Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
7,
No.
2,
April
2017,
pp.
905
–
912
ISSN:
2088-8708
905
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
Impr
o
v
ed
T
iming
Estimation
Using
Iterati
v
e
Normalization
T
echnique
f
or
OFDM
Systems
Suy
oto
1
,
Iskandar
2
,
Sugihartono
3
,
and
Adit
K
ur
niawan
4
1,2,3,4
School
of
Electrical
Engineering
and
Informatics,
Institut
T
eknologi
Bandung
(ITB),
Indonesia
1
Research
Center
of
Informatics,
Lembag
a
Ilmu
Pengetahuan
Indonesia
(LIPI),
Indonesia
Article
Inf
o
Article
history:
Recei
v
ed
Oct
25,
2016
Re
vised
Feb
9,
2017
Accepted
Feb
24,
2017
K
eyw
ord:
OFDM
multipath
channel
timing
estimation
high
delay
spread
ABSTRA
CT
Con
v
entional
timing
estimation
schemes
based
on
autocorrelation
e
xperience
performance
de
gradation
in
the
multipath
channel
en
vironment
with
high
delay
spread.
T
o
o
v
ercome
this
problem,
we
proposed
an
impro
v
ement
of
the
timing
estimat
ion
for
the
OFDM
system
based
on
statistical
change
of
symmetrical
correlator
.
The
ne
w
method
uses
iterati
v
e
normalization
technique
to
the
correlator
output
before
the
detection
based
on
statistical
change
of
sym-
metric
correlator
is
applied.
Thus,
it
increases
the
detection
probability
and
achie
v
es
better
performance
than
pre
vi
ously
published
methods
in
t
he
multipath
en
vironment.
Computer
simulation
sho
ws
that
our
method
is
v
ery
rob
ust
in
the
f
ading
multipath
channel.
Copyright
c
2017
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Suyoto
Research
Center
of
Informatics,
Lembag
a
Ilmu
Pengetahuan
Indonesia
K
omplek
LIPI
Gedung
20.
lt.3
Jl.
Cisitu
No.21/154D
Bandung
40135
+62222504711
yoto@informatika.lipi.go.id
1.
INTR
ODUCTION
Orthogonal
Frequenc
y
Di
vision
Mul
tiple
xing
(OFDM)
systems
of
fer
high
bandwidth
ef
ficienc
y
and
rob
ust
ag
ainst
multipath
delay
.
Hence,
OFDM
systems
ha
v
e
been
widely
adopted
for
a
high
dat
a
rate,
wireless
communi-
cation
systems,
such
as
WLAN
[1],
D
VB-T2
[2],
and
WMAN
802.16m
[3].
Both
of
D
VB-T2
and
WMAN
802.16m
are
supporting
applications
that
run
in
a
high
speed
mobility
en
vironment.
Recently
OFDM
technique
is
also
used
for
cogniti
v
e
radio
systems,
which
t
h
e
use
of
frequenc
y
spectrum
in
the
OFDM
systems
can
be
done
as
ef
ficiently
as
possible
[4]-[5].
Ho
we
v
er
,
OFDM
systems
need
strict
timing
synchronization
between
transmitter
and
recei
v
er
,
as
an
error
in
timing
estimation
gi
v
e
rise
to
InterSymbol
Interference
(ISI)
and
can
decrease
the
o
v
erall
performance
of
OFDM
systems
[6]-[7].
F
or
symbol
timing
estim
ation,
Schmidl
[8]
used
a
preamble
consists
of
tw
o
identical
parts
for
symbol
timing
estimation.
But,
the
timing
metric
of
Schmidl’
s
method
has
a
plateau,
which
causes
a
lar
ge
v
ariance
in
the
timing
of
fset
est
imation.
T
o
decrease
the
plateau,
Mi
nn
[9]
proposed
a
ne
w
training
symbol
with
four
identical
parts.
It
results
a
sharper
timing
metric
than
Schmidl’
s
method,
ho
we
v
er
,
it
still
has
ambiguity
due
to
some
side-lobes
at
a
side
of
the
peak
correlation
re
gion,
thus
estimation
v
ariance
is
still
lar
ge.
In
order
to
reduce
the
v
ariance,
P
ark
[10]
proposed
a
sharper
timing
metric
using
symmetric
correlation
property
of
the
preamble.
Y
et,
the
timing
metric
of
P
ark’
s
method
has
tw
o
lar
ge
side-lobes
.
T
o
eliminate
the
side-lobes
of
P
ark’
s
timing
m
etric,
Y
i
[11]
proposed
a
ne
w
preamble
structure
that
has
symmetric
correlation
property
.
The
perform
ance
of
all
the
abo
v
e-mentioned
approaches
decrease
in
multipath
channel
en
vironments.
T
o
o
v
ercome
this
problem,
Cho
[12]
proposed
a
met
h
od
that
e
xploits
statistical
change
of
symmetric
corre-
lator
.
It
reduces
the
multipath
channel
ef
fect,
hence
the
v
ariance
of
the
timing
of
fset
estimation
is
small.
Ho
we
v
er
,
Cho’
s
method
generates
error
detection
if
the
correlation
magnitude
on
the
first
arri
ving
path
is
much
smaller
than
the
strongest
path.
T
o
o
v
ercome
this
problem,
we
proposed
an
iterati
v
e
normalization
technique
to
the
correlator
output
before
the
detection
based
on
statistical
change
of
symmetric
correlator
is
applied.
Considering
the
v
ery
small
correla-
tion
magnitude
on
the
first
arri
ving
path,
we
attempt
to
increase
the
correlation
magnitude
on
the
first
a
rri
ving
path
to
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.v7i2.pp905-912
Evaluation Warning : The document was created with Spire.PDF for Python.
906
ISSN:
2088-8708
Figure
1.
The
Block
diagram
of
OFDM
transmission
systems
(synch.:
synchronization).
produce
an
estimation
method
with
better
performance.
Our
e
xperimental
results
sho
w
that
the
ne
w
timing
estimator
achie
v
es
better
performance
than
pre
viously
published
methods.
2.
OFDM
SIGN
AL
MODEL
Fig.
1
sho
ws
an
OFDM
transmission
system
that
consists
of
a
sequence
of
OFDM
symbols,
where
each
of
the
OFDM
symbol
which
has
a
duration
of
T
s
seconds
is
generated
by
a
number
of
N
s
points
In
v
erse
F
ast
F
ourier
T
ransform
(IFFT)
from
a
block
of
sub-symbols
f
C
k
g
.
Cyclic
Prefix
(CP)
with
a
length
of
N
g
is
added
at
the
start
of
the
OFDM
sym
bol
that
is
longer
than
the
duration
of
the
Channel
Impulse
Response
(CIR).
Thus,
the
OFDM
signal
transmitted
through
the
frequenc
y
selecti
v
e
f
ading
channel
with
a
delay
spread
length
of
L
ch
is
e
xpressed
as
follo
ws:
y
(
d
)
=
L
ch
1
X
m
=0
h
(
m
)
x
(
d
m
)
+
w
(
d
)
;
(1)
where
d
is
time
inde
x,
h
(
m
)
is
the
channel
impulse
response,
w
(
d
)
is
white
Gaussian
noise
with
zero
mean,
and
x
(
d
)
is
the
output
signal
from
IFFT
describes
as
follo
ws:
x
(
d
)
=
N
1
X
k
=0
C
k
e
j
2
k
d=
N
s
:
(2)
The
delay
of
the
recei
ving
signal
r
(
d
)
at
the
recei
v
er
can
be
modelled
as
follo
ws:
r
(
d
)
=
y
(
d
d
)
e
j
2
d
N
s
f
;
(3)
where
d
is
an
unkno
wn
inte
ger
-v
alued
of
arri
v
al
time
of
an
OFDM
symbol
and
f
is
the
Carrier
Frequenc
y
Of
fset
(CFO)
normalized
to
the
subcarrier
spacing.
3.
PR
OPOSED
METHOD
3.1.
Symmetric
Corr
elator
In
time
domain,
the
form
of
P
ark’
s
preamble
is
defined
as
follo
ws
[10]:
P
P
ar
k
=
[
A
N
s
=
4
B
N
s
=
4
A
N
s
=
4
B
N
s
=
4
]
;
(4)
IJECE
V
ol.
7,
No.
2,
April
2017:
905
–
912
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
907
where
A
N
s
=
4
represents
samples
with
length
N
s
=
4
generated
by
IFFT
of
a
Pseudo
Noise
(PN)
sequence,
and
A
N
s
=
4
represents
the
conjug
ate
of
A
N
s
=
4
.
B
N
s
=
4
is
symmetric
of
A
N
s
=
4
and
is
generated
by
the
method
in
[10].
Thus,
the
symmetric
correlator
T
(
d
)
is
defined
as:
T
(
d
)
=
N
s
=
2
1
X
k
=1
r
(
d
+
k
)
r
(
d
+
N
s
k
)
:
(5)
3.2.
Statistical
Pr
operty
of
Symmetric
Corr
elator
As
in
[12],
the
Probability
Distrib
ution
Function
(PDF)
of
T
(
d
)
is
defined
as
follo
ws:
(
d
)
=
N
s
=
2
1
X
k
=1
r
(
d
+
N
s
=
2
k
)
r
(
d
+
N
s
=
2
+
k
)
;
(6)
for
j
d
d
0
j
<
L
r
,
(
d
)
follo
w
a
comple
x
normal
distrib
ution
as:
(
d
)
C
N
(0
;
K
4
r
)
;
d
=
2
L
C
N
(
K
h
2
(
d
d
0
)
2
x
e
2
f
;
K
4
r
)
;
d
2
L;
(7)
where
L
r
is
the
number
of
ident
ical
part
of
the
preamble
(
L
r
=
N
s
=
2)
,
K
=
(
N
s
=
2
1)
,
2
r
=
2
x
+
2
w
;
=
P
m
j
h
(
m
)
j
2
,
2
x
=
1
N
s
P
N
s
1
k
=0
j
x
p
(
k
)
j
2
,
2
w
is
noise
v
ariance,
and
x
p
(
k
)
denote
the
preamble
signal
in
t
ime
domain.
d
0
indicates
the
start
of
preamble
(
d
0
=
0)
,
which
corresponds
to
the
first
arri
ving
path
and
L
(=
(
d
0
;
d
0
+
1
;
:::;
d
0
+
L
ch
))
is
multipath
channel
inde
x.
Accordingly
,
for
correlator
length
(
L
r
>
j
d
d
0
j
)
,
T
(
d
)
is
a
Rician
random
v
ariable
with
PDF:
f
(
T
(
d
);
2
;
v
(
d
))
=
T
(
d
)
2
exp
(
T
2
(
d
)
+
v
2
(
d
)
2
2
)
I
0
(
T
(
d
)
v
(
d
)
2
)
;
(8)
where
I
0
(
x
)
is
modified
the
first
kind
of
Bassel
function
with
order
zero,
v
(
d
)
=
K
j
h
2
(
d
d
0
)
j
2
x
;
d
2
L
0
;
d
=
2
L;
(9)
and
2
=
K
4
r
=
2
.
3.3.
T
iming
Estimation
Based
on
Statistical
Change
of
Symmetric
Corr
elator
From
the
PDF
deri
v
ed
in
(8),
[12]
observ
es
the
statistical
change
of
T
(
d
)
upon
the
reception
of
the
preamble.
Then,
by
the
Generalized
Lik
elihood
Ratio
(GLR)
approach,
the
timing
metric
is
defined
as:
M
T
(
d
)
=
exp
1
2
(
d
)
+
1
I
0
p
(
d
)
2
2(
d
)
;
(10)
where
(
d
)
=
T
2
(
d
)
2
0
(
d
)
,
2
0
(
d
)
=
1
2
J
P
J
1
k
=0
T
2
(
d
k
)
,
and
J
is
the
observ
ation
length
for
detection.
Thus,
the
timing
estimation
is
defined
as:
^
d
=
ar
g
max
|
{z
}
d
(
M
0
T
(
d
))
;
(11)
where
M
0
T
(
d
)
=
M
T
(
d
)
j
;
T
(
d
)
>
R
0
;
other
w
ise;
(12)
and
R
is
the
threshold,
which
set
to
a
v
oid
F
alse
Alarm
in
Eq.
(12).
The
Probability
of
F
alse
Alarm
(
P
F
A
)
is
deri
v
ed
from
Eq.
(8)
at
v
(
d
)
=
0
as:
Impr
o
ved
T
iming
Estimation
Using
Iter
ative
Normalization
T
ec
hnique
for
OFDM
...
(Suyoto)
Evaluation Warning : The document was created with Spire.PDF for Python.
908
ISSN:
2088-8708
P
F
A
=
exp
(
R
2
=
2
2
)
;
(13)
if
2
replaced
by
2
0
,
the
threshold
can
be
obtained
for
the
gi
v
en
F
alse
Alarm
rate
as:
R
=
q
2
2
0
l
og
P
F
A
:
(14)
3.4.
The
Pr
oposed
T
iming
Estimation
Cho’
s
technique
e
xploits
the
statistics
of
T
(
d
)
change
upon
the
reception
of
t
he
preamble.
It
detects
the
change
of
parameter
v
(
d
)
from
0
for
d
<
d
0
to
v
(
d
)
=
K
j
h
2
(0)
j
2
x
at
d
=
d
0
.
This
technique
generates
error
in
detecting
the
first
arri
ving
path
when
the
g
ain
on
the
first
channel
path
(
j
h
2
(0)
j
)
is
much
smaller
than
the
strongest
path
(
j
h
2
(
m
)
j
)
,
where
m
is
1
;
2
;
:::;
L
ch
1
.
Thus,
it
mak
es
the
correlation
magnitude
on
the
first
arri
ving
path
much
smaller
than
the
stronger
path
and
causes
(
d
0
)
<
(
d
s
)
,
where
d
s
is
time
inde
x
on
the
stronger
path.
Therefore,
Cho’
s
detection
technique
f
ails
to
detect
the
first
arri
ving
path.
T
o
o
v
ercome
this
problem,
we
proposed
an
iterati
v
e
normalization
technique
to
be
applied
to
the
correlator
T
(
d
)
before
the
detection
based
on
statistical
change
of
symmetric
correlator
is
applied.
It
increases
the
corre
lation
magnitude
on
the
first
arri
ving
path
and
suppress
the
correlation
magnitude
on
other
paths,
which
are
associated
with
the
time
side-lobes
that
are
sometimes
can
appear
as
the
stronger
path.
In
other
w
ords,
we
gi
v
e
higher
weighting
f
actor
to
other
paths
than
to
the
first
arri
ving
path.
Hence,
making
the
v
alue
of
(
d
0
)
(
d
s
)
.
Thus,
Cho’
s
detection
technique
can
successfully
detect
the
first
arri
ving
path.
Cho
met
hod
is
actually
second-order
normalization
technique,
b
ut
this
technique
can
not
be
applied
directly
to
the
iterati
v
e
normalization
technique
because
it
does
not
has
a
stable
performa
n
c
e
when
the
number
of
iterations
is
increased.
This
is
due
to
P
ark’
s
timing
metric
which
is
compliant
with
WMAN
802.16m
[3]
systems
has
tw
o
lar
ge
lobes
so
that
the
short
of
observ
ation
length
(Cho’
s
observ
ation
length
less
than
or
equal
to
the
channel
length)
from
Cho’
s
method
can
not
be
used
for
iterati
v
e
technique.
W
e
set
the
observ
ation
length
for
iterati
v
e
normalizat
ion
equal
to
the
number
of
identical
parts
(
L
r
),
since
the
magnitude
of
side-lobes
depend
on
the
number
of
identical
parts
of
the
preamble.
This
is
done
to
achie
v
e
stable
performance
until
q
iterations.
The
iterati
v
e
normalization
technique
Z
i
(
d
)
is
e
xpressed
as:
Z
i
(
d
)
=
s
Z
2
i
1
(
d
)
2
Z
(
i
1)
(
d
)
;
(15)
where
i
is
the
inde
x
of
iteration
and
2
Z
i
(
d
)
is
the
v
ariance
of
correlator
at
i
iteration
and
is
defined
as:
2
Z
i
(
d
)
=
1
N
nor
m
N
nor
m
1
X
k
=0
Z
2
i
(
d
k
)
;
(16)
where
N
nor
m
is
the
observ
ation
length
for
iterati
v
e
normalization.
Our
proposed
method
is
performed
as
follo
ws.
First,
we
set
Z
0
(
d
)
=
T
(
d
)
,
and
then
the
iteration
process
is
applied
to
(15)
for
i
=
1
to
q
,
where
q
is
the
number
of
iteration.
After
obtaining
Z
q
(
d
)
,
we
set
back
T
(
d
)
=
Z
q
(
d
)
.
Then,
the
timing
estimation
can
be
calculated
using
(10)
and
(11).
Fig.
2
Sho
ws
the
simulation
result
using
Cho’
s
method
(Fig.
2(c))
compared
to
that
using
our
proposed
method
with
q
=
3
(Fig.
2(d)).
Those
figures
repre
sent
normalized
v
alue
ag
ainst
their
maximum
v
alue.
The
correct
timing
point
d
0
(the
first
arri
ving
path)
is
inde
x
ed
as
0.
Under
such
situations,
we
can
observ
e
that
Cho’
s
method
f
ails
to
detect
the
first
arri
ving
path
because
the
correlation
magnitude
on
the
first
arri
ving
path
much
smaller
than
the
stronger
path
(Fig.
2(a)),
hence
(
d
0
)
<
(
d
s
)
.
Meanwhile,
our
proposed
method
ca
n
detect
the
first
arri
ving
path;
this
impro
v
ement
can
be
inferred
from
the
rise
of
the
correlation
magnitude
v
(
d
0
)
and
the
decrease
of
the
correlation
magnitude
on
other
paths
(Fig.
2(b)),
hence
(
d
0
)
(
d
s
)
and
Cho’
s
detection
technique
can
successfully
detect
the
first
arri
ving
path.
4.
RESUL
TS
AND
DISCUSSION
In
this
part,
we
tested
the
performance
of
the
proposed
method
using
computer
simulation
in
the
term
of
timing
metric
and
measure
the
Mean
Squared
Error
(MSE)
of
symbol
timing.
The
MSE
of
symbol
timing
is
defined
as
E
[(
t
estimation
t
of
f
set
)
2
]
,
which
i
n
di
cates
the
a
v
erage
squared
dif
ference
between
the
estimation
time
at
recei
v
er
and
IJECE
V
ol.
7,
No.
2,
April
2017:
905
–
912
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
909
Figure
2.
Comparison
detection
under
the
V
ehicular
B
channel
[13]
with
SNR
=
20
dB,
N
s
=
2048
;
and
N
g
=
256
on
symmetric
correlator
output
(
T
(
d
)
).
T
able
1.
Comple
xity
Comparison
Method
Number
of
Comple
x
Number
of
Comple
x
Multiplication
Addition
P
ark
et
al.
N
s
=
2
N
s
=
2
1
Cho
and
P
ark
N
s
=
2
N
s
=
2
+
J
3
Proposed
with
q=2
N
s
=
2
N
s
=
2
+
J
+
2
N
nor
m
5
Proposed
with
q=3
N
s
=
2
N
s
=
2
+
J
+
3
N
nor
m
6
the
time
of
fset
caused
by
transmission.
W
e
run
our
simulation
at
sampling
rate
0.1
s
,
CP
is
set
to
1
=
8
of
the
OFDM
symbol,
and
16-QAM
is
used
as
data
modulation.
The
simulation
is
conducted
on
the
V
ehicular
B
multipath
channel
model
with
v
ehicle
speed
set
to
120
km/hour
[13].
Note
that
we
use
N
s
=
2048
under
the
V
ehicular
B
channel,
so
that
the
duration
of
CP
is
longer
than
the
duration
of
CIR.
The
CFO
is
modelled
as
uniform
random
v
ariable
distrib
uted
in
range
3
and
P
F
A
is
set
to
10
6
.
The
observ
ation
length
for
detection
is
set
to
J
=
N
g
=
2
and
the
observ
ation
length
for
iterati
v
e
normalization
is
set
to
N
nor
m
=
N
s
=
2
.
MSE
of
symbol
timing
under
the
V
ehicular
B
channel
are
sho
wn
in
Fig.
3.
F
or
that
channel
model,
the
proposed
method
outperforms
other
methods
sho
wn
in
a
much
smaller
MSE,
which
indicate
that
the
stable
timing
position
can
be
accomplished
with
less
number
of
preamble
detection.
P
ark’
s
method
has
the
lo
west
performance,
this
is
due
to
autocorrelation
technique
yields
a
delayed
timing
estimate.
The
proposed
method
has
better
performance
than
Cho’
s
method,
this
is
because
at
e
v
ery
iteration
in
iterati
v
e
no
r
malization
technique
increasing
the
g
ain
of
correlation
magnitude
on
the
first
arri
ving
path
and
pressing
the
others
path
g
ain,
while
in
the
Cho’
s
method,
the
detection
is
made
without
iterati
v
e
normalization
technique
so
that
the
v
ery
small
g
ain
of
correlation
magnitude
on
the
first
arri
ving
path
causes
a
f
ailure
in
detecting
the
first
arri
ving
path
(the
correct
timing
point).
Note
that
the
proposed
method
with
q
=
3
is
better
than
the
proposed
method
with
q
=
2
in
the
e
xpense
of
increasing
comple
xity
.
When
we
increase
q
>
3
,
we
find
that
the
performance
does
not
significantly
impro
v
ed.
Impr
o
ved
T
iming
Estimation
Using
Iter
ative
Normalization
T
ec
hnique
for
OFDM
...
(Suyoto)
Evaluation Warning : The document was created with Spire.PDF for Python.
910
ISSN:
2088-8708
Figure
3.
Performance
of
three
methods
under
V
ehicular
B
channel.
The
comple
xity
of
the
proposed
method
in
comparison
with
the
pre
vious
methods
sho
wn
in
the
T
able
1.
In
the
proposed
method
with
q
=
2
,
we
need
N
s
=
2
comple
x
multiplication
and
N
s
=
2
2
comple
x
addition
to
calculate
T
(
d
)
2
.
Then,
it
needs
2
di
vision
a
n
d
2
N
nor
m
2
comple
x
addition
to
calculate
iterati
v
e
normalization.
After
that,
it
needs
1
di
vision
and
J
1
comple
x
addition
to
obtain
(
d
)
.
In
the
proposed
method
with
q
=
3
,
we
need
N
s
=
2
comple
x
multiplication
and
N
s
=
2
2
comple
x
addition
to
calculate
T
(
d
)
2
.
Then,
it
needs
3
di
vision
and
3
N
nor
m
3
comple
x
addition
to
calculate
iterati
v
e
normalization.
After
that,
it
needs
1
di
vision
and
J
1
comple
x
addition
to
obtain
(
d
)
.
W
e
can
write
(15)
as
Z
2
i
(
d
)
=
Z
2
i
1
(
d
)
2
Z
(
i
1)
(
d
)
so,
the
root
equation
can
be
a
v
oided
and
is
not
considered
in
comple
xity
analysis.
From
T
able
1,
we
can
observ
e
that
our
proposed
method
can
be
realized
with
comparable
comple
xity
to
the
pre
vious
methods.
Thus,
our
propos
ed
estimator
can
pro
vide
an
impro
v
ed
performance
with
a
slight
additional
comple
xity
than
pre
vious
methods.
5.
CONCLUSION
W
e
already
proposed
an
impro
v
ement
of
the
timing
estimation
based
on
statistical
change
of
symmetric
correlator
.
It
uses
iterati
v
e
normalization
technique
to
the
correlator
output
before
the
detection
based
on
statistical
change
of
symmetric
correlator
is
applied.
This
technique
increases
the
detection
probability
and
a
chie
v
es
superior
estimation
performance
in
multipath
en
vironments.
The
proposed
estimator
achie
v
es
better
performance
than
pre
vious
published
methods
as
sho
wn
in
smaller
MSE.
Hence,
the
propos
ed
estimator
appropriate
to
be
implemented
for
timing
synchronization
in
mobile
OFDM
systems
with
high
delay
spread
en
vironment.
A
CKNO
WLEDGEMENT
The
author
w
ould
lik
e
to
thank
the
Editor
and
anon
ymous
re
vie
wers
for
their
helpful
comments
and
sugges-
tions
in
impro
ving
the
quality
of
this
paper
and
the
Indonesia
Endo
wment
Fund
for
Education
(LPDP)
for
their
support
to
our
w
ork
in
this
research.
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art
11:
W
ireless
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ysical
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ol.
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2017:
905
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912
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISSN:
2088-8708
911
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ei
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e
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em
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2014.
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and
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2009.
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M.
Schmidl
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C.
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ust
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y
and
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iming
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for
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”
IEEE
T
rans.
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mun.,
v
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45,
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1997.
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.
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Bhar
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v
a,
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v
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pp.
242244,
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2000.
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ark
and
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Cheon,
C.
Kang,
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Hong,
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el
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iming
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2003.
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el
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y
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e
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”
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2008.
[12]
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and
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P
ark,
”
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iming
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Based
on
Statistical
Change
of
Symmetric
Correlator
for
OFDM
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”
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BIOGRAPHIES
OF
A
UTHORS
Suy
oto
is
a
researcher
with
Research
Center
for
Informatics,
Indonesian
Institute
of
Sciences
since
2005.
He
obtained
bachelor
and
master
de
gree
in
electrical
engineering
from
Bandung
Institute
of
T
echnology
,
Indonesia,
in
2002
and
2009
respecti
v
ely
.
His
researches
are
in
fields
of
digital
systems,
signal
processing,
and
wireless
telecommunication.
His
research
focuses
on
timing
syn-
chronization
of
high
speed
mobile
OFDM.
He
is
af
filiated
with
IEEE
as
student
member
.
He
is
currently
w
orking
to
w
ard
Doctoral
de
gree
at
School
of
El
ectrical
Engineering
and
Informatics,
In-
stitut
T
eknologi
Bandung
(ITB),
Bandung,
Indonesia.
Iskandar
completed
his
B.E.
and
M.E.
de
grees
all
in
communications
engineering
from
Institut
T
eknologi
Bandung
(ITB),
Indonesia
in
1995
and
2000,
respecti
v
ely
.
In
March
2007,
he
recei
v
ed
his
Ph.D
de
gree
from
the
Graduate
School
of
Global
Information
and
T
elecommunication
Studies
(GITS),
W
aseda
Uni
v
ersity
,
Japan.
Since
April
1997,
he
joined
the
Department
of
Electrical
Engi-
neering,
ITB,
as
lecturer
.
His
major
research
interests
are
in
the
areas
of
radio
propag
ation,
channel
modelling,
mobile
communication,
stratospheric
platform,
and
millimetre
w
a
v
e
band.
Impr
o
ved
T
iming
Estimation
Using
Iter
ative
Normalization
T
ec
hnique
for
OFDM
...
(Suyoto)
Evaluation Warning : The document was created with Spire.PDF for Python.
912
ISSN:
2088-8708
Sugihartono
Sugihartono
recei
v
ed
the
B.E.
de
gree
in
Electrical
Engineering
from
Institut
T
eknologi
Bandung,
Indonesia
in
1973.
He
recei
v
ed
master
and
doctor
de
grees
from
the
Ecole
Nationale
Superieure
de
l’Aeronautique
et
de
l’Espace,
T
oulouse,
France,
in
1982
and
1987
respec-
ti
v
ely
.
Dr
.
Sugihartono
is
currently
Associate
Professor
at
the
School
of
Electrical
Engineering
and
Informatics,
Institut
T
eknologi
Bandung,
Indonesia.
His
research
interest
co
v
ers
digital
communi-
cation
system
and
communication
signal
processing.
Adit
K
ur
niawan
recei
v
ed
B.
Eng.
in
Electrical
Engineering
from
Bandung
Institute
of
T
echnology
,
Indonesia,
in
1986.
He
then
recei
v
ed
M.
Eng.
and
Ph.D
in
T
elecommuni
cation
Engineering
from
the
RMIT
Uni
v
ersity
and
the
Uni
v
ersity
of
South
Australia,
respecti
v
ely
in
1996
and
2003.
He
is
currently
Professor
at
School
of
Electrical
Engineering
and
Informatics,
Bandung
Institute
of
T
echnology
,
Indonesia.
His
research
interests
co
v
er
the
area
of
Antenna
and
W
a
v
e
Propag
ation,
and
W
ireless
Communications.
IJECE
V
ol.
7,
No.
2,
April
2017:
905
–
912
Evaluation Warning : The document was created with Spire.PDF for Python.