p
lay
th
e
ex
p
er
im
en
tal
r
esu
lts
o
f
th
e
im
p
lem
en
tatio
n
o
f
OFDM
s
ig
n
al
r
ec
o
g
n
itio
n
b
ased
o
n
t
h
e
co
r
r
elatio
n
ap
p
r
o
ac
h
u
s
in
g
th
e
NI
USR
P
-
2
9
3
0
p
latf
o
r
m
,
t
h
e
d
i
s
p
lay
ed
r
esu
lts
ar
e
av
er
ag
ed
o
v
e
r
2
0
0
0
r
ea
lizatio
n
s
.
W
e
h
av
e
g
en
e
r
ated
a
n
OF
DM
s
ig
n
al
r
an
d
o
m
ly
f
r
o
m
s
ix
OFDM
s
tan
d
ar
d
s
,
3
GPP(L
T
E
)
,
W
iMAX
(
I
E
E
E
8
0
2
.
1
6
)
,
DVB
-
T
2
K,
I
E
E
E
8
0
2
.
22
-
1
K
,
I
E
E
E
8
0
2
.
2
2
-
2
K
,
an
d
I
E
E
E
8
0
2
.
2
2
-
4
K
.
Th
e
s
ig
n
al
is
m
o
d
u
lated
b
y
N=
6
4
s
u
b
ca
r
r
ier
s
u
n
co
d
ed
QPSK
,
th
e
len
g
th
o
f
th
e
cy
clic
p
r
ef
ix
(
CP
)
is
f
ix
ed
at
4
with
K=
2
0
s
y
m
b
o
ls
av
ailab
le
at
th
e
OFDM
r
ec
eiv
er
.
I
n
p
r
ac
tice,
th
e
p
e
r
f
o
r
m
an
ce
o
f
o
u
r
m
eth
o
d
ca
n
o
n
l
y
b
e
o
b
s
er
v
ed
t
h
r
o
u
g
h
th
e
d
etec
tio
n
v
alu
e
b
elo
w
th
e
th
r
esh
o
ld
η
d
ep
e
n
d
in
g
o
f
S
N
R
a
n
d
P
fa
p
ar
am
eter
s
g
iv
en
b
y
(
9
)
in
o
r
d
e
r
to
d
is
tin
g
u
is
h
an
OFDM
s
ig
n
al
f
r
o
m
an
o
th
e
r
ty
p
e
o
f
d
ig
ital
s
ig
n
al.
T
h
e
c
h
ip
d
u
r
atio
n
T
c
o
f
OFDM
s
y
m
b
o
l f
o
r
ea
ch
s
tan
d
ar
d
s
y
s
tem
is
s
to
ck
ed
in
o
u
r
d
atab
ase.
I
n
Fig
u
r
e
6
,
we
p
l
o
t
th
e
d
ec
i
s
io
n
cr
iter
ia
v
er
s
u
s
th
e
SNR
f
o
r
OFDM
s
ig
n
als
o
f
th
e
s
ix
s
tan
d
ar
d
s
m
en
tio
n
ed
a
b
o
v
e
.
As
we
ca
n
n
o
tice
wh
en
a
p
p
r
o
a
ch
in
g
to
S
N
R
=
-
2
dB
,
th
e
d
ec
is
io
n
cr
iter
i
a
ten
d
s
to
war
d
s
0
,
wh
ich
s
h
o
ws
th
e
g
o
o
d
p
e
r
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
to
d
etec
t
th
e
OFDM
s
ig
n
al
s
u
cc
ess
f
u
lly
.
Mo
r
eo
v
er
,
th
e
MA
T
L
AB
s
im
u
latio
n
r
esu
lts
an
d
th
e
USR
P
im
p
lem
en
tatio
n
m
ea
s
u
r
em
e
n
ts
ar
e
clo
s
er
to
ea
ch
o
th
er
.
Fig
u
r
e
6
.
Dete
ctio
n
c
r
iter
ia
(
D
c
)
v
s
.
SNR
f
o
r
OFDM
s
ig
n
als
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4
7
5
2
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l.
23
,
No
.
2
,
Au
g
u
s
t 2
0
2
1
:
1
1
2
7
-
1
1
3
6
1234
Fig
u
r
e
7
illu
s
tr
ates
th
e
p
r
o
b
a
b
ilit
y
o
f
d
etec
tio
n
v
e
r
s
u
s
SNR
f
o
r
t
h
e
s
ix
s
tan
d
ar
d
s
b
ased
o
n
OFDM
m
o
d
u
latio
n
.
W
e
p
lo
t
th
e
d
etec
tio
n
p
r
o
b
ab
ilit
y
d
e
f
in
ed
b
y
=
(
<
|
)
,
wh
er
e
is
th
e
d
ec
is
io
n
cr
iter
ia
(
9
)
an
d
is
th
e
th
r
esh
o
ld
(
1
0
)
.
W
e
n
o
tice
th
at
o
u
r
m
et
h
o
d
d
em
o
n
s
tr
ates
alwa
y
s
a
s
tr
o
n
g
p
e
r
f
o
r
m
an
ce
to
d
etec
t
OFDM
s
ig
n
als
am
o
n
g
o
th
e
r
d
ig
ital
s
ig
n
als
in
a
s
y
s
tem
atic
an
d
in
tellig
en
t
way
ev
en
with
lo
w
SNR
v
alu
es.
W
e
ca
n
also
s
ee
as
il
lu
s
tr
ated
in
t
h
e
f
i
g
u
r
es
t
h
at
t
h
e
cu
r
v
es
o
b
tain
ed
b
y
MA
T
L
AB
s
im
u
latio
n
an
d
im
p
lem
en
tatio
n
m
ea
s
u
r
e
m
en
t
s
o
n
th
e
USR
P
s
o
f
twar
e
d
ef
in
ed
r
ad
i
o
h
av
e
th
e
s
am
e
b
e
h
av
io
r
an
d
ar
e
m
o
r
e
clo
s
ely
r
elate
d
to
ea
ch
o
th
er
.
Fig
u
r
e
7
.
Dete
ctio
n
p
r
o
b
ab
ilit
y
(
P
D
)
o
f
r
ec
o
g
n
itio
n
OFDM
s
y
s
tem
s
v
s
.
SNR
5.
CO
NCLU
SI
O
N
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
d
escr
i
b
ed
in
th
is
p
ap
er
allo
ws
u
s
t
o
r
ec
o
g
n
ize
OFDM
s
ig
n
al
u
s
ed
b
y
th
e
wir
eless
s
tan
d
ar
d
s
p
r
esen
t
in
a
R
ad
io
Fre
q
u
en
cy
(
R
F)
r
ec
eiv
er
b
y
u
s
in
g
o
n
ly
a
p
ar
ticu
lar
p
r
o
p
er
t
y
o
f
th
e
s
ec
o
n
d
-
o
r
d
er
s
tatis
tics
.
W
e
h
a
v
e
an
aly
ze
d
m
ath
em
atica
lly
a
n
d
n
u
m
er
ically
th
r
o
u
g
h
a
s
er
i
es
o
f
eq
u
atio
n
s
an
d
s
im
u
latio
n
r
esu
lts
th
at
i
llu
s
tr
a
te
th
e
ef
f
icien
cy
an
d
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
m
eth
o
d
.
C
o
m
p
ar
ed
to
th
e
liter
atu
r
e,
we
h
av
e
also
co
n
s
i
d
er
ed
m
o
r
e
r
ea
lis
tic
s
itu
atio
n
s
s
in
ce
s
ev
er
al
r
ec
o
g
n
itio
n
m
eth
o
d
s
f
o
r
wir
eless
co
m
m
u
n
icatio
n
s
y
s
tem
s
h
av
e
b
ee
n
p
r
esen
te
d
co
n
s
id
er
i
n
g
o
n
ly
s
y
n
th
etic
m
o
d
els.
Fo
r
th
at
,
we
h
av
e
s
et
u
p
a
test
p
latf
o
r
m
b
ased
o
n
th
e
USR
P
s
o
f
twar
e
d
ef
in
ed
r
ad
io
t
o
g
en
er
ate
r
ea
l
OFDM
s
ig
n
als.
T
h
e
p
er
f
o
r
m
an
ce
o
f
o
u
r
m
eth
o
d
is
illu
s
tr
ated
b
y
a
r
ea
l
im
p
lem
en
tatio
n
u
s
in
g
NI
USR
P
-
2
9
3
0
h
ar
d
war
e
d
ev
ice,
wh
ich
p
r
o
v
id
es
good
e
x
p
er
im
e
n
tal
r
esu
lt
s
th
at
ar
e
clo
s
er
to
th
o
s
e
o
f
M
o
n
te
C
ar
lo
s
im
u
latio
n
s
ex
ec
u
ted
b
y
MA
T
L
AB
s
o
f
twar
e
.
Mo
r
eo
v
er
,
th
e
m
eth
o
d
s
h
o
ws
th
at
it
i
s
ab
s
o
lu
tely
r
o
b
u
s
t
an
d
m
o
r
e
ef
f
icien
t
in
lo
w
S
N
R
v
alu
es.
T
h
e
f
u
tu
r
e
wo
r
k
will
b
e
to
p
r
o
p
o
s
e
a
m
eth
o
d
to
d
is
cr
im
in
ate
s
tan
d
ar
d
s
b
etwe
en
th
em
.
W
e
will
al
s
o
s
tu
d
y
th
e
co
n
tr
ib
u
tio
n
o
f
ar
tific
ial
in
tellig
en
ce
in
th
e
co
n
tex
t
o
f
co
g
n
iti
v
e
r
ad
io
.
RE
F
E
R
E
NC
E
S
[1
]
M
.
Alib
a
k
h
sh
i
k
e
n
a
ri
,
e
t
a
l
.
,
"
A
Co
m
p
re
h
e
n
si
v
e
S
u
r
v
e
y
o
f
“
M
e
tam
a
teria
l
Tran
sm
issio
n
-
Li
n
e
Ba
se
d
An
ten
n
a
s
:
De
sig
n
,
Ch
a
ll
e
n
g
e
s,
a
n
d
Ap
p
li
c
a
ti
o
n
s
,
"
in
IE
EE
Acc
e
ss
,
v
o
l.
8
,
p
p
.
1
4
4
7
7
8
-
1
4
4
8
0
8
,
2
0
2
0
,
d
o
i:
1
0
.
1
1
0
9
/ACCES
S
.
2
0
2
0
.
3
0
1
3
6
9
8
.
[2
]
P
.
Die
g
o
a
n
d
C.
He
rn
a
n
d
e
z
,
"
C
o
g
n
it
i
v
e
ra
d
io
f
o
r
TVW
S
u
sa
g
e
,
"
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
,
C
o
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l.
1
7
,
n
o
.
6
,
p
p
.
2
7
3
5
-
2
7
4
6
,
De
c
e
m
b
e
r
2
0
1
9
,
d
o
i:
1
0
.
1
2
9
2
8
/t
e
lk
o
m
n
i
k
a
.
v
1
7
i
6
.
1
3
1
1
1
.
[3
]
J.
M
it
o
la,
“
Co
g
n
it
i
v
e
Ra
d
i
o
:
An
In
te
g
ra
ted
A
g
e
n
t
a
rc
h
it
e
c
tu
re
f
o
r
S
o
ftwa
re
De
fin
e
d
Ra
d
i
o
,
”
Ph
d
t
h
e
sis,
R
o
y
a
l
In
stit
u
te
o
f
T
e
c
h
n
o
lo
g
y
(S
t
o
c
k
h
o
l
m
,
S
we
d
e
n
),
2
0
0
0
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esian
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4
7
5
2
P
erfo
r
ma
n
ce
ev
a
lu
a
tio
n
o
f n
e
w
b
lin
d
OF
DM sig
n
a
l reco
g
n
i
tio
n
…
(
Mo
h
a
me
d
F
ir
d
a
o
u
s
s
i
)
1235
[4
]
S
.
Ha
y
k
in
,
“
Co
g
n
it
i
v
e
ra
d
i
o
:
Br
a
in
-
e
m
p
o
we
re
d
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s,
”
IEE
E
J
o
u
r
n
a
l
o
n
S
e
l
e
c
ted
Are
a
s
i
n
Co
mm
u
n
ica
ti
o
n
s,
S
p
e
c
ia
l
Iss
u
e
o
n
Co
g
n
it
ive
Ne
two
rk
s
,
v
o
l.
2
3
,
p
p
.
2
0
1
-
2
2
0
,
F
e
b
.
2
0
0
5
,
d
o
i:
1
0
.
1
1
0
9
/JS
AC.
2
0
0
4
.
8
3
9
3
8
0
.
[5
]
T.
Yu
c
e
k
a
n
d
H.
Ars
lan
,
"
A
s
u
rv
e
y
o
f
sp
e
c
tru
m
se
n
sin
g
a
lg
o
rit
h
m
s
fo
r
c
o
g
n
i
ti
v
e
ra
d
io
a
p
p
li
c
a
ti
o
n
s,"
IEE
E
Co
mm
u
n
ica
ti
o
n
s S
u
rv
e
y
s
and
T
u
t
o
ria
ls
,
v
o
l.
1
1
,
n
o
.
1
,
p
p
.
1
1
6
-
1
3
0
,
2
0
0
9
,
d
o
i:
1
0
.
1
1
0
9
/S
URV
.
2
0
0
9
.
0
9
0
1
0
9
.
[6
]
D.
Ba
o
,
L.
De
Vito
,
a
n
d
S
.
Ra
p
u
a
n
o
,
"
A
h
ist
o
g
ra
m
-
b
a
se
d
se
g
m
e
n
tatio
n
m
e
th
o
d
fo
r
wi
d
e
b
a
n
d
sp
e
c
tru
m
se
n
sin
g
in
c
o
g
n
i
ti
v
e
ra
d
io
s,"
IEE
E
T
ra
n
sa
c
t
io
n
s
o
n
In
stru
me
n
ta
ti
o
n
a
n
d
M
e
a
su
re
me
n
t
,
v
o
l.
6
2
,
n
o
.
7
,
p
p
.
1
9
0
0
-
1
9
0
8
,
2
0
1
3
,
d
o
i:
1
0
.
1
1
0
9
/T
IM
.
2
0
1
3
.
2
2
5
1
8
2
1
.
[7
]
E.
Ax
e
ll
,
G
.
Leu
s,
E.
G
.
Lars
so
n
,
a
n
d
H.
V
.
P
o
o
r
,
"
S
p
e
c
tru
m
se
n
si
n
g
fo
r
c
o
g
n
it
i
v
e
ra
d
i
o
:
S
tate
o
f
t
h
e
a
rt
a
n
d
re
c
e
n
t
a
d
v
a
n
c
e
s,"
IEE
E
S
i
g
n
a
l
Pro
c
e
ss
in
g
M
a
g
a
zi
n
e
,
v
o
l.
2
9
,
n
o
.
3
,
p
p
.
1
0
1
-
1
1
6
,
2
0
1
2
,
d
o
i:
1
0
.
1
1
0
9
/
M
S
P
.
2
0
1
2
.
2
1
8
3
7
7
1
.
[8
]
F.
-
Z.
El
Ba
h
i
,
H.
G
h
e
n
n
io
u
i
a
n
d
M
.
Zo
u
a
k
,
"
S
p
e
c
tru
m
se
n
sin
g
tec
h
n
i
q
u
e
o
f
OFDM
si
g
n
a
l
u
n
d
e
r
n
o
ise
u
n
c
e
rtain
t
y
b
a
se
d
o
n
M
e
a
n
Am
b
i
g
u
it
y
F
u
n
c
ti
o
n
f
o
r
c
o
g
n
it
iv
e
ra
d
io
,
”
P
h
y
sic
a
l
Co
mm
u
n
ica
t
io
n
,
v
o
l.
3
3
,
p
p
.
1
4
2
-
1
5
0
,
A
p
ri
l
2
0
1
9
,
d
o
i:
1
0
.
1
0
1
6
/
j.
p
h
y
c
o
m
.
2
0
1
8
.
1
2
.
0
2
2
.
[9
]
R.
De
e
p
a
a
n
d
Y.
Ra
v
in
d
e
r,
"
A
sta
ti
stica
l
a
p
p
ro
a
c
h
to
s
p
e
c
tru
m
se
n
sin
g
u
si
n
g
b
a
y
e
s
fa
c
to
r
a
n
d
p
-
Va
lu
e
s,
"
In
ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
El
e
c
trica
l
a
n
d
Co
m
p
u
ter
En
g
in
e
e
rin
g
(IJ
E
CE)
,
v
o
l.
9
,
n
o
.
4
,
p
p
.
2
9
1
0
-
2
9
1
7
,
Au
g
u
st
2
0
1
9
,
d
o
i:
1
0
.
1
1
5
9
1
/
ij
e
c
e
.
v
9
i
4
.
p
p
2
9
1
0
-
2
9
1
7
.
[1
0
]
S
.
Dik
m
e
se
,
S
.
S
ri
n
iv
a
sa
n
,
a
n
d
M
.
Re
n
fo
rs,
"
F
F
T
a
n
d
F
il
ter
Ba
n
k
Ba
se
d
S
p
e
c
tru
m
S
e
n
s
i
n
g
a
n
d
S
p
e
c
tru
m
Util
iza
ti
o
n
f
o
r
Co
g
n
ti
v
e
Ra
d
io
s
,
"
2
0
1
2
5
th
I
n
ter
n
a
ti
o
n
a
l
S
y
mp
o
siu
m
o
n
C
o
mm
u
n
ica
ti
o
n
s,
C
o
n
t
ro
l
a
n
d
S
ig
n
a
l
Pro
c
e
ss
in
g
,
2
0
1
2
,
p
p
.
1
-
5
,
d
o
i:
1
0
.
1
1
0
9
/I
S
CCS
P
.
2
0
1
2
.
6
2
1
7
7
8
8
.
[1
1
]
J.
A.
C.
Bin
g
h
a
m
,
"
M
u
lt
ica
rrier
m
o
d
u
lati
o
n
fo
r
d
a
ta
tran
sm
issio
n
:
a
n
id
e
a
wh
o
se
ti
m
e
h
a
s
c
o
m
e
,
"
I
EE
E
Co
mm
u
ll
.
M
a
g
.
,
v
o
l
.
2
8
,
n
o
.
5
,
p
p
.
5
-
1
4
,
1
9
9
0
,
d
o
i:
1
0
.
1
1
0
9
/
3
5
.
5
4
3
4
2
.
[1
2
]
R.
V.
Ne
e
a
n
d
R.
P
r
a
sa
d
,
“
OFDM
fo
r
Wi
r
e
les
s M
u
lt
ime
d
ia C
o
m
m
u
n
ica
ti
o
n
s,”
Arte
c
h
Ho
u
se
,
2
0
0
0
.
[1
3
]
IEE
E
C
o
m
p
u
ter
S
o
c
iety
LAN
M
AN
S
tan
d
a
rd
s
Co
m
m
it
tee
,
"
W
irele
ss
LAN
m
e
d
iu
m
a
c
c
e
ss
c
o
n
tro
l
(M
AC)
a
n
d
p
h
y
sic
a
l
la
y
e
r
(P
HY
)
sp
e
c
ifi
c
a
ti
o
n
s,"
AN
S
I/IE
E
E
S
td
.
8
0
2
.
1
1
-
1
9
9
9
,
1
9
9
9
.
[1
4
]
Lo
c
a
l
a
n
d
M
e
tro
p
o
li
tan
Are
a
Ne
two
rk
s
-
P
a
rt
1
6
,
Air
In
terfa
c
e
fo
r
F
ix
e
d
Br
o
a
d
b
a
n
d
W
irele
ss
Ac
c
e
ss
S
y
ste
m
s,
IEE
E
S
ta
n
d
a
rd
IE
EE
8
0
2
.
1
6
0
-
2
0
0
1
.
[1
5
]
B.
Wan
g
a
n
d
L.
G
e
,
"
A
n
o
v
e
l
a
lg
o
rit
h
m
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ti
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sig
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l,
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2
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5
I
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ter
n
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t
io
n
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l
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fer
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n
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les
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mm
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n
s,
Ne
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o
rk
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g
a
n
d
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o
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g
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5
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0
9
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CNM
.
2
0
0
5
.
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5
4
4
0
3
1
.
[1
6
]
D.
G
rima
ld
i,
S
.
Ra
p
u
n
a
o
,
a
n
d
G
.
Tru
g
li
a
,
"
A
n
a
u
t
o
m
a
ti
c
d
i
g
i
tal
m
o
d
u
lati
o
n
c
las
sifier
fo
r
m
e
a
su
re
m
e
n
t
o
n
tele
c
o
m
m
u
n
ica
ti
o
n
n
e
two
r
k
s,"
I
EE
E
T
r
a
n
sa
c
ti
o
n
s
o
n
In
str
u
me
n
ta
ti
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n
a
n
d
M
e
a
s
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re
me
n
t
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p
p
.
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,
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0
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,
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o
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0
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0
9
/T
IM
.
2
0
0
7
.
8
9
5
6
7
5
.
[1
7
]
W.
Ak
m
o
u
c
h
e
,
"
De
tec
ti
o
n
o
f
m
u
l
ti
c
a
rrier
m
o
d
u
latio
n
s
u
si
n
g
4
th
o
r
d
e
r
c
u
m
u
lan
ts,
"
in
Pr
o
c
.
IEE
E
M
IL
COM
,
1
9
9
9
,
p
p
.
4
3
2
-
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3
6
,
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0
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1
0
9
/M
ILC
OM.
1
9
9
9
.
8
2
2
7
2
0
.
[1
8
]
P
.
S
.
Th
a
k
u
r,
S
.
M
a
d
a
n
,
a
n
d
M
.
M
a
d
a
n
,
“
Au
to
m
a
ti
c
Clas
sifica
ti
o
n
o
f
Wi
M
AX
P
h
y
sic
a
l
Lay
e
r
OFD
M
S
ig
n
a
ls
Us
in
g
Ne
u
ra
l
Ne
two
r
k
,
”
Ne
x
t
-
Ge
n
e
ra
ti
o
n
Ne
two
rk
s,
2
0
1
8
.
[1
9
]
A.
Bo
u
z
e
g
z
i,
P
.
Cib
lat
a
n
d
P
.
Ja
ll
o
n
,
“
Ne
w
a
lg
o
rit
h
m
s
fo
r
b
li
n
d
re
c
o
g
n
i
-
ti
o
n
o
f
OFDM
b
a
se
d
sy
ste
m
s,
”
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ig
n
a
l
Pro
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e
ss
in
g
,
v
o
l
.
9
0
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n
o
.
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,
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p
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0
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si
g
p
r
o
.
2
0
0
9
.
0
9
.
0
1
7
.
[2
0
]
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-
X.
S
o
c
h
e
lea
u
,
S
.
Ho
u
c
k
e
,
P
.
C
ib
lat,
a
n
d
A.
Aïss
a
-
El
-
Be
y
,
“
Co
g
n
it
iv
e
OFDM
sy
ste
m
d
e
tec
ti
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n
u
sin
g
p
i
lo
t
t
o
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e
s
se
c
o
n
d
a
n
d
t
h
ird
-
o
rd
e
r
c
y
c
lo
s
tatio
n
a
rit
y
,
”
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g
n
a
l
Pro
c
e
ss
,
v
o
l.
9
1
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n
o
.
2
,
p
p
.
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5
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-
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6
8
,
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e
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.
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0
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1
,
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o
i:
1
0
.
1
0
1
6
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.
si
g
p
r
o
.
2
0
1
0
.
0
7
.
0
0
3
.
[2
1
]
P
.
D.
S
u
tt
o
n
,
K.
E.
No
lan
,
a
n
d
L
.
E.
Do
y
le,
"
Cy
c
lo
sta
ti
o
n
a
r
y
sig
n
a
tu
re
s
in
p
ra
c
ti
c
a
l
c
o
g
n
i
ti
v
e
ra
d
i
o
a
p
p
li
c
a
ti
o
n
s,
"
IEE
E
J
.
S
e
l.
Are
a
s C
o
mm
u
n
.
,
v
o
l.
2
6
,
n
o
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1
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p
p
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4
,
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n
.
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0
0
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1
0
.
1
1
0
9
/JS
AC.
2
0
0
8
.
0
8
0
1
0
3
.
[2
2
]
F.
-
X.
S
o
c
h
e
lea
u
,
P
.
C
ib
lat,
a
n
d
S
.
Ho
u
c
k
e
,
"
OFDM
sy
ste
m
i
d
e
n
ti
fi
c
a
ti
o
n
f
o
r
c
o
g
n
i
ti
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e
ra
d
io
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a
se
d
o
n
p
il
o
t
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in
d
u
c
e
d
c
y
c
lo
sta
ti
o
n
a
rit
y
,
"
2
0
0
9
IE
EE
W
ire
les
s
Co
mm
u
n
ica
ti
o
n
s
a
n
d
Ne
tw
o
rk
in
g
C
o
n
fer
e
n
c
e
,
2
0
0
9
,
p
p
.
1
-
6
,
d
o
i:
1
0
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1
1
0
9
/W
CNC.
2
0
0
9
.
4
9
1
7
8
4
0
.
[2
3
]
A.
Al
-
Ha
b
a
sh
n
a
,
O.
A.
Do
b
re
,
R.
Ve
n
k
a
tes
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n
,
a
n
d
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C.
P
o
p
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sc
u
,
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o
n
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rd
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y
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M
AX
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n
d
LT
E
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sig
n
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l
s
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n
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p
li
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ti
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sp
e
c
tru
m
a
w
a
re
n
e
s
s
in
c
o
g
n
it
iv
e
ra
d
io
sy
ste
m
s,"
IEE
E
J
.
S
e
l.
T
o
p
ics
S
ig
n
a
l
Pro
c
e
ss
.
,
v
o
l.
6
,
n
o
.
1
,
p
p
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6
-
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2
,
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e
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.
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0
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2
,
d
o
i:
1
0
.
1
1
0
9
/JS
T
S
P
.
2
0
1
1
.
2
1
7
4
7
7
3
[2
4
]
W.
Je
rjaw
i,
Y.
A.
El
d
e
m
e
rd
a
sh
,
a
n
d
O.
A.
Do
b
re
,
"
S
e
c
o
n
d
-
o
r
d
e
r
c
y
c
lo
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se
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F
DMA
sig
n
a
ls
f
o
r
c
o
g
n
it
iv
e
ra
d
io
s
y
ste
m
s,"
IEE
E
T
r
a
n
sa
c
ti
o
n
s
o
n
In
stru
me
n
ta
ti
o
n
a
n
d
M
e
a
su
re
me
n
t
,
v
o
l.
6
4
,
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o
.
3
,
p
p
.
8
2
3
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8
3
3
,
M
a
r.
2
0
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5
,
d
o
i:
1
0
.
1
1
0
9
/T
IM
.
2
0
1
4
.
2
3
5
7
5
9
2
.
[2
5
]
K.
Tek
b
iy
i
k
,
H.
Tu
ğ
re
l,
a
n
d
G
.
K.
Ka
ra
b
u
lu
t,
a
n
d
G
.
Ay
y
il
d
iz,
“
Bli
n
d
re
c
o
g
n
i
ti
o
n
o
f
OFDM
si
g
n
a
ls
b
a
s
e
d
o
n
c
y
c
lo
sta
ti
o
n
a
ry
sig
n
a
l
a
n
a
l
y
sis,”
2
4
t
h
I
n
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
T
e
lec
o
mm
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ica
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s
(ICT
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p
.
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0
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1
1
0
9
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T.
2
0
1
7
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7
9
9
8
2
3
0
[2
6
]
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.
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a
o
u
ss
i,
H.
G
h
e
n
n
i
o
u
i,
a
n
d
M
.
El
Ka
m
il
i,
”
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w
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lg
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m
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se
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u
sin
g
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e
c
o
n
d
-
Ord
e
r
S
tatist
ics
,
”
in
Pro
c
e
ss
in
g
2
0
1
5
IEE
E
W
INCOM
,
p
p
.
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-
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,
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d
o
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0
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.
2
0
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5
.
7
3
8
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3
3
6
.
[2
7
]
M
.
On
e
r
a
n
d
F
.
Jo
n
d
ra
l,
"
On
t
h
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x
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ti
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tru
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li
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y
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m
s,"
IEE
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J
o
u
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n
a
l
o
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lec
ted
Are
a
s
in
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mm
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0
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7
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.
[2
8
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Na
ti
o
n
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l
I
n
stru
m
e
n
ts
Co
.
,
”
USRP
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”
[On
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].
Av
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:
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s:/
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c
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n
d
Tele
c
o
m
m
u
n
ica
ti
o
n
s
fr
o
m
t
h
e
M
o
h
a
m
e
d
V
Un
i
v
e
rsity
.
His
m
a
in
re
se
a
rc
h
in
tere
sts
a
re
s
ig
n
a
l/
ima
g
e
p
ro
c
e
ss
in
g
i
n
c
lu
d
i
n
g
b
l
in
d
so
u
rc
e
s
se
p
a
ra
ti
o
n
,
d
a
ta
a
n
a
ly
ti
c
,
d
e
b
lu
rri
n
g
a
n
d
c
o
g
n
i
ti
v
e
ra
d
io
.
Mo
h
a
m
e
d
El
K
a
m
il
i
re
c
e
iv
e
d
t
h
e
P
h
.
D.
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
i
e
n
c
e
a
n
d
Op
e
ra
ti
o
n
s
Re
se
a
rc
h
fro
m
th
e
M
o
h
a
m
m
e
d
V
Un
iv
e
rsity
,
Ra
b
a
t,
M
o
ro
c
c
o
.
He
h
a
v
e
c
o
-
a
u
th
o
re
d
m
a
n
y
j
o
u
r
n
a
l
a
rti
c
les
a
n
d
b
o
o
k
c
h
a
p
ters
,
a
n
d
m
a
n
y
c
o
n
fe
re
n
c
e
p
u
b
li
c
a
ti
o
n
s.
He
a
c
ts
a
s
a
re
v
iew
e
r
fo
r
p
ro
fe
ss
io
n
a
l
p
u
b
li
c
a
ti
o
n
s,
p
re
stig
io
u
s
in
tern
a
ti
o
n
a
l
jo
u
r
n
a
ls
a
n
d
i
n
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s,
a
s
IEE
E
Ne
two
r
k
M
a
g
a
z
in
e
,
c
o
m
p
u
te
r
c
o
m
m
u
n
ica
t
io
n
j
o
u
r
n
a
l
(Co
m
Co
m
),
I
EE
E
IC
C,
IEE
E
G
lo
b
c
o
m
c
o
n
fe
re
n
c
e
,
IEE
E
IW
CM
C
,
IEE
E
WCNC,
A
DH
OCN
ET
.
Cu
rre
n
tl
y
,
h
e
is
a
f
u
ll
-
ti
m
e
As
so
c
iate
P
ro
fe
ss
o
r
i
n
Co
m
p
u
ter
Ne
two
rk
s
a
t
Co
m
p
u
t
e
r
En
g
in
e
e
rin
g
De
p
a
rtme
n
t,
Hi
g
h
e
r
S
c
h
o
o
l
o
f
Tec
h
n
o
lo
g
y
,
Ha
ss
a
n
II
Un
iv
e
rsit
y
o
f
Ca
sa
b
lan
c
a
,
M
o
r
o
c
c
o
.
He
is
a
p
e
rm
a
n
e
n
t
m
e
m
b
e
r
o
f
th
e
re
se
a
rc
h
tea
m
“
Big
Da
ta
&
In
tern
e
t
o
f
S
k
il
ls
”
o
f
th
e
“
Co
m
p
u
ter
S
c
ien
c
e
&
S
m
a
rt
S
y
ste
m
s
(C3
S
)
La
b
o
ra
to
r
y
”
.
His
c
u
rre
n
t
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
Ne
two
r
k
in
g
G
a
m
e
s,
De
sig
n
o
f
c
o
m
m
u
n
ica
ti
o
n
p
r
o
to
c
o
ls
f
o
r
wire
les
s
n
e
two
r
k
s,
Wi
re
les
s
M
AC
p
r
o
to
c
o
ls
d
e
sig
n
a
n
d
e
v
a
lu
a
ti
o
n
,
i
n
telli
g
e
n
t
wire
les
s
n
e
two
rk
s
a
n
d
lea
rn
i
n
g
a
lg
o
rit
h
m
s,
Co
g
n
it
iv
e
ra
d
io
a
n
d
De
lay
To
l
e
ra
n
t
Ne
two
rk
,
I
o
T
a
n
d
D
2
D
c
o
m
m
u
n
ica
ti
o
n
s.
Dr.
EL
KA
M
I
LI
is
a
fo
u
n
d
e
r
a
n
d
th
e
p
re
sid
e
n
t
o
f
t
h
e
M
o
r
o
c
c
a
n
M
o
b
il
e
Co
m
p
u
ti
n
g
a
n
d
In
tell
ig
e
n
t
Em
b
e
d
d
e
d
-
S
y
ste
m
s
S
o
c
iety
(M
o
b
it
ic),
h
tt
p
:/
/www
.
m
o
b
it
ic.
o
rg
/.
He
h
a
s
a
lso
c
o
-
fo
u
n
d
e
d
th
e
I
n
te
rn
a
ti
o
n
a
l
Co
n
fe
re
n
c
e
o
n
Wi
re
les
s
Ne
two
rk
s
a
n
d
M
o
b
il
e
Co
m
m
u
n
ica
ti
o
n
s
(W
INCO
M
,
h
tt
p
:
//
ww
w.win
c
o
m
-
c
o
n
f.
o
r
g
)
te
c
h
n
ica
ll
y
su
p
p
o
rt
b
y
IEE
E
Co
m
S
o
c
.
Dr.
El
Ka
m
il
i
a
c
ts
a
s
a
lo
c
a
l
c
h
a
ir
o
f
th
e
f
o
u
r
th
e
d
it
i
o
n
o
f
WI
NCO
M
th
a
t
h
e
ld
in
fe
z
M
o
ro
c
c
o
,
Oc
to
b
e
r
2
6
-
2
9
,
2
0
1
6
,
a
n
d
h
e
a
c
ts
a
s
a
Vic
e
-
G
e
n
e
ra
l
C
h
a
ir
fo
r
th
e
se
v
e
n
t
h
e
d
it
io
n
o
f
WI
NCO
M
,
Oc
to
b
e
r
2
9
-
N
o
v
e
m
b
e
r
1
,
2
0
1
9
.
Du
ri
n
g
Ja
n
u
a
r
y
2
0
1
7
to
m
a
rc
h
2
0
2
0
,
He
wa
s
a
m
e
m
b
e
r
o
f
t
h
e
IEE
E
M
o
r
o
c
c
o
S
e
c
ti
o
n
c
o
m
m
it
tee
;
h
e
h
a
s
o
c
c
u
p
i
e
d
t
h
e
p
o
siti
o
n
o
f
Co
n
fe
re
n
c
e
Co
o
rd
in
a
t
o
r
Mo
h
a
m
e
d
La
m
r
in
i
re
c
e
iv
e
d
t
h
e
P
h
D
d
e
g
re
e
fr
o
m
t
h
e
Cla
u
d
e
Be
rn
a
rd
–
Ly
o
n
Un
i
v
e
rsity
i
n
1
9
9
3
.
He
is
c
u
rre
n
tl
y
a
p
r
o
fe
ss
o
r
o
f
c
o
m
p
u
ter
sc
ien
c
e
a
t
U
S
M
BA
-
F
e
z
Un
iv
e
rsity
.
He
is
a
ls
o
a
m
e
m
b
e
r
o
f
t
h
e
LP
AIS
Lab
o
ra
to
r
y
.
His
re
se
a
rc
h
in
tere
sts
i
n
c
lu
d
e
so
ftwa
re
q
u
a
li
ty
(CM
M
I,
S
i
x
sig
m
a
,
IS
O 9
0
0
1
),
I
n
d
u
strial
E
n
g
i
n
e
e
rin
g
(
M
e
th
o
d
s a
n
d
sta
ti
stica
l
t
o
o
ls).
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