Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
7,
No.
4,
August
2017,
pp.
1934
–
1940
ISSN:
2088-8708
1934
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
P
erf
ormance
Ev
aluation
of
Ener
gy
Detector
Based
Spectrum
Sensing
f
or
Cogniti
v
e
Radio
using
NI
USRP-2930
F
.
Z.
El
Bahi,
H.
Ghennioui,
and
M.
Zouak
Laboratoire
Signaux,
Syst
`
emes
et
Composants
(LSSC),
F
acult
´
e
des
Sciences
et
T
echniques
de
F
`
es
(FSTF)
Uni
v
ersit
´
e
Sidi
Mohamed
Ben
Abdellah
(USMB
A),
Route
Immouzzer
,
B.P
.
2202,
F
`
es-Maroc
Article
Inf
o
Article
history:
Recei
v
ed:
Feb
18,
2017
Re
vised:
May
28,
2017
Accepted:
Jun
12,
2017
K
eyw
ord:
Ener
gy
Detector
Spectrum
Sensing
Cogniti
v
e
Radio
Primary
User
Secondary
User
USRP
MA
TLAB
ABSTRA
CT
This
paper
presents
the
performance
e
v
aluation
of
the
Ener
gy
Detector
technique,
which
is
one
of
the
most
popular
Spectrum
Sensing
(SS)
technique
for
Cogniti
v
e
Radio
(CR).
SS
is
the
ability
to
detect
the
presence
of
a
Primary
User
(PU)
(i.e.
licensed
user)
in
order
to
allo
w
a
Secondary
User
(SU)
(i.e
unlice
nsed
user)
to
access
PU’
s
frequenc
y
band
using
CR,
so
that
the
a
v
ailable
frequenc
y
bands
can
be
used
ef
ficiently
.
W
e
used
for
implementation
an
Uni
v
ersal
Softw
are
Radio
Peripheral
(USRP),
which
is
the
most
used
Softw
are
Defined
Radio
(SDR)
de
vice
for
research
in
wireless
communications.
Experimental
measurements
sho
w
that
the
Ener
gy
Detector
can
obtain
good
performances
in
lo
w
Signal
to
Noise
Ratio
(SNR)
v
alues.
Furthermore,
computer
simulations
using
MA
TLAB
are
closer
to
those
of
USRP
measurements.
Copyright
c
2017
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
F
atima
Zahra
EL
B
AHI,
Laboratoire
Signaux,
Syst
`
emes
et
Composants
(LSSC),
F
acult
´
e
des
Sciences
et
T
echniques
de
F
`
es
(FSTF),
Uni
v
ersit
´
e
Sidi
Mohamed
Ben
Abdellah
(USMB
A),
Route
Immouzzer
,
B.P
.
2202,
F
`
es-Maroc.
Email:
f
atimazahra.elbahi@usmba.ac.ma
1.
INTR
ODUCTION
In
recent
years,
the
wireless
applications
and
de
vices
ha
v
e
de
v
eloped
and
increased
rapidly
.
Since
the
access
to
electromagnetic
spectrum
is
fix
ed
and
limited,
thus
the
a
v
ailable
frequenc
y
bands
are
inef
ficiently
utilized
which
causes
the
spectrum
scarcity
problem
[1].
In
order
to
use
the
a
v
ailable
spectrum
ef
ficiently
,
man
y
studies
and
re-
searches
ha
v
e
propo
s
ed
a
ne
w
concept
called
Cogniti
v
e
Radio
(CR).
This
concept
is
based
on
the
opportunistic
usage
of
radio
frequenc
y
bands
by
allo
wing
Secondary
Users
(SUs,
i.e.
unlicensed
users)
to
e
xploit
frequenc
y
bands
of
Primary
Users
(PUs,
i.e.
licensed
users).
CR
is
a
radio
for
wireless
communications
able
to
change
its
parameters,
related
to
either
transmission
or
reception,
autonomously
and
dynamically
based
on
the
electromagnetic
en
vironment
and
communication
requirements,
in
order
to
perform
an
ef
ficient
communication
without
interfering
with
PUs
[2].
CR
has
the
capability
of
sensing
the
spectrum,
thus
detect
the
presence
PU’
s
signal,
using
dif
ferent
spectrum
sensing
techniques.
These
techniques
are
subdi
vided
into
tw
o
cate
gories:
cooperati
v
e
and
non-cooperati
v
e
sensing
techniques.
The
first
cate
gory
is
based
on
sharing
information,
in
other
w
ords,
the
detection
of
Primary
User’
s
signal
is
performed
by
combining
results
from
multiple
cogniti
v
e
radios
that
w
orks
together
[3].
The
most
important
adv
antage
of
this
technique
is
the
capability
of
decreasing
sensing
time
and
impro
ving
the
sensing
accurac
y
.
The
second
cate
gory
,
non-cooperati
v
e
sensing
techniques,
is
also
kno
wn
as
primary
transmitter
detection,
because
the
detection
of
PU’
s
signal
is
based
only
on
the
recei
v
ed
signal
at
a
SU.
The
most
common
sensing
techniques
that
belong
to
this
cate
gory
are:
ener
gy
detector
[4],
matched
filter
detector
and
c
yclostationary
detector
[5].
Ener
gy
detector
is
the
most
popular
spectrum
sensing
technique,
it
can
also
be
considered
as
the
most
spectrum
sensing
technique
used
in
practice
because
of
its
lo
w
implementation
comple
xity
.
The
concept
of
Ener
gy
detector
is
based
only
on
computing
the
total
ener
gy
of
the
recei
v
ed
signal,
then
comparing
is
to
a
specified
threshold
in
order
to
decide
the
presence
or
absence
of
a
PU’
s
signal,
thus
no
prior
kno
wledge
of
the
PU’
s
signal
is
required,
only
the
v
ariance
of
the
noise
is
needed.
In
this
paper
,
we
present
an
implementation
of
the
Ener
gy
Detector
Based
Spectrum
Sensing,
using
an
Uni
v
er
-
sal
Softw
are
Radio
Peripheral
(USRP),
a
Softw
are
Defined
Radio
(SDR)
de
vice,
in
order
to
e
v
aluate
the
performance
J
ournal
Homepage:
http://iaesjournal.com/online/inde
x.php/IJECE
I
ns
t
it
u
t
e
o
f
A
d
v
a
nce
d
Eng
ine
e
r
i
ng
a
nd
S
cie
nce
w
w
w
.
i
a
e
s
j
o
u
r
n
a
l
.
c
o
m
,
DOI:
10.11591/ijece.v7i4.pp1934-1940
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1935
of
the
spectrum
sensing
technique.
W
e
used
for
the
implementation
an
NI
USRP-2930,
which
belongs
to
the
USRP
se-
ries
of
the
National
Instruments’
brand.
F
or
the
programming,
we
used
LabVIEW
and
MA
TLAB
for
both
transmission
and
reception.
W
e
implemented
for
transmission
an
OFDM
(Orthogonal
Frequenc
y
Di
vision
Multiple
xing)
Signal,
which
is
used
as
a
Primary
User’
s
signal.
W
e
chooses
the
OFDM
modulation
because
it
is
the
most
used
in
wireless
communication,
due
to
its
high
bandwidth
ef
ficienc
y
[6],
such
as
W
iF
i
[7],
3GPP/L
TE
[8]
(3rd
Generation
P
artnership
Project/Long
T
erm
Ev
olution)
f
o
r
the
do
wnlink,
D
VBT
[9]
(Digital
V
ideo
Broadcast
T
errestrial)
and
W
iMAX
[10]
(W
orldwide
Interoperability
for
Micro
w
a
v
e
Access).
The
performance
e
v
aluation
of
the
Ener
gy
Detector
is
based
on
e
xperimental
measurements
obtained
using
USRP’
s
recei
v
er
,
which
included
the
Ener
gy
Detector
algorithm.
The
rest
of
this
paper
is
or
g
anized
as
follo
ws.
In
section
2,
we
introduce
the
Ener
gy
Detector
based
sensing
after
defining
the
problem
formulation.
Section
3
defines
the
implementation
details
using
NI
USRP-2930.
Finally
,
in
section
4,
the
e
xperim
ental
results
obtained
from
the
implementation
are
pro
vided
in
order
to
illustrate
the
performance
of
Ener
gy
Detector
.
2.
ENERGY
DETECT
OR
B
ASED
SENSING
2.1.
Pr
oblem
F
ormulation
In
our
system
model,
a
SU
senses
the
presence
of
a
PU.
In
order
to
perform
a
good
detection
of
a
spectrum
opportunity
,
tw
o
h
ypotheses,
H
0
and
H
1
are
defined
respecti
v
ely
for
the
absence
and
the
presence
of
a
PU
signal.
Hence,
our
h
ypothesis
model
for
transmitter
detection
can
be
e
xpressed
as
follo
ws:
(
H
0
:
s
(
l
)
=
n
(
l
)
H
1
:
s
(
l
)
=
x
(
l
)
+
n
(
l
)
;
(1)
where,
s
(
l
)
represents
the
recei
v
ed
data,
x
(
l
)
is
the
transmitted
signal
by
the
primary
user
and
n
(
l
)
denotes
the
White
Gaussian
noise
independent
from
the
transmitted
signal,
with
zero
mean
and
v
ariance
2
n
.
From
the
tw
o
h
ypotheses,
tw
o
probabilities
describe
the
performance
of
the
spectrum
sensing
technique:
the
f
alse-alarm
probability
P
f
a
,
which
is
the
probability
of
declaring
wrongly
H
1
and
the
detection
probability
P
d
,
which
is
the
probability
of
declaring
correctly
H
1
.
The
main
purpose
of
all
spectrum
sensing
techniques
i
s
to
maximize
the
detection
probability
for
a
lo
w
f
alse-alarm
probability
.
2.2.
Ener
gy
Detector
Ener
gy
Det
ector
,
also
kno
wn
as
radiometry
,
is
the
most
popular
and
widely
used
spectrum
sensing
technique
because
of
its
lo
w
computational
and
implementation
comple
xities.
It
is
a
simple
sensing
technique
that
does
not
need
prior
kno
wledge
of
the
PU
’
s
signal,
only
the
v
alue
of
the
White
Gaussian
Noise
is
needed.
Urk
o
witz
[4]
w
as
the
first
to
in
v
estig
ate
the
detection
of
an
unkno
wn
signal
in
a
White
noise
channel
using
the
ener
gy
detector
based
sensing.
The
PU
signal
is
detected
by
comparing
the
total
ener
gy
of
the
recei
v
ed
signal,
o
v
er
a
specified
time
duration,
with
a
threshold.
Thus,
the
test
statistics
of
the
ener
gy
detector
is
written
as:
T
E
D
=
1
L
L
X
l
=0
j
s
(
l
)
j
2
;
(2)
where,
L
denotes
the
size
of
the
observ
ation
sequence.
The
presence
of
a
PU’
s
signal
is
thus
detected
if
the
ener
gy
is
greater
than
the
threshold.
The
decision
is
then
e
xpressed
as
follo
ws:
T
E
D
H
1
?
H
0
;
(3)
where,
denotes
the
threshold.
The
Probability
Density
Function
(PDF)
of
the
test
statistics
T
E
D
can
be
modeled
as
a
Gaussian
distrib
ution
[11]
according
to
the
tw
o
h
ypothesis
as
follo
ws:
8
>
<
>
:
H
0
:
T
E
D
N
2
n
;
4
n
L
H
1
:
T
E
D
N
2
x
+
2
n
;
(
2
x
+
2
n
)
2
L
;
(4)
P
erformance
Evaluation
of
Ener
gy
Detector
using
NI
USRP-2930
(F
.
Z.
El
Bahi)
Evaluation Warning : The document was created with Spire.PDF for Python.
1936
ISSN:
2088-8708
where,
2
n
represents
the
v
ariance
of
the
White
Gaussian
noise
and
2
x
denotes
the
v
ariance
of
the
transmitted
PU’
s
signal.
Based
on
the
PDF
of
the
test
statistics,
t
he
detection
probability
P
d
and
the
f
alse-alarm
probability
P
f
a
can
be
e
xpressed
as:
P
f
a
=
P
r
(
T
E
D
>
j
H
0
)
=
Q
2
n
1
p
L
(5)
P
d
=
P
r
(
T
E
D
>
j
H
1
)
=
Q
2
n
1
s
L
2
+
1
!
;
(6)
where,
=
2
x
2
n
denotes
the
Signal
to
Noise
Ratio
(SNR)
and
Q
(
:
)
is
the
Marcum
Q-function
defined
as:
Q
(
y
)
=
1
p
2
R
1
y
e
u
2
2
du
.
F
or
a
tar
get
f
alse-alarm
probability
,
the
v
alue
of
the
threshold
can
be
calculate
d
by
in
v
erting
the
relation
described
in
Eq.
6
as
follo
ws:
=
2
n
Q
1
(
P
f
a
)
p
L
+
1
;
(7)
where,
Q
1
(
:
)
is
the
in
v
erse
Marcum
Q-function.
3.
IMPLEMENT
A
TION
DET
AILS
In
this
section,
we
pro
vide
the
practical
implementation
details
of
the
Ener
gy
Detector
bas
ed
sensing.
W
e
used
for
both
transmission
and
reception
an
NI
USRP-2930
(Uni
v
ersal
Softw
are
Radio
Peripheral)
and
a
Desktop
Computer
with
LabVIEW
2014
and
MA
TLAB
R2013a
to
control
the
USRP
using
a
Gig
abit
Ethernet
Cable
as
sho
wn
in
Figure
1.
Figure
1.
Implementation’
s
structure
3.1.
NI
USRP-2930
NI
USRP-2930
is
a
Softw
are
Defined
Radio
(SDR)
transcei
v
er
,
able
to
transmit
and
recei
v
e
RF
(Radio
Fre-
quenc
y)
signals,
from
the
USRP
series
of
the
National
Instruments’
brand,
it
is
widely
used
for
both
teaching
and
research
in
wireless
communications.
Furthermore,
it
enables
a
wide
range
of
RF
applications
co
v
ering
common
standards
such
as
GSM
Cellular
,
broadcast
radio,
W
iFi,
GPS
and
digital
TV
.
The
USRP
hardw
are
is
a
straightforw
ard
RF
platform
for
rapid
prototyping
applicat
ions
such
as
spectrum
monitoring
and
ph
ysical
layer
communication.
It
has
the
ability
to
transmit
and
recei
v
e
RF
signals
across
a
frequenc
y
range
from
50
MHz
to
2.2
GHz.
Moreo
v
er
,
the
NI
USRP-2930
has
an
inte
grated
GPS-disciplined
clock
that
pro
vides
GPS
position
information,
impro
v
ed
frequenc
y
accurac
y
and
synchronization
capabilities
[12].
3.2.
T
ransmitter
The
programming
and
design
parts
of
the
transmitter
are
implemented
in
LabVIEW
in
order
to
control
the
NI
USRP-2930.
F
or
our
e
xperiment
,
we
consider
an
OFDM
(Orthogonal
Frequenc
y
Di
vision
Multiple
xing)
transmitted
signal
with
a
carrier
frequenc
y
set
to
200
Mhz.
As
seen
in
Figure
2,
the
transmitter’
s
front
pane
l
is
di
vided
into
tw
o
parts.
The
left
part
contains
three
blocks;
USRP
P
arameters,
OFDM
signal
parameters
and
Deb
ug
(Figure
3).
In
the
OFDM
signal
parameters
block,
we
can
choose
the
OFDM
standard
to
transmit
among
the
follo
wing
ones:
3GPP/L
TE,
W
iMax
802.16,
D
VBT
-2K
and
802.22-1K.
The
right
part
of
the
transmitter
represents
the
PSD
(Po
wer
Spectrum
Density)
of
the
OFDM
transmitted
signal.
IJECE
V
ol.
7,
No.
4,
August
2017:
1934
–
1940
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1937
Figure
2.
T
ransmitter’
s
front
panel.
Figure
3.
T
ransmitter’
s
tab
control.
3.3.
Recei
v
er
In
the
same
w
ay
as
the
transmitter
,
we
ha
v
e
implemented
the
recei
v
er
in
LabVIEW
as
sho
wn
in
Figure
4.
The
recei
v
er’
s
front
panel
is
also
di
vided
into
tw
o
parts,
the
left
one
contains
three
blocks;
the
USRP
P
arameters,
Ener
gy
Detector
and
Deb
ug.
The
recei
v
er’
s
deb
ug
part
is
the
same
as
the
one
of
the
transmitter
.
In
the
Ener
gy
Detector
part,
as
sho
wn
in
Figure
5,
we
calculate
the
P
d
(Detection
Probability)
for
a
specific
v
alue
of
P
f
a
(F
alse-alarm
Probability)
and
realizations.
The
right
part
of
the
recei
v
er
contains
the
PSD
of
the
recei
v
ed
OFDM
signal,
which
is
almost
the
same
as
the
one
of
the
OFDM
transmitted
signal.
Figure
4.
Recei
v
er’
s
front
panel.
P
erformance
Evaluation
of
Ener
gy
Detector
using
NI
USRP-2930
(F
.
Z.
El
Bahi)
Evaluation Warning : The document was created with Spire.PDF for Python.
1938
ISSN:
2088-8708
Figure
5.
Recei
v
er’
s
tab
control.
4.
RESUL
TS
AND
DISCUSSION
In
this
section,
we
present
e
xperimental
results
of
the
implementation
of
the
Ener
gy
Detector
based
sensing
using
NI
USRP-2930
and
a
v
eraged
o
v
er
1000
realizations.
4.1.
Effect
of
SNR
In
this
e
xperiment,
we
test
the
impact
of
dif
ferent
SNR
(Signal
to
Noise
Ratio)
v
alues
on
the
Detection
Probability
(
P
d
).
W
e
fix
the
F
alse-alarm
Probability
(
P
f
a
)
to
0.01
and
v
ary
the
SNR
v
alue
from
-24
dB
to
0
dB
with
a
step
of
2
dB.
Each
measurement
result
is
the
a
v
erage
v
alue
of
1000
measurement
results
for
the
same
SNR
v
alue.
W
e
generated
an
OFDM
signal
with
64
subcarriers,
10
symbols
and
c
yclic
prefix
equals
to
8.
Figure
6a
and
6b
sho
w
the
MA
TLAB
simulation
(Computer
simulations)
and
measurement
results
(USRP
implementation)
of
the
P
d
v
ersus
SNR
of
a
3GPP/L
TE
signal
and
D
VBT
-2K
signal
respecti
v
el
y
.
W
e
can
notice
from
both
figures
that
the
P
d
increases
with
the
SNR
v
alues.
Thus,
the
lar
ger
SNR,
the
better
the
detecti
on
of
the
OFDM
PU.
Furthermore,
USRP
implementations
are
closer
to
MA
TLAB
simulations.
(a)
(b)
Figure
6.
Detection
probability
(
P
d
)
vs.
Signal
to
Noise
Ratio
(SNR)
(with
P
f
a
=0.01)
of
(a)
3GPP/L
TE
signal
and
(b)
D
VBT
-2K
signal.
IJECE
V
ol.
7,
No.
4,
August
2017:
1934
–
1940
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
1939
4.2.
Recei
v
er
Operation
Characteristic
In
order
to
e
xamine
the
ef
fect
of
P
f
a
on
the
detection
performance
of
the
Ener
gy
Detect
or
,
we
fix
the
v
alue
of
the
SNR
to
-10.4
dB
and
v
ary
the
P
f
a
from
0.1
to
1
with
a
step
of
0.1.
The
achie
v
ed
P
d
as
a
function
of
P
f
a
is
called
the
R
OC
(Recei
v
er
Operating
Characteristic)
curv
e.
W
ith
the
same
w
ay
as
the
abo
v
e
e
xperiment,
each
result
is
a
v
eraged
o
v
er
1000
measurements.
Figure
7a
and
7b
represent
the
MA
TLAB
simulation
and
USRP
implementation
of
the
P
d
v
ersus
P
f
a
of
a
3GPP/L
TE
signal
and
D
VBT
-2K
signal
respecti
v
ely
.
As
sho
wn
in
those
figures,
USRP
implementations
are
closer
to
MA
TLAB
simulations
and
the
Ener
gy
Detector
achie
v
es
good
performance
for
all
P
f
a
v
alues.
(a)
(b)
Figure
7.
Detection
probability
(
P
d
)
vs.
F
alse-alarm
probability
(
P
f
a
)
(with
SNR=-10.4
dB)
of
(a)
3GPP/L
TE
signal
and
(b)
D
VBT
-2K
signal.
5.
CONCLUSION
In
this
paper
,
we
ha
v
e
pro
vided
an
e
xperimental
performance
e
v
aluation
of
the
Ener
gy
Detector
based
sensing
using
NI
USRP-2930,
which
is
a
Softw
are
Defined
Radio
(SDR)
transcei
v
er
.
W
e
ha
v
e
tested
the
impact
of
SNR
on
the
detection
probability
.
Furthermore,
a
R
OC
curv
e
w
as
obtained
for
a
lo
w
SNR
v
alue.
Experimental
results
sho
ws
that
the
Ener
gy
Detector
achie
v
es
good
performances
for
lo
w
SNR
v
alues
and
for
all
P
f
a
v
alues
and
are
closer
to
those
of
computer
simulations
using
MA
TLAB.
REFERENCES
[1]
S.
S.
Ali,
C.
Liu,
and
M.
Jin,
”Minimum
Eigen
v
alue
Detection
for
Spectrum
Sensing
in
Cogniti
v
e
Radio,
”
Int.
J
.
Electr
.
Comput.
Eng
.
,
v
ol.
4,
no.
4,
p.
623,
2014.
[2]
Y
.
Saleem
and
M.
H.
Rehmani,
”Primary
radio
user
acti
vity
models
for
cogniti
v
e
radio
netw
orks:
A
surv
e
y
,
”
J
.
Netw
.
Comput.
Appl.
,
v
ol.
43,
pp.
116,
2014.
[3]
M.
S.
Hossain,
M.
I.
Abdullah,
and
M.
A.
Hossain,
”Hard
combination
data
fusion
for
cooperati
v
e
spectrum
sensing
in
cogniti
v
e
radio,
”
Int.
J
.
Electr
.
Comput.
Eng
.
,
v
ol.
2,
no.
6,
p.
811,
2012.
[4]
H.
Urk
o
witz,
”Ener
gy
detection
of
unkno
wn
deterministic
signals,
”
Pr
oc.
IEEE
,
v
ol.
55,
no.
4,
pp.
523-531,
1967.
P
erformance
Evaluation
of
Ener
gy
Detector
using
NI
USRP-2930
(F
.
Z.
El
Bahi)
Evaluation Warning : The document was created with Spire.PDF for Python.
1940
ISSN:
2088-8708
[5]
H.
Sun,
A.
Nallanathan,
C.-X.
W
ang,
and
Y
.
Chen,
”W
ideband
spectrum
sens
ing
for
cogniti
v
e
radio
netw
orks:
a
surv
e
y
,
”
IEEE
W
ir
el.
Commun.
,
v
ol.
20,
no.
2,
pp.
7481,
2013.
[6]
M.
Hu,
Y
.
Li,
X.
Lu,
and
H.
Zhang,
”T
one
reserv
ation
to
minimize
nonlinearity
impact
on
OFDM
signals,
”
IEEE
T
r
ans.
V
eh.
T
ec
hnol.
,
v
ol.
64,
no.
9,
pp.
43104314,
2015.
[7]
C.
Smith
and
J.
Me
yer
,
”3G
W
ireless
with
W
iMAX
and
W
iFi:
802.16
and
802.11.
”
McGr
aw-Hill
Pr
ofessional
,
2005.
[8]
H.
Holma
and
A.
T
oskala,
”HSDP
A/HSUP
A
for
UMTS:
high
speed
radio
access
for
mobile
communications”.
J
ohn
W
ile
y
and
Sons
,
2007.
[9]
U.
Ladeb
usch
and
C.
A.
Liss,
”T
errestrial
D
VB
(D
VB-T):
A
broadcast
technology
for
st
ationary
portable
and
mobile
use,
”
Pr
oc.
IEEE
,
v
ol.
94,
no.
1,
pp.
183193,
2006.
[10]
O.
A.
Dobre,
R.
V
enkatesan,
and
D.
C.
Popescu,
”Second-order
c
yclostationarity
of
mobile
W
iMAX
and
L
TE
OFDM
signals
and
application
to
spectrum
a
w
areness
in
cogniti
v
e
radio
systems,
”
IEEE
J
.
Sel.
T
op.
Signal
Pr
o-
cess.
,
v
ol.
6,
no.
1,
pp.
2642,
2012.
[11]
Y
.-C.
Liang,
Y
.
Zeng,
E.
C.
Y
.
Peh,
and
A.
T
.
Hoang,
”Sensing-throughput
tradeof
f
for
cogniti
v
e
radio
netw
orks,
”
IEEE
T
r
ans.
W
ir
el.
Commun.
,
v
ol.
7,
no.
4,
pp.
1326-1337,
2008.
[12]
National
Instruments
Co.,
”USRP-292x/293x
Datasheet,
”
[Online].
A
v
ailable:
http://www
.ni.com/datasheet/pdf/en/ds-355
,
accessed
3
February
2017.
BIOGRAPHIES
OF
A
UTHORS
F
atima
Zahra
EL
B
AHI
is
an
engineer
in
Netw
ork
and
T
elecommunication,
graduated
in
2014
from
National
School
of
Applied
Sciences
of
T
angier
,
Morocco.
She
is
currently
w
orking
to
w
ard
Ph.D.
de
gre
e
in
the
laboratory
of
Signal,
Systems
and
Components,
in
F
aculty
of
Sciences
and
T
echnologies
of
Fez
at
Sidi
Mohammed
Ben
Abdelah
Uni
v
ersity
,
Morocco.
Her
main
research
interests
are
signal
processing
and
cogniti
v
e
radio.
Hicham
GHENNIOUI
is
an
assistant
director
of
Signals,
Systems
and
Components
Laboratory
at
F
aculty
of
Sciences
and
T
echnologies,
Fez,
Morocco.
Since
2011,
he
is
a
full-time
Associate
Pro-
fessor
at
the
F
aculty
of
Sciences
and
T
echnologies,
Fez.
He
recei
v
ed
the
Ph.D
de
gree
in
Computer
Science
and
T
elecommunications
in
2008,
jointly
from
Mohamed
V
Uni
v
ersity
and
the
T
oulon
Uni-
v
ersity
.
In
2004,
recei
v
ed
the
D.E.S.A.
de
gree
in
Computer
Science
and
T
elecommunications
from
the
Mohamed
V
Uni
v
ersity
.
His
main
research
interests
are
signal/image
processing
including
blind
sources
separation,
data
analytic,
deblurring
and
cogniti
v
e
radio.
Mohcine
ZOU
AK
is
the
director
of
the
Cancer
Research
I
nstitute
of
Fez,
Morocco.
President
of
the
Conference
of
D
eans
of
F
aculties
Scientific
Morocco
(CCMS).
V
ice
P
resident
of
International
Conference
of
Heads
of
Uni
v
ersities
and
Scientific
Institutions
of
French
e
xpression
CIR
UISEF
.
Since
2005,
he
is
a
professor
of
Higher
Education
at
F
aculty
of
Sciences
and
T
echnologies,
Fez,
Morocco.
He
assured
lessons
in
the
areas
of
signal
processing,
electronic
systems
and
telecommu-
nications
as
well
as
in
the
fie
lds
of
stochastic
estimation.
His
research
acti
vities
mainly
concern
the
signal
processing
and
telecommunications.
IJECE
V
ol.
7,
No.
4,
August
2017:
1934
–
1940
Evaluation Warning : The document was created with Spire.PDF for Python.