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b
er
o
f
r
esear
c
h
p
a
p
er
s
w
er
e
p
r
ese
n
ted
o
n
t
h
e
R
a
k
e
r
ec
eiv
er
[
1
7
]
w
h
ic
h
al
s
o
in
clu
d
es
t
h
e
s
tu
d
y
o
f
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
p
ar
tial,
ch
ip
,
o
r
s
y
m
b
o
l
d
el
a
y
s
s
p
ac
i
n
g
R
a
k
e
w
it
h
ch
a
n
n
e
l
esti
m
ato
r
[
1
8
]
.
I
ts
ex
ec
u
t
io
n
w
a
s
ad
d
itio
n
all
y
s
tu
d
ied
w
it
h
t
w
o
co
m
b
in
i
n
g
s
tr
at
eg
ies
th
e
MR
C
a
n
d
S
L
C
(
Sq
u
ar
e
L
a
w
C
o
m
b
i
n
er
)
in
[
1
9
]
.
L
ik
e
w
is
e
a
Selecti
v
e
R
ak
e
(
SR
a
k
e)
r
ec
eiv
er
w
h
ic
h
tr
ac
k
s
th
e
s
tr
o
n
g
est
L
m
u
ltip
ath
co
m
p
o
n
en
ts
is
p
r
o
p
o
s
ed
in
[
2
0
]
w
i
th
its
e
x
e
cu
tio
n
ex
h
ib
ited
.
I
n
[
2
1
]
th
e
p
er
f
o
r
m
a
n
ce
f
o
r
UW
B
co
m
m
u
n
ica
tio
n
s
y
s
te
m
s
u
s
i
n
g
d
if
f
er
en
t o
p
ti
m
al
m
o
d
el
tech
n
iq
u
es i
n
a
R
AKE
r
ec
eiv
e
r
is
in
v
esti
g
ated
.
T
h
e
w
av
elet
tr
a
n
s
f
o
r
m
(
W
T
)
tech
n
iq
u
e
i
s
a
m
o
d
er
n
ar
ea
o
f
m
at
h
e
m
a
tic
s
t
h
at
is
a
p
p
lied
f
o
r
co
m
p
r
es
s
i
n
g
s
i
g
n
a
ls
a
n
d
i
m
a
g
es
a
n
d
r
e
m
o
v
i
n
g
n
o
i
s
e
f
r
o
m
th
eir
co
e
f
f
icien
ts
[
2
2
]
.
T
h
e
s
ig
n
al
-
to
n
o
is
e
r
atio
(
SNR
)
ca
n
b
e
i
m
p
r
o
v
ed
b
y
u
s
i
n
g
a
W
T
ap
p
r
o
ac
h
th
at
d
ec
o
m
p
o
s
e
s
t
h
e
s
i
g
n
al
i
n
to
d
if
f
er
en
t
s
ca
les
a
n
d
d
if
f
er
e
n
t
le
v
els
o
f
r
eso
l
u
tio
n
.
W
av
elet
v
id
eo
co
m
p
r
ess
io
n
w
a
s
ev
a
lu
ated
a
n
d
ac
h
ie
v
ed
in
[
`
2
3
]
,
[
2
4
]
f
o
r
w
id
eb
an
d
,
m
u
lt
i
-
ca
r
r
ier
,
co
d
e
-
d
iv
i
s
io
n
,
m
u
lt
ip
le
ac
ce
s
s
(
MC
-
C
DM
A
)
a
n
d
a
r
ak
e
r
ec
eiv
er
o
v
er
ad
d
itiv
e,
w
h
ite
Ga
u
s
s
ian
n
o
is
e
(
A
W
G
N)
an
d
th
e
R
a
y
lei
g
h
f
ad
i
n
g
c
h
an
n
el.
A
n
o
v
e
l
w
a
v
elet
r
a
k
e
r
ec
eiv
er
(
W
R
)
b
ased
o
n
co
n
ti
n
u
o
u
s
w
a
v
elet
tr
a
n
s
f
o
r
m
(
C
W
T
)
w
a
s
p
r
esen
ted
in
[
2
5
]
,
an
d
it
s
h
o
w
ed
en
h
a
n
ce
m
en
t
i
n
p
er
f
o
r
m
an
c
e
w
it
h
a
les
s
co
m
p
le
x
r
ec
eiv
er
.
I
n
[
2
6
]
,
[
2
7
]
,
w
ie
n
er
f
ilter
s
wer
e
u
s
ed
to
esti
m
ate
th
e
ch
a
n
n
el.
T
h
is
ap
p
r
o
ac
h
o
f
ch
an
n
el
esti
m
atio
n
is
k
n
o
w
n
as
m
i
n
i
m
u
m
m
ea
n
s
q
u
ar
e
er
r
o
r
(
MM
SE)
s
o
lu
tio
n
,
w
h
ic
h
p
r
o
v
id
es
m
o
r
e
r
eliab
le
esti
m
atio
n
in
f
a
s
t
f
ad
in
g
ch
a
n
n
el.
Ho
w
e
v
er
to
ca
lcu
late
th
e
o
p
ti
m
a
l
w
ien
er
f
ilter
co
ef
f
ic
ien
t
s
,
r
eq
u
ir
e
m
atr
ix
i
n
v
er
s
io
n
ca
lcu
latio
n
w
h
ic
h
is
h
ig
h
l
y
c
o
m
p
le
x
.
I
n
[
2
8
]
a
lin
e
ar
p
r
e
d
ictio
n
f
i
lter
w
a
s
in
tr
o
d
u
ce
s
w
h
ich
i
s
co
m
p
letel
y
b
ased
o
n
d
ec
is
io
n
-
f
ee
d
b
ac
k
m
o
d
e.
T
h
is
m
eth
o
d
o
v
er
co
m
e
s
th
e
co
m
p
u
tatio
n
a
l
co
m
p
lex
it
y
in
tr
o
d
u
ce
d
b
y
t
h
e
w
ie
n
er
f
i
lter
ap
p
r
o
ac
h
.
I
n
th
i
s
p
ap
er
,
an
ad
ap
tiv
e
co
n
tin
u
o
u
s
w
a
v
elet
tr
a
n
s
f
o
r
m
s
(
AC
W
T
)
b
ased
r
ak
e
r
ec
eiv
er
is
p
r
o
p
o
s
e
d
an
d
i
m
p
le
m
e
n
ted
w
h
er
e
th
e
f
i
n
g
er
g
ai
n
s
a
n
d
t
h
e
d
ela
y
s
ar
e
u
p
d
ated
o
v
er
th
e
tr
ai
n
i
n
g
p
er
io
d
.
L
MS
al
g
o
r
ith
m
is
u
s
ed
f
o
r
th
e
u
p
d
ati
n
g
d
ela
y
s
an
d
g
ain
s
o
f
t
h
e
f
in
g
er
.
2.
SYST
E
M
M
O
DE
L
T
w
o
p
o
p
u
lar
m
o
d
u
latio
n
t
y
p
e
s
b
ein
g
co
n
s
id
er
ed
f
o
r
UW
B
a
r
e
p
u
ls
e
-
p
o
s
i
tio
n
m
o
d
u
latio
n
(
P
P
M)
an
d
p
u
ls
e
a
m
p
lit
u
d
e
m
o
d
u
lat
io
n
(
P
A
M)
.
Her
e,
w
e
co
n
s
id
er
s
y
s
t
e
m
s
o
f
t
h
e
P
A
M
t
y
p
e,
an
d
a
m
o
d
el
f
o
r
s
in
g
le
-
u
s
er
P
A
M
UW
B
tr
an
s
m
is
s
io
n
o
v
e
r
ch
a
n
n
el
s
w
i
th
I
SI.
W
e
h
av
e
ass
u
m
ed
a
b
aseb
a
n
d
tr
an
s
m
is
s
io
n
s
y
s
te
m
,
an
d
h
en
ce
all
s
i
g
n
a
l
s
ar
e
ass
u
m
ed
to
b
e
r
ea
l
-
v
alu
ed
.
T
h
e
ze
r
o
-
m
ea
n
in
d
ep
en
d
e
n
t
an
d
id
en
t
ical
l
y
d
is
tr
ib
u
ted
(
i.i.
d
)
d
ata
s
y
m
b
o
ls
{s
n
}
ar
e
p
ass
ed
th
r
o
u
g
h
a
u
n
it
e
n
er
g
y
p
u
ls
e
s
h
ap
in
g
f
ilter
p
(
t)
w
h
ich
in
cl
u
d
es
th
e
ef
f
ec
t
s
o
f
th
e
tr
an
s
m
it
a
n
te
n
n
a.
No
te
t
h
at
w
e
r
eq
u
ir
e
t
h
e
p
u
l
s
e
s
h
ap
e
to
b
e
u
n
ch
a
n
g
in
g
f
r
o
m
s
y
m
b
o
l
p
er
io
d
t
o
s
y
m
b
o
l
p
er
io
d
,
b
u
t
o
u
r
m
o
d
el
is
g
e
n
e
r
al
en
o
u
g
h
to
in
c
lu
d
e
eit
h
er
ti
m
e
h
o
p
p
in
g
(
T
H)
o
r
d
ir
ec
t
s
e
q
u
en
ce
(
DS)
b
lo
ck
s
p
r
ea
d
in
g
i
f
,
f
o
r
ex
a
m
p
le,
p
(
t)
is
th
e
s
u
m
o
f
s
ev
er
al
d
ela
y
ed
Gau
s
s
ia
n
m
o
n
o
c
y
cles.
P
u
ls
e
w
a
v
e
f
o
r
m
g
e
n
er
atio
n
is
a
k
ey
tec
h
n
o
lo
g
y
i
n
UW
B
s
y
s
te
m
.
T
h
er
e
ar
e
s
o
m
e
g
o
o
d
ca
n
d
id
ates,
s
u
c
h
as
Her
m
i
te
p
o
ly
n
o
m
ial
p
u
ls
e,
Ga
u
s
s
ian
p
u
l
s
e,
n
ar
r
o
w
p
u
ls
e
b
ased
o
n
a
s
in
u
s
o
id
al
s
i
g
n
al,
etc.
Gau
s
s
ia
n
p
u
ls
e
h
as
s
o
m
e
u
n
iq
u
e
f
ea
tu
r
e
s
,
w
h
ich
ar
e
g
o
o
d
f
o
r
UW
B
s
y
s
te
m
.
So
i
n
t
h
is
p
ap
er
,
s
ec
o
n
d
o
r
d
er
d
er
iv
ativ
e
o
f
Gau
s
s
ia
n
p
u
ls
e
i
s
e
m
p
lo
y
ed
as th
e
p
u
ls
e
s
ig
n
al
o
f
UW
B
.
T
h
e
ex
p
r
ess
io
n
o
f
Gau
s
s
ia
n
p
u
l
s
e
is
:
(
)
√
(
1
)
W
h
er
e
A
is
th
e
s
i
g
n
a
l
a
m
p
lit
u
d
e
an
d
is
th
e
p
u
ls
e
s
h
ap
in
g
f
ac
to
r
.
T
h
e
ex
p
r
ess
io
n
o
f
s
e
co
n
d
o
r
d
er
d
er
iv
ativ
e
o
f
Ga
u
s
s
ia
n
p
u
l
s
e
is
:
(
)
√
,
(
)
-
(
2
)
Seco
n
d
o
r
d
er
d
e
r
iv
ativ
e
o
f
Ga
u
s
s
ian
p
u
ls
e
i
s
k
n
o
w
n
as
m
o
n
o
cy
cle.
Af
ter
p
u
l
s
e
s
h
ap
in
g
,
t
h
e
s
i
g
n
al
u
n
d
er
g
o
es
th
e
e
f
f
ec
ts
o
f
a
ch
an
n
el
w
i
th
M
p
at
h
s
w
h
o
s
e
r
esp
o
n
s
e
g
iv
e
n
b
y
:
(
)
∑
(
)
(
(
)
)
(
3
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
8
,
No
.
5
,
Octo
b
er
2
0
1
8
:
3
4
4
4
–
3
4
5
2
3446
W
h
er
e
α
(
m
)
an
d
τ
(
m
)
ar
e
th
e
g
ain
a
n
d
d
ela
y
i
n
tr
o
d
u
ce
d
b
y
t
h
e
m
th
p
at
h
o
f
t
h
e
ch
a
n
n
el.
(
)
∑
∑
(
)
(
(
)
)
(
)
(
4
)
W
h
er
e
T
is
th
e
s
y
m
b
o
l
r
ate
a
n
d
w
(
t)
is
ad
d
iti
v
e
n
o
is
e.
T
h
e
n
o
is
e
is
as
s
u
m
ed
to
b
e
a
ze
r
o
-
m
ea
n
w
id
e
s
en
s
e
s
tat
io
n
ar
y
p
r
o
ce
s
s
th
a
t
is
u
n
co
r
r
elate
d
w
it
h
t
h
e
d
ata,
an
d
it
m
a
y
b
e
co
lo
r
ed
d
u
e
to
n
ar
r
o
w
b
a
n
d
in
ter
f
er
er
s
.
I
n
th
e
ca
s
e
o
f
n
o
I
SI
an
d
w
h
e
n
th
e
n
o
is
e
i
s
A
W
GN,
th
e
o
p
ti
m
al
r
ec
eiv
er
is
a
f
ilter
m
a
tc
h
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to
th
e
r
ec
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ed
w
a
v
e
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o
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m
(
i.e
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th
e
c
o
m
b
i
n
ed
r
esp
o
n
s
e
o
f
th
e
c
h
an
n
el
an
d
tr
a
n
s
m
it
p
u
l
s
e
s
h
ap
es
)
.
T
y
p
icall
y
,
th
is
i
s
i
m
p
le
m
en
ted
i
n
a
R
A
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v
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e
w
it
h
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f
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wh
ich
ca
n
b
e
r
ep
r
esen
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it
h
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esp
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(
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(
5
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W
h
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m
o
d
el
p
lace
s
n
o
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tr
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n
s
o
n
t
h
e
s
p
ac
in
g
o
f
th
e
R
A
KE
d
ela
y
s
θ
l
a
n
d
β
l
is
th
e
w
e
ig
h
t
o
f
th
e
m
th
f
i
n
g
er
,
th
e
s
a
m
p
led
o
u
t
p
u
t o
f
th
e
R
A
KE
r
ec
eiv
er
i
s
th
en
:
,
(
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(
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-
(
6
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Fro
m
ab
o
v
e
w
e
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et
:
∑
∑
∑
(
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(
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(
(
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̌
(
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(
7
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W
h
er
e
(
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(
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(
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(
8
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is
th
e
ti
m
e
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au
to
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r
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is
t
h
e
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ilter
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n
o
is
e.
I
t
i
s
w
e
ll
k
n
o
w
n
th
at
th
e
o
p
ti
m
al
co
m
b
i
n
er
f
o
r
th
e
A
W
GN
m
u
lt
ip
ath
ch
a
n
n
el
i
s
M
R
C
,
w
h
er
e
L
=M
f
i
n
g
er
s
.
W
h
e
n
t
h
e
r
ec
eiv
ed
s
ig
n
als
o
n
ea
c
h
f
i
n
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er
ar
e
o
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t
h
o
g
o
n
a
l
(
as
is
th
e
ca
s
e
w
h
en
t
h
er
e
is
n
o
I
SI)
,
MRC
attai
n
s
th
e
m
atch
e
d
f
ilter
b
o
u
n
d
[
7
]
.
Ho
w
e
v
er
,
w
h
e
n
th
e
I
SI
b
ec
o
m
es
s
ig
n
i
f
ica
n
t
th
e
o
r
th
o
g
o
n
a
lit
y
o
f
t
h
e
p
ath
s
is
v
io
lated
u
n
less
ca
r
e
is
tak
e
n
i
n
th
e
d
esi
g
n
o
f
t
h
e
p
u
l
s
e
s
h
ap
e
(
as
in
lo
n
g
-
co
d
ed
DS
-
C
DM
A
s
y
s
te
m
s
w
h
er
e
s
u
cc
e
s
s
i
v
e
s
y
m
b
o
ls
ar
e
n
ea
r
l
y
o
r
th
o
g
o
n
al)
.
Fo
r
UW
B
-
b
ased
h
ig
h
r
ate
W
P
A
Ns
i
t
i
s
an
ti
cip
ated
th
at
s
p
r
ea
d
in
g
co
d
es
w
ill
b
e
s
h
o
r
t,
an
d
th
er
ef
o
r
e
MR
C
is
s
u
b
o
p
ti
m
al
ev
en
w
h
e
n
th
e
n
o
is
e
is
A
W
G
N.
T
h
is
m
o
tiv
ate
s
a
s
m
ar
ter
ch
o
ice
o
f
co
m
b
in
in
g
w
ei
g
h
ts
β
m
,
an
d
f
i
n
g
er
d
ela
y
s
θ
m
,
to
co
m
b
i
n
e
t
h
e
s
i
g
n
al
en
er
g
y
w
h
ile
co
m
p
en
s
ati
n
g
f
o
r
th
e
ef
f
ec
t
s
o
f
I
SI
a
n
d
n
ar
r
o
w
b
an
d
i
n
ter
f
er
e
n
ce
.
3.
P
RO
P
O
SE
D
RAK
E
R
E
C
E
I
VE
R
3
.
1
.
Co
ntinuo
us
w
a
v
elet
t
ra
ns
f
o
r
m
W
av
elets
ar
e
d
ef
i
n
ed
as
s
m
all
w
a
v
ef
o
r
m
s
w
i
th
d
i
f
f
er
e
n
t
o
s
cillato
r
y
s
tr
u
ctu
r
e
t
h
at
i
s
n
o
n
-
ze
r
o
f
o
r
a
li
m
ited
p
er
io
d
o
f
ti
m
e
(
o
r
s
p
ac
e)
.
T
h
e
w
a
v
elet
tr
an
s
f
o
r
m
i
s
a
m
u
l
ti
-
r
e
s
o
lu
tio
n
an
al
y
s
is
s
c
h
e
m
e
w
h
er
e
a
s
i
g
n
al
is
d
ec
o
m
p
o
s
ed
i
n
to
d
if
f
er
e
n
t
f
r
eq
u
en
c
y
co
m
p
o
n
e
n
ts
(
i.e
.
a
t
d
if
f
er
en
t
s
ca
le
s
)
.
T
h
e
w
a
v
el
et
tr
an
s
f
o
r
m
b
asi
s
f
u
n
ctio
n
s
ar
e
d
er
iv
ed
w
it
h
v
a
r
io
u
s
co
n
ti
n
u
o
u
s
s
ca
li
n
g
a
n
d
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i
f
t
p
ar
a
m
eter
s
,
a
an
d
b
r
esp
ec
tiv
el
y
,
f
r
o
m
t
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m
o
th
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w
a
v
elet
(
MW
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f
u
n
ctio
n
ψ(
t)
as f
o
llo
w
s
:
(
)
√
(
)
(
1
0
)
T
h
er
e
ar
e
v
ar
io
u
s
MW
li
k
e,
D
au
b
ec
h
ie
s
w
a
v
elet
s
f
a
m
il
y
,
M
o
r
let
an
d
Me
x
ica
n
Hat.
T
h
e
C
W
T
o
f
th
e
s
ig
n
al
s
(
t)
is
d
e
f
in
ed
b
y
t
h
e
w
a
v
elet
co
ef
f
icie
n
ts
g
i
v
en
b
y
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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&
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p
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I
SS
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2
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-
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P
erfo
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ma
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E
va
lu
a
tio
n
o
f A
d
a
p
tive
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u
o
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s
W
a
ve
let
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r
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n
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fo
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m
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ke
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vith
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3447
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1
1
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W
h
er
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W
(
a,
b
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r
ep
r
esen
ts
th
e
s
i
m
ilar
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y
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et
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th
e
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t
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th
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r
r
elatio
n
b
et
w
ee
n
s
(
t)
a
n
d
ψ
ab
(
t)
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T
h
e
s
ca
le
„
a‟
i
s
p
h
y
s
icall
y
d
e
f
i
n
ed
as
an
i
n
v
er
s
e
p
r
o
p
o
r
tio
n
al
to
th
e
f
r
eq
u
e
n
c
y
.
3.
2
.
P
ro
po
s
ed
ra
k
e
re
ce
iv
er
I
n
t
h
is
p
r
o
p
o
s
ed
ad
ap
tiv
e
co
n
tin
u
o
u
s
w
a
v
elet
tr
an
s
f
o
r
m
(
AC
W
T
)
b
ased
r
ak
e
r
ec
eiv
er
,
t
h
e
co
n
tin
u
o
u
s
w
av
ele
t
tr
an
s
f
o
r
m
o
f
ea
ch
m
u
lt
ip
ath
co
m
p
o
n
e
n
t
o
f
r
ec
eiv
ed
s
ig
n
al
i
s
tak
e
n
f
ir
s
t
an
d
is
co
r
r
elate
d
w
it
h
t
h
e
te
m
p
late
s
ig
n
al
o
r
r
ef
er
en
ce
s
i
g
n
al,
w
h
ich
i
s
a
co
n
tin
u
o
u
s
w
av
e
let
tr
an
s
f
o
r
m
o
f
tr
an
s
m
itted
p
u
l
s
e
o
v
e
r
d
if
f
er
en
t
s
ca
le
s
.
Ma
x
i
m
u
m
r
atio
co
m
b
i
n
er
i
s
u
s
ed
to
co
m
b
in
e
all
th
e
f
in
g
er
s
o
u
tp
u
t.
Af
ter
th
e
MR
C
co
m
b
i
n
er
d
ec
is
io
n
lo
g
ic
is
ap
p
lied
an
d
d
ata
is
esti
m
ated
.
B
ased
o
n
th
e
esti
m
ated
d
ata
an
d
th
e
ac
tu
al
d
esire
d
d
ata,
er
r
o
r
d
ata
is
g
en
er
ated
a
n
d
L
M
S
eq
u
alize
r
s
ar
e
u
s
ed
o
n
t
h
is
er
r
o
r
d
ata.
T
h
e
L
MS
eq
u
alize
r
u
p
d
ates
t
h
e
ch
an
n
el
co
e
f
f
icien
ts
.
B
ased
o
n
t
h
ese
c
h
a
n
n
e
l
co
ef
f
icie
n
ts
r
ak
e
p
ar
a
m
eter
s
s
u
ch
as
d
ela
y
an
d
f
i
n
g
er
g
a
in
s
ar
e
r
ec
o
m
p
u
ted
an
d
u
p
d
ated
.
3.
3
.
Ada
ptiv
e
L
M
S e
qu
a
lizer
An
ad
ap
tiv
e
eq
u
a
lizer
is
a
n
eq
u
alize
r
th
at
a
u
to
m
atica
ll
y
a
d
ap
ts
to
ti
m
e
-
v
ar
y
in
g
p
r
o
p
er
ties
o
f
t
h
e
co
m
m
u
n
icatio
n
c
h
a
n
n
e
l
.
T
h
e
m
o
s
t
w
ell
-
k
n
o
w
n
ad
ap
tiv
e
alg
o
r
ith
m
s
ar
e
t
h
e
L
MS
a
n
d
th
e
r
ec
u
r
s
i
v
e
lea
s
t
s
q
u
ar
es
al
g
o
r
ith
m
s
.
E
ac
h
o
f
th
e
m
h
as
it
s
o
w
n
u
n
iq
u
e
p
r
o
p
er
ties
an
d
ap
p
licatio
n
s
.
I
n
t
h
i
s
p
ap
er
,
w
e
u
s
e
th
e
L
MS
alg
o
r
it
h
m
d
u
e
to
it
s
s
i
m
p
licit
y
,
g
o
o
d
s
tab
ilit
y
,
an
d
r
o
b
u
s
tn
e
s
s
to
s
i
g
n
al
s
tatis
tic
s
I
t
i
s
a
s
to
ch
a
s
tic
g
r
ad
ien
t
d
e
s
ce
n
t
m
e
th
o
d
t
h
at
u
s
e
s
t
h
e
g
r
ad
ien
t
v
ec
to
r
o
f
t
h
e
f
il
ter
tap
w
ei
g
h
ts
to
co
n
v
er
g
e
o
n
t
h
e
o
p
ti
m
a
l
W
ein
er
s
o
lu
tio
n
.
Fro
m
th
e
m
et
h
o
d
o
f
s
teep
est d
escen
t,
t
h
e
w
ei
g
h
t
v
ec
to
r
eq
u
a
tio
n
is
g
iv
e
n
b
y
:
(
)
(
)
,
(
*
(
)
+
)
-
(
1
2
)
W
h
er
e
μ
is
th
e
s
tep
-
s
ize
p
ar
a
m
eter
an
d
co
n
tr
o
ls
th
e
co
n
v
er
g
en
ce
c
h
ar
ac
ter
is
tic
s
o
f
th
e
L
MS
alg
o
r
ith
m
,
e
2
(
n
)
is
t
h
e
m
ea
n
s
q
u
ar
e
er
r
o
r
b
etw
ee
n
th
e
o
u
tp
u
t
y
(
n
)
an
d
t
h
e
r
ef
er
en
ce
s
ig
n
al
w
h
ic
h
is
g
iv
e
n
b
y
,
(
)
,
(
)
̂
(
)
-
(
1
3
)
I
n
o
r
d
er
to
ac
h
iev
e
a
f
ast
in
iti
al
co
n
v
er
g
e
n
ce
s
p
ee
d
an
d
to
r
etain
a
f
a
s
t
tr
ac
k
i
n
g
ab
il
it
y
i
n
th
e
s
tead
y
s
tate,
lar
g
e
v
al
u
e
f
o
r
s
tep
s
i
ze
is
c
h
o
s
en
.
On
th
e
o
t
h
er
h
an
d
,
lar
g
e
s
tep
s
ize
w
il
l
r
esu
lt
in
lar
g
e
s
tead
y
m
alad
j
u
s
t
m
e
n
t e
r
r
o
r
.
4.
SI
M
UL
AT
I
O
N
R
E
S
UL
T
S
A
ND
ANA
L
YS
I
S
T
h
is
s
ec
tio
n
p
r
ese
n
ts
t
h
e
s
i
m
u
latio
n
r
es
u
lts
a
n
d
th
e
p
er
f
o
r
m
a
n
ce
an
al
y
s
is
o
f
th
e
AC
W
T
r
ak
e
r
ec
eiv
er
o
v
er
th
e
co
n
v
e
n
tio
n
al
r
ak
e
r
ec
eiv
er
an
d
th
e
C
W
T
r
ak
e
r
ec
e
iv
er
.
Fo
r
s
i
m
u
latio
n
p
u
r
p
o
s
e
w
e
h
av
e
c
h
o
s
en
t
h
e
tr
an
s
m
itted
p
u
l
s
e
as
Ga
u
s
s
ian
2
nd
o
r
d
er
d
er
iv
ate
p
u
ls
e
o
f
p
u
l
s
e
w
id
th
0
.
5
n
s
w
it
h
a
s
a
m
p
li
n
g
p
er
io
d
o
f
0
.
0
5
n
s
.
Fra
m
e
ti
m
e
o
f
2
0
n
s
s
ec
o
n
d
s
i
s
s
elec
ted
w
i
th
a
co
n
s
tr
ain
s
u
c
h
th
at
o
n
e
p
u
l
s
e
p
er
f
r
a
m
e.
UW
B
ch
an
n
els
C
M1
,
C
M2
,
C
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O
N
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h
e
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g
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an
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p
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th
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UW
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s
y
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ev
elo
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m
en
t
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ak
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.
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ased
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th
i
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f
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W
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ak
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s
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esig
n
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d
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m
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m
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th
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s
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s
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id
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e
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ased
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d
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n
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r
(
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M1
to
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n
clu
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th
at
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m
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8
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ak
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h
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o
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W
T
r
ak
e
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a
s
s
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o
w
n
an
SN
R
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m
p
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v
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n
t
o
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2
d
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,
3
d
B
,
1
0
d
B
an
d
2
d
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r
esp
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if
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er
en
t U
W
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ch
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n
els
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M1
,
C
M2
,
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M3
an
d
C
M4
.
RE
F
E
R
E
NC
E
S
[1
]
Ra
z
a
li
N
g
a
h
a
n
d
Ya
ss
e
r
Zah
e
d
i,
“
UW
B
Co
m
m
u
n
ica
ti
o
n
s:
P
re
se
n
t
a
n
d
Fu
t
u
re
”
,
IEE
E
Asia
-
P
a
c
if
ic
Co
n
fer
e
n
c
e
o
n
Ap
p
li
e
d
El
e
c
tro
m
a
g
n
e
ti
c
s
(
AP
ACE
)
,
2
0
1
6
,
p
p
3
7
3
-
3
7
8
.
[2
]
V
ik
a
s
G
o
y
a
l
a
n
d
B.
S
.
D
h
a
li
w
a
l,
“
Op
ti
m
a
l
P
u
lse
G
e
n
e
ra
ti
o
n
f
o
r
th
e
Im
p
ro
v
e
m
e
n
t
o
f
Ul
tra
W
i
d
e
b
a
n
d
S
y
ste
m
P
e
rf
o
rm
a
n
c
e
”
,
In
t.
Co
n
f.
Rec
e
n
t
Ad
v
a
n
c
e
s
i
n
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
t
a
ti
o
n
a
l
S
c
ien
c
e
s,
IEE
E
Exp
lo
re
d
ig
it
a
l
li
b
ra
ry
wit
h
,
I
S
BN:
9
7
8
-
1
-
4
7
9
9
-
2
2
9
1
-
8
h
e
ld
a
t
U
IE
T
,
P
u
n
j
a
b
Un
iv
e
rs
it
y
,
Ch
a
n
d
ig
a
rh
,
M
a
rc
h
6
-
8
,
2
0
1
4
.
[3
]
T
a
b
a
a
,
M
.
a
n
d
Dio
u
,
C.
,
“
A
L
o
w
-
Co
st
M
a
n
y
-
to
-
On
e
W
S
N
A
rc
h
it
e
c
tu
re
Ba
s
e
d
o
n
UW
B
-
IR
a
n
d
DW
P
T
”
,
In
te
rn
a
t
io
n
a
l
c
o
n
fer
e
n
c
e
o
n
C
o
n
tro
l
,
De
c
isio
n
a
n
d
In
f
o
rm
a
ti
o
n
t
e
c
h
n
o
l
o
g
ies
Co
DIT
2
0
1
4
IE
EE
,
M
e
tz,
p
p
.
3
-
5
,
No
v
.
2
0
1
4
.
[4
]
Ch
e
h
a
it
ly
,
e
t
a
l
.,
“
A
L
o
w
-
Co
st
M
.
T
a
b
a
a
2
0
8
De
sig
n
o
f
T
ra
n
c
e
iv
e
r
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se
d
o
n
DW
P
T
f
o
r
W
S
N
”,
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
M
icr
o
e
lec
tro
n
ics
I
CM
2
0
1
5
,
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sa
b
lan
c
a
,
p
p
.
20
-
2
3
,
De
c
.
2
0
1
5
.
[5
]
L
.
S
to
ica
,
e
t
a
l
.,
“
A
n
Ultra
w
id
e
b
a
n
d
S
y
ste
m
A
rc
h
it
e
c
tu
re
f
o
r
tag
b
a
se
d
W
irele
ss
S
e
n
so
r
Ne
t
w
o
rk
s
”,
IEE
E
T
ra
n
s.
Veh
.
T
e
c
h
n
o
l.
,
v
o
l.
5
4
,
p
p
.
1
6
3
2
-
1
6
4
5
,
2
0
0
5
.
[6
]
L
.
Y.
A
sta
n
in
a
n
d
A
.
A
.
Ko
sty
le
v
,
“
Ultra
w
id
e
b
a
n
d
Ra
d
a
r
M
e
a
su
re
m
e
n
ts
A
n
a
l
y
sis
a
n
d
P
r
o
c
e
ss
in
g
”
,
L
o
n
d
o
n
U
.
K.:
IEE
,
1
9
9
7
.
[7
]
“
Re
v
isio
n
o
f
P
a
rt
1
5
o
f
th
e
Co
m
m
is
sio
n
‟s
Ru
les
Re
g
a
r
d
in
g
Ultra
-
W
id
e
b
a
n
d
T
ra
n
sm
i
ss
io
n
F
e
d
e
ra
l
Co
m
m
u
n
ica
ti
o
n
s Co
m
m
i
ss
io
n
”
,
1
st R
e
p
.
a
n
d
Or
d
e
r
,
2
0
0
2
.
[8
]
T
.
W
.
Ba
rre
tt
,
“
Histo
ry
o
f
Ultra
-
w
id
e
b
a
n
d
C
o
m
m
u
n
ica
ti
o
n
s
a
n
d
Ra
d
a
r:
P
a
rt
I,
UW
B
Co
m
m
u
n
ica
ti
o
n
s
”
,
M
icr
o
w.
J
.
,
p
p
.
2
2
-
5
6
,
Ja
n
.
2
0
0
1
.
[9
]
M
.
Z.
W
in
a
n
d
R.
A
.
S
c
h
o
l
tz,
“
Im
p
u
lse
Ra
d
i
o
:
Ho
w
it
w
o
rk
s
”
,
IEE
E
Co
mm
u
n
.
L
e
tt
.
,
v
o
l
.
2
,
p
p
.
3
6
-
3
8
,
F
e
b
.
1
9
9
8
.
[1
0
]
M
.
Z.
W
in
a
n
d
R.
A
.
S
c
h
o
lt
z
,
“
Ultra
-
w
id
e
Ba
n
d
w
id
th
T
i
m
e
-
h
o
p
p
i
n
g
S
p
re
a
d
-
s
p
e
c
tru
m
I
m
p
u
lse
Ra
d
io
f
o
r
W
ir
e
les
s
M
u
lt
i
p
le
-
a
c
c
e
ss
Co
m
m
u
n
ica
ti
o
n
s
”
,
IEE
E
T
ra
n
s.
Co
mm
u
n
.
,
v
o
l.
4
8
,
p
p
.
6
7
9
-
6
8
9
,
A
p
r.
2
0
0
0
.
[1
1
]
F
.
Ra
m
irez
-
M
irele
s,
“
P
e
rf
o
rm
a
n
c
e
o
f
Ultra
w
id
e
b
a
n
d
S
S
M
A
u
sin
g
T
i
m
e
Ho
p
p
in
g
a
n
d
M
-
a
ry
P
P
M
”
,
IEE
E
J
.
S
e
lec
t.
Are
a
s Co
mm
u
n
.
,
v
o
l
.
1
9
,
p
p
.
1
1
8
6
-
1
1
9
6
,
2
0
0
1
.
[1
2
]
R.
T
.
Ho
c
to
r
a
n
d
H.W
.
T
o
m
li
n
so
n
,
“
A
n
Ov
e
r
v
ie
w
o
f
De
la
y
-
h
o
p
p
e
d
,
T
ra
n
sm
it
ted
Re
f
e
r
e
n
c
e
RF
Co
m
m
u
n
ica
ti
o
n
s
”
,
in
T
e
c
h
n
ica
l
I
n
f
o
rm
a
ti
o
n
S
e
rie
s
:
G.E
.
Res
e
a
rc
h
a
n
d
De
v
e
lo
p
me
n
t
Ce
n
ter
,
Ja
n
.
2
0
0
2
,
p
p
.
1
-
2
9
.
[1
3
]
J.
R.
F
o
e
rste
r,
“
T
h
e
P
e
rf
o
rm
a
n
c
e
o
f
a
Dire
c
t
S
e
q
u
e
n
c
e
S
p
re
a
d
Ultra
-
w
id
e
b
a
n
d
S
y
ste
m
in
th
e
P
re
se
n
c
e
o
f
M
u
lt
ip
a
th
,
Na
rro
w
b
a
n
d
In
terf
e
re
n
c
e
,
a
n
d
M
u
l
ti
u
se
r
I
n
terf
e
re
n
c
e
”
,
in
Pro
c
.
2
0
0
2
U
W
BS
T
,
2
0
0
2
,
p
p
.
8
7
-
9
2
.
[1
4
]
Hu
a
S
h
a
o
;
No
rm
a
n
C.
Be
a
u
li
e
u
,
“
Dire
c
t
se
q
u
e
n
c
e
a
n
d
ti
m
e
h
o
p
p
i
n
g
se
q
u
e
n
c
e
d
e
sig
n
s
f
o
r
n
a
rro
w
b
a
n
d
in
terf
e
re
n
c
e
m
it
ig
a
ti
o
n
i
n
im
p
u
lse
ra
d
io
UW
B
sy
ste
m
s
”
,
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
C
o
mm
u
n
ic
a
ti
o
n
s
,
v
o
l.
5
9
,
n
o
.
7
,
2
0
1
1
,
p
p
1
9
5
7
-
1
9
6
5
.
[1
5
]
J.
D.
Ch
o
i
a
n
d
W
.
E.
S
tark
,
“
P
e
rf
o
rm
a
n
c
e
A
n
a
l
y
sis
o
f
Ultra
-
w
id
e
b
a
n
d
S
p
re
a
d
-
sp
e
c
tru
m
C
o
m
m
u
n
ica
t
io
n
s
in
Na
rro
w
b
a
n
d
In
terf
e
re
n
c
e
”
,
in
Pro
c
.
2
0
0
2
M
IL
COM
,
2
0
0
2
.
[1
6
]
Jo
h
n
D.
Ch
o
i,
a
n
d
W
a
y
n
e
E.
S
ta
rk
“
P
e
rf
o
r
m
a
n
c
e
o
f
Ultra
-
W
id
e
b
a
n
d
Co
m
m
u
n
ica
ti
o
n
s
w
it
h
S
u
b
o
p
ti
m
a
l
Re
c
e
iv
e
r
s
in
M
u
lt
i
p
a
th
C
h
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I
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6
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.
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7
]
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F
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t
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8
]
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.
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.
5
2
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.
3
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rc
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.
Evaluation Warning : The document was created with Spire.PDF for Python.