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1
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[
5
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Op
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P
As
n
ea
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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6930
T
E
L
KOM
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A
T
elec
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m
m
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m
p
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C
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tr
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l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
1
-
1
3
2
s
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r
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n
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m
p
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[
7
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-
[
1
1
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.
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er
ef
o
r
e,
d
ig
ital
p
r
ed
is
to
r
tio
n
(
DP
D
)
r
e
m
ai
n
s
o
n
e
o
f
th
e
m
o
s
t
p
r
ac
tical
an
d
ef
f
icie
n
t
ap
p
r
o
ac
h
es
f
o
r
P
A
lin
ea
r
izatio
n
,
as
it
co
m
p
e
n
s
ate
s
f
o
r
n
o
n
l
in
ea
r
it
ies
b
y
ap
p
ly
in
g
an
in
v
er
s
e
tr
an
s
f
er
f
u
n
ctio
n
p
r
io
r
to
am
p
li
f
icat
io
n
.
R
ec
en
t
l
y
,
d
ee
p
lear
n
in
g
-
b
a
s
e
d
m
o
d
els
h
a
v
e
d
e
m
o
n
s
tr
ated
o
u
ts
tan
d
i
n
g
ca
p
ab
ilit
ies
in
m
o
d
eli
n
g
n
o
n
li
n
ea
r
itie
s
a
n
d
co
m
p
en
s
ati
n
g
s
i
g
n
al
d
is
to
r
tio
n
s
t
h
a
n
k
s
t
o
th
eir
s
tr
o
n
g
f
u
n
ctio
n
ap
p
r
o
x
i
m
atio
n
p
r
o
p
er
ties
[
1
2
]
.
A
r
ch
itect
u
r
es
s
u
c
h
as
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
e
t
w
o
r
k
s
(
C
NN
s
)
,
lo
n
g
s
h
o
r
t
-
ter
m
m
e
m
o
r
y
(
L
ST
M)
n
et
w
o
r
k
s
,
g
en
er
ati
v
e
ad
v
er
s
ar
i
al
n
et
w
o
r
k
s
(
G
A
N
s
)
,
a
n
d
au
to
e
n
co
d
er
s
(
A
E
s
)
[
1
3
]
-
[
1
6
]
h
av
e
b
ee
n
ex
p
lo
r
ed
f
o
r
tr
an
s
m
itter
o
p
ti
m
iza
ti
o
n
,
P
A
li
n
ea
r
izatio
n
,
a
n
d
s
ig
n
al
r
esto
r
at
io
n
.
I
n
th
is
w
o
r
k
,
w
e
p
r
o
p
o
s
e
an
au
to
en
co
d
er
-
b
ased
OFDM
-
P
A
(
A
E
-
O
FDM
-
P
A
)
s
y
s
te
m
t
h
at
p
er
f
o
r
m
s
en
d
-
to
-
en
d
lear
n
in
g
to
j
o
in
tl
y
li
n
ea
r
ize
th
e
n
o
n
lin
ea
r
r
e
s
p
o
n
s
e
o
f
a
m
o
d
if
ied
R
ap
p
-
b
ased
P
A
a
n
d
to
co
m
p
e
n
s
ate
f
o
r
R
a
y
le
ig
h
f
ad
in
g
.
U
n
li
k
e
co
n
v
en
t
io
n
al
DP
D
ap
p
r
o
ac
h
es,
th
e
p
r
o
p
o
s
ed
m
o
d
el
lear
n
s
th
e
o
p
tim
a
l
m
ap
p
in
g
b
et
w
ee
n
tr
an
s
m
itted
a
n
d
r
ec
eiv
ed
s
ig
n
al
s
d
ir
ec
tl
y
t
h
r
o
u
g
h
d
ata
-
d
r
iv
e
n
tr
ain
i
n
g
,
ac
h
ie
v
i
n
g
b
o
th
d
is
to
r
tio
n
m
it
ig
at
io
n
an
d
lo
w
co
m
p
u
tatio
n
a
l c
o
m
p
lex
it
y
.
T
h
e
r
em
ai
n
d
er
o
f
th
i
s
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
Sectio
n
2
p
r
esen
ts
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
o
lo
g
y
,
in
cl
u
d
in
g
t
h
e
s
y
s
te
m
m
o
d
el
a
n
d
A
E
-
b
a
s
ed
DP
D
f
r
a
m
e
w
o
r
k
.
Sectio
n
3
d
is
c
u
s
s
es
th
e
s
i
m
u
latio
n
r
esu
lts
a
n
d
co
m
p
ar
ati
v
e
an
al
y
s
is
.
Fi
n
all
y
,
s
ec
tio
n
4
co
n
cl
u
d
es th
e
p
ap
er
an
d
o
u
tl
i
n
es p
o
s
s
ib
le
d
ir
ec
tio
n
s
f
o
r
f
u
t
u
r
e
w
o
r
k
.
2.
M
E
T
H
O
DO
L
O
G
Y
2
.
1
.
O
v
er
v
ie
w
o
f
ex
is
t
ing
t
ec
hn
iq
ues
DP
D
is
w
id
el
y
ad
o
p
ted
as
an
ef
f
ec
ti
v
e
tec
h
n
iq
u
e
f
o
r
p
o
w
er
a
m
p
li
f
ier
li
n
ea
r
izatio
n
.
C
o
n
v
e
n
tio
n
a
l
DP
D
s
c
h
e
m
e
s
,
s
u
c
h
as
lo
o
k
-
u
p
tab
le
(
L
UT
)
,
Vo
lter
r
a
s
er
i
es,
an
d
m
e
m
o
r
y
p
o
l
y
n
o
m
ial
m
o
d
el
s
[
1
7
]
,
[
1
8
]
,
atte
m
p
t
to
ap
p
r
o
x
i
m
ate
t
h
e
i
n
v
er
s
e
b
eh
av
io
r
o
f
th
e
P
A
a
n
d
ap
p
ly
it
b
ef
o
r
e
a
m
p
li
f
icat
io
n
.
Ho
w
e
v
er
,
th
e
ir
p
er
f
o
r
m
a
n
ce
i
s
li
m
ited
b
y
m
o
d
elin
g
ac
c
u
r
ac
y
,
s
e
n
s
iti
v
it
y
to
m
e
m
o
r
y
ef
f
ec
t
s
,
an
d
d
ep
en
d
en
ce
o
n
p
ar
a
m
ete
r
tu
n
in
g
.
R
ec
en
t
ad
v
a
n
ce
s
in
d
ee
p
lear
n
in
g
h
a
v
e
i
n
tr
o
d
u
ce
d
d
ata
-
d
r
iv
e
n
ap
p
r
o
ac
h
es c
ap
ab
le
o
f
d
ir
ec
tl
y
lear
n
in
g
co
m
p
le
x
n
o
n
lin
e
ar
m
ap
p
i
n
g
s
f
r
o
m
d
ata
[
1
9
]
.
A
r
ch
itect
u
r
es
s
u
ch
as
C
N
Ns
,
L
ST
Ms,
an
d
au
to
e
n
co
d
er
s
A
E
s
h
av
e
s
h
o
w
n
p
r
o
m
i
s
i
n
g
r
es
u
lt
s
f
o
r
P
A
lin
ea
r
izatio
n
an
d
en
d
-
to
-
e
n
d
tr
an
s
m
itter
o
p
ti
m
izat
io
n
.
T
h
e
p
r
o
p
o
s
ed
AE
-
OF
DM
-
P
A
m
o
d
el
b
u
ild
s
u
p
o
n
th
i
s
p
ar
ad
ig
m
b
y
lev
er
a
g
in
g
an
A
E
-
b
ased
f
r
a
m
e
w
o
r
k
to
j
o
in
tly
m
iti
g
ate
P
A
n
o
n
lin
ea
r
it
ies an
d
c
h
an
n
el
d
is
to
r
tio
n
s
w
h
ile
m
ain
tain
in
g
l
o
w
c
o
m
p
u
tatio
n
al
co
m
p
lex
i
t
y
.
2
.
2
.
Sy
s
t
em
m
o
del a
nd
cla
s
s
ica
l dig
it
a
l pre
dis
t
o
rt
io
n
2
.
2
.
1
.
O
F
D
M
s
ig
na
l m
o
de
l
OFDM
i
s
a
m
u
lt
icar
r
ier
m
o
d
u
latio
n
tech
n
iq
u
e
th
a
t
en
ab
le
s
h
i
g
h
d
ata
r
ates
a
n
d
r
o
b
u
s
t
n
ess
a
g
ain
s
t
I
SI.
T
h
e
b
aseb
an
d
OFDM
s
ig
n
al
is
g
e
n
er
ated
u
s
i
n
g
th
e
i
n
v
e
r
s
e
d
is
cr
ete
Fo
u
r
ier
tr
an
s
f
o
r
m
(
I
DFT
)
[
2
0
]
,
w
h
ic
h
m
ap
s
f
r
eq
u
en
c
y
-
d
o
m
ai
n
m
o
d
u
latio
n
s
y
m
b
o
ls
to
t
h
e
ti
m
e
d
o
m
ai
n
as:
(
)
=
1
√
∑
−
1
=
0
2
,
0
≤
≤
(
1
)
w
h
er
e
d
en
o
tes
th
e
n
u
m
b
er
o
f
s
u
b
ca
r
r
ier
s
,
is
th
e
tr
an
s
m
i
t
ted
s
y
m
b
o
l
o
n
th
e
-
t
h
s
u
b
ca
r
r
ier
,
is
th
e
f
r
eq
u
en
c
y
o
f
t
h
e
-
th
s
u
b
ca
r
r
ier
an
d
is
th
e
OF
DM
s
y
m
b
o
l d
u
r
atio
n
.
A
c
y
cl
ic
p
r
ef
ix
(
CP
)
is
in
s
er
t
ed
b
y
co
p
y
i
n
g
t
h
e
en
d
o
f
ea
ch
s
y
m
b
o
l
to
its
b
eg
in
n
in
g
t
o
m
ai
n
tai
n
o
r
th
o
g
o
n
alit
y
i
n
m
u
ltip
at
h
ch
a
n
n
el
s
.
A
t
t
h
e
r
ec
ei
v
er
,
th
e
C
P
is
r
e
m
o
v
ed
,
an
d
ch
a
n
n
el
e
q
u
al
izatio
n
e
n
ab
les
t
h
e
r
ec
o
v
er
y
o
f
tr
a
n
s
m
itted
d
ata
s
y
m
b
o
ls
.
A
s
ig
n
i
f
ica
n
t
c
h
alle
n
g
e
in
O
FDM
i
s
t
h
e
p
r
esen
c
e
o
f
a
n
o
n
-
co
n
s
ta
n
t
en
v
elo
p
e,
c
h
ar
ac
ter
ized
b
y
h
i
g
h
p
o
w
er
p
ea
k
s
co
m
p
ar
ed
to
th
e
a
v
er
ag
e
s
i
g
n
al
p
o
w
er
.
S
p
ec
if
icall
y
,
as
th
e
n
u
m
b
er
o
f
s
u
b
ca
r
r
ier
s
in
cr
ea
s
es,
t
h
e
s
i
g
n
a
l
(
)
f
o
llo
w
s
a
co
m
p
lex
Gau
s
s
ia
n
p
r
o
ce
s
s
b
ased
o
n
th
e
ce
n
tr
al
li
m
it
t
h
eo
r
e
m
(
C
L
T
)
[
2
1
]
.
T
o
m
ea
s
u
r
e,
th
e
a
m
p
l
itu
d
e
f
lu
c
t
u
atio
n
s
in
t
h
e
OF
DM
s
i
g
n
al,
th
e
p
ea
k
to
av
er
ag
e
p
o
w
er
r
atio
is
co
m
m
o
n
l
y
e
m
p
l
o
y
ed
.
T
h
e
P
A
P
R
r
atio
is
ex
p
r
ess
ed
as
:
=
10
10
(
0
≤
≤
−
1
|
(
)
|
2
[
|
(
)
|
2
]
)
(2
)
w
h
er
e,
{
.
}
is
th
e
s
tatis
tical
ex
p
ec
t
atio
n
o
p
er
ato
r
,
an
d
(
)
is
th
e
b
ase
b
an
d
OFDM
s
i
g
n
a
l in
t
h
e
ti
m
e
d
o
m
ai
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
Dee
p
lea
r
n
in
g
-
b
a
s
ed
p
o
w
er a
mp
lifi
er lin
ea
r
iz
a
tio
n
in
OF
DM syst
em
s
…
(
Mer
ye
m
Ma
mia
B
en
o
s
ma
n
)
3
T
h
e
p
r
o
b
ab
ilit
y
th
at
th
e
P
A
P
R
ex
ce
ed
s
a
g
i
v
en
th
r
es
h
o
ld
δ
i
s
ch
ar
ac
ter
ized
u
s
in
g
t
h
e
co
m
p
l
e
m
en
tar
y
cu
m
u
lat
iv
e
d
is
tr
ib
u
tio
n
f
u
n
ct
i
o
n
(
C
C
D
F):
[
(
(
)
)
]
=
[
(
(
)
)
>
δ]
(
3
)
w
h
er
e
[
.
]
d
en
o
tes
th
e
p
r
o
b
a
b
ilit
y
o
p
er
ato
r
,
an
d
is
th
e
P
A
P
R
th
r
es
h
o
ld
.
A
lar
g
e
P
A
P
R
ca
n
d
r
iv
e
n
o
n
li
n
ea
r
co
m
p
o
n
en
t
s
s
u
c
h
as
p
o
w
er
a
m
p
li
f
ier
s
i
n
to
s
atu
r
atio
n
,
ca
u
s
in
g
s
ig
n
al
d
is
to
r
tio
n
an
d
s
p
ec
tr
al
r
eg
r
o
w
t
h
.
2
.
2
.
2
.
P
o
w
er
a
m
p
lifie
r
m
o
del
(
R
a
pp
m
o
d
if
ied)
T
h
e
p
o
w
er
a
m
p
lifie
r
is
a
n
es
s
en
tia
l
co
m
p
o
n
e
n
t
i
n
t
h
e
tr
an
s
m
i
s
s
io
n
ch
a
in
a
s
it
i
s
r
esp
o
n
s
ib
le
f
o
r
a
m
p
li
f
y
i
n
g
th
e
s
ig
n
al.
I
ts
b
eh
av
io
r
is
d
escr
ib
ed
b
y
it
s
t
r
an
s
f
er
f
u
n
c
tio
n
,
w
h
ich
d
ef
in
es
th
e
r
elatio
n
s
h
i
p
b
et
w
ee
n
th
e
in
p
u
t
an
d
o
u
tp
u
t
s
i
g
n
al
s
.
T
h
e
am
p
lit
u
d
e
m
o
d
u
latio
n
t
o
am
p
lit
u
d
e
m
o
d
u
latio
n
(
A
M
/
A
M
)
ch
ar
ac
ter
is
tic
i
n
d
icate
s
h
o
w
t
h
e
a
m
p
lit
u
d
e
o
f
th
e
o
u
tp
u
t
s
ig
n
a
l
v
ar
ies
w
it
h
r
esp
ec
t
to
th
e
in
p
u
t
s
i
g
n
al
’
s
a
m
p
lit
u
d
e.
On
th
e
o
th
er
h
an
d
,
AM
/P
M
ch
ar
ac
ter
is
t
ic
d
escr
i
b
es
th
e
p
h
ase
s
h
if
t
b
et
w
ee
n
t
h
e
i
n
p
u
t
an
d
o
u
tp
u
t
s
ig
n
al
s
[
2
2
]
.
T
h
e
am
p
li
f
ied
s
i
g
n
al
ca
n
b
e
ex
p
r
es
s
ed
as:
(
)
=
(
(
)
)
=
(
(
)
)
(
(
)
+
(
(
)
)
)
(
4
)
w
h
er
e,
(
)
an
d
(
)
ar
e
r
esp
ec
tiv
el
y
th
e
m
o
d
u
l
u
s
an
d
th
e
p
h
a
s
e
o
f
th
e
s
i
g
n
al
(
)
.
(
.
)
d
escr
ib
es
th
e
A
M/
AM
co
n
v
er
s
io
n
a
n
d
(
.
)
d
escr
ib
es A
M
/P
M
co
n
v
er
s
io
n
o
f
th
e
a
m
p
lifie
r
.
I
n
th
is
s
t
u
d
y
,
th
e
R
ap
p
m
o
d
if
i
ed
m
o
d
el
is
e
m
p
lo
y
ed
to
r
e
p
r
esen
t
th
e
s
ta
tic
n
o
n
li
n
ea
r
b
eh
av
io
r
o
f
th
e
P
A
.
I
t
ca
p
tu
r
e
s
b
o
th
AM
/
AM
an
d
A
M/P
M
d
i
s
to
r
tio
n
s
as
f
o
llo
w
s
.
Fi
g
u
r
e
1
(
a)
illu
s
tr
ates
t
h
e
A
M/
AM
ch
ar
ac
ter
is
tic
o
f
t
h
e
R
ap
p
m
o
d
if
ied
m
o
d
el
an
d
Fi
g
u
r
e
1
(
b
)
s
h
o
w
s
A
M
/P
M
ch
ar
ac
ter
is
tics
.
(
(
)
)
=
(
)
(
1
+
(
(
)
)
2
)
1
2
(
5
)
(
(
)
)
=
(
(
)
)
1
+
(
(
)
′
)
(
6
)
w
h
er
e,
(
)
is
th
e
m
o
d
u
l
u
s
o
f
t
h
e
in
p
u
t
OFDM
s
ig
n
al,
is
th
e
P
A
g
ain
.
T
h
e
p
ar
am
eter
a
d
j
u
s
ts
th
e
ch
ar
ac
ter
is
tic
b
y
co
n
tr
o
llin
g
t
h
e
tr
an
s
itio
n
b
et
w
ee
n
th
e
li
n
ea
r
ar
ea
an
d
th
e
s
atu
r
atio
n
ar
ea
o
f
th
e
A
M/
A
M
ch
ar
ac
ter
is
tic.
is
th
e
s
atu
r
ati
o
n
v
o
ltag
e
o
f
th
e
P
A
.
T
h
e
a
n
d
′
p
a
r
am
eter
s
co
n
tr
o
l
th
e
lev
el
o
f
p
h
ase
d
is
to
r
tio
n
in
tr
o
d
u
ce
d
b
y
t
h
e
P
A
.
(
a)
(
b
)
Fig
u
r
e
1
.
C
h
ar
ac
ter
is
t
ics o
f
t
h
e
m
o
d
if
ied
R
ap
p
m
o
d
el:
(
a)
AM
/AM
co
n
v
er
s
io
n
a
n
d
(
b
)
A
M/P
M
co
n
v
er
s
io
n
T
h
e
in
p
u
t
b
ac
k
-
o
f
f
(
I
B
O
)
is
an
i
m
p
o
r
tan
t
p
ar
a
m
eter
th
at
d
e
f
i
n
es
t
h
e
o
p
er
atin
g
p
o
in
t
o
f
th
e
a
m
p
lifie
r
r
elativ
e
to
it
s
s
at
u
r
atio
n
lev
el.
I
t
is
e
x
p
r
ess
ed
as
t
h
e
r
atio
b
e
t
w
ee
n
t
h
e
s
a
tu
r
atio
n
p
o
w
er
an
d
th
e
av
er
a
g
e
in
p
u
t
p
o
w
er
.
A
h
i
g
h
er
I
B
O
v
alu
e
in
d
icate
s
th
at
t
h
e
a
m
p
li
f
ier
o
p
er
ates
in
a
m
o
r
e
lin
ea
r
r
eg
i
o
n
,
en
s
u
r
in
g
lo
w
er
d
is
to
r
tio
n
b
u
t
r
ed
u
ce
d
p
o
w
er
ef
f
icien
c
y
,
w
h
ile
a
lo
w
er
I
B
O
v
alu
e
i
n
cr
ea
s
es
e
f
f
icie
n
c
y
at
th
e
co
s
t
o
f
s
tr
o
n
g
er
n
o
n
li
n
ea
r
ef
f
ec
t
s
.
T
h
e
I
B
O
is
m
at
h
e
m
a
ticall
y
d
ef
i
n
ed
as:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
un
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
1
-
1
3
4
(
)
=
10
l
og
10
(
[
|
(
)
|
2
]
)
(
7
)
w
h
er
e,
d
en
o
tes th
e
s
atu
r
atio
n
p
o
w
er
o
f
th
e
,
[
|
(
)
|
2
]
d
en
o
tes th
e
a
v
er
ag
e
in
p
u
t p
o
w
er
.
2
.
2
.
3
.
Cla
s
s
ica
l
m
e
m
o
ry
po
ly
no
m
i
a
l D
P
D
T
o
co
m
p
en
s
ate
f
o
r
P
A
n
o
n
lin
ea
r
itie
s
,
DP
D
tec
h
n
iq
u
es
ar
e
ap
p
lied
b
ef
o
r
e
a
m
p
lif
ica
tio
n
.
T
h
e
p
r
in
cip
le
is
to
in
tr
o
d
u
c
e
a
n
o
n
lin
ea
r
i
n
v
er
s
e
f
u
n
ctio
n
,
d
e
n
o
ted
as
(
.
)
,
s
u
ch
t
h
at
t
h
e
ca
s
ca
d
e
(
.
)
(
.
)
(
p
r
ed
is
to
r
ter
f
o
llo
w
ed
b
y
a
m
p
l
if
ier
)
b
eh
av
e
s
ap
p
r
o
x
i
m
atel
y
l
in
ea
r
l
y
.
T
h
e
co
n
ce
p
t is d
ep
icted
in
Fig
u
r
e
2.
Fig
u
r
e
2
.
Dig
ital p
r
ed
is
to
r
tio
n
m
ec
h
a
n
i
s
m
f
o
r
p
o
w
er
a
m
p
li
f
i
er
lin
ea
r
izatio
n
I
n
th
is
w
o
r
k
,
th
e
m
e
m
o
r
y
p
o
ly
n
o
m
ia
l
m
o
d
el
i
s
ad
o
p
ted
as
a
clas
s
ical
b
en
c
h
m
ar
k
f
o
r
P
A
lin
ea
r
izatio
n
.
T
h
e
p
r
ed
is
to
r
ted
s
i
g
n
al
(
)
is
ex
p
r
ess
ed
as:
(
)
=
∑
∑
,
(
−
)
|
(
−
)
|
−
1
=
1
,
3
,
5
,
.
.
−
1
=
0
(
8
)
w
h
er
e
,
d
en
o
tes
th
e
p
o
ly
n
o
m
i
al
o
r
d
e
r
,
th
e
m
e
m
o
r
y
d
ep
th
,
an
d
,
th
e
co
m
p
le
x
co
ef
f
icie
n
t
s
o
b
tain
ed
v
ia
least sq
u
ar
es e
s
ti
m
a
tio
n
.
I
n
th
e
i
m
p
le
m
e
n
ted
al
g
o
r
ith
m
,
th
e
b
asi
s
f
u
n
ctio
n
s
ar
e
b
u
ilt
f
r
o
m
t
h
e
P
A
o
u
tp
u
t
s
i
g
n
al,
an
d
th
e
co
ef
f
icie
n
t
s
ar
e
o
p
ti
m
iz
ed
to
ap
p
r
o
x
im
a
te
th
e
i
n
v
er
s
e
o
f
t
h
e
a
m
p
lifie
r
’
s
n
o
n
lin
ea
r
r
esp
o
n
s
e.
T
h
is
ap
p
r
o
ac
h
,
k
n
o
w
n
a
s
m
e
m
o
r
y
p
o
l
y
n
o
m
ial
DP
D,
o
f
f
er
s
a
g
o
o
d
tr
ad
e
-
o
f
f
b
et
w
ee
n
p
er
f
o
r
m
a
n
ce
an
d
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
,
m
a
k
in
g
it
w
id
el
y
u
s
ed
in
p
r
ac
tical
r
ad
io
f
r
eq
u
en
c
y
(
RF
)
s
y
s
te
m
s
[
2
3
]
.
Ho
w
e
v
er
,
its
ef
f
ec
ti
v
en
e
s
s
d
ec
r
ea
s
es
f
o
r
h
ig
h
l
y
n
o
n
li
n
ea
r
o
r
m
e
m
o
r
y
-
i
n
ten
s
i
v
e
P
A
s
,
m
o
tiv
a
tin
g
th
e
u
s
e
o
f
d
ee
p
lear
n
in
g
–
b
ased
DP
D
ar
ch
itect
u
r
es e
x
p
lo
r
ed
in
th
is
p
ap
er
.
2
.
3
.
E
nd
-
to
-
end lea
rning
f
o
r
co
m
m
u
nica
t
io
n sy
s
t
e
m
s
Dee
p
lear
n
i
n
g
h
a
s
e
m
er
g
ed
as
a
tr
an
s
f
o
r
m
ati
v
e
to
o
l
i
n
co
m
m
u
n
icat
io
n
s
y
s
te
m
s
,
o
f
f
er
i
n
g
th
e
ab
ilit
y
to
m
o
d
el
co
m
p
lex
n
o
n
li
n
ea
r
r
elatio
n
s
h
ip
s
th
r
o
u
g
h
d
ata
-
d
r
iv
en
lear
n
i
n
g
.
R
at
h
er
th
a
n
d
esig
n
in
g
ea
ch
b
lo
ck
o
f
th
e
tr
a
n
s
m
is
s
io
n
ch
ai
n
s
ep
ar
atel
y
,
t
h
e
e
n
d
-
to
-
en
d
lear
n
in
g
p
ar
ad
ig
m
tr
ai
n
s
th
e
en
t
ir
e
s
y
s
te
m
j
o
in
tl
y
,
f
r
o
m
t
h
e
tr
an
s
m
i
tter
to
th
e
r
ec
ei
v
er
,
th
r
o
u
g
h
d
if
f
er
en
t
iab
le
m
o
d
els.
T
h
e
co
n
ce
p
t
w
as
f
ir
s
t
in
s
p
ir
e
d
b
y
th
e
s
tr
u
ct
u
r
e
o
f
au
to
en
c
o
d
er
s
A
E
s
,
w
h
er
e
th
e
en
co
d
er
r
ep
r
esen
ts
th
e
tr
an
s
m
i
tter
,
th
e
ch
a
n
n
el
ac
ts
as
a
s
to
ch
asti
c
la
y
er
in
tr
o
d
u
cin
g
i
m
p
air
m
e
n
t
s
,
an
d
th
e
d
ec
o
d
er
r
ep
r
ese
n
ts
th
e
r
ec
eiv
er
.
T
h
e
n
et
w
o
r
k
is
tr
ai
n
ed
to
m
i
n
i
m
ize
t
h
e
d
i
f
f
er
e
n
ce
b
et
w
ee
n
t
h
e
tr
an
s
m
itted
a
n
d
r
ec
o
v
er
ed
m
es
s
ag
e
s
,
th
u
s
au
to
m
atica
l
l
y
lear
n
i
n
g
o
p
ti
m
al
s
ig
n
al
r
ep
r
esen
ta
tio
n
s
f
o
r
th
e
g
i
v
en
c
h
a
n
n
el
a
n
d
h
a
r
d
war
e
i
m
p
air
m
e
n
ts
.
R
ec
en
t
l
y
,
s
e
v
er
al
n
e
u
r
al
DP
D
f
r
a
m
e
w
o
r
k
s
h
a
v
e
b
ee
n
p
r
o
p
o
s
ed
,
in
clu
d
i
n
g
C
NN
-
an
d
L
S
T
M
-
b
ased
ar
ch
itect
u
r
es
as
w
e
ll
as
te
m
p
o
r
al
co
n
v
o
lu
tio
n
al
n
et
w
o
r
k
s
(
T
C
N
)
-
DP
D
f
o
r
m
o
d
e
lin
g
P
A
n
o
n
li
n
ea
r
itie
s
an
d
m
e
m
o
r
y
e
f
f
ec
t
s
[
2
4
]
,
[
2
5
]
.
Ho
w
e
v
er
,
m
o
s
t o
f
t
h
ese
ap
p
r
o
ac
h
es tr
ea
t p
r
ed
is
to
r
tio
n
an
d
eq
u
a
lizatio
n
as
s
ep
ar
ate
task
s
.
I
n
co
n
tr
ast,
th
e
p
r
o
p
o
s
ed
A
E
-
OF
D
M
-
P
A
f
r
a
m
e
w
o
r
k
j
o
in
tl
y
p
er
f
o
r
m
s
P
A
lin
ea
r
i
za
tio
n
an
d
ch
an
n
el
co
m
p
e
n
s
at
io
n
in
a
n
en
d
-
to
-
e
n
d
lear
n
in
g
p
r
o
ce
s
s
,
p
r
o
v
id
in
g
en
h
an
ce
d
r
o
b
u
s
t
n
ess
a
n
d
ad
ap
tab
ilit
y
w
i
th
o
u
t
ex
p
licit C
SI
m
o
d
elin
g
.
2
.
3
.
1
.
Aut
o
enco
der
princip
le
An
au
to
e
n
co
d
er
is
a
ty
p
e
o
f
n
eu
r
al
n
et
w
o
r
k
d
esi
g
n
ed
f
o
r
u
n
s
u
p
er
v
is
ed
en
d
-
to
-
e
n
d
lear
n
i
n
g
[
2
6
]
.
I
t
lear
n
s
to
r
ec
o
n
s
tr
u
ct
its
i
n
p
u
t
at
th
e
o
u
tp
u
t
̂
af
ter
p
ass
i
n
g
th
r
o
u
g
h
a
b
o
ttlen
ec
k
r
ep
r
ese
n
tati
o
n
:
̂
=
σ
(
ℎ
ℎ
−
1
+
ℎ
)
;
ℎ
=
1
,
…
,
(
9
)
w
h
er
e,
ℎ
an
d
ℎ
d
en
o
te
th
e
w
eig
h
t
m
atr
i
x
a
n
d
b
ias
v
ec
to
r
,
(
.
)
is
t
h
e
ac
tiv
atio
n
f
u
n
ctio
n
an
d
th
e
n
u
m
b
er
o
f
h
id
d
en
la
y
er
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
Dee
p
lea
r
n
in
g
-
b
a
s
ed
p
o
w
er a
mp
lifi
er lin
ea
r
iz
a
tio
n
in
OF
DM syst
em
s
…
(
Mer
ye
m
Ma
mia
B
en
o
s
ma
n
)
5
T
h
e
en
co
d
er
(
)
m
ap
s
th
e
i
n
p
u
t
d
ata
in
to
a
laten
t
r
ep
r
es
en
tatio
n
,
w
h
ile
th
e
d
ec
o
d
er
(
)
r
ec
o
n
s
tr
u
ct
s
it
as
p
r
esen
ted
in
Fig
u
r
e
3
.
C
o
m
m
o
n
ac
ti
v
atio
n
f
u
n
ctio
n
s
i
n
clu
d
e
th
e
R
eL
U
,
s
ig
m
o
id
,
li
n
ea
r
,
an
d
So
f
tMa
x
f
u
n
ctio
n
s
.
T
r
ain
in
g
ai
m
s
to
m
in
i
m
ize
t
h
e
ca
te
g
o
r
ical
cr
o
s
s
-
en
tr
o
p
y
lo
s
s
:
ℒ
(
,
̂
)
=
−
∑
l
og
(
̂
)
(
1
0
)
wh
er
e,
an
d
̂
ar
e
th
e
tr
u
e
a
n
d
p
r
ed
icted
o
n
e
-
h
o
t
e
n
co
d
ed
s
y
m
b
o
l
s
,
r
esp
ec
tiv
e
l
y
,
Ne
t
w
o
r
k
p
ar
a
m
eter
s
(
,
)
ar
e
o
p
tim
ized
u
s
i
n
g
s
to
ch
as
tic
g
r
ad
ien
t d
escen
t (
SGD)
[
2
7
]
:
+
=
−
ℒ
(
,
̂
)
(
1
1
)
T
h
e
tr
an
s
m
itter
’
s
o
u
tp
u
t
is
co
n
s
tr
ain
ed
to
s
atis
f
y
av
e
r
ag
e
p
o
w
er
o
r
a
m
p
litu
d
e
li
m
it
s
,
s
u
c
h
as
[
|
(
)
|
2
]
≤
1
.
A
s
ill
u
s
tr
ated
in
Fig
u
r
e
4
,
th
is
s
tr
u
ct
u
r
e
allo
w
s
th
e
au
to
en
co
d
er
to
ac
t
as
a
co
m
p
lete
co
m
m
u
n
icatio
n
s
y
s
te
m
,
w
it
h
t
h
e
en
co
d
er
f
u
n
ctio
n
i
n
g
a
s
th
e
tr
an
s
m
itter
,
an
d
t
h
e
d
ec
o
d
er
as th
e
r
ec
eiv
er
.
Fig
u
r
e
3
.
A
r
ch
itectu
r
e
o
f
a
n
a
u
to
en
co
d
er
f
o
r
laten
t r
ep
r
esen
tat
io
n
lear
n
in
g
Fig
u
r
e
4
.
Au
to
en
co
d
er
ar
ch
ite
ctu
r
e
f
o
r
en
d
-
to
-
e
n
d
co
m
m
u
n
icatio
n
s
y
s
te
m
d
esi
g
n
2
.
4
.
P
r
o
po
s
ed
AE
-
O
F
DM
-
P
A
f
ra
m
ew
o
rk
W
e
p
r
o
p
o
s
e
an
en
d
-
to
-
e
n
d
au
t
o
en
co
d
er
-
b
ased
OFDM
s
y
s
te
m
(
A
E
-
O
FDM
-
P
A
)
t
h
at
j
o
in
tl
y
m
i
tig
a
tes
n
o
n
li
n
ea
r
p
o
w
er
a
m
p
lif
ier
d
is
t
o
r
tio
n
s
an
d
R
a
y
lei
g
h
ch
a
n
n
el
ef
f
ec
ts
.
T
h
e
m
o
d
el,
s
h
o
w
n
i
n
Fig
u
r
e
5
,
in
teg
r
ates
an
A
E
-
T
X
(
en
co
d
er
)
an
d
an
A
E
-
R
X
(
d
ec
o
d
er
)
tr
ain
ed
to
g
eth
er
t
h
r
o
u
g
h
en
d
-
to
-
e
n
d
lear
n
in
g
.
A
m
o
d
i
f
ied
R
ap
p
m
o
d
el
is
u
s
ed
to
e
m
u
l
ate
P
A
n
o
n
li
n
ea
r
it
y
,
w
h
i
le
e
m
b
ed
d
ed
p
ilo
t
s
eq
u
e
n
ce
s
al
l
o
w
t
h
e
A
E
-
R
X
to
esti
m
ate
th
e
c
h
an
n
el
i
m
p
licitl
y
,
eli
m
in
ati
n
g
th
e
n
ee
d
f
o
r
ex
p
licit
C
SI
esti
m
atio
n
.
B
y
lear
n
in
g
f
r
o
m
d
ata,
th
e
AE
-
OF
DM
-
P
A
s
y
s
te
m
co
m
p
en
s
ate
s
b
o
th
a
m
p
litu
d
e
an
d
p
h
ase
d
is
to
r
tio
n
s
,
ac
h
iev
i
n
g
j
o
i
n
t
lin
ea
r
izatio
n
an
d
ch
an
n
el
eq
u
al
izatio
n
.
T
h
is
d
at
a
-
d
r
iv
e
n
ap
p
r
o
ac
h
en
h
a
n
ce
s
r
o
b
u
s
tn
e
s
s
a
n
d
s
p
ec
tr
al
ef
f
icie
n
c
y
co
m
p
ar
ed
w
it
h
co
n
v
e
n
tio
n
al
DP
D
m
e
th
o
d
s
t
h
at
r
el
y
o
n
e
x
p
licit
P
A
o
r
c
h
an
n
el
m
o
d
eli
n
g
.
T
h
e
A
E
-
O
FDM
-
P
A
m
o
d
el
i
n
Fig
u
r
e
5
in
cl
u
d
es:
1.
On
e
A
E
-
T
X
b
lo
ck
p
er
s
u
b
ca
r
r
ier
:
f
o
r
1
6
-
QA
M
m
o
d
u
lat
io
n
2.
A
r
ea
l/co
m
p
le
x
co
n
v
er
s
io
n
b
lo
ck
: f
o
r
co
n
v
er
ti
n
g
m
o
d
u
lated
o
u
tp
u
t in
to
co
m
p
lex
s
y
m
b
o
ls
3.
A
m
o
d
u
latio
n
b
lo
ck
b
y
in
v
er
s
e
f
ast Fo
u
r
ier
tr
an
s
f
o
r
m
(
I
FF
T
)
: f
o
r
s
y
m
b
o
l
m
o
d
u
latio
n
4.
A
c
y
clic
p
r
ef
i
x
ad
d
itio
n
b
lo
ck
:
f
o
r
eli
m
i
n
ati
n
g
i
n
ter
s
y
m
b
o
l i
n
t
er
f
er
e
n
ce
ca
u
s
ed
b
y
t
h
e
m
u
lt
ip
ath
ch
an
n
el
5.
A
p
o
w
er
a
m
p
li
f
ier
ad
d
itio
n
b
lo
ck
6.
A
p
ar
allel/s
er
ia
l
co
n
v
er
s
io
n
b
l
o
ck
7.
A
co
m
p
le
x
/r
ea
l
co
n
v
er
s
io
n
b
lo
ck
8.
A
ch
an
n
el
b
lo
ck
:
R
a
y
leig
h
c
h
an
n
el
o
n
e
-
tap
9.
A
r
ea
l/co
m
p
le
x
co
n
v
er
s
io
n
b
lo
ck
10.
A
s
er
ial
/
p
ar
allel
co
n
v
er
s
io
n
b
l
o
ck
11.
A
c
y
c
lic
p
r
ef
i
x
r
e
m
o
v
al
b
lo
ck
12.
A
F
FT
d
em
o
d
u
latio
n
b
lo
ck
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
un
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
1
-
1
3
6
13.
An
eq
u
aliz
at
io
n
b
lo
ck
f
o
r
ea
ch
o
u
tp
u
t o
f
th
e
p
r
ev
io
u
s
b
lo
ck
14.
A
co
m
p
le
x
/r
ea
l
co
n
v
er
s
io
n
b
lo
ck
15.
An
A
E
-
R
X
b
lo
ck
f
o
r
ea
ch
s
u
b
ca
r
r
ier
: f
o
r
s
y
m
b
o
l d
e
m
o
d
u
lat
i
o
n
,
th
is
b
lo
ck
h
as t
h
e
s
a
m
e
ar
c
h
itect
u
r
e
as
a
s
in
g
le
-
ca
r
r
ier
r
ec
eiv
er
16.
A
p
ar
allel/s
e
r
ia
l c
o
n
v
er
s
io
n
b
l
o
ck
: to
r
ec
o
v
er
th
e
s
eq
u
e
n
ce
o
f
b
in
ar
y
w
o
r
d
s
.
Fig
u
r
e
5
.
Deta
iled
s
tr
u
ct
u
r
e
o
f
A
E
-
OFD
M
-
PA
2
.
4
.
1
.
Cha
nn
el
equa
liza
t
io
n
T
o
en
ab
le
ch
an
n
el
es
ti
m
atio
n
w
it
h
i
n
t
h
e
A
E
-
R
X,
p
ilo
t
s
eq
u
e
n
ce
s
ar
e
i
n
s
er
ted
in
to
t
h
e
OFD
M
f
r
a
m
e.
T
h
ese
p
ilo
ts
ar
e
k
n
o
w
n
to
b
o
th
tr
an
s
m
itter
an
d
r
ec
eiv
er
,
an
d
ar
e
o
r
th
o
g
o
n
al
to
d
ata
s
y
m
b
o
ls
to
p
r
ev
en
t
in
ter
f
er
e
n
ce
.
T
h
e
esti
m
a
ted
ch
an
n
el
is
o
b
tain
ed
as:
=
∗
|
|
2
(
1
2
)
w
h
er
e,
is
th
e
esti
m
ate
d
ch
an
n
el,
an
d
r
ep
r
ese
n
t
th
e
tr
an
s
m
itted
an
d
r
ec
eiv
ed
p
ilo
t
s
y
m
b
o
ls
,
r
esp
ec
tiv
el
y
.
T
h
e
A
E
-
R
X
r
ec
ei
v
es
f
o
u
r
i
n
p
u
ts
,
t
h
e
r
ea
l
an
d
i
m
a
g
in
ar
y
p
ar
ts
o
f
t
h
e
r
ec
eiv
ed
s
i
g
n
al
(
,
)
,
an
d
th
e
r
ea
l
an
d
i
m
a
g
in
ar
y
p
ar
ts
o
f
th
e
e
s
ti
m
ated
ch
a
n
n
e
l
(
,
)
,
as
d
ep
icted
in
Fig
u
r
e
6
.
T
h
is
co
n
f
ig
u
r
atio
n
en
ab
les
t
h
e
A
E
-
R
X
to
j
o
in
tl
y
co
r
r
ec
t
am
p
lit
u
d
e
a
n
d
p
h
ase
d
is
to
r
tio
n
s
b
y
lear
n
in
g
to
e
x
p
lo
it
th
e
i
m
p
lic
it
C
SI
r
ath
er
th
a
n
r
el
y
i
n
g
o
n
ex
p
licit
an
al
y
tical
ch
a
n
n
el
m
o
d
els
.
Du
r
in
g
tr
ai
n
in
g
,
th
e
a
u
to
en
c
o
d
er
is
f
ed
w
ith
b
atc
h
es
o
f
s
ize
,
eac
h
co
n
tain
in
g
OFD
M
f
r
a
m
e
s
au
g
m
e
n
ted
w
ith
id
e
n
tical
p
il
o
t
s
eq
u
en
ce
s
ac
r
o
s
s
all
s
u
b
ca
r
r
ier
s
.
Gr
ad
ien
ts
ar
e
co
m
p
u
te
d
f
o
r
ea
ch
b
atch
to
u
p
d
ate
th
e
n
et
w
o
r
k
p
ar
a
m
ete
r
s
(
,
)
v
ia
s
to
c
h
asti
c
g
r
ad
ien
t
d
es
ce
n
t,
en
s
u
r
i
n
g
s
tab
le
co
n
v
er
g
en
ce
an
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n
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el
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m
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en
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o
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as
ill
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s
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ated
i
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Fi
g
u
r
e
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.
On
ce
tr
ai
n
ed
,
th
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A
E
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OFD
M
-
P
A
s
y
s
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e
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its
a
lin
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ized
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n
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tr
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s
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er
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e
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ates
f
o
r
b
o
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P
A
n
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lin
ea
r
it
y
an
d
ch
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n
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el
d
is
to
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s
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it
h
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ex
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SI
m
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h
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ata
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r
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en
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s
r
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u
s
tn
es
s
an
d
ad
ap
tab
ilit
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co
m
p
ar
ed
to
tr
a
d
itio
n
al
DP
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m
et
h
o
d
s
th
at
d
ep
en
d
o
n
ac
cu
r
ate
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aly
tical
m
o
d
els
o
r
ex
p
licit
ch
an
n
el
i
n
f
o
r
m
atio
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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as
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ated
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am
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ch
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e
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k
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ai
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R
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all
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e
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n
s
u
r
e
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ical
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eliab
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iti
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e
r
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o
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v
en
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al
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e
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o
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ial
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ig
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tio
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-
DP
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ap
p
r
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n
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g
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p
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ly
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ia
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r
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er
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an
d
a
m
e
m
o
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ep
th
o
f
1
.
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h
is
s
y
n
t
h
etic
d
ataset
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n
s
u
r
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u
l
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n
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o
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er
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ch
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n
el
an
d
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A
ch
ar
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ter
is
t
ics,
g
u
ar
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teein
g
r
ep
r
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d
u
cib
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en
ab
li
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m
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ar
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n
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et
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n
th
e
p
r
o
p
o
s
ed
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ee
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lear
n
in
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ased
p
r
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d
is
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m
e
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t
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ical
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DP
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m
e
th
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d
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T
ab
le
1
p
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th
e
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ar
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m
eter
s
o
f
th
e
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E
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P
A
lay
er
s
an
d
t
h
e
co
r
r
esp
o
n
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g
tr
ai
n
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n
f
i
g
u
r
atio
n
.
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h
is
s
y
n
th
e
tic
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ataset
allo
w
s
f
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ll
co
n
tr
o
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v
er
b
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th
ch
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d
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ter
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n
s
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r
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ass
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s
s
m
e
n
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o
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th
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r
o
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o
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m
o
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el
u
n
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er
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li
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d
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o
n
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itio
n
s
.
T
ab
le
1
.
A
r
ch
itect
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o
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Co
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esu
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s
T
h
e
co
n
s
tellatio
n
a
n
al
y
s
i
s
in
Fig
u
r
e
8
co
m
p
ar
es
t
h
e
p
r
o
p
o
s
ed
A
E
-
OFDM
-
P
A
s
y
s
te
m
(
Fi
g
u
r
e
8
(
a)
)
w
it
h
th
e
co
n
v
en
t
io
n
al
OFDM
-
DP
D
s
c
h
e
m
e
(
Fi
g
u
r
e
8
(
b
)
)
u
n
d
er
a
h
ig
h
l
y
n
o
n
li
n
ea
r
co
n
d
i
tio
n
,
w
h
er
e
t
h
e
P
A
o
p
er
ate
s
n
ea
r
s
atu
r
at
io
n
(
I
B
O=
3
d
B
)
.
I
n
th
e
co
n
v
e
n
tio
n
a
l
OFDM
-
DP
D
s
y
s
te
m
,
t
h
e
o
b
j
e
ctiv
e
i
s
to
p
r
eser
v
e
th
e
s
ta
n
d
ar
d
1
6
-
Q
A
M
co
n
s
te
llatio
n
at
th
e
P
A
in
p
u
t
(
b
lu
e
p
o
in
ts
)
.
T
h
e
DP
D
b
lo
ck
co
m
p
e
n
s
ates
f
o
r
th
e
n
o
n
li
n
ea
r
b
eh
av
io
r
o
f
th
e
a
m
p
lif
ier
,
i
m
p
r
o
v
i
n
g
t
h
e
r
ec
eiv
ed
co
n
s
tellat
io
n
(
g
r
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n
)
co
m
p
ar
ed
w
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th
th
e
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e
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el
y
d
is
to
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ted
o
u
tp
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t
af
ter
th
e
P
A
an
d
ch
an
n
el
(
r
ed
)
.
H
o
w
ev
er
,
th
is
ap
p
r
o
ac
h
d
ep
en
d
s
o
n
ac
c
u
r
ate
P
A
m
o
d
elin
g
an
d
ac
ts
as a
n
e
x
ter
n
al
co
r
r
ec
tio
n
m
o
d
u
le.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
un
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
1
-
1
3
8
I
n
co
n
tr
ast,
th
e
A
E
-
OFDM
-
P
A
f
r
a
m
e
w
o
r
k
e
m
p
lo
y
s
an
en
d
-
to
-
en
d
lear
n
i
n
g
s
tr
ateg
y
th
at
j
o
in
tl
y
o
p
tim
izes
tr
an
s
m
is
s
io
n
a
n
d
r
ec
ep
tio
n
.
T
h
e
A
E
-
T
X
lear
n
s
a
n
o
n
-
s
ta
n
d
ar
d
co
n
s
tellatio
n
at
th
e
tr
an
s
m
i
tter
(
b
lu
e)
th
at
is
i
n
h
er
e
n
tl
y
r
o
b
u
s
t
to
n
o
n
li
n
ea
r
ities
,
w
h
ile
th
e
A
E
-
R
X
r
ec
o
n
s
tr
u
ct
s
a
clea
n
1
6
-
Q
A
M
-
li
k
e
co
n
s
tellat
io
n
(
g
r
ee
n
)
at
th
e
r
e
ce
iv
er
d
esp
ite
s
tr
o
n
g
d
is
to
r
tio
n
(
r
ed
)
.
T
h
is
b
eh
av
io
r
d
em
o
n
s
tr
ates
th
a
t
th
e
A
E
-
OFDM
-
P
A
n
o
t
o
n
l
y
co
m
p
en
s
ates
f
o
r
P
A
a
n
d
ch
an
n
el
i
m
p
air
m
en
ts
b
u
t
al
s
o
lear
n
s
to
a
d
ap
t
its
m
o
d
u
latio
n
s
ch
e
m
e
to
m
a
x
i
m
ize
r
o
b
u
s
tn
e
s
s
u
n
d
er
h
ar
s
h
n
o
n
li
n
ea
r
co
n
d
itio
n
s
s
u
ch
as
I
B
O=
3
d
B
.
T
h
ese
f
in
d
i
n
g
s
co
n
f
ir
m
th
e
ef
f
ec
ti
v
e
n
ess
o
f
t
h
e
j
o
i
n
t
A
E
-
T
X/
A
E
-
R
X
o
p
ti
m
izat
io
n
in
m
i
tig
a
tin
g
n
o
n
li
n
ea
r
d
is
to
r
tio
n
s
w
it
h
o
u
t
r
eq
u
ir
in
g
e
x
p
licit P
A
m
o
d
eli
n
g
.
(
a)
(
b
)
Fig
u
r
e
8
.
C
o
n
s
tel
latio
n
co
m
p
a
r
is
o
n
o
f
(
a)
A
E
-
OF
DM
-
P
A
a
n
d
(
b
)
OFDM
-
DP
D
3
.
2
.
B
lo
ck
er
ro
r
ra
t
e
(
B
L
E
R
)
re
s
ults
A
d
etaile
d
co
m
p
ar
is
o
n
o
f
t
h
e
B
L
E
R
p
er
f
o
r
m
a
n
ce
w
as
ca
r
r
i
ed
o
u
t
am
o
n
g
t
h
e
p
r
o
p
o
s
ed
A
E
-
OFDM
-
P
A
s
y
s
te
m
,
th
e
co
n
v
en
tio
n
al
OFDM
an
d
OFDM
-
P
A
s
ch
e
m
es,
an
d
th
e
r
ef
er
en
ce
O
FDM
-
DP
D
ap
p
r
o
ac
h
.
T
h
e
r
esu
lt
s
,
p
r
esen
ted
i
n
Fi
g
u
r
e
9
,
ass
ess
t
h
e
r
o
b
u
s
t
n
e
s
s
o
f
t
h
ese
s
y
s
te
m
s
u
n
d
er
s
e
v
e
r
e
n
o
n
lin
ea
r
d
is
to
r
tio
n
co
n
d
itio
n
s
,
w
it
h
t
h
e
P
A
o
p
er
atin
g
at
a
n
I
B
O
o
f
3
d
B
.
T
o
en
s
u
r
e
h
i
g
h
s
tatis
tical
ac
cu
r
ac
y
,
t
h
e
s
i
m
u
latio
n
s
e
tu
p
w
a
s
r
ef
i
n
ed
to
en
ab
le
r
eliab
l
e
B
L
E
R
esti
m
atio
n
d
o
w
n
to
1
0
⁻⁵.
T
h
e
OFDM
-
P
A
c
u
r
v
e
(
r
ed
)
s
h
o
w
s
a
clea
r
p
er
f
o
r
m
a
n
ce
d
eg
r
ad
atio
n
d
u
e
to
th
e
a
m
p
lifie
r
’
s
n
o
n
li
n
ea
r
it
y
,
r
ea
ch
i
n
g
an
er
r
o
r
f
lo
o
r
ab
o
v
e
3
×1
0
⁻²
at
h
ig
h
E
b
/N0
v
alu
es.
I
n
co
n
tr
ast,
th
e
OFDM
-
DP
D
s
y
s
te
m
(
g
r
ee
n
)
ef
f
ec
ti
v
el
y
co
m
p
e
n
s
ate
s
f
o
r
th
ese
n
o
n
li
n
ea
r
ities
,
ac
h
iev
in
g
p
er
f
o
r
m
a
n
ce
co
m
p
a
r
ab
le
to
th
e
id
ea
l
OFDM
r
ef
er
en
ce
(
m
ag
e
n
ta)
,
w
h
ic
h
r
ep
r
esen
ts
t
h
e
u
p
p
er
b
o
u
n
d
f
o
r
li
n
ea
r
izatio
n
.
T
h
e
p
r
o
p
o
s
ed
A
E
-
O
FDM
-
P
A
m
o
d
e
l
(
c
y
a
n
)
ex
h
ib
it
s
a
s
tr
o
n
g
ab
ili
t
y
to
m
it
ig
ate
b
o
th
n
o
n
li
n
ea
r
an
d
ch
an
n
el
-
i
n
d
u
ce
d
im
p
air
m
en
t
s
.
At
/
0
=2
0
d
B
,
it
a
ch
iev
e
s
a
B
L
E
R
o
f
ap
p
r
o
x
i
m
a
tel
y
5
×1
0
⁻⁴,
co
r
r
esp
o
n
d
in
g
to
m
o
r
e
th
a
n
a
7
0
-
f
o
ld
i
m
p
r
o
v
e
m
e
n
t
o
v
e
r
th
e
u
n
co
m
p
e
n
s
ated
OFDM
-
P
A
.
A
lt
h
o
u
g
h
it
s
p
er
f
o
r
m
a
n
ce
d
o
es
n
o
t
ex
ac
tl
y
m
atc
h
t
h
at
o
f
t
h
e
a
n
al
y
tic
all
y
m
o
d
eled
DP
D,
th
e
A
E
-
OFDM
-
P
A
r
e
m
ai
n
s
h
ig
h
l
y
co
m
p
et
iti
v
e.
A
t
a
tar
g
e
t
B
L
E
R
o
f
1
0
⁻³,
it
r
eq
u
ir
es
o
n
l
y
ab
o
u
t
2
d
B
h
i
g
h
e
r
E
b
/N0
th
a
n
t
h
e
o
p
ti
m
ized
OFDM
-
DP
D
r
ef
er
en
ce
.
T
h
ese
f
i
n
d
in
g
s
v
alid
ate
t
h
e
ca
p
ab
ilit
y
o
f
t
h
e
p
r
o
p
o
s
ed
en
d
-
to
-
e
n
d
l
ea
r
n
in
g
f
r
a
m
e
w
o
r
k
to
p
r
eser
v
e
s
ig
n
al
in
teg
r
it
y
a
n
d
co
n
f
ir
m
t
h
at
d
ee
p
lear
n
in
g
-
b
a
s
ed
co
m
p
en
s
atio
n
ca
n
s
e
r
v
e
as
a
r
o
b
u
s
t
an
d
m
o
d
el
-
f
r
ee
alter
n
at
i
v
e
to
tr
ad
itio
n
al
DP
D
m
et
h
o
d
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
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L
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o
m
m
u
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o
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Dee
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mp
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DM syst
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ye
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mia
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en
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9
Fig
u
r
e
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.
B
L
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R
p
er
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o
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ased
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te
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o
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n
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s
i
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g
t
w
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k
e
y
s
p
ec
tr
al
m
e
tr
ics:
t
h
e
p
o
w
er
s
p
ec
tr
al
d
en
s
i
t
y
(
P
SD)
an
d
th
e
ad
j
ac
en
t
c
h
an
n
el
lea
k
ag
e
r
atio
(
AC
L
R
)
.
As
s
h
o
w
n
in
Fi
g
u
r
e
1
0
,
t
h
e
P
SD
r
ev
ea
l
s
t
h
e
i
m
p
ac
t
o
f
th
e
P
A
n
o
n
li
n
ea
r
it
y
o
n
s
p
ec
tr
al
s
p
r
ea
d
in
g
u
n
d
er
t
h
e
ch
alle
n
g
i
n
g
I
B
O
=
3
d
B
co
n
d
itio
n
.
I
n
th
e
co
n
v
e
n
tio
n
al
OFDM
-
P
A
s
y
s
te
m
,
s
tr
o
n
g
n
o
n
li
n
ea
r
it
y
ca
u
s
e
s
s
ig
n
i
f
ica
n
t
s
p
ec
tr
al
r
eg
r
o
w
t
h
,
r
aisi
n
g
t
h
e
o
u
t
-
of
-
b
an
d
n
o
i
s
e
f
lo
o
r
to
ab
o
u
t
−1
0
.
5
d
B
/
Hz
(
at
n
o
r
m
alize
d
f
r
eq
u
en
c
y
±
0
.
4
)
.
W
h
en
a
co
n
v
e
n
tio
n
al
D
P
D
b
ased
o
n
a
m
e
m
o
r
y
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o
l
y
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o
m
ial
m
o
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el
is
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p
lied
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a
s
l
ig
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t
r
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ctio
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er
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m
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r
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d
le
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el
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r
o
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i
m
atel
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4
d
B
/Hz.
Ho
w
e
v
er
,
t
h
i
s
i
m
p
r
o
v
e
m
en
t r
e
m
ai
n
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li
m
ited
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p
ar
ticu
lar
l
y
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o
r
s
ig
n
als e
x
h
ib
itin
g
m
e
m
o
r
y
e
f
f
ec
ts
.
I
n
co
n
tr
ast,
th
e
p
r
o
p
o
s
ed
A
E
-
OF
DM
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A
s
y
s
te
m
e
f
f
e
ctiv
el
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u
p
p
r
ess
e
s
s
p
ec
tr
al
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eg
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o
w
th
,
m
ai
n
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n
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g
t
h
e
o
u
t
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of
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b
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d
l
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el
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s
e
to
−2
0
d
B
/Hz,
w
h
ich
is
n
ea
r
l
y
id
e
n
tical
to
th
at
o
f
an
id
ea
l
lin
ea
r
s
y
s
te
m
.
T
h
is
r
ep
r
esen
ts
a
n
i
m
p
r
o
v
e
m
en
t o
f
ab
o
u
t 9
.
5
d
B
o
v
er
th
e
u
n
co
m
p
en
s
ated
OFDM
-
P
A
an
d
n
ea
r
l
y
6
d
B
o
v
er
th
e
co
n
v
en
t
io
n
al
DP
D.
T
h
ese
r
esu
lts
d
e
m
o
n
s
tr
ate
th
a
t
th
e
au
to
e
n
co
d
er
is
ca
p
ab
le
o
f
lear
n
i
n
g
a
s
ig
n
a
l
r
ep
r
esen
tatio
n
t
h
at
i
n
h
er
e
n
tl
y
m
i
n
i
m
izes sp
ec
tr
al
d
is
to
r
tio
n
d
esp
ite
h
ar
d
w
ar
e
n
o
n
li
n
ea
r
itie
s
.
T
h
e
A
C
P
R
r
e
s
u
l
ts
,
s
h
o
w
n
in
Fig
u
r
e
1
1
,
f
u
r
t
h
er
co
n
f
ir
m
th
e
s
e
f
i
n
d
i
n
g
s
.
T
h
e
v
er
tical
a
x
is
r
e
p
r
esen
t
s
th
e
AC
P
R
m
ea
s
u
r
ed
in
d
B
at
th
e
P
A
o
u
tp
u
t,
w
h
ile
t
h
e
h
o
r
izo
n
tal
ax
is
co
r
r
esp
o
n
d
s
to
th
e
d
if
f
er
e
n
t
OFD
M
-
b
ased
tr
an
s
m
is
s
io
n
s
c
h
e
m
es.
T
h
e
u
n
co
m
p
en
s
ated
OF
DM
-
P
A
s
y
s
te
m
e
x
h
ib
its
th
e
s
tr
o
n
g
es
t
d
eg
r
ad
atio
n
(
≈
−1
2
.
5
d
B
)
,
w
h
ile
t
h
e
co
n
v
e
n
tio
n
al
DP
D
ac
h
ie
v
es
o
n
l
y
m
o
d
er
ate
im
p
r
o
v
e
m
en
t
(
≈
−7
.
0
d
B
)
.
B
y
co
m
p
ar
i
s
o
n
,
th
e
A
E
-
O
FDM
-
P
A
s
y
s
te
m
ac
h
iev
e
s
an
A
C
L
R
o
f
ap
p
r
o
x
im
atel
y
−1
8
.
5
d
B
,
co
r
r
esp
o
n
d
in
g
to
g
a
in
s
o
f
ab
o
u
t
6
d
B
an
d
1
1
.
5
d
B
o
v
er
th
e
OFDM
-
P
A
a
n
d
DP
D
s
y
s
te
m
s
,
r
esp
ec
tiv
el
y
.
Fig
u
r
e
1
0
.
P
SD p
er
f
o
r
m
an
ce
f
o
r
d
if
f
er
en
t O
FDM
-
b
ased
s
y
s
te
m
s
Fig
u
r
e
1
1
.
C
o
m
p
ar
is
o
n
o
f
AC
P
R
p
er
f
o
r
m
a
n
ce
h
ig
h
li
g
h
ti
n
g
A
E
-
O
FDM
-
P
A
a
n
d
OFDM
-
DP
D
s
ch
e
m
es
Ov
er
all,
th
ese
s
p
e
ctr
al
r
esu
l
ts
co
n
f
ir
m
th
a
t
th
e
A
E
-
O
FDM
-
P
A
ap
p
r
o
ac
h
n
o
t
o
n
l
y
r
ed
u
ce
s
s
p
ec
tr
al
r
eg
r
o
w
t
h
to
n
ea
r
-
id
ea
l
lev
e
ls
b
u
t
also
p
r
o
v
id
es
th
e
h
ig
h
e
s
t
m
ea
s
u
r
ab
le
g
ai
n
in
A
C
L
R
.
T
h
is
m
ak
e
s
it
a
m
o
r
e
r
o
b
u
s
t
an
d
s
p
ec
tr
all
y
ef
f
ic
ien
t
alter
n
ativ
e
to
co
n
v
en
t
io
n
al
li
n
e
ar
izatio
n
tec
h
n
iq
u
es.
I
t
s
h
o
u
ld
b
e
n
o
ted
th
at
all
r
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lt
s
w
er
e
o
b
tain
ed
f
r
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m
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i
m
u
latio
n
s
w
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h
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t
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d
w
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th
e
-
lo
o
p
o
r
r
ea
l
P
A
m
ea
s
u
r
e
m
en
ts
.
F
u
t
u
r
e
w
o
r
k
w
il
l
f
o
cu
s
o
n
ex
p
er
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tal
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R
F
h
ar
d
w
ar
e
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d
ex
ten
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m
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MI
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atio
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s
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a
n
d
w
id
eb
an
d
f
ad
in
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ch
a
n
n
els.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
un
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
1
-
1
3
10
3
.
4
.
Ana
ly
s
is
o
f
t
he
CCDF
a
nd
P
AP
R
Fig
u
r
e
1
2
p
r
esen
ts
th
e
C
C
DF
,
a
k
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y
m
etr
ic
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s
ed
to
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y
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P
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P
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r
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m
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ased
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T
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66
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88
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u
c
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FF
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P
A
,
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FF
T
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ar
e
ex
clu
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ed
f
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s
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al
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is
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ce
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le
p
ar
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ter
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ar
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co
m
m
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ased
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C
o
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ar
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w
it
h
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ap
p
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th
e
A
E
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ex
h
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ce
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s
s
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e
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etr
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n
clu
d
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n
g
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,
AC
L
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a
nd
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lt
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ial
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e
A
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u
s
es
its
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itio
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m
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lex
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t
y
s
tr
ateg
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to
p
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en
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izati
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h
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De
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ch
itect
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ased
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h
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ai
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m
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p
lex
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ical
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icall
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s
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w
it
h
r
ec
u
r
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en
t
n
eu
r
al
n
e
t
w
o
r
k
(
R
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)
o
r
t
r
a
n
s
f
o
r
m
er
-
b
ased
d
esig
n
s
[
2
8
]
.
T
h
is
b
alan
ce
d
tr
ad
e
-
o
f
f
b
et
wee
n
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m
p
lex
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a
n
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s
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if
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h
e
A
E
’
s
ad
o
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tio
n
,
o
f
f
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n
g
s
u
p
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io
r
s
p
ec
tr
al
ef
f
icie
n
c
y
,
r
o
b
u
s
t
n
e
s
s
,
an
d
n
o
n
li
n
ea
r
it
y
m
iti
g
atio
n
w
h
i
le
m
ai
n
tai
n
i
n
g
p
r
ac
tical
co
m
p
u
tatio
n
al
f
ea
s
ib
ilit
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.