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Ac
c
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a
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q
u
a
li
t
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i
n
d
ica
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ial
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r
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ti
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n
c
e
in
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term
e
v
o
lu
ti
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E)
n
e
two
r
k
s.
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re
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l
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wo
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sc
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rio
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e
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n
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s
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u
c
tu
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te
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id
l
y
d
u
e
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se
r
m
o
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il
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n
a
c
c
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ra
te
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stim
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ti
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a
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to
s
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b
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li
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d
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u
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li
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o
S
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r
b
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u
se
rs
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n
d
n
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two
rk
o
p
e
ra
t
o
rs.
Trad
it
io
n
a
l
Ka
lma
n
f
il
ter
(KF)
a
p
p
ro
a
c
h
e
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ften
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g
g
le
wit
h
t
h
e
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o
n
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li
n
e
a
r
a
n
d
ti
m
e
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v
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r
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g
n
a
tu
re
o
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les
s
c
h
a
n
n
e
ls,
e
sp
e
c
ially
u
n
d
e
r
u
n
p
re
d
icta
b
le
m
o
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il
it
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p
a
tt
e
r
n
s.
T
h
is
p
a
p
e
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p
ro
p
o
se
s
a
n
imp
r
o
v
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d
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ti
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m
e
th
o
d
b
a
se
d
o
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th
e
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ten
d
e
d
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lma
n
f
il
ter
(EKF
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wh
ic
h
m
o
d
e
ls n
o
n
-
li
n
e
a
r
sy
ste
m
d
y
n
a
m
ics
m
o
re
e
ffe
c
ti
v
e
ly
.
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h
e
m
e
th
o
d
is
imp
lem
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n
ted
i
n
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-
S
im,
a
n
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ly
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si
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g
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AT
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n
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lu
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ted
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n
d
M
a
n
h
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n
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su
lt
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s
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o
w t
h
a
t
a
c
ro
ss
m
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li
ty
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s
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h
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t
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re
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M
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ra
ti
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S
INR)
sta
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ro
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stn
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su
lt
s
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h
e
imp
o
rtan
c
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o
f
m
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it
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a
wa
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e
stim
a
ti
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teg
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e
n
h
a
n
c
in
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LT
E
n
e
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o
rk
a
d
a
p
tab
il
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y
a
n
d
th
r
o
u
g
h
p
u
t.
K
ey
w
o
r
d
s
:
C
h
an
n
el
q
u
ality
in
d
icato
r
esti
m
atio
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E
x
ten
d
ed
Kalm
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n
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ilter
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o
n
g
ter
m
ev
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tio
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Mo
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els
Sig
n
al
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to
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in
ter
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er
e
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p
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s
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n
o
is
e
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atio
T
h
is i
s
a
n
o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Hilar
y
U.
E
ze
a
Dep
ar
tm
en
t o
f
E
lectr
ical
an
d
E
lectr
o
n
ic
E
n
g
i
n
ee
r
in
g
,
Facu
lty
o
f
E
n
g
in
ee
r
i
n
g
,
Fed
e
r
al
Un
i
v
er
s
ity
Oy
e
E
k
iti
E
k
iti State,
Nig
er
ia
E
m
ail:
h
ilar
y
.
ez
ea
@
f
u
o
y
e.
e
d
u
.
n
g
1.
I
NT
RO
D
UCT
I
O
N
I
n
lo
n
g
te
r
m
e
v
o
lu
tio
n
(
L
T
E
)
n
etwo
r
k
s
an
d
o
t
h
er
wir
e
less
m
o
b
ile
co
m
m
u
n
icatio
n
n
etwo
r
k
s
,
ac
cu
r
ate
k
n
o
wled
g
e
o
f
ch
an
n
el
co
n
d
itio
n
s
is
f
u
n
d
am
en
tal
t
o
en
s
u
r
in
g
ef
f
icien
t
s
p
ec
tr
u
m
u
tili
za
tio
n
,
o
p
tim
al
s
ch
ed
u
lin
g
,
an
d
co
n
s
is
ten
t
s
er
v
ice
q
u
ality
[
1
]
.
I
n
m
o
d
elin
g
wir
eless
co
m
m
u
n
icatio
n
s
y
s
tem
s
,
u
n
d
er
s
tan
d
in
g
th
e
in
h
er
en
t
ch
ar
ac
ter
is
tics
o
f
th
e
ch
an
n
el
h
elp
s
in
ca
p
tu
r
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n
g
th
e
d
y
n
am
is
m
ass
o
ciate
d
with
ch
an
n
el
s
tate
v
ar
iatio
n
.
T
h
e
ch
an
n
el
ch
a
r
ac
t
er
is
tics
co
u
ld
b
e
q
u
an
tifie
d
as
ch
an
n
el
q
u
ality
in
d
icato
r
(
C
QI
)
o
r
ch
a
n
n
el
s
tate
in
f
o
r
m
atio
n
(
C
SI)
.
W
h
ile
th
e
C
SI
p
r
o
v
id
es
d
etailed
in
f
o
r
m
a
tio
n
ab
o
u
t
th
e
ch
an
n
el
co
n
d
iti
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n
s
an
d
is
u
s
ed
f
o
r
lin
k
ad
ap
tatio
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b
ea
m
f
o
r
m
in
g
,
an
d
o
th
er
t
r
an
s
m
is
s
io
n
tech
n
iq
u
es,
th
e
C
QI
p
r
o
v
id
es
a
q
u
an
tized
m
ea
s
u
r
e
o
f
ch
an
n
el
q
u
ality
a
n
d
is
u
s
ed
to
d
eter
m
in
e
th
e
m
o
d
u
latio
n
an
d
co
d
in
g
s
ch
em
e
(
MCS
)
m
o
s
t
s
u
itab
le
f
o
r
s
ig
n
al
tr
an
s
m
is
s
io
n
[
2
]
.
I
n
L
T
E
s
y
s
tem
s
,
C
QI
esti
m
atio
n
p
lay
s
a
p
iv
o
tal
r
o
le
in
d
y
n
a
m
ic
r
eso
u
r
ce
allo
ca
tio
n
,
en
ab
lin
g
th
e
eNo
d
eB
to
ass
ig
n
a
p
p
r
o
p
r
iate
MCS
b
ased
o
n
r
ea
l
-
tim
e
ch
a
n
n
el
c
o
n
d
iti
o
n
s
.
Acc
u
r
ate
C
QI
r
ep
o
r
tin
g
is
cr
u
cial
f
o
r
m
ain
ta
in
in
g
q
u
ality
o
f
s
er
v
ice
(
Qo
S)
,
r
ed
u
ci
n
g
r
etr
an
s
m
is
s
io
n
s
,
an
d
en
s
u
r
in
g
s
p
ec
tr
al
ef
f
icien
cy
,
p
ar
ticu
la
r
ly
in
h
ete
r
o
g
en
e
o
u
s
n
etwo
r
k
s
an
d
f
o
r
u
s
er
s
lo
ca
ted
in
r
eg
io
n
s
with
p
o
o
r
s
ig
n
al
co
v
er
ag
e
,
s
u
ch
as
ce
ll
ed
g
es.
I
n
o
th
e
r
w
o
r
d
s
,
C
QI
ac
cu
r
ac
y
d
ir
ec
tly
im
p
ac
ts
th
r
o
u
g
h
p
u
t
an
d
r
eliab
i
lity
[
3
]
.
E
s
tim
atin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
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T
elec
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u
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p
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ch
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n
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q
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a
lity in
d
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ca
to
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ma
tio
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s
in
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ex
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e
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K
a
lma
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filt
er in
…
(
Hila
r
y
U.
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1167
th
e
C
SI
ac
cu
r
ately
in
a
wir
e
less
f
ast
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f
ad
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g
ch
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ly
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allen
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tai
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n
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th
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wh
ich
is
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elativ
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s
tr
aig
h
tf
o
r
wa
r
d
.
Ho
wev
er
,
C
QI
esti
m
atio
n
in
m
o
b
ile
en
v
ir
o
n
m
en
ts
p
o
s
es
a
s
ig
n
if
ican
t
ch
allen
g
e
d
u
e
to
th
e
h
ig
h
l
y
d
y
n
am
ic
n
atu
r
e
o
f
wir
eless
ch
an
n
els,
wh
ich
ar
e
in
f
lu
en
ce
d
b
y
f
ac
to
r
s
s
u
ch
as
f
ad
in
g
,
i
n
ter
f
er
en
ce
,
an
d
v
ar
y
in
g
u
s
er
m
o
b
ilit
y
p
atter
n
s
[
4
]
.
T
h
ese
f
ac
to
r
s
ar
e
m
o
r
e
p
r
o
n
o
u
n
ce
d
in
h
ig
h
m
o
b
ilit
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ce
n
ar
io
s
,
as
it
b
ec
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m
es
m
o
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e
ch
all
en
g
in
g
to
esti
m
ate
th
e
ch
an
n
els
ac
cu
r
ately
[
5
]
.
As
u
s
er
s
m
o
v
e,
ch
an
n
el
c
o
n
d
itio
n
s
ca
n
ch
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e
u
n
p
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ly
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o
u
td
ated
o
r
in
ac
cu
r
ate
C
QI
r
e
p
o
r
ts
th
at
d
eg
r
ad
e
lin
k
ad
ap
tat
io
n
an
d
o
v
er
all
n
etwo
r
k
p
er
f
o
r
m
an
ce
[
5
]
,
[
6
]
.
I
n
d
eter
m
in
in
g
ch
an
n
el
co
n
d
i
tio
n
s
as
a
f
u
n
ctio
n
o
f
tim
e,
it
is
n
ec
es
s
ar
y
to
p
u
t
in
to
p
er
s
p
ec
tiv
e
th
e
r
ea
lity
o
f
v
al
u
e
d
ep
r
ec
iatio
n
o
r
ag
ein
g
a
r
is
in
g
f
r
o
m
th
e
ef
f
ec
t
o
f
th
e
d
if
f
e
r
en
ce
b
et
wee
n
th
e
tim
e
o
f
m
ea
s
u
r
em
en
t
an
d
th
e
tim
e
o
f
u
s
ag
e
o
f
th
e
m
ea
s
u
r
ed
v
alu
es.
I
f
s
u
b
s
tan
tial
tim
e
elap
s
es
b
etwe
en
th
e
s
u
b
m
is
s
io
n
o
f
th
e
C
QI
r
ep
o
r
t
an
d
its
u
s
e
in
d
ec
is
io
n
-
m
ak
in
g
(
s
u
ch
as
s
ch
ed
u
lin
g
d
ec
i
s
io
n
s
)
,
th
e
r
ep
o
r
t
’
s
r
elev
an
ce
m
a
y
b
e
s
ig
n
if
ican
t
ly
d
eg
r
a
d
ed
,
p
o
ten
tially
lea
d
in
g
to
r
ed
u
ce
d
n
etwo
r
k
s
p
ec
tr
al
ef
f
icien
cy
[
7
]
.
C
o
n
s
eq
u
en
tly
,
it
is
r
ec
o
m
m
e
n
d
ed
th
at
th
e
esti
m
atio
n
b
ias
is
v
er
y
clo
s
e
to
ze
r
o
,
s
u
ch
th
at
t
h
e
esti
m
ated
v
alu
e
d
o
es
n
o
t
d
ev
iate
m
u
ch
f
r
o
m
th
e
ac
tu
al
co
n
d
itio
n
o
f
th
e
c
h
an
n
el.
T
h
e
tr
a
d
itio
n
al
Kalm
an
f
ilter
(
KF)
is
a
m
o
d
el
-
b
ased
iter
ativ
e
tech
n
iq
u
e
th
at
u
tili
ze
s
a
s
er
ies
o
f
o
b
s
er
v
atio
n
s
to
o
b
tain
a
m
o
r
e
ac
c
u
r
ate
esti
m
ate
o
f
th
e
s
tate
p
ar
am
eter
s
[
8
]
.
Alth
o
u
g
h
th
e
KF
tech
n
iq
u
es
ar
e
ef
f
ec
tiv
e
f
o
r
li
n
ea
r
s
y
s
tem
s
,
th
ey
o
f
ten
ex
h
ib
it
r
e
d
u
ce
d
ac
cu
r
ac
y
in
th
e
p
r
esen
ce
o
f
n
o
n
-
lin
ea
r
c
h
an
n
el
v
ar
iatio
n
s
,
co
m
m
o
n
ly
o
b
s
er
v
ed
in
r
an
d
o
m
o
r
i
r
r
eg
u
la
r
m
o
b
ilit
y
s
ce
n
ar
io
s
[
9
]
.
Sev
er
al
tech
n
iq
u
es
h
av
e
b
ee
n
ad
o
p
ted
in
wir
eless
n
etwo
r
k
s
f
o
r
esti
m
atin
g
th
e
C
QI
.
R
ao
an
d
Naid
u
[
1
0
]
p
r
o
p
o
s
ed
a
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SNR
)
esti
m
atio
n
alg
o
r
ith
m
f
o
r
o
r
th
o
g
o
n
al
f
r
e
q
u
en
c
y
d
iv
is
io
n
m
u
ltip
le
ac
ce
s
s
(
OFDMA
)
s
y
s
tem
s
i
n
wh
ich
th
e
o
r
th
o
g
o
n
al
f
r
e
q
u
en
cy
d
iv
is
io
n
m
u
ltip
lex
in
g
(
OFDM
)
tr
ain
in
g
s
y
m
b
o
ls
ar
e
em
p
lo
y
ed
in
ev
al
u
atin
g
th
e
n
o
is
e
v
ar
ia
n
ce
,
wh
i
le
s
ec
o
n
d
-
o
r
d
er
m
o
m
en
ts
o
f
th
e
r
ec
eiv
ed
s
y
m
b
o
ls
ar
e
u
s
ed
in
esti
m
atin
g
th
e
s
ig
n
al
p
lu
s
n
o
is
e
p
o
wer
.
Simu
lat
io
n
r
esu
lts
d
em
o
n
s
tr
ate
co
m
p
ar
ab
le
p
er
f
o
r
m
an
ce
with
th
eo
r
etica
l
an
aly
s
is
,
co
m
p
lem
en
ted
b
y
its
o
u
ts
tan
d
in
g
p
er
f
o
r
m
an
ce
wh
en
b
en
ch
m
a
r
k
ed
ag
ain
s
t
s
elec
ted
esti
m
atio
n
m
eth
o
d
s
.
Similar
ly
,
to
im
p
r
o
v
e
SNR
esti
m
ati
o
n
in
OFDM
n
etwo
r
k
s
,
L
in
g
[
1
1
]
ad
o
p
ted
a
n
ap
p
r
o
ac
h
th
at
alig
n
ed
with
t
h
e
n
etwo
r
k
’
s
n
o
n
-
lin
ea
r
ity
f
ea
t
u
r
es
b
y
e
m
p
lo
y
i
n
g
th
e
ex
ten
d
ed
Kalm
an
f
ilter
in
g
tech
n
iq
u
e.
C
o
m
p
a
r
ativ
e
an
aly
s
is
r
ev
ea
led
th
at
th
e
ex
ten
d
ed
Kalm
an
f
ilter
(
E
KF
)
esti
m
at
o
r
o
u
tp
er
f
o
r
m
s
th
e
least
s
q
u
ar
es
(
L
S)
an
d
m
in
im
u
m
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MM
SE)
tech
n
iq
u
es.
I
n
p
u
r
s
u
it
o
f
e
v
en
lo
wer
b
it
e
r
r
o
r
r
ate
(
B
E
R
)
,
Kap
il
et
a
l
.
[
1
2
]
p
r
o
p
o
s
ed
a
m
o
d
if
ied
ex
te
n
d
ed
Kalm
an
f
ilter
(
ME
KF)
to
jo
in
tly
esti
m
ate
th
e
ch
an
n
el
r
esp
o
n
s
e
an
d
a
u
to
-
r
e
g
r
ess
iv
e
(
AR
)
m
o
d
el
co
ef
f
icien
ts
,
co
m
b
in
in
g
th
e
f
ast
co
n
v
e
r
g
en
ce
r
ate
o
f
E
KF
an
d
th
e
co
r
r
elatio
n
f
ea
tu
r
e
o
f
2
D
in
ter
p
o
latio
n
u
s
in
g
least
s
q
u
ar
es
(
2
DI
L
S).
Alth
o
u
g
h
it
a
ch
iev
ed
lo
wer
B
E
R
th
an
E
KF a
n
d
2
DI
L
S,
ME
KF is p
r
o
n
e
to
esti
m
atio
n
e
r
r
o
r
s
a
n
d
co
m
es with
h
i
g
h
er
c
o
m
p
u
tatio
n
al
co
m
p
lex
ity
.
I
n
an
o
th
e
r
s
tu
d
y
,
T
a
n
g
et
a
l
.
[
1
3
]
p
r
o
p
o
s
ed
a
KF
-
b
ased
c
h
a
n
n
el
esti
m
atio
n
m
eth
o
d
f
o
r
2
×
2
an
d
4
×
4
s
p
ac
e
-
tim
e
b
lo
ck
c
o
d
in
g
m
u
ltip
le
-
in
p
u
t
an
d
m
u
ltip
le
-
o
u
t
p
u
t
o
r
t
h
o
g
o
n
al
f
r
e
q
u
en
c
y
d
i
v
is
io
n
m
u
ltip
lex
in
g
(
STBC
MI
M
O
-
OFDM)
s
y
s
te
m
s
in
d
y
n
am
ic
en
v
ir
o
n
m
en
ts
,
u
s
in
g
o
r
th
o
g
o
n
al
s
p
ac
e
-
tim
e
c
o
d
ewo
r
d
s
an
d
p
ilo
t
s
eq
u
en
ce
s
to
s
u
p
p
r
ess
an
ten
n
a
in
ter
f
er
e
n
ce
b
e
f
o
r
e
ap
p
l
y
in
g
th
e
KF
’
s
p
r
ed
ictio
n
–
u
p
d
ate
p
r
o
ce
s
s
with
n
o
is
e
s
u
p
p
r
ess
io
n
.
T
h
is
a
p
p
r
o
ac
h
a
ch
iev
ed
s
tr
o
n
g
B
E
R
an
d
n
o
r
m
alize
d
m
ea
n
s
q
u
ar
e
e
r
r
o
r
(
NM
SE)
p
er
f
o
r
m
an
ce
,
b
u
t
at
th
e
co
s
t
o
f
i
n
cr
ea
s
ed
c
o
m
p
u
tatio
n
al
lo
ad
d
u
e
t
o
iter
ativ
e
KF
p
r
o
ce
s
s
in
g
an
d
p
ilo
t
d
esig
n
.
Ku
m
ar
an
d
Ma
lles
war
i
[
1
4
]
in
teg
r
ated
th
e
E
KF
with
a
s
liced
m
u
lti
-
m
o
d
u
lu
s
alg
o
r
ith
m
(
SMM
A)
f
o
r
im
p
r
o
v
in
g
OFDM
-
MI
MO
s
y
s
tem
s
,
o
u
tp
er
f
o
r
m
i
n
g
tr
ad
itio
n
al
m
u
lti
-
m
o
d
u
lu
s
alg
o
r
ith
m
s
in
ter
m
s
o
f
B
E
R
an
d
in
ter
-
s
y
m
b
o
l
in
ter
f
er
en
ce
m
et
r
ics.
R
ajen
d
er
et
a
l
.
[
6
]
p
r
o
v
id
es
a
co
m
p
r
eh
en
s
iv
e
r
ev
iew
o
f
Kalm
an
f
ilter
-
b
ased
ch
an
n
e
l
esti
m
atio
n
ca
p
ab
ilit
ies
ac
r
o
s
s
OFDM
an
d
MI
MO
-
STBC
s
y
s
tem
s
,
h
ig
h
lig
h
tin
g
b
o
th
ac
c
u
r
ac
y
a
n
d
co
m
p
u
tatio
n
al
d
e
m
an
d
s
.
Similar
ly
,
Dr
ak
s
h
ay
in
i
an
d
Ko
u
n
t
e
[
8
]
class
if
ied
tech
n
iq
u
es
in
to
m
o
d
el
-
b
ased
an
d
d
ee
p
lear
n
in
g
-
b
ased
ca
teg
o
r
ies,
n
o
tin
g
th
at
wh
ile
KF
y
ield
s
h
ig
h
ly
ac
cu
r
ate
esti
m
ates
th
r
o
u
g
h
iter
ativ
e
o
b
s
er
v
atio
n
,
it c
o
m
es with
s
u
b
s
tan
tial c
o
m
p
u
tatio
n
al
co
m
p
le
x
ity
.
B
u
ild
in
g
o
n
th
e
s
tr
en
g
th
s
o
f
KF
ap
p
r
o
ac
h
es,
s
ev
er
al
wo
r
k
s
h
av
e
ad
ap
ted
t
h
em
s
p
ec
if
ically
f
o
r
C
QI
p
r
ed
ictio
n
a
n
d
d
y
n
a
m
ic
r
eso
u
r
ce
o
p
tim
izatio
n
.
Fo
r
in
s
tan
ce
,
Su
lth
an
a
an
d
Nak
k
ee
r
an
[
1
5
]
a
d
d
r
ess
ed
th
e
u
n
r
ea
lis
tic
ass
u
m
p
tio
n
o
f
p
er
f
ec
t
C
QI
in
ea
r
lier
r
esear
ch
b
y
p
r
e
d
ictin
g
SNR
f
r
o
m
im
p
er
f
ec
t
C
QI
u
s
in
g
Kalm
an
f
ilter
in
g
.
T
h
e
p
r
e
d
icte
d
SNR
was
th
en
u
s
ed
to
esti
m
ate
tr
an
s
m
is
s
io
n
r
ates
an
d
d
esi
g
n
p
r
io
r
ity
u
tili
ties
f
o
r
s
ch
ed
u
lin
g
d
ec
is
io
n
s
.
T
eix
eir
a
an
d
T
im
o
teo
[
1
6
]
,
L
T
E
r
eso
u
r
ce
allo
ca
tio
n
was
en
h
an
ce
d
b
y
u
s
in
g
a
KF
-
b
ased
p
r
ed
ictio
n
m
eth
o
d
f
o
r
d
eter
m
in
in
g
t
h
e
d
ata
r
ate.
Ho
wev
er
,
p
ar
am
eter
f
in
e
-
tu
n
in
g
was
n
o
t
co
n
s
id
er
ed
.
I
n
a
r
elate
d
s
tu
d
y
B
is
was
et
a
l
.
[
1
7
]
,
m
u
ltip
le
lin
ea
r
r
eg
r
ess
io
n
was
u
s
ed
t
o
esti
m
ate
f
u
tu
r
e
th
r
o
u
g
h
p
u
t,
f
o
llo
wed
b
y
KF
co
r
r
ec
tio
n
to
m
itig
ate
p
r
ed
ictio
n
an
d
m
ea
s
u
r
em
en
t
er
r
o
r
s
.
T
h
is
ap
p
r
o
a
ch
d
eliv
er
ed
tim
ely
an
d
ac
c
u
r
ate
th
r
o
u
g
h
p
u
t
p
r
e
d
ictio
n
s
with
o
u
t
o
v
er
f
itti
n
g
,
m
ak
in
g
it
s
u
itab
le
f
o
r
e
n
er
g
y
-
co
n
s
tr
ain
ed
L
T
E
d
ev
ices,
th
o
u
g
h
with
lim
ited
p
er
f
o
r
m
an
ce
in
h
ig
h
ly
d
y
n
am
ic
ch
an
n
els.
E
x
ten
d
in
g
t
h
e
p
r
ed
ictiv
e
f
r
am
ewo
r
k
to
s
p
ec
tr
u
m
m
an
a
g
em
en
t,
T
i
m
ó
teo
et
a
l
.
[
1
8
]
a
p
p
lied
th
e
Kalm
an
-
T
ak
e
n
s
f
ilter
(
KT
F)
f
o
r
r
ea
l
-
tim
e
5
G
s
p
ec
tr
u
m
allo
ca
tio
n
.
B
y
m
in
i
m
izin
g
r
o
o
t
m
ea
n
s
q
u
ar
e
er
r
o
r
(
R
MSE
)
,
th
e
m
eth
o
d
ef
f
ec
ti
v
ely
ca
p
tu
r
ed
tr
af
f
ic
d
y
n
am
ics,
o
p
tim
ized
th
r
o
u
g
h
p
u
t
an
d
laten
cy
,
an
d
a
d
ap
ted
well
to
h
ig
h
-
d
em
an
d
s
ce
n
ar
i
o
s
.
No
n
eth
eless
,
it
s
p
er
f
o
r
m
an
ce
d
e
p
en
d
s
h
ea
v
il
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ataset
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ic
p
ar
am
eter
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n
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n
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er
s
tan
d
i
n
g
in
ter
-
p
a
r
am
eter
d
ep
en
d
e
n
cies.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
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1
6
9
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6
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,
Vo
l.
23
,
No
.
5
,
Octo
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e
r
20
25
:
1
1
6
6
-
1
1
7
6
1168
W
ith
th
e
r
is
e
o
f
m
ac
h
in
e
lear
n
in
g
,
d
ee
p
lear
n
in
g
-
b
ased
a
p
p
r
o
ac
h
es
f
o
r
C
QI
an
d
ch
an
n
el
esti
m
atio
n
h
av
e
b
ee
n
ex
ten
s
iv
ely
in
v
e
s
tig
ated
,
o
f
f
er
in
g
n
ew
o
p
p
o
r
tu
n
ities
f
o
r
p
atter
n
ex
tr
ac
ti
o
n
an
d
lo
n
g
-
ter
m
p
r
ed
ictio
n
.
A
co
m
p
a
r
ativ
e
an
aly
s
is
in
J
ian
g
an
d
Sch
o
tten
[
1
9
]
s
h
o
wed
th
at
wh
ile
r
ec
u
r
r
en
t
n
eu
r
al
n
etwo
r
k
(
R
NN)
-
b
ased
p
r
ed
icto
r
s
ex
h
ib
it
h
ig
h
er
c
o
m
p
u
tatio
n
al
co
m
p
lex
ity
th
an
KF
-
b
ased
p
r
ed
icto
r
s
,
b
o
th
ac
h
iev
e
co
m
p
ar
ab
le
s
in
g
le
-
s
tep
ac
cu
r
ac
y
,
th
o
u
g
h
R
NNs
d
em
o
n
s
tr
ate
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
m
u
lti
-
s
tep
p
r
ed
ictio
n
.
I
n
v
e
h
icu
lar
s
y
s
tem
s
,
Kim
an
d
Han
[
4
]
p
r
o
p
o
s
ed
a
n
r
ec
ei
v
ed
s
ig
n
al
s
tr
en
g
th
in
d
icato
r
(
R
SS
I
)
-
d
r
iv
en
l
ong
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
STM
)
-
b
ased
C
QI
p
r
ed
icto
r
th
at
o
u
tp
er
f
o
r
m
e
d
co
n
v
en
tio
n
al
tim
e
-
s
er
ies
m
o
d
els,
wh
ile
Qu
et
a
l
.
[
2
0
]
p
r
o
p
o
s
ed
a
tem
p
o
r
al
-
s
p
atial
co
llab
o
r
ativ
e
f
r
am
ewo
r
k
co
m
b
in
in
g
b
in
a
r
y
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
B
PS
O)
,
m
ax
-
r
e
lev
an
ce
an
d
m
in
-
r
ed
u
n
d
an
c
y
(
MRMR
)
f
ea
tu
r
e
s
elec
tio
n
,
d
ee
p
n
eu
r
al
n
etwo
r
k
(
DNN)
,
an
d
atten
tio
n
m
ec
h
a
n
is
m
s
,
y
ield
in
g
p
r
o
ac
tiv
e
l
o
n
g
ter
m
ev
o
lu
tio
n
-
r
ailway
(
L
T
E
-
R
)
b
ase
s
tatio
n
m
ain
ten
an
ce
,
th
o
u
g
h
with
s
i
g
n
if
ican
t
c
o
m
p
u
tatio
n
al
d
em
an
d
s
.
Fo
r
u
n
m
an
n
e
d
ae
r
ial
v
eh
icle
(
UAV
)
u
ltra
-
r
eliab
le
lo
w
-
laten
cy
co
m
m
u
n
icatio
n
s
,
B
ar
to
li
an
d
Ma
r
ab
is
s
i
[
2
1
]
ap
p
lied
d
ee
p
r
ec
u
r
r
e
n
t
n
eu
r
al
n
etwo
r
k
s
(
DR
NNs)
with
L
STM
,
wh
ich
r
ed
u
ce
s
d
ec
o
d
e
er
r
o
r
p
r
o
b
ab
ilit
y
an
d
im
p
r
o
v
es
th
r
o
u
g
h
p
u
t.
Ho
we
v
er
,
th
is
ap
p
r
o
ac
h
is
lim
ited
to
tem
p
o
r
a
l CQI
d
ata
an
d
n
e
g
lects sp
atial
/f
r
eq
u
en
c
y
co
r
r
elatio
n
s
.
Fu
r
th
er
m
o
r
e
,
C
walin
a
et
a
l
.
[
2
2
]
m
o
d
ele
d
a
n
o
n
-
lin
ea
r
r
elatio
n
s
h
ip
b
etwe
en
ch
an
n
el
p
ar
a
m
eter
s
an
d
b
lo
ck
e
r
r
o
r
r
ate
(
B
L
E
R
)
,
ac
h
iev
in
g
a
g
ain
o
f
u
p
to
4
0
%
o
v
er
lin
ea
r
m
o
d
els
with
lo
w
co
m
p
u
tatio
n
al
co
m
p
lex
ity
.
Similar
ly
,
Dio
u
f
et
a
l
.
[
2
3
]
ap
p
lied
DNN
an
d
L
STM
to
r
ea
l
4
G
d
ataset
s
,
ac
h
iev
in
g
lo
w
R
MSE
an
d
s
tr
o
n
g
p
r
ed
ictio
n
ac
cu
r
a
cy
,
b
u
t
r
e
q
u
ir
in
g
lar
g
e,
h
ig
h
-
q
u
ality
d
atasets
f
o
r
tr
ain
in
g
.
Ad
v
an
ce
d
wir
eless
s
ce
n
ar
io
s
,
s
u
ch
as
v
eh
icle
-
to
-
v
eh
icle
(
V2
V)
,
in
d
u
s
tr
ial
I
o
T
(
I
I
o
T
)
,
R
I
S
-
b
ased
s
y
s
tem
s
,
an
d
m
m
W
av
e
MI
MO
,
h
av
e
m
o
tiv
ated
th
e
d
ev
elo
p
m
en
t
o
f
s
p
ec
ialized
an
d
h
y
b
r
id
s
ch
em
es.
I
n
V2
V
an
d
I
I
o
T
n
e
two
r
k
s
,
L
iao
et
a
l
.
[
2
4
]
d
esig
n
e
d
two
B
ay
esian
f
ilter
-
b
ased
ch
an
n
el
esti
m
atio
n
tech
n
iq
u
es
-
b
asis
ex
ten
d
e
d
m
o
d
el
-
u
n
s
ce
n
ted
Kalm
an
f
ilter
(
B
E
M
-
UKF)
,
o
f
f
er
in
g
s
tr
o
n
g
r
o
b
u
s
tn
ess
at
h
ig
h
co
m
p
lex
ity
,
a
n
d
B
asis
E
x
ten
d
ed
M
o
d
el
-
e
x
ten
d
ed
Kalm
an
f
ilter
(
B
E
M
-
E
KF)
,
with
m
o
d
er
ate
r
o
b
u
s
tn
ess
at
lo
wer
co
m
p
lex
ity
.
Fo
r
in
d
u
s
tr
ial
s
u
b
n
etwo
r
k
s
,
Ga
u
tam
et
a
l
.
[
2
5
]
in
tr
o
d
u
ce
d
a
v
ar
iatio
n
al
d
ee
p
s
tate
s
p
ac
e
m
o
d
el
(
v
DSSM)
with
s
p
ar
s
e
s
tu
d
en
t
-
t
p
r
o
ce
s
s
r
eg
r
ess
io
n
a
n
d
m
o
d
if
ied
u
n
s
ce
n
ted
KF,
e
n
s
u
r
in
g
u
ltra
-
r
eliab
le
B
L
E
R
co
n
tr
o
l
d
esp
ite
th
e
n
ee
d
f
o
r
r
ea
l
-
tim
e
v
alid
atio
n
.
I
n
5
G/6
G
C
SI
p
r
ed
ictio
n
,
So
s
z
k
a
[
2
6
]
h
ig
h
lig
h
ted
th
e
p
o
te
n
tial o
f
L
STM
R
NNs
ac
r
o
s
s
s
u
b
-
6
GHz
a
n
d
m
m
W
av
e,
o
p
tim
izin
g
f
ea
tu
r
es
a
n
d
h
i
d
d
en
lay
er
s
b
u
t
s
tr
ess
in
g
th
e
n
ee
d
f
o
r
m
o
r
e
m
ea
s
u
r
em
en
t
-
d
r
iv
en
s
tu
d
ies.
I
n
m
m
W
av
e
M
I
MO
s
y
s
tem
s
,
Hu
an
g
et
a
l
.
[
2
7
]
co
m
b
in
ed
least
s
q
u
ar
e
esti
m
at
io
n
(
L
SE)
an
d
s
p
ar
s
e
m
ess
ag
e
p
ass
in
g
(
SMP)
to
ex
p
lo
it
ch
an
n
el
s
p
ar
s
ity
,
r
ea
ch
i
n
g
n
ea
r
-
cr
am
er
-
r
ao
lo
wer
b
o
u
n
d
ac
c
u
r
ac
y
with
in
f
iv
e
iter
atio
n
s
,
th
o
u
g
h
a
d
jace
n
t
-
en
tr
y
co
r
r
elatio
n
r
e
m
ain
s
u
n
ad
d
r
ess
ed
.
R
ec
o
n
f
ig
u
r
ab
le
in
tellig
en
t
s
u
r
f
ac
es
(
R
I
S)
-
ass
i
s
ted
s
y
s
tem
s
wer
e
tar
g
eted
in
W
ei
et
a
l
.
[
2
8
]
,
wh
ich
p
r
o
p
o
s
e
d
p
ar
allel
f
ac
to
r
an
al
y
s
is
(
PA
R
AFA
C
)
d
ec
o
m
p
o
s
itio
n
u
s
i
n
g
alter
n
atin
g
least
s
q
u
ar
es
(
AL
S)
an
d
v
ec
t
o
r
ap
p
r
o
x
im
ate
m
ess
ag
e
p
ass
in
g
(
VAM
P)
alg
o
r
ith
m
s
,
b
o
th
o
f
wh
ich
o
u
tp
er
f
o
r
m
e
d
b
en
ch
m
ar
k
s
ch
em
es
a
n
d
ac
h
iev
ed
n
ea
r
-
p
e
r
f
ec
t
s
u
m
r
ate
p
er
f
o
r
m
an
ce
.
C
o
n
s
tr
ain
ts
o
n
R
I
S
elem
en
t
n
u
m
b
e
r
s
an
d
tr
ain
in
g
s
y
m
b
o
l
len
g
th
s
,
as
well
as
esti
m
atio
n
am
b
ig
u
ity
,
we
r
e
n
o
ted
as
lim
itatio
n
s
.
Fin
ally
,
Ser
u
n
in
et
a
l
.
[
2
9
]
d
ev
elo
p
ed
a
C
SI
-
RS
-
b
ased
C
QI
ev
alu
atio
n
m
eth
o
d
in
v
o
l
v
in
g
n
o
is
e
esti
m
atio
n
,
SNR
tr
an
s
f
o
r
m
atio
n
,
an
d
MCS
s
elec
tio
n
,
ac
h
iev
in
g
ac
cu
r
ate
C
QI
r
ep
o
r
tin
g
u
n
d
er
a
d
d
itiv
e
wh
ite
g
a
u
s
s
ian
n
o
is
e
(
AW
G
N
)
co
n
d
itio
n
s
,
b
u
t
r
eq
u
ir
i
n
g
f
u
r
th
er
e
v
alu
atio
n
i
n
co
m
p
lex
f
ad
in
g
e
n
v
ir
o
n
m
en
ts
.
T
h
e
KF
’
s
lim
itatio
n
,
ex
em
p
lifie
d
b
y
r
e
d
u
ce
d
ac
cu
r
ac
y
in
m
o
d
elin
g
n
o
n
-
lin
ea
r
ch
an
n
el
b
eh
av
io
r
,
m
o
tiv
ates
ex
p
lo
r
i
n
g
th
e
E
K
F,
wh
ich
u
s
es
f
ir
s
t
-
o
r
d
er
li
n
ea
r
izatio
n
to
ac
co
m
m
o
d
ate
n
o
n
-
lin
ea
r
s
y
s
tem
d
y
n
am
ics
an
d
h
as
b
ee
n
s
h
o
wn
to
im
p
r
o
v
e
esti
m
atio
n
in
tim
e
-
v
ar
y
in
g
ch
an
n
els.
T
h
is
s
tu
d
y
p
r
o
p
o
s
es
an
E
KF
-
b
ased
C
QI
esti
m
atio
n
ap
p
r
o
ac
h
f
o
r
L
T
E
n
etwo
r
k
s
an
d
ev
alu
ates
its
p
er
f
o
r
m
an
ce
ag
ain
s
t
th
e
clas
s
ical
K
F
u
n
d
er
two
d
is
tin
ct
m
o
b
ilit
y
m
o
d
els:
th
e
s
tr
u
ctu
r
ed
Ma
n
h
att
an
m
o
d
el
an
d
th
e
u
n
s
tr
u
ctu
r
e
d
r
an
d
o
m
d
ir
ec
tio
n
m
o
d
el.
Usi
n
g
L
T
E
-
Sim
f
o
r
s
ig
n
al
-
to
-
in
ter
f
er
e
n
ce
-
p
lu
s
-
n
o
i
s
e
r
atio
(
SIN
R
)
e
x
tr
ac
tio
n
a
n
d
MA
T
L
AB
f
o
r
an
aly
s
is
,
th
e
wo
r
k
d
em
o
n
s
tr
ates
h
o
w
th
e
E
KF
tech
n
iq
u
e
en
h
an
ce
s
C
QI
esti
m
atio
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ac
c
u
r
ac
y
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n
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er
n
o
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ea
r
m
o
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n
d
itio
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s
.
T
h
e
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lts
ar
e
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v
an
t
f
o
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im
p
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o
v
in
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T
E
n
etwo
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a
p
tab
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th
r
o
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p
u
t,
an
d
Q
o
S in
r
ea
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w
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ld
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lo
y
m
e
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ts
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T
h
e
r
est
o
f
th
e
p
ap
er
is
s
tr
u
ctu
r
ed
as
f
o
llo
ws:
s
ec
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2
p
r
esen
ts
th
e
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y
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tem
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o
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p
r
o
v
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d
etailed
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atio
n
o
f
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m
atio
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in
g
KF
an
d
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K
F;
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3
d
is
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s
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es
th
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im
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latio
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an
d
se
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4
co
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e
p
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2.
M
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H
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2
.
1
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Sy
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ter
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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3
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W
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2
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2
.
E
s
t
im
a
t
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m
et
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T
o
esti
m
ate
th
e
C
QI
ef
f
ec
tiv
ely
,
two
f
ilter
in
g
a
p
p
r
o
ac
h
es
wer
e
em
p
lo
y
e
d
:
th
e
KF
f
o
r
lin
ea
r
s
y
s
tem
s
,
an
d
th
e
E
KF
f
o
r
s
y
s
tem
s
with
n
o
n
-
lin
ea
r
d
y
n
am
ics.
T
h
ese
f
ilter
in
g
tech
n
i
q
u
es
wer
e
s
elec
ted
b
ec
a
u
s
e
o
f
th
eir
ef
f
ec
tiv
en
ess
in
h
an
d
lin
g
n
o
i
s
y
m
ea
s
u
r
em
en
ts
an
d
th
eir
s
u
itab
ilit
y
f
o
r
c
h
an
n
el
s
tate
esti
m
atio
n
in
wir
eless
co
m
m
u
n
icatio
n
s
y
s
tem
s
.
T
h
e
f
o
llo
win
g
s
u
b
s
ec
tio
n
s
d
etail
th
eir
u
n
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er
ly
in
g
p
r
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cip
les,
m
ath
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atica
l
f
o
r
m
u
latio
n
s
,
an
d
a
p
p
licatio
n
t
o
L
T
E
C
QI
esti
m
atio
n
.
2
.
2
.
1
.
K
a
l
m
a
n
f
ilte
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T
h
e
KF
is
a
c
o
m
p
u
tatio
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m
eth
o
d
t
h
at
u
s
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a
s
tate
-
s
p
ac
e
m
o
d
el
to
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m
ate
th
e
s
tate
o
f
a
s
y
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tem
o
r
p
r
o
ce
s
s
in
th
e
tim
e
d
o
m
ain
.
I
t
lev
er
ag
es
th
e
r
elatio
n
s
h
ip
b
etwe
en
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s
tem
’
s
s
tat
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an
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m
ea
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r
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eq
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atio
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s
to
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r
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iv
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m
ate
th
e
s
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th
m
in
im
al
m
ea
n
s
q
u
ar
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er
r
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r
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3
1
]
,
[
3
2
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.
F
o
r
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m
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with
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eled
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er
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tial
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atio
n
s
[
3
3
]
.
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s
ed
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in
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tem
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in
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ewo
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k
[
3
4
]
.
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m
atio
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p
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s
s
in
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p
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id
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b
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at
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th
e
esti
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ate
[
6
]
.
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h
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esti
m
atio
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r
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tech
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iq
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e
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ig
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r
r
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n
t state
[
3
5
]
.
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n
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m
atin
g
th
e
C
QI
u
s
in
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e
KF
,
th
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ased
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ter
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T
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er
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e
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u
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1
5
]
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|
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6
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7
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6
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ile
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atr
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[
1
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.
Similar
ly
,
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e
in
(
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r
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ile
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Kalm
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m
in
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ize
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m
atio
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er
r
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an
d
is
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in
(
8
)
as
[
1
8
]
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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:
1
6
9
3
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6
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3
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n
o
n
-
lin
ea
r
s
y
s
tem
s
with
lin
ea
r
m
o
d
els
[
3
2
]
.
Alth
o
u
g
h
th
e
KF
is
ef
f
ec
tiv
e
f
o
r
m
a
n
y
esti
m
atio
n
p
r
o
b
lem
s
,
its
lim
itatio
n
to
f
in
ite
-
d
im
en
s
io
n
al
s
tate
r
e
p
r
e
s
en
tatio
n
s
m
ak
es
it
u
n
s
u
itab
le
f
o
r
s
y
s
tem
s
with
n
o
n
-
lin
ea
r
d
y
n
am
ics.
T
o
ad
d
r
es
s
th
is
lim
itatio
n
,
th
e
E
KF,
wh
ich
p
r
o
v
id
es
a
f
ir
s
t
-
o
r
d
er
lin
ea
r
izatio
n
o
f
n
o
n
-
lin
e
ar
s
y
s
tem
s
,
was
d
ev
elo
p
ed
.
T
h
e
en
h
an
ce
m
e
n
t
ac
h
iev
e
d
b
y
E
KF
is
a
r
esu
lt
o
f
its
ca
p
ab
ilit
y
to
a
p
p
r
o
x
im
ate
n
o
n
-
lin
ea
r
f
ilter
in
g
p
r
o
b
lem
s
u
s
in
g
T
ay
lo
r
p
o
ly
n
o
m
ial
ex
p
an
s
io
n
[
3
6
]
.
T
h
is
lin
ea
r
izatio
n
en
a
b
les
th
e
E
KF
to
a
p
p
ly
th
e
iter
ativ
e
an
d
co
r
r
ec
tio
n
p
r
o
ce
s
s
es
o
f
th
e
KF
to
s
y
s
tem
s
th
at
ar
e
non
-
lin
ea
r
,
s
u
ch
as
th
e
tim
e
-
v
ar
y
in
g
c
h
an
n
els
[
3
7
]
.
E
KF
co
m
p
r
is
es
two
s
tag
es,
wh
ich
in
c
lu
d
e
th
e
p
r
ed
ictio
n
s
tag
e
an
d
th
e
co
r
r
ec
tio
n
s
tag
e
[
3
8
]
.
I
n
th
e
p
r
e
d
ictio
n
s
tag
e,
an
esti
m
ated
cu
r
r
en
t
s
tate
o
f
th
e
ch
an
n
el
an
d
th
e
er
r
o
r
c
o
v
ar
ian
c
e
esti
m
ate
ar
e
u
s
ed
to
ca
lcu
late
th
e
esti
m
ates f
o
r
th
e
n
e
x
t state
[
1
2
]
.
|
−
1
=
(
−
1
,
,
0
)
(
1
1
)
T
h
e
(
1
1
)
im
p
lies
th
at
th
e
s
tate
tr
an
s
itio
n
m
o
d
el
is
a
d
if
f
er
en
t
iab
le
f
u
n
ctio
n
,
u
n
lik
e
th
e
ca
s
e
o
f
KF
,
wh
e
r
e
it
is
d
ef
in
ed
as
a
lin
ea
r
f
u
n
ctio
n
.
Si
m
ilar
ly
,
th
e
(
1
2
)
s
h
o
ws
th
at
th
e
m
ea
s
u
r
em
en
t
m
o
d
el
c
o
u
ld
b
e
d
ef
in
e
d
as
a
n
o
n
-
lin
ea
r
f
u
n
ctio
n
.
=
ℎ
(
)
+
(
1
2
)
|
−
1
=
−
1
+
−
1
(
1
3
)
T
h
e
(
1
3
)
p
r
o
v
i
d
es
th
e
er
r
o
r
c
o
v
ar
ian
ce
esti
m
ate,
wh
e
r
e
is
th
e
s
tate
tr
an
s
itio
n
m
atr
ix
,
is
th
e
tr
an
s
p
o
s
e
o
f
th
e
s
tate
tr
an
s
itio
n
m
atr
ix
a
n
d
(
−
1
)
r
ep
r
esen
ts
th
e
co
v
a
r
ian
ce
o
f
th
e
n
o
is
e.
I
n
th
e
co
r
r
ec
tio
n
s
tag
e,
t
h
e
p
r
ed
icted
esti
m
ate
is
s
u
b
ject
ed
to
a
co
r
r
ec
ti
o
n
al
p
r
o
ce
s
s
u
s
in
g
th
e
o
b
s
er
v
atio
n
m
o
d
el
to
m
in
im
i
ze
th
e
er
r
o
r
co
v
ar
ian
ce
o
f
th
e
esti
m
ato
r
,
r
esu
ltin
g
in
an
im
p
r
o
v
e
d
esti
m
ate,
as
s
h
o
wn
in
(
1
4
)
.
T
h
e
(
1
5
)
g
iv
es
an
u
p
d
ated
er
r
o
r
co
v
ar
ian
ce
es
tim
ate.
%
=
|
−
1
+
(
−
ℎ
(
|
−
1
,
0
)
)
(
1
4
)
=
|
−
1
(
−
)
(
1
5
)
W
h
er
e
is
th
e
KF
g
iv
en
b
y
:
=
|
−
1
(
|
−
1
+
)
−
1
(
1
6
)
W
h
er
e
|
−
1
+
is
th
e
in
n
o
v
atio
n
co
v
a
r
ian
ce
.
T
h
e
m
atr
ix
in
v
er
s
io
n
i
n
(
1
5
)
i
n
cr
ea
s
es
co
m
p
lex
ity
,
a
n
d
th
er
e
is
alwa
y
s
a
tr
ad
e
-
o
f
f
b
et
wee
n
co
m
p
u
tatio
n
al
co
m
p
lex
i
ty
an
d
th
e
E
KF e
s
tim
atio
n
ac
cu
r
ac
y
[
3
6
]
.
2
.
2
.
3
.
Det
er
m
ina
t
i
o
n o
f
t
he
key
pa
ra
m
e
t
er
s
in t
he
f
ilte
ring
pro
ce
s
s
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
KF
an
d
E
KF
d
ep
en
d
s
o
n
th
e
ap
p
r
o
p
r
iat
e
s
elec
tio
n
o
f
p
a
r
am
eter
s
s
u
ch
as
in
itial
s
tate,
co
v
ar
ian
ce
m
atr
ices,
an
d
p
r
o
ce
s
s
/m
ea
s
u
r
em
en
t
n
o
is
e
co
v
ar
ian
ce
s
.
I
n
im
p
lem
en
tin
g
KF
an
d
E
KF
f
o
r
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
I
mp
r
o
ve
d
ch
a
n
n
el
q
u
a
lity in
d
i
ca
to
r
esti
ma
tio
n
u
s
in
g
ex
ten
d
e
d
K
a
lma
n
filt
er in
…
(
Hila
r
y
U.
E
z
ea
)
1171
C
QI
esti
m
atio
n
in
th
is
s
tu
d
y
,
k
ey
p
ar
am
ete
r
s
wer
e
ch
o
s
en
th
r
o
u
g
h
a
co
m
b
in
atio
n
o
f
em
p
ir
ical
an
aly
s
is
,
s
im
u
latio
n
-
b
ased
tu
n
in
g
,
an
d
r
ef
er
en
ce
to
L
T
E
s
p
ec
if
icatio
n
s
.
T
h
e
s
elec
tio
n
p
r
o
ce
s
s
is
d
etailed
as f
o
llo
ws:
a.
I
n
itial
s
tate
(
0
):
f
o
r
b
o
th
t
h
e
K
F
an
d
E
KF
im
p
lem
en
tatio
n
s
,
th
e
in
itial
s
tate
was
s
et
to
th
e
f
ir
s
t
C
QI
v
alu
e
o
b
tain
ed
f
r
o
m
th
e
L
T
E
-
Sim
s
im
u
latio
n
o
u
tp
u
t,
with
th
e
ass
u
m
p
tio
n
th
at
th
e
in
itial
m
ea
s
u
r
ed
C
QI
is
a
r
ea
s
o
n
ab
le
ap
p
r
o
x
im
atio
n
o
f
th
e
tr
u
e
ch
an
n
el
q
u
ality
.
b.
I
n
itial
co
v
ar
ian
ce
m
atr
ix
(
0
):
t
h
e
in
itial
er
r
o
r
co
v
ar
ian
ce
m
a
tr
ix
was
ch
o
s
en
to
b
e
a
d
iag
o
n
al
m
atr
ix
with
r
elativ
ely
lar
g
e
v
al
u
es,
r
ef
lectin
g
th
e
in
itial
u
n
ce
r
tain
t
y
.
Fo
r
b
o
th
esti
m
atio
n
tech
n
iq
u
es,
th
e
s
am
e
in
itial
co
v
ar
ian
ce
m
atr
ix
v
alu
e
was
u
s
ed
to
allo
w
f
o
r
a
f
air
c
o
m
p
ar
is
o
n
b
etwe
en
KF
an
d
E
KF.
Ho
wev
er
,
d
if
f
er
en
ce
s
in
th
eir
u
n
d
e
r
ly
i
n
g
ass
u
m
p
tio
n
s
an
d
alg
o
r
ith
m
ic
s
tr
u
ctu
r
es
led
to
d
is
tin
ct
p
er
f
o
r
m
a
n
ce
ch
ar
ac
ter
is
tics
,
esp
ec
ially
in
n
o
n
-
lin
ea
r
s
y
s
tem
s
.
c.
Pro
ce
s
s
n
o
is
e
c
o
v
a
r
ian
ce
(
):
t
h
e
m
atr
ix
was
tu
n
ed
ex
p
er
i
m
en
tally
b
y
r
u
n
n
i
n
g
L
T
E
-
Si
m
s
ce
n
ar
io
s
with
v
ar
y
in
g
v
alu
es
an
d
co
m
p
ar
in
g
t
h
e
esti
m
ated
C
QI
ag
ain
s
t
r
ef
er
en
ce
v
alu
es.
T
h
e
o
p
tim
al
v
alu
e
,
m
in
im
ized
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
MSE
)
,
was
s
elec
ted
to
b
ala
n
ce
r
esp
o
n
s
iv
en
ess
to
ch
an
n
el
v
ar
iatio
n
s
an
d
th
e
s
m
o
o
th
in
g
o
f
r
an
d
o
m
f
lu
ct
u
atio
n
s
.
d.
Me
asu
r
em
en
t
n
o
is
e
co
v
a
r
ian
c
e
(
)
:
t
h
is
was
d
er
i
v
ed
f
r
o
m
th
e
v
ar
ian
ce
o
f
C
QI
m
ea
s
u
r
em
en
t
er
r
o
r
s
i
n
th
e
s
im
u
latio
n
,
ca
lcu
lated
as
th
e
v
ar
ian
ce
b
etwe
en
th
e
s
im
u
lato
r
’
s
in
s
tan
tan
eo
u
s
C
QI
o
u
tp
u
t
an
d
a
m
o
v
in
g
-
av
er
a
g
e
r
ef
er
en
ce
C
Q
I
o
v
er
th
e
s
am
e
p
er
io
d
.
T
h
is
d
er
iv
atio
n
en
s
u
r
ed
th
at
ac
cu
r
ately
r
ef
lecte
d
th
e
in
h
er
en
t
n
o
is
e
lev
el
o
f
th
e
C
QI
r
ep
o
r
tin
g
p
r
o
ce
s
s
in
th
e
s
im
u
lated
L
T
E
en
v
ir
o
n
m
en
t.
e.
Kalm
an
g
ain
:
i
n
b
o
th
KF
an
d
E
KF,
th
e
Kalm
an
g
ain
was
c
o
m
p
u
ted
d
y
n
am
ically
at
ea
ch
s
tep
f
r
o
m
th
e
ch
o
s
en
,
,
an
d
u
p
d
ated
c
o
v
ar
i
an
ce
v
alu
es.
No
f
ix
ed
g
ai
n
was
im
p
o
s
ed
,
allo
win
g
th
e
f
ilter
to
ad
ju
s
t
weig
h
tin
g
b
etwe
en
p
r
ed
ictio
n
an
d
m
ea
s
u
r
em
e
n
t a
d
ap
tiv
el
y
.
f.
State
tr
an
s
itio
n
an
d
o
b
s
er
v
atio
n
m
atr
ices
:
i
n
KF
im
p
lem
e
n
tatio
n
,
b
o
th
m
atr
ices
wer
e
s
et
to
u
n
ity
to
m
o
d
el
a
d
i
r
ec
t
r
elatio
n
s
h
ip
b
etwe
en
th
e
p
r
e
v
io
u
s
an
d
cu
r
r
en
t
s
tates,
as
well
as
b
etwe
en
th
e
s
tate
an
d
o
b
s
er
v
atio
n
.
I
n
co
n
tr
ast,
i
n
E
KF
im
p
lem
en
tatio
n
,
t
h
e
s
tate
tr
an
s
itio
n
J
ac
o
b
ian
(
)
an
d
m
ea
s
u
r
em
en
t
J
ac
o
b
ian
(
)
wer
e
r
ec
alcu
lated
at
ea
ch
iter
atio
n
b
ased
o
n
th
e
n
o
n
-
lin
ea
r
s
tate
an
d
m
ea
s
u
r
em
en
t
m
o
d
els
d
er
iv
ed
f
r
o
m
th
e
c
h
an
n
el
m
a
p
p
in
g
.
T
h
ese
m
atr
ices
e
n
s
u
r
e
d
co
r
r
ec
t
lin
ea
r
izatio
n
f
o
r
p
r
e
d
ictio
n
-
u
p
d
ate
cy
cles.
2
.
3
.
Sim
ula
t
i
o
n
s
et
up
Simu
latio
n
s
wer
e
co
n
d
u
cted
u
s
in
g
L
T
E
-
Sim,
wh
ich
p
r
o
v
i
d
ed
a
r
o
b
u
s
t
p
latf
o
r
m
f
o
r
m
o
d
elin
g
L
T
E
s
y
s
tem
b
eh
av
io
r
u
n
d
er
v
ar
io
u
s
s
ce
n
ar
io
s
.
MA
T
L
AB
was
s
u
b
s
eq
u
en
tly
em
p
l
o
y
ed
f
o
r
p
o
s
t
-
s
im
u
latio
n
d
ata
p
r
o
ce
s
s
in
g
,
s
tatis
tical
an
aly
s
i
s
,
an
d
v
is
u
aliza
tio
n
o
f
th
e
o
b
tain
ed
r
esu
lts
.
T
h
e
s
im
u
latio
n
en
v
ir
o
n
m
en
t
was
co
n
f
ig
u
r
ed
to
em
u
late
r
ea
lis
tic
L
T
E
d
o
wn
lin
k
co
n
d
itio
n
s
.
2
.
3
.
1
.
Sim
ula
t
io
n pa
ra
m
et
er
s
T
h
e
L
T
E
-
Sim
s
im
u
latio
n
s
o
f
t
war
e
was
u
s
ed
to
ex
tr
ac
t
t
h
e
SIN
R
f
r
o
m
th
e
esti
m
ated
C
QI
,
an
d
th
e
p
lo
ts
wer
e
ca
r
r
ied
o
u
t
u
s
in
g
MA
T
L
AB
.
T
o
esti
m
ate
th
e
c
h
an
n
el
q
u
ality
,
th
e
ad
a
p
tiv
e
m
o
d
u
latio
n
a
n
d
co
d
in
g
(
AM
C
)
m
o
d
u
le
in
L
T
E
-
Sim
was
m
o
d
if
ied
,
an
d
th
e
esti
m
ated
ch
an
n
el
q
u
ality
was
u
s
e
d
to
d
eter
m
in
e
th
e
SIN
R
.
Deta
il
s
o
f
th
e
s
im
u
latio
n
p
ar
am
eter
s
ar
e
p
r
esen
ted
i
n
T
ab
le
1
.
T
ab
le
1
.
Simu
latio
n
p
ar
am
eter
s
P
a
r
a
me
t
e
r
V
a
l
u
e
u
se
d
C
e
l
l
s
c
e
n
a
r
i
o
S
i
n
g
l
e
-
c
e
l
l
C
e
l
l
r
a
d
i
u
s
1
k
m
N
u
mb
e
r
o
f
R
B
s
50
B
a
n
d
w
i
d
t
h
10
M
H
z
F
r
a
me
s
t
r
u
c
t
u
r
e
F
D
D
U
E
s
p
e
e
d
3
k
m/
h
r
P
r
o
p
a
g
a
t
i
o
n
m
o
d
e
l
P
ED
-
A
,
Ty
p
i
c
a
l
U
r
b
a
n
M
o
b
i
l
i
t
y
m
o
d
e
l
s
M
a
n
h
a
t
t
a
n
,
r
a
n
d
o
m
S
c
h
e
d
u
l
i
n
g
t
ype
D
o
w
n
l
i
n
k
sc
h
e
d
u
l
i
n
g
a
l
g
o
r
i
t
h
m w
i
t
h
i
mp
e
r
f
e
c
t
C
Q
I
(
D
S
A
)
S
i
mu
l
a
t
i
o
n
d
u
r
a
t
i
o
n
5
0
0
s
2
.
3
.
2
.
M
o
bil
it
y
m
o
dels
T
h
e
m
o
b
ilit
y
m
o
d
els
co
n
s
id
er
ed
in
th
is
wo
r
k
ar
e
th
e
Ma
n
h
a
ttan
m
o
b
ilit
y
m
o
d
el
an
d
th
e
r
an
d
o
m
m
o
b
ilit
y
m
o
d
el.
I
n
th
e
co
n
te
x
t
o
f
L
T
E
n
etwo
r
k
s
im
u
latio
n
s
,
th
e
Ma
n
h
attan
m
o
b
ilit
y
a
n
d
r
an
d
o
m
d
ir
ec
tio
n
m
o
b
ilit
y
m
o
d
els
ar
e
co
m
m
o
n
ly
u
s
ed
to
s
im
u
late
u
s
er
m
o
v
em
en
t,
with
th
e
Ma
n
h
attan
m
o
d
el
r
ep
r
esen
tin
g
m
o
v
em
en
t
al
o
n
g
a
g
r
id
-
lik
e
p
ath
,
with
tem
p
o
r
al
d
e
p
en
d
e
n
cies,
g
eo
g
r
a
p
h
ic
r
estrictio
n
s
b
u
t
with
n
o
s
p
atial
d
ep
en
d
e
n
cies
an
d
th
e
r
a
n
d
o
m
d
ir
ec
tio
n
m
o
d
el
r
ep
r
esen
tin
g
r
an
d
o
m
m
o
v
em
en
t
b
etwe
en
p
o
in
ts
,
with
n
o
tem
p
o
r
al
d
e
p
en
d
e
n
cy
,
n
o
r
s
p
a
tial
d
ep
en
d
en
cy
,
n
o
r
g
eo
g
r
ap
h
ic
r
estrictio
n
s
[
3
9
]
.
Mo
b
ilit
y
m
o
d
els
s
ig
n
if
ican
tly
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
TEL
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
23
,
No
.
5
,
Octo
b
e
r
20
25
:
1
1
6
6
-
1
1
7
6
1172
af
f
ec
t
SIN
R
in
L
T
E
n
etwo
r
k
s
,
s
in
ce
u
s
er
m
o
b
ilit
y
ca
n
le
ad
to
ch
an
g
es
in
s
ig
n
al
s
tr
en
g
th
as
m
o
b
ile
u
s
er
m
o
v
es a
way
f
r
o
m
th
e
eN
o
d
eB,
in
ter
f
er
en
ce
lev
els,
an
d
c
h
an
n
el
co
n
d
itio
n
s
s
u
ch
as p
ath
lo
s
s
o
r
f
ad
in
g
[
4
0
]
.
3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
SIN
R
p
lo
ts
f
o
r
th
e
KF
an
d
th
e
E
KF
esti
m
atio
n
s
ar
e
s
h
o
wn
in
Fig
u
r
es
1
an
d
2
,
r
esp
e
ctiv
ely
.
I
n
Fig
u
r
e
1
,
th
e
SIN
R
f
o
r
th
e
r
an
d
o
m
m
o
b
ilit
y
m
o
d
el
d
r
o
p
s
b
elo
w
1
0
d
B
f
o
r
ap
p
r
o
x
im
ately
8
5
%
o
f
th
e
o
b
s
er
v
atio
n
p
er
i
o
d
.
I
n
co
n
tr
ast,
f
o
r
th
e
Ma
n
h
atta
n
m
o
b
ilit
y
m
o
d
el,
t
h
e
SIN
R
r
em
ai
n
s
ab
o
v
e
1
0
d
B
f
o
r
o
v
er
5
0
%
o
f
th
e
o
b
s
er
v
atio
n
p
e
r
io
d
.
T
h
is
in
d
icate
s
th
at
th
e
KF
i
s
b
etter
at
esti
m
atin
g
th
e
SIN
R
f
o
r
u
s
er
s
f
o
llo
win
g
th
e
Ma
n
h
attan
m
o
b
ilit
y
m
o
d
el
th
an
f
o
r
th
o
s
e
with
r
an
d
o
m
d
ir
ec
tio
n
m
o
b
ilit
y
.
Fig
u
r
e
2
f
u
r
th
er
illu
s
tr
ate
th
at,
f
o
r
u
s
er
s
with
r
an
d
o
m
d
ir
ec
tio
n
m
o
b
ilit
y
,
t
h
e
SIN
R
ex
ce
e
d
s
1
0
d
B
f
o
r
ar
o
u
n
d
4
5
%
o
f
t
h
e
o
b
s
er
v
atio
n
p
er
i
o
d
,
with
a
p
ea
k
v
alu
e
r
ea
c
h
in
g
6
0
d
B
.
C
o
n
v
er
s
ely
,
u
s
er
s
with
th
e
Ma
n
h
attan
m
o
b
ilit
y
m
o
d
e
l
ex
p
er
ien
ce
SIN
R
v
alu
es d
r
o
p
p
in
g
b
elo
w
1
0
d
B
f
o
r
ab
o
u
t 7
0
% o
f
th
e
o
b
s
er
v
ati
o
n
p
er
i
o
d
.
Fig
u
r
e
1
.
SIN
R
E
s
tim
atio
n
s
u
s
in
g
KF
Fig
u
r
e
2
.
SIN
R
E
s
tim
atio
n
u
s
in
g
E
KF
Fo
r
th
e
Ma
n
h
attan
m
o
b
ilit
y
m
o
d
el,
th
e
SIN
R
est
im
ated
u
s
in
g
th
e
E
KF
ar
e
h
ig
h
er
th
an
th
o
s
e
esti
m
ated
with
th
e
KF
at
th
e
e
ar
ly
s
tag
es
o
f
esti
m
atio
n
.
Ho
wev
er
,
th
is
t
r
en
d
r
ev
er
s
es
in
t
h
e
later
s
tag
es.
T
h
is
o
b
s
er
v
atio
n
m
ay
b
e
d
u
e
to
n
o
n
-
lin
ea
r
s
tar
t
-
u
p
tr
an
s
ien
ts
a
n
d
g
r
ea
te
r
in
itial
u
n
ce
r
tain
ty
at
th
e
ea
r
ly
s
tag
e,
wh
ich
th
e
E
KF
m
an
a
g
es
to
ca
p
tu
r
e
m
o
r
e
ef
f
ec
tiv
ely
.
As
th
e
esti
m
atio
n
p
r
o
g
r
ess
es,
th
e
ch
an
n
el
s
tatis
tic
s
b
ec
o
m
es
m
o
r
e
lin
ea
r
,
m
ak
in
g
th
e
KF
m
o
r
e
s
u
itab
le
f
o
r
later
s
tag
es.
I
n
co
n
tr
ast,
in
a
r
an
d
o
m
m
o
b
ilit
y
m
o
d
el,
th
e
m
o
v
em
e
n
t
o
f
u
s
er
s
ar
e
u
n
p
r
ed
ictab
le
an
d
n
o
n
-
lin
ea
r
.
I
n
th
ese
s
itu
atio
n
s
,
th
e
E
KF,
wh
ich
,
is
s
p
ec
if
ically
d
esig
n
ed
to
h
a
n
d
le
n
o
n
-
lin
ea
r
s
y
s
tem
s
,
p
r
o
v
id
es
a
m
o
r
e
a
c
cu
r
ate
esti
m
ate
o
f
th
e
SIN
R
.
I
n
s
u
m
m
ar
y
,
wh
ile
b
o
th
KF
an
d
E
KF
tech
n
iq
u
es
ar
e
v
alid
o
p
tio
n
s
f
o
r
SI
NR
es
tim
atio
n
,
th
e
E
KF
d
em
o
n
s
tr
ates
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
in
L
T
E
n
etwo
r
k
s
ch
ar
ac
ter
ized
b
y
n
o
n
-
lin
ea
r
d
y
n
am
ics.
T
o
co
n
te
x
tu
alize
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
im
p
r
o
v
e
d
C
QI
esti
m
atio
n
m
eth
o
d
,
a
c
o
m
p
a
r
ativ
e
an
aly
s
is
o
f
th
e
s
im
u
latio
n
r
esu
lts
o
b
ta
in
ed
was
ca
r
r
ie
d
o
u
t
u
s
in
g
r
e
ce
n
t
liter
atu
r
e
f
in
d
in
g
s
.
Fig
u
r
e
1
s
h
o
ws
th
at
KF
esti
m
ates
SIN
R
m
o
r
e
ac
cu
r
ate
ly
in
s
tr
u
ct
u
r
ed
m
o
b
ilit
y
p
atte
r
n
s
,
alig
n
in
g
with
th
e
f
in
d
in
g
s
in
[
1
3
]
,
wh
er
e
KF
d
em
o
n
s
tr
ated
s
tr
o
n
g
B
E
R
an
d
NM
SE
p
er
f
o
r
m
an
ce
u
n
d
er
s
tr
u
ctu
r
ed
ch
a
n
n
el
co
n
d
itio
n
s
.
Ho
wev
er
,
it
r
eq
u
ir
es
iter
ativ
e
p
r
o
ce
s
s
in
g
an
d
p
ilo
t
d
esig
n
.
I
n
a
s
im
ilar
m
an
n
er
,
[
1
6
]
r
e
p
o
r
te
d
im
p
r
o
v
em
e
n
ts
in
th
r
o
u
g
h
p
u
t
an
d
a
r
ed
u
ctio
n
in
p
ac
k
et
lo
s
s
with
L
T
E
-
Sim
wh
en
u
s
in
g
KF
-
b
as
ed
p
r
e
d
ictio
n
s
,
p
a
r
ticu
lar
ly
in
co
n
tr
o
lled
o
r
less
v
ar
iab
le
m
o
b
ilit
y
s
ce
n
ar
io
s
,
wh
ich
alig
n
s
with
o
u
r
r
esu
lts
f
r
o
m
th
e
Ma
n
h
attan
m
o
b
ilit
y
m
o
d
el.
On
th
e
o
th
er
h
an
d
,
Fig
u
r
e
2
s
u
p
p
o
r
ts
th
e
f
in
d
in
g
s
o
f
[
1
1
]
,
w
h
ich
in
d
icate
d
th
at
th
e
E
KF
o
u
tp
er
f
o
r
m
ed
b
o
th
L
S
an
d
MM
SE
m
eth
o
d
s
in
n
o
n
-
li
n
ea
r
OFDM
ch
an
n
els.
Als
o
,
th
e
r
esu
lts
ar
e
co
n
s
is
ten
t
with
[
1
4
]
,
h
ig
h
lig
h
tin
g
th
e
E
KF
’
s
s
u
p
er
io
r
a
b
ilit
y
to
m
an
a
g
e
in
t
er
-
s
y
m
b
o
l
in
ter
f
er
e
n
ce
a
n
d
n
o
n
-
lin
ea
r
ities
co
m
p
ar
ed
to
t
r
a
d
itio
n
al
alg
o
r
ith
m
s
.
Ou
r
f
in
d
i
n
g
s
also
alig
n
with
[
2
4
]
,
wh
er
e
th
e
au
th
o
r
s
d
e
m
o
n
s
tr
ated
th
at
E
KF
-
b
ased
esti
m
ato
r
s
m
ain
tain
r
o
b
u
s
tn
ess
in
d
y
n
am
ic
an
d
u
n
p
r
e
d
ictab
le
m
o
b
ilit
y
en
v
ir
o
n
m
en
ts
,
s
u
ch
as
V2
V
a
n
d
I
I
o
T
n
etwo
r
k
s
.
I
n
co
m
p
ar
is
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a
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p
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ed
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r
s
in
[
4
]
,
o
u
r
a
p
p
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o
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h
p
r
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a
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in
[
1
2
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ac
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els.
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CO
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wh
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er
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is
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o
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atter
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ch
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T
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is
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ateg
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.
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DATA AV
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
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3
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TEL
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20
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:
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1174
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[
1
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[
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tri
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e
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ic
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n
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h
a
s
p
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b
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sh
e
d
o
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r
5
0
p
a
p
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in
re
p
u
tab
le,
h
ig
h
im
p
a
c
t
jo
u
rn
a
ls.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
m
a
m
il
u
s.a
h
a
n
e
k
u
@u
n
n
.
e
d
u
.
n
g
.
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c
e
n
t
C.
Chi
jin
d
u
o
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tain
e
d
h
is
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En
g
.
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lec
tri
c
a
l
a
n
d
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e
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tr
o
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ic
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n
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i
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a
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.
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n
g
.
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m
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ter
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c
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c
e
a
n
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g
in
e
e
ri
n
g
)
fro
m
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n
a
m
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ra
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tate
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rsity
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n
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u
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tate
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iv
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rsity
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f
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o
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o
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sp
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o
b
tai
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e
d
h
is
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h
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.
in
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m
p
u
t
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r
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g
in
e
e
rin
g
fro
m
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n
a
m
d
i
Az
ik
iwe
Un
i
v
e
rsity
,
Aw
k
a
,
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a
m
b
ra
S
tate
,
Nig
e
ria
i
n
2
0
1
6
.
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is
a
n
As
so
c
iate
P
ro
fe
ss
o
r
o
f
C
o
m
p
u
ter
En
g
in
e
e
rin
g
,
Un
iv
e
rsity
o
f
Ni
g
e
ria
Ns
u
k
k
a
,
En
u
g
u
S
tate
,
Nig
e
ria.
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h
a
s
su
c
c
e
ss
fu
ll
y
su
p
e
rv
ise
d
a
n
d
g
ra
d
u
a
ted
3
P
h
.
D
.
a
n
d
7
M
a
ste
rs
S
tu
d
e
n
ts,
w
h
il
e
c
u
r
re
n
tl
y
su
p
e
rv
i
sin
g
a
h
a
n
d
fu
l
o
f
st
u
d
e
n
ts
a
t
b
o
t
h
t
h
e
M
a
ste
rs
a
n
d
P
h
.
D
.
lev
e
ls.
His
re
se
a
rc
h
i
n
tere
sts
i
n
c
lu
d
e
:
d
i
g
it
a
l
ima
g
e
p
ro
c
e
ss
in
g
,
m
a
c
h
in
e
lea
rn
in
g
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
,
re
n
e
wa
b
le
e
n
e
rg
y
sy
st
e
m
s
a
n
d
m
a
teria
ls,
wire
l
e
ss
se
n
so
r
n
e
tw
o
rk
s
a
n
d
sy
ste
m
s
.
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re
c
e
iv
e
d
th
e
Ce
rti
f
ica
te
o
f
M
e
rit
a
wa
rd
a
s
th
e
Be
st
G
ra
d
u
a
ti
n
g
S
t
u
d
e
n
t
in
t
h
e
De
p
a
rt
m
e
n
t
o
f
El
e
c
tri
c
a
l
a
n
d
E
lec
tro
n
ic
En
g
i
n
e
e
rin
g
,
An
a
m
b
ra
S
tate
Un
iv
e
rsity
o
f
Tec
h
n
o
l
o
g
y
i
n
1
9
8
8
.
He
is
a
m
e
m
b
e
r
o
f
Nig
e
rian
S
o
c
iety
o
f
En
g
in
e
e
rs,
I
EE
E
Nig
e
ria
S
e
c
ti
o
n
,
a
n
d
Ni
g
e
ria
Co
m
p
u
ter
S
o
c
iety
(NCS
).
He
h
a
s
o
v
e
r
4
5
j
o
u
r
n
a
l
p
u
b
li
c
a
ti
o
n
s
a
n
d
o
n
e
p
a
ten
te
d
w
o
rk
t
o
h
is
c
re
d
i
t.
He
c
a
n
b
e
c
o
n
tac
ted
at
e
m
a
il
:
v
in
c
e
n
t.
c
h
ij
i
n
d
u
@u
n
n
.
e
d
u
.
n
g
.
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