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g
r
a
m
m
a
b
le
g
ate
ar
r
a
y
s
(
FP
GAs)
h
av
e
m
ad
e
it
p
o
s
s
ib
le
to
r
u
n
ML
al
g
o
r
ith
m
s
d
ir
ec
tly
o
n
th
e
r
a
d
io
h
ar
d
war
e,
ac
h
iev
in
g
r
ea
l
-
tim
e
p
er
f
o
r
m
an
ce
with
r
ed
u
ce
d
lat
en
cy
an
d
p
o
wer
c
o
n
s
u
m
p
tio
n
[
4
]
.
Ap
p
r
o
ac
h
es
s
u
ch
as
h
y
b
r
id
c
o
n
v
o
l
u
tio
n
al
n
eu
r
al
n
etwo
r
k
-
lo
n
g
s
h
o
r
t
-
ter
m
m
em
o
r
y
(
C
NN
-
L
STM
)
m
o
d
els
f
u
r
th
er
s
tr
en
g
th
en
t
h
e
ab
i
lity
o
f
SDR
s
y
s
tem
s
to
r
ec
o
g
n
ize
m
o
d
u
latio
n
f
o
r
m
ats
ac
cu
r
ately
,
s
u
p
p
o
r
tin
g
i
n
tellig
en
t
an
d
ad
ap
tiv
e
co
m
m
u
n
icatio
n
s
tr
ateg
ies
[
5
]
.
I
n
ad
d
itio
n
,
r
ec
en
t
s
tu
d
ie
s
h
ig
h
lig
h
t
th
e
g
r
o
win
g
im
p
o
r
tan
ce
o
f
co
o
p
e
r
ativ
e
an
d
M
L
-
en
ab
led
s
p
ec
tr
u
m
s
en
s
in
g
m
eth
o
d
s
,
w
h
ich
p
la
y
a
cr
u
cial
r
o
le
in
im
p
r
o
v
in
g
d
etec
tio
n
p
er
f
o
r
m
an
ce
an
d
s
p
e
ctr
u
m
u
tili
za
tio
n
i
n
co
m
p
lex
r
a
d
io
en
v
ir
o
n
m
en
ts
[
6
]
.
R
esear
ch
o
n
m
u
lti
-
b
an
d
SDR
ar
ch
itectu
r
es
also
em
p
h
asizes
th
e
n
ee
d
f
o
r
h
ig
h
ly
r
ec
o
n
f
ig
u
r
ab
le
r
ad
io
s
ca
p
ab
le
o
f
m
ee
tin
g
t
h
e
d
iv
er
s
e
o
p
er
atio
n
al
r
e
q
u
ir
em
en
ts
o
f
n
ex
t
-
g
e
n
er
atio
n
n
etwo
r
k
s
[
7
]
.
T
o
g
eth
er
,
th
ese
ad
v
an
ce
m
en
ts
p
o
in
t
to
war
d
t
h
e
d
ev
elo
p
m
e
n
t
o
f
an
FP
GA
-
b
ased
,
ML
-
en
ab
led
SDR
s
y
s
tem
ca
p
ab
le
o
f
ad
ju
s
tin
g
tr
an
s
m
is
s
io
n
p
a
r
am
eter
s
s
u
ch
as
m
o
d
u
latio
n
ty
p
e
an
d
s
p
ec
tr
u
m
u
s
ag
e
in
r
ea
l
tim
e
b
ased
o
n
f
ee
d
b
ac
k
f
r
o
m
th
e
co
m
m
u
n
icatio
n
ch
an
n
el.
Su
ch
ad
ap
ta
b
ilit
y
is
ess
en
ti
al
f
o
r
ac
h
ie
v
in
g
th
e
r
eliab
ilit
y
,
ef
f
icien
cy
,
an
d
r
e
s
p
o
n
s
iv
en
ess
ex
p
ec
ted
in
5
G
ap
p
licatio
n
s
in
clu
d
in
g
m
a
s
s
iv
e
m
ac
h
in
e
-
ty
p
e
co
m
m
u
n
icatio
n
s
(
m
MT
C
)
,
u
ltra
-
r
eliab
le
lo
w
-
laten
cy
c
o
m
m
u
n
icatio
n
s
(
UR
L
L
C
)
,
an
d
en
h
an
ce
d
m
o
b
ile
b
r
o
ad
b
an
d
(
eM
B
B
)
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
T
h
e
ev
o
lu
tio
n
o
f
SDR
h
as
b
e
en
clo
s
ely
tied
to
th
e
d
ev
el
o
p
m
en
t
o
f
r
ec
o
n
f
ig
u
r
ab
le
a
r
ch
it
ec
tu
r
es
an
d
ad
ap
tiv
e
s
ig
n
al
p
r
o
ce
s
s
in
g
m
eth
o
d
s
.
Sin
ce
th
e
ea
r
ly
2
0
1
0
s
,
r
esear
ch
er
s
h
av
e
em
p
h
asized
th
e
in
teg
r
atio
n
o
f
SDR
wi
th
in
tellig
en
t
alg
o
r
i
th
m
s
to
ad
d
r
ess
th
e
ch
alle
n
g
es
o
f
s
p
ec
tr
u
m
s
ca
r
city
,
in
ter
f
er
en
ce
,
a
n
d
h
eter
o
g
en
eity
in
m
o
d
er
n
wir
eless
n
etwo
r
k
s
.
Ulv
er
s
o
y
[
8
]
p
r
o
v
id
ed
a
co
m
p
r
eh
e
n
s
iv
e
o
v
er
v
iew
o
f
SDR
f
r
am
ewo
r
k
s
an
d
th
eir
lim
itatio
n
s
in
h
ar
d
war
e
ad
ap
tab
ilit
y
.
Hay
k
in
[
9
]
in
tr
o
d
u
ce
d
co
g
n
i
tiv
e
r
ad
io
as
a
k
ey
p
ar
ad
ig
m
f
o
r
en
a
b
lin
g
d
y
n
am
i
c
s
p
ec
tr
u
m
ac
ce
s
s
,
wh
ic
h
s
et
t
h
e
f
o
u
n
d
atio
n
f
o
r
ML
-
SDR
in
teg
r
atio
n
.
W
ith
th
e
em
er
g
en
ce
o
f
5
G,
ad
v
a
n
ce
d
m
o
d
u
latio
n
r
ec
o
g
n
itio
n
an
d
ad
ap
tiv
e
c
o
d
in
g
b
ec
a
m
e
cr
iti
ca
l,
as
o
u
tlin
e
d
b
y
Yu
ce
k
an
d
Ar
s
lan
[
1
0
]
,
w
h
o
s
u
r
v
ey
ed
v
ar
io
u
s
s
p
ec
tr
u
m
s
e
n
s
in
g
an
d
ad
ap
tatio
n
tech
n
iq
u
es.
R
ec
en
t
s
tu
d
ies
h
av
e
f
o
cu
s
ed
o
n
th
e
u
s
e
o
f
d
ee
p
lear
n
in
g
m
o
d
els
to
im
p
r
o
v
e
PHY
-
lay
er
d
ec
is
io
n
-
m
ak
in
g
an
d
m
o
d
u
latio
n
class
if
icatio
n
.
Wan
g
[
1
1
]
d
e
m
o
n
s
tr
ate
d
h
o
w
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
s
co
u
l
d
en
h
an
ce
m
o
d
u
latio
n
r
ec
o
g
n
itio
n
ac
c
u
r
ac
y
in
d
y
n
a
m
ic
en
v
ir
o
n
m
e
n
ts
.
L
eCu
n
et
a
l
.
[
1
2
]
h
ig
h
lig
h
ted
th
e
p
o
wer
o
f
d
ee
p
lear
n
in
g
in
g
en
er
al
s
ig
n
al
p
r
o
ce
s
s
in
g
task
s
,
in
s
p
ir
in
g
its
u
s
e
in
co
m
m
u
n
icatio
n
lay
er
s
.
Li
a
o
et
a
l
.
[
1
3
]
f
u
r
t
h
er
ex
ten
d
ed
th
is
b
y
p
r
o
p
o
s
in
g
a
d
ee
p
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
m
o
d
el
f
o
r
a
d
ap
tiv
e
m
o
d
u
latio
n
in
5
G
n
et
wo
r
k
s
.
I
n
a
d
d
itio
n
,
Li
a
n
g
et
a
l
.
[
1
4
]
ap
p
lied
m
u
lt
i
-
u
s
er
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
f
o
r
s
p
ec
tr
u
m
s
h
ar
i
n
g
,
p
r
o
v
in
g
ef
f
ec
ti
v
e
in
r
ea
l
-
tim
e
m
u
lti
-
ag
en
t
s
ce
n
ar
io
s
.
Ye
et
a
l
.
[
1
5
]
s
h
o
wca
s
ed
t
h
e
u
s
e
o
f
DNNs
f
o
r
OFDM
s
ig
n
al
d
etec
tio
n
an
d
ch
an
n
el
esti
m
atio
n
,
r
ed
u
cin
g
b
it
er
r
o
r
r
ate
(
B
E
R
)
u
n
d
er
n
o
is
e
an
d
f
ad
in
g
.
Similar
ly
,
W
an
g
et
a
l
.
[
1
6
]
r
ev
iewe
d
th
e
c
o
n
v
e
r
g
en
ce
o
f
ed
g
e
co
m
p
u
tin
g
with
d
ee
p
le
ar
n
in
g
f
o
r
wir
eless
ap
p
licatio
n
s
,
p
r
o
p
o
s
in
g
n
o
v
el
ed
g
e
-
SDR
s
y
s
tem
s
.
Oth
er
wo
r
k
s
,
s
u
ch
as
T
an
g
et
a
l
.
[
1
7
]
,
im
p
lem
en
ted
DR
L
f
o
r
5
G
r
eso
u
r
ce
allo
ca
tio
n
,
ac
h
iev
in
g
l
o
w
laten
cy
a
n
d
h
ig
h
th
r
o
u
g
h
p
u
t
in
d
e
n
s
e
e
n
v
ir
o
n
m
en
ts
.
B
ey
o
n
d
m
o
d
u
l
atio
n
an
d
f
ilter
in
g
,
r
esear
ch
er
s
s
u
ch
as
Z
ap
p
o
n
e
et
a
l
.
[
1
8
]
an
d
R
estu
cc
ia
an
d
Me
lo
d
ia
[
1
9
]
em
p
h
asi
ze
d
f
u
ll
-
s
tack
ML
ap
p
licatio
n
s
,
f
r
o
m
MA
C
lay
er
s
ch
ed
u
lin
g
t
o
R
F
lo
o
p
lear
n
in
g
.
Mo
r
e
r
ec
en
t
liter
atu
r
e
ex
p
lo
r
es
ML
-
b
ased
ad
ap
tatio
n
o
f
b
ea
m
f
o
r
m
in
g
wei
g
h
ts
,
f
in
ite
im
p
u
ls
e
r
esp
o
n
s
e
(
FIR)
f
ilter
tap
s
co
n
f
ig
u
r
atio
n
s
,
an
d
p
o
we
r
co
n
tr
o
l
m
ec
h
an
is
m
s
.
T
h
is
liter
atu
r
e
p
r
o
v
id
es
a
r
o
b
u
s
t
f
o
u
n
d
a
tio
n
f
o
r
d
ev
elo
p
in
g
a
n
FP
GA
-
b
ased
ML
-
en
ab
le
d
SDR
ca
p
ab
le
o
f
ad
ap
tin
g
in
r
e
al
tim
e
to
v
ar
io
u
s
5
G
an
d
b
io
m
ed
ical
tr
an
s
m
is
s
io
n
d
em
an
d
s
.
3.
P
RO
P
O
SE
D
M
E
T
H
O
DO
L
O
G
Y
T
h
e
p
r
o
p
o
s
ed
ML
-
en
ab
led
SDR
ar
ch
itectu
r
e
is
d
esig
n
e
d
to
d
y
n
am
ically
a
d
ju
s
t
cr
itical
b
aseb
an
d
p
r
o
ce
s
s
in
g
p
a
r
am
eter
s
s
u
ch
a
s
m
o
d
u
latio
n
o
r
d
er
,
FIR
f
ilter
co
n
f
i
g
u
r
atio
n
,
an
d
ca
r
r
ier
f
r
e
q
u
en
cy
in
r
ea
l
tim
e
b
ased
o
n
en
v
ir
o
n
m
en
tal
an
d
ch
an
n
el
co
n
d
itio
n
s
.
T
h
is
ad
ap
tab
ilit
y
is
es
s
en
tial
f
o
r
s
u
p
p
o
r
tin
g
ad
v
a
n
ce
d
5
G
ap
p
licatio
n
s
in
clu
d
in
g
UR
L
L
C
an
d
ed
g
e
-
b
ased
b
io
m
e
d
ical
co
m
m
u
n
icatio
n
.
T
h
e
s
y
s
tem
b
eg
in
s
with
a
R
O
M
-
b
ased
in
p
u
t
b
lo
ck
th
at
s
to
r
e
s
p
r
e
-
r
ec
o
r
d
ed
elec
tr
o
ca
r
d
i
o
g
r
am
(
E
C
G
)
s
ig
n
als,
u
s
ed
as
r
ep
r
esen
tativ
e
b
io
m
ed
ical
d
ata
f
o
r
m
o
d
u
lati
o
n
.
T
h
e
in
co
m
in
g
d
ata
is
p
ass
ed
th
r
o
u
g
h
a
s
er
ial
-
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J I
n
f
&
C
o
m
m
u
n
T
ec
h
n
o
l
I
SS
N:
2252
-
8
7
7
6
A
n
ma
ch
in
e
le
a
r
n
in
g
-
e
n
h
a
n
ce
d
r
ec
o
n
fig
u
r
a
b
le
s
o
ftw
a
r
e
d
efin
ed
r
a
d
io
…
(
V
ija
ya
B
h
a
s
ka
r
C
h
a
la
mp
a
lem
)
701
to
-
p
ar
allel
co
n
v
e
r
ter
to
f
o
r
m
at
th
e
b
its
tr
ea
m
f
o
r
m
o
d
u
latio
n
p
r
o
ce
s
s
in
g
.
A
r
ec
o
n
f
ig
u
r
a
b
le
b
aseb
an
d
m
o
d
u
lato
r
th
en
m
a
p
s
th
e
i
n
p
u
t
s
y
m
b
o
l
s
to
o
n
e
o
f
f
o
u
r
s
u
p
p
o
r
ted
m
o
d
u
latio
n
s
ch
em
es:
B
PS
K,
QPSK,
1
6
-
QAM
,
o
r
OQAM
.
T
h
e
s
elec
tio
n
o
f
th
e
m
o
d
u
latio
n
ty
p
e
is
co
n
tr
o
ll
ed
b
y
a
2
-
b
it
in
p
u
t
(
MO
D_
SE
L
)
g
en
er
ate
d
b
y
th
e
ML
in
f
er
en
ce
e
n
g
in
e
b
ased
o
n
ch
an
n
el
f
ee
d
b
ac
k
s
u
ch
as
s
ig
n
al
-
to
-
n
o
is
e
r
atio
(
SNR
)
an
d
B
E
R
.
T
h
e
m
o
d
u
lated
o
u
tp
u
t
is
f
ilter
ed
u
s
in
g
an
in
ter
p
o
latio
n
FIR
f
ilter
,
wh
ich
im
p
r
o
v
es
s
p
ec
tr
al
ef
f
icien
cy
an
d
r
e
d
u
ce
s
in
ter
-
s
y
m
b
o
l
in
ter
f
e
r
en
ce
.
T
h
e
f
ilte
r
is
co
n
f
ig
u
r
ab
le
f
o
r
4
,
8
,
o
r
1
6
tap
s
,
an
d
u
s
es
d
is
tr
ib
u
ted
ar
ith
m
etic
(
DA)
an
d
ca
n
o
n
ical
s
ig
n
ed
d
ig
it
(
C
SD)
tech
n
iq
u
es
to
m
in
im
ize
co
m
p
u
tatio
n
a
l
co
m
p
lex
ity
an
d
p
o
wer
c
o
n
s
u
m
p
tio
n
.
T
h
e
ap
p
r
o
p
r
iate
f
ilter
tap
len
g
th
is
s
elec
ted
u
s
in
g
a
2
-
b
it
FIL
T
E
R
_
SEL
in
p
u
t,
also
p
r
o
v
id
ed
b
y
th
e
ML
en
g
in
e
.
T
o
en
a
b
le
f
r
eq
u
en
cy
tr
a
n
s
latio
n
f
o
r
R
F
tr
an
s
m
is
s
io
n
,
th
e
s
y
s
tem
in
clu
d
es
a
d
ig
itally
co
n
tr
o
ll
ed
all
-
d
ig
ital
p
h
ase
-
lo
ck
e
d
lo
o
p
(
ADPL
L
)
th
at
g
en
er
ate
s
s
in
e
an
d
co
s
in
e
wav
ef
o
r
m
s
f
o
r
I
/Q
m
o
d
u
latio
n
.
T
h
e
ADPL
L
d
esig
n
is
f
u
ll
y
d
ig
ital
an
d
o
p
tim
ized
f
o
r
f
a
s
t
lo
ck
in
g
an
d
lo
w
jitt
er
,
en
ab
lin
g
p
r
ec
is
e
ca
r
r
ier
g
en
er
atio
n
ac
r
o
s
s
a
wid
e
f
r
e
q
u
en
cy
r
an
g
e.
T
h
e
f
in
al
s
ig
n
al
is
p
as
s
ed
th
r
o
u
g
h
an
R
F
am
p
lifie
r
f
o
r
tr
an
s
m
is
s
io
n
v
ia
an
an
ten
n
a.
T
h
e
en
tire
s
y
s
tem
is
im
p
lem
en
ted
o
n
a
Xilin
x
Z
y
n
q
So
C
,
with
Ver
ilo
g
u
s
ed
f
o
r
th
e
d
ata
p
ath
lo
g
ic
an
d
Py
th
o
n
u
s
ed
to
co
n
tr
o
l
th
e
ML
in
f
e
r
en
ce
th
at
s
elec
ts
s
y
s
tem
p
ar
am
eter
s
.
T
h
is
co
-
d
esig
n
ap
p
r
o
ac
h
e
n
ab
le
s
lo
w
-
laten
cy
,
r
ea
l
-
tim
e
r
ec
o
n
f
ig
u
r
atio
n
s
u
itab
le
f
o
r
ad
ap
tiv
e
5
G
SDR
ap
p
licatio
n
s
.
T
h
e
f
u
n
ctio
n
al
o
r
g
an
izatio
n
o
f
th
e
p
r
o
p
o
s
ed
SDR
f
r
am
ewo
r
k
is
d
ep
icte
d
in
Fig
u
r
e
1
,
ea
c
h
m
ajo
r
co
m
p
o
n
en
t
r
a
n
g
in
g
f
r
o
m
th
e
m
o
d
u
lato
r
an
d
r
ec
o
n
f
ig
u
r
ab
le
FIR
f
ilter
to
t
h
e
ADPL
L
an
d
ML
d
ec
is
io
n
en
g
in
e
is
p
o
s
itio
n
ed
to
s
h
o
w
its
r
o
le
in
en
ab
lin
g
ad
ap
tiv
e
5
G
-
o
r
ien
ted
E
C
G
tr
a
n
s
m
is
s
io
n
.
T
h
e
r
ec
o
n
f
ig
u
r
ab
le
m
o
d
u
lato
r
ac
ce
p
ts
a
2
-
b
it
MO
D_
SEL
in
p
u
t
to
ch
o
o
s
e
b
etwe
en
f
o
u
r
m
o
d
u
latio
n
s
ch
em
es
-
B
PS
K
(
0
0
)
,
QPSK
(
0
1
)
,
1
6
-
QAM
(
1
0
)
,
an
d
OQAM
(
1
1
)
.
T
h
e
s
y
m
b
o
l
m
ap
p
in
g
lo
g
ic
is
im
p
lem
en
ted
u
s
in
g
Ver
ilo
g
F
SM
s
an
d
lo
o
k
u
p
tab
les.
Fo
r
in
s
tan
ce
,
th
e
B
PS
K
o
u
tp
u
t c
an
b
e
m
o
d
eled
as:
y
(
t)
=
A·
co
s
(
2
πf
_
ct
+
πb
)
,
wh
er
e
b
∈
{0
,
1
}
T
h
e
FIR
f
ilter
in
g
b
l
o
ck
s
u
p
p
o
r
ts
4
-
,
8
-
,
an
d
1
6
-
tap
co
n
f
ig
u
r
atio
n
s
,
s
elec
ted
u
s
in
g
a
2
-
b
it
FIL
T
E
R
_
SEL
in
p
u
t.
Fil
ter
co
ef
f
icien
ts
ar
e
im
p
lem
e
n
ted
u
s
in
g
DA
an
d
C
SD
r
ep
r
esen
tatio
n
s
to
r
ed
u
ce
lo
g
ic
co
m
p
lex
ity
a
n
d
p
o
wer
co
n
s
u
m
p
tio
n
.
T
h
e
f
ilter
o
u
tp
u
t is co
m
p
u
ted
as:
y
[
n
]
=
Σ
_
{i=
0
}^{
N
-
1
}
h
[
i]
·
x
[
n
-
i]
,
wh
er
e
N
=
4
,
8
,
o
r
1
6
T
h
e
ADPL
L
m
o
d
u
le
g
en
e
r
ates
q
u
ad
r
at
u
r
e
ca
r
r
ier
s
f
o
r
u
p
co
n
v
er
s
io
n
.
I
t
u
s
es
a
16
-
b
it
s
in
e/co
s
in
e
lo
o
k
u
p
tab
le
with
d
ig
ital
p
h
as
e
ac
cu
m
u
lato
r
.
T
h
e
lo
o
p
b
an
d
wid
th
an
d
s
ettlin
g
tim
e
ar
e
o
p
tim
ized
u
s
in
g
th
e
eq
u
atio
n
:
t_
s
ettle
≈
4
.
6
/ζ
ω
_
n
,
wh
e
r
e
ζ
=
d
am
p
in
g
f
ac
to
r
,
ω
_
n
=
n
atu
r
al
f
r
eq
u
e
n
cy
T
h
e
ML
m
o
d
el
is
tr
ain
e
d
u
s
in
g
a
d
ataset
o
f
c
h
an
n
el
p
ar
a
m
eter
s
in
clu
d
in
g
r
ec
eiv
ed
s
ig
n
al
s
tr
en
g
th
in
d
icato
r
(
R
SS
I
)
,
B
E
R
,
an
d
SNR
.
A
d
ec
is
io
n
tr
ee
class
if
ier
o
r
a
d
ee
p
Q
-
l
ea
r
n
in
g
ag
e
n
t
is
u
s
ed
to
in
f
er
o
p
tim
al
MO
D_
SEL
a
n
d
FIL
T
E
R
_
SEL
v
alu
es
in
r
ea
l
tim
e.
F
o
r
e
x
am
p
le,
an
in
p
u
t
SNR
o
f
2
2
d
B
m
ay
r
esu
lt
i
n
MO
D_
SEL
=1
0
(
1
6
-
QAM
)
a
n
d
FIL
T
E
R
_
SEL
=0
1
(
8
-
tap
)
f
o
r
a
tr
ad
e
o
f
f
b
etwe
en
th
r
o
u
g
h
p
u
t
a
n
d
n
o
is
e
r
esil
ien
ce
.
T
h
is
en
d
-
t
o
-
en
d
p
ip
elin
e
s
u
p
p
o
r
ts
r
ea
l
-
tim
e
E
C
G
s
ig
n
al
tr
an
s
m
is
s
io
n
o
p
tim
ized
f
o
r
5
G
en
v
ir
o
n
m
en
ts
with
SNR
v
ar
iatio
n
s
f
r
o
m
1
0
t
o
3
0
d
B
,
ac
h
ie
v
in
g
B
E
R
b
elo
w
1
0
⁻³
at
2
2
d
B
an
d
b
elo
w
1
0
⁻⁵
at
2
6
d
B
with
a
d
ap
tiv
e
co
n
f
i
g
u
r
atio
n
.
T
h
e
s
y
s
tem
is
im
p
lem
e
n
ted
o
n
a
Xilin
x
Z
y
n
q
So
C
.
Ver
ilo
g
R
T
L
is
u
s
ed
f
o
r
SDR
d
atap
ath
,
wh
ile
Py
t
h
o
n
-
b
ased
ML
in
f
e
r
en
ce
r
u
n
s
o
n
AR
M
C
o
r
tex
-
A9
.
AXI
i
n
ter
f
ac
e
b
r
id
g
es
th
e
d
ec
is
io
n
o
u
tp
u
ts
with
m
o
d
u
lat
io
n
an
d
f
ilter
co
n
tr
o
l lin
es.
Fig
u
r
e
2
illu
s
tr
ates
th
e
o
p
e
r
a
tio
n
al
f
lo
w
o
f
th
e
p
r
o
p
o
s
ed
ML
-
en
ab
led
SDR
s
y
s
tem
,
d
e
tailin
g
th
e
s
eq
u
en
tial
p
r
o
ce
s
s
in
g
s
tag
es
f
r
o
m
d
ata
ac
q
u
is
itio
n
to
m
o
d
u
l
atio
n
,
f
ilter
in
g
,
f
r
eq
u
en
cy
s
y
n
t
h
esis
,
an
d
a
d
ap
tiv
e
r
ec
o
n
f
ig
u
r
ati
o
n
d
r
iv
e
n
b
y
m
ac
h
in
e
-
lear
n
in
g
d
ec
is
io
n
s
.
T
ab
le
1
s
u
m
m
ar
izes
th
e
o
u
tc
o
m
es
o
f
f
u
n
ctio
n
al
s
im
u
latio
n
s
p
er
f
o
r
m
ed
o
n
ea
c
h
s
u
b
s
y
s
tem
o
f
t
h
e
p
r
o
p
o
s
ed
SDR
ar
ch
itectu
r
e,
co
n
f
ir
m
i
n
g
co
r
r
ec
t
o
p
er
atio
n
o
f
th
e
m
o
d
u
latio
n
,
f
ilter
in
g
,
f
r
e
q
u
en
cy
s
y
n
th
esis
,
an
d
ML
co
n
t
r
o
l
m
o
d
u
les
p
r
io
r
to
h
ar
d
war
e
s
y
n
th
esis
.
Fig
u
r
e
3
p
r
esen
ts
th
e
s
y
n
th
esis
r
esu
lts
o
f
th
e
p
r
o
p
o
s
ed
SDR
ar
ch
it
ec
tu
r
e,
s
u
m
m
ar
izin
g
th
e
r
eso
u
r
ce
u
tili
za
tio
n
an
d
tim
in
g
p
er
f
o
r
m
an
ce
o
b
tain
e
d
f
r
o
m
FP
GA
im
p
lem
en
tatio
n
.
T
o
ev
al
u
ate
th
e
en
er
g
y
ef
f
ic
ien
cy
o
f
th
e
ar
c
h
itectu
r
e,
T
a
b
le
2
p
r
esen
ts
th
e
p
o
wer
c
o
n
s
u
m
p
tio
n
d
is
tr
ib
u
tio
n
ac
r
o
s
s
in
d
iv
id
u
al
SDR
m
o
d
u
les,
h
ig
h
lig
h
tin
g
th
e
co
n
tr
ib
u
tio
n
o
f
ea
ch
b
lo
ck
t
o
th
e
to
tal
d
y
n
am
ic
p
o
wer
.
Fig
u
r
e
4
h
ig
h
lig
h
ts
h
o
w
th
e
p
r
o
p
o
s
ed
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d
esig
n
im
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r
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r
lier
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en
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All
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s
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ased
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a
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SU
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u
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I
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m
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e
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ch
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o
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ates
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im
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ed
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d
m
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ile
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n
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i
g
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n
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itio
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e
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to
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R
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,
an
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ap
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licatio
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s
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if
ic
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em
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n
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s
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ak
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s
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s
u
ited
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o
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e
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m
p
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in
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i
o
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ed
ical
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d
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L
C
tr
af
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s
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ar
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s
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er
e
laten
c
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n
d
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b
u
s
tn
ess
ar
e
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itical.
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h
ese
o
u
tco
m
es
co
n
f
ir
m
t
h
at
th
e
ML
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SDR
f
r
am
ewo
r
k
is
a
v
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ca
n
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i
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ate
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t
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5
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c
o
m
m
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