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lect
rica
l a
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Co
m
pu
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I
J
E
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Vo
l.
15
,
No
.
6
,
Decem
b
er
20
25
,
p
p
.
5
4
4
3
~
5
4
5
2
I
SS
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.
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5443
J
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ttp
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ec
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esco
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co
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Co
nv
o
lutiona
l neural network
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bea
mfo
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desig
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nerg
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f
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e M
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ticle
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Dec
2
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5
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2
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M
il
li
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ter
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wa
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m
Wav
e
)
m
a
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m
u
lt
i
p
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in
p
u
t
m
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lt
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o
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tp
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t
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M
IM
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tec
h
n
o
l
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y
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r
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g
s
sig
n
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fica
n
t
imp
ro
v
e
m
e
n
ts
i
n
d
a
ta
tra
n
sm
issio
n
ra
tes
fo
r
c
o
m
m
u
n
ica
ti
o
n
sy
ste
m
s.
A
k
e
y
to
th
e
d
e
sig
n
o
f
m
m
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e
M
-
M
IM
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sy
ste
m
s
is
b
e
a
m
fo
rm
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g
tec
h
n
i
q
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e
s,
wh
ic
h
f
o
c
u
s
sig
n
a
ls
to
wa
rd
sp
e
c
ifi
c
d
irec
ti
o
n
s
b
u
t
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ly
o
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e
x
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e
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e
,
e
n
e
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g
y
-
i
n
ten
si
v
e
ra
d
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o
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q
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e
n
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y
(RF
)
c
h
a
in
s.
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o
a
d
d
re
ss
th
is
issu
e
,
h
y
b
ri
d
b
e
a
m
fo
rm
e
rs
(HB)
h
a
v
e
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e
e
n
in
tro
d
u
c
e
d
a
s
a
p
a
rti
a
l
so
l
u
ti
o
n
,
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n
d
d
e
e
p
lea
rn
in
g
(DL)
h
a
s
p
r
o
v
e
n
e
ffe
c
ti
v
e
fo
r
HB
d
e
sig
n
.
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o
we
v
e
r,
p
re
v
i
o
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s
wo
rk
s
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t
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izin
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m
a
c
h
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rn
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g
(M
L)
n
e
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rk
s
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a
v
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p
rima
ril
y
f
o
c
u
se
d
o
n
th
e
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e
c
tral
e
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n
c
y
(S
E)
m
e
tri
c
fo
r
c
o
n
stru
c
ti
n
g
HB.
In
t
h
is
p
a
p
e
r,
we
p
re
se
n
t
a
c
o
n
v
o
lu
ti
o
n
a
l
n
e
u
r
a
l
n
e
two
r
k
(CNN
)
a
rc
h
it
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c
tu
re
w
h
o
se
l
o
ss
fu
n
c
ti
o
n
is
d
e
fin
e
d
t
o
m
a
x
imi
z
e
e
n
e
rg
y
e
fficie
n
c
y
(EE
)
d
irec
tl
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.
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h
e
n
e
two
rk
jo
i
n
tl
y
lea
rn
s
a
n
a
lo
g
a
n
d
d
i
g
it
a
l
b
e
a
m
fo
rm
e
rs
b
y
e
v
a
lu
a
ti
n
g
EE
(
t
h
ro
u
g
h
p
u
t
p
e
r
t
o
tal
p
o
we
r,
in
c
l
u
d
in
g
p
h
a
se
sh
ift
e
rs,
sw
it
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h
e
s,
d
ig
it
a
l
-
to
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a
n
a
l
o
g
c
o
n
v
e
rters
(
DA
Cs
)
,
a
n
d
RF
c
h
a
in
s)
a
n
d
se
lec
ti
n
g
th
e
c
o
n
fi
g
u
ra
ti
o
n
th
a
t
y
ield
s
t
h
e
h
ig
h
e
st
EE
.
Th
e
CN
N
tak
e
s
a
c
h
a
n
n
e
l
m
a
tri
x
a
s
in
p
u
t
a
n
d
o
u
tp
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ts
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a
n
d
b
a
se
b
a
n
d
b
e
a
m
fo
rm
e
r
m
a
tri
c
e
s.
S
imu
latio
n
re
su
lt
s
v
a
li
d
a
te
th
e
e
f
fe
c
ti
v
e
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ss
o
f
th
e
p
r
o
p
o
se
d
M
-
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IM
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sc
h
e
m
e
,
a
c
h
iev
in
g
si
g
n
ifi
c
a
n
t
EE
imp
r
o
v
e
m
e
n
ts
b
y
o
p
t
imiz
in
g
h
y
b
ri
d
p
re
c
o
d
i
n
g
a
n
d
re
d
u
c
in
g
RF
c
h
a
in
u
sa
g
e
.
K
ey
w
o
r
d
s
:
C
o
n
v
o
lu
tio
n
al
n
eu
r
al
n
etwo
r
k
Dee
p
lear
n
in
g
E
n
er
g
y
ef
f
icien
cy
Hy
b
r
id
b
ea
m
f
o
r
m
er
s
M
-
MI
MO
T
h
is i
s
a
n
o
p
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a
c
c
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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
:
Han
an
e
Ay
ad
L
T
T
L
ab
o
r
at
o
r
y
,
Dep
ar
tm
en
t
o
f
T
elec
o
m
m
u
n
icatio
n
,
Ab
o
u
B
ak
er
B
elk
aïd
Un
iv
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s
ity
T
lem
ce
n
,
1
3
0
0
,
Alg
e
r
ia
E
m
ail:
ay
ad
.
h
an
an
e@
u
n
iv
-
tle
m
ce
n
.
d
z
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
Ma
s
s
iv
e
m
u
ltip
le
-
in
p
u
t
m
u
ltip
le
-
o
u
tp
u
t
(
M
-
MI
MO
)
h
as
b
ec
o
m
e
a
co
r
n
er
s
to
n
e
o
f
5
G
an
d
is
p
r
o
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d
to
p
lay
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q
u
ally
ce
n
tr
al
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o
le
in
6
G,
wh
er
e
it is
ex
p
ec
ted
to
ad
d
r
ess
th
e
d
em
an
d
s
f
o
r
u
ltra
-
h
ig
h
d
ata
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ates,
m
ass
iv
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co
n
n
ec
tiv
ity
,
an
d
lo
w
laten
c
y
b
y
lev
er
a
g
in
g
la
r
g
e
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te
n
n
a
a
r
r
ay
s
to
en
h
a
n
ce
s
p
ec
tr
al
ef
f
icien
cy
(
SE)
,
co
v
er
ag
e
,
an
d
lin
k
r
elia
b
ilit
y
[
1
]
,
[
2
]
.
At
m
illi
m
eter
-
wav
e
(
m
m
W
av
e)
f
r
eq
u
e
n
cies,
f
u
lly
d
ig
ital
(
FD)
b
ea
m
f
o
r
m
in
g
d
em
a
n
d
s
as
m
an
y
p
o
wer
-
h
u
n
g
r
y
R
F
ch
ain
s
as
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ten
n
as,
m
ak
in
g
it
im
p
r
a
ctica
l
f
o
r
r
ea
l
-
wo
r
l
d
s
y
s
tem
s
.
Hy
b
r
id
p
r
ec
o
d
in
g
(
HP)
ad
d
r
ess
es
th
is
b
y
s
p
litt
in
g
th
e
b
ea
m
f
o
r
m
in
g
in
to
a
lo
w
-
d
im
en
s
io
n
al
d
ig
ital
s
tag
e
an
d
an
an
al
o
g
n
etwo
r
k
o
f
p
h
ase
s
h
if
ter
s
o
r
s
witch
e
s
,
th
er
eb
y
ap
p
r
o
x
im
atin
g
FD
p
er
f
o
r
m
an
ce
with
s
ig
n
if
ican
tly
f
ewe
r
R
F
ch
ain
s
[
3
]
–
[
4
]
.
Mo
s
t
HP
s
ch
e
m
es
h
av
e
b
ee
n
d
e
v
elo
p
e
d
to
m
ax
i
m
ize
SE,
in
clu
d
in
g
s
p
atially
s
p
ar
s
e
co
d
eb
o
o
k
s
[
3
]
,
alter
n
atin
g
‐
m
in
im
izatio
n
al
g
o
r
ith
m
s
[
5
]
,
s
u
b
s
eq
u
e
n
t
in
n
o
v
atio
n
s
in
tr
o
d
u
ce
d
d
y
n
am
ic‐
s
tr
ea
m
ass
ig
n
m
en
t
f
o
r
u
p
lin
k
m
u
ltiu
s
er
o
r
th
o
g
o
n
al
f
r
eq
u
en
cy
d
iv
is
io
n
m
u
ltip
l
ex
in
g
(
OFDM)
[
6
]
,
s
witch
‐
b
ased
an
alo
g
n
etwo
r
k
s
[
7
]
,
an
d
p
er
‐
R
F‐c
h
ain
clu
s
ter
in
g
in
ce
n
tr
alize
d
ce
ll‐f
r
ee
s
y
s
tem
s
[
8
]
,
Mo
r
e
r
ec
en
t
co
n
tr
ib
u
tio
n
s
lev
er
ag
e
m
et
h
eu
r
is
tic
o
p
tim
izatio
n
s
s
u
ch
as
g
en
etic‐
alg
o
r
ith
m
b
ased
HP
[
9
]
,
an
d
b
ea
m
‐
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N
:
2
0
8
8
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8
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I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
4
4
3
-
5
4
5
2
5444
d
iv
is
io
n
m
u
ltip
le
ac
ce
s
s
with
ze
r
o
‐
f
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cin
g
an
d
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ea
r
‐
d
ig
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SE
with
f
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R
F
ch
ain
s
[
1
0
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,
Ad
d
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o
n
all
y
,
r
el
ay
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r
e
f
l
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t
in
g
in
tel
li
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t
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h
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n
g
SE
in
ce
ll
‐
f
r
e
e
M
-
M
I
MO
[
1
1
]
.
As
n
e
tw
o
r
k
s
d
e
n
s
it
y
,
e
n
e
r
g
y
ef
f
ic
ie
n
c
y
(
E
E
)
h
as
em
er
g
e
d
as
a
k
e
y
d
esi
g
n
c
r
ite
r
i
o
n
.
T
e
ch
n
i
q
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es
s
u
c
h
as
l
o
w
‐
r
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ti
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DACs
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a
d
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p
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s
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b
a
r
r
ay
all
o
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ati
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n
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n
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ch
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n
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m
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x
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ti
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f
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tl
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i
m
p
r
o
v
e
d
E
E
i
n
m
ass
i
v
e
MI
MO
[
1
2
]
–
[
1
3
]
.
R
ec
en
t
w
o
r
k
li
k
e
h
y
b
r
i
d
a
n
al
o
g
a
n
d
d
i
g
it
al
B
e
a
m
f
o
r
m
e
r
s
w
it
h
l
o
w
r
es
o
l
u
ti
o
n
(
HAND
B
AL
L
)
[
1
4
]
f
u
r
t
h
er
in
te
g
r
at
es
s
e
n
s
i
n
g
wi
th
co
ar
s
e‐
q
u
a
n
t
iz
ed
b
ea
m
f
o
r
m
in
g
.
Ho
we
v
e
r
,
f
ew
h
y
b
r
i
d
p
r
e
co
d
i
n
g
s
t
u
d
ies
em
b
e
d
E
E
d
ir
ec
t
ly
i
n
t
o
t
h
ei
r
o
p
t
im
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za
t
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n
,
i
n
s
t
ea
d
r
e
ly
in
g
o
n
h
e
u
r
is
ti
c
p
o
we
r
r
e
d
u
c
ti
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n
s
f
o
ll
o
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n
g
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d
r
i
v
e
n
d
esi
g
n
.
R
ec
en
tly
,
d
ee
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lear
n
in
g
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DL
)
h
as
p
r
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v
e
n
ef
f
ec
tiv
e
f
o
r
HB
d
esig
n
.
I
n
th
e
co
n
tex
t
o
f
HP
d
esig
n
,
C
NN
-
b
ased
m
eth
o
d
s
h
av
e
b
ee
n
em
p
lo
y
ed
to
jo
i
n
tly
o
p
tim
iz
e
an
alo
g
an
d
d
ig
ital
co
m
p
o
n
e
n
ts
u
n
d
er
im
p
er
f
ec
t
C
SI,
im
p
r
o
v
in
g
SE
o
v
er
co
n
v
en
tio
n
al
d
esig
n
s
[
1
5
]
,
[
1
6
]
.
Fu
r
th
er
en
h
an
ce
m
en
ts
ad
d
r
es
s
E
E
b
y
in
teg
r
atin
g
ad
ap
tiv
e
f
u
lly
-
co
n
n
ec
ted
n
et
wo
r
k
s
[
1
7
]
.
C
o
m
p
lem
en
tar
y
s
tu
d
ies
ex
p
lo
r
e
C
NNs
f
o
r
s
u
b
ar
r
ay
co
n
f
ig
u
r
atio
n
s
[
1
8
]
,
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
f
o
r
d
y
n
am
ic
b
ea
m
s
elec
tio
n
[
1
9
]
,
an
d
d
ee
p
u
n
f
o
ld
in
g
f
o
r
f
ast,
tr
ain
ab
le
HP
[
2
0
]
.
Un
s
u
p
er
v
is
ed
ap
p
r
o
ac
h
es
also
elim
in
ate
th
e
n
ee
d
f
o
r
lab
eled
d
ata
an
d
co
d
eb
o
o
k
s
,
p
r
o
v
in
g
ef
f
ec
ti
v
e
in
d
is
tr
ib
u
ted
an
d
q
u
an
tized
s
ettin
g
s
[
2
1
]
–
[
2
2
]
.
I
n
p
ar
allel,
ex
ten
s
iv
e
s
u
r
v
ey
s
o
f
m
ac
h
i
n
e
l
ea
r
n
in
g
i
n
m
ass
iv
e
MI
MO
h
ig
h
lig
h
t
b
o
th
th
e
o
p
p
o
r
tu
n
ities
an
d
o
p
en
c
h
allen
g
es
in
ap
p
ly
i
n
g
DL
to
h
y
b
r
id
b
ea
m
f
o
r
m
in
g
[
2
3
]
.
T
o
th
e
b
est
o
f
o
u
r
k
n
o
wled
g
e
,
th
is
is
th
e
f
ir
s
t
DL
-
b
ased
HP
o
p
tim
izatio
n
th
at
d
ir
ec
tly
in
co
r
p
o
r
ates
E
E
cr
iter
ia.
Ou
r
s
o
lu
tio
n
is
a
C
NN
b
ased
HB
d
esig
n
f
o
r
m
m
W
av
e
M
-
MI
MO
th
at
d
ir
ec
tly
tar
g
ets
E
E
r
ath
er
th
an
SE.
Sp
ec
if
ically
,
we
in
tr
o
d
u
ce
a
n
E
E
-
awa
r
e
lo
s
s
f
u
n
ctio
n
th
at
co
m
b
in
es
SE
an
d
r
ea
lis
tic
h
ar
d
war
e
p
o
wer
co
n
s
u
m
p
tio
n
(
p
h
ase
s
h
if
ter
s
,
s
witch
es,
D
AC
s
,
R
F
c
h
ain
s
)
,
s
teer
in
g
th
e
n
etwo
r
k
t
o
war
d
b
ea
m
f
o
r
m
er
co
n
f
ig
u
r
atio
n
s
th
at
m
a
x
im
ize
E
E
.
W
e
em
b
ed
th
is
h
a
r
d
war
e
p
o
wer
m
o
d
el
in
th
e
C
NN
s
o
th
at
b
o
th
a
n
alo
g
an
d
d
ig
ital w
eig
h
tin
g
s
in
tr
in
s
ically
ac
co
u
n
t f
o
r
ac
tu
al
e
n
er
g
y
u
s
a
g
e.
T
h
e
o
r
g
a
n
izatio
n
o
f
t
h
is
ar
ticle
is
as
f
o
llo
ws:
s
ec
tio
n
2
p
r
e
s
en
ts
th
e
th
eo
r
etica
l
f
o
u
n
d
ati
o
n
s
o
f
th
e
s
tu
d
y
,
in
clu
d
i
n
g
th
e
s
ig
n
al
m
o
d
el,
en
er
g
y
e
f
f
icien
cy
,
a
n
d
p
r
o
b
lem
f
o
r
m
u
latio
n
.
T
h
e
p
r
o
p
o
s
ed
C
NN
-
b
ased
b
ea
m
f
o
r
m
in
g
s
tr
ateg
y
is
also
d
escr
ib
ed
i
n
th
is
s
ec
tio
n
.
S
ec
tio
n
3
d
etails
th
e
s
im
u
latio
n
en
v
ir
o
n
m
e
n
t
an
d
s
y
s
tem
p
ar
am
eter
s
u
s
ed
in
MA
T
L
AB
,
alo
n
g
with
th
e
C
NN
tr
ain
in
g
s
tr
ateg
y
an
d
h
y
p
er
p
ar
a
m
eter
co
n
f
ig
u
r
atio
n
.
Sectio
n
4
r
ep
o
r
ts
an
d
an
al
y
ze
s
th
e
s
im
u
lati
o
n
r
esu
lts
,
h
i
g
h
lig
h
tin
g
th
e
p
er
f
o
r
m
an
ce
g
ain
s
ac
h
iev
ed
in
ter
m
s
o
f
en
er
g
y
an
d
s
p
ec
tr
al
e
f
f
icien
cy
u
n
d
er
v
ar
io
u
s
R
F
co
n
f
ig
u
r
ati
o
n
s
.
Fin
ally
,
s
ec
tio
n
5
co
n
clu
d
es th
e
p
ap
er
a
n
d
o
u
tlin
es p
o
ten
tial d
ir
ec
tio
n
s
f
o
r
f
u
t
u
r
e
wo
r
k
.
No
tatio
n
:
d
en
o
tes
a
s
ca
lar
,
a
is
a
v
ec
to
r
an
d
A
is
a
m
atr
i
x
.
Fo
r
a
v
ec
to
r
a,
th
e
n
o
tatio
n
[
]
d
en
o
tes
its
i
-
th
elem
en
t.
Similar
ly
,
f
o
r
a
m
atr
ix
A
,
[
]
:
,
an
d
[
]
,
r
ep
r
esen
t
th
e
i
-
th
co
lu
m
n
an
d
th
e
(
i,
j)
-
th
en
tr
y
,
r
esp
ec
tiv
ely
.
T
h
e
s
u
p
er
s
cr
ip
ts
(
.
)
an
d
(
.
)
in
d
icate
tr
an
s
p
o
s
e,
an
d
Her
m
itian
o
p
er
atio
n
s
.
T
h
e
Fro
b
en
iu
s
n
o
r
m
is
r
ep
r
esen
ted
b
y
‖
.
‖
an
d
is
an
id
en
tity
m
atr
ix
o
f
s
ize
.
I
n
th
is
co
n
tex
t,
[
]
:
,
r
ef
er
s
to
th
e
f
u
ll
co
lu
m
n
v
ec
to
r
co
m
p
o
s
ed
o
f
al
l r
o
ws at
th
e
i
-
th
co
lu
m
n
p
o
s
itio
n
.
2.
T
H
E
O
R
E
T
I
CA
L
F
O
UNDA
T
I
O
N
AND
P
RO
P
O
S
E
D
AP
P
RO
ACH
2
.
1
.
SI
G
NA
L
M
O
D
E
L
Fig
u
r
e
1
d
e
p
icts
a
m
m
W
av
e
M
-
MI
MO
s
y
s
tem
eq
u
ip
p
ed
w
ith
tr
an
s
m
itti
n
g
an
ten
n
as
th
at
s
er
v
es
a
s
in
g
le
-
u
s
er
m
o
b
ile
s
tatio
n
with
r
ec
eiv
in
g
an
ten
n
as.
T
h
e
tr
an
s
m
itter
p
r
o
v
i
d
es
s
t
r
ea
m
s
o
f
d
ata
s
y
m
b
o
ls
to
th
e
r
ec
eiv
er
o
v
er
th
e
n
etwo
r
k
.
Fig
u
r
e
1
s
h
o
ws
h
o
w
th
e
b
ase
s
tatio
n
(
B
S)
p
r
ec
o
d
es
th
e
d
ata
s
tr
ea
m
s
u
s
in
g
×
d
ig
ital
p
r
ec
o
d
e
r
s
an
d
×
an
alo
g
p
r
ec
o
d
e
r
s
.
T
h
e
d
is
cr
ete
-
tim
e
d
ata
s
tr
ea
m
s
ar
e
r
ep
r
esen
ted
b
y
t
h
e
v
ec
to
r
s
=
[
1
,
2
,
…
,
]
.
T
h
e
co
v
a
r
ian
ce
m
atr
ix
o
f
s
v
ec
to
r
is
[
]
=
/
u
n
d
er
th
e
ass
u
m
p
tio
n
o
f
in
d
e
p
en
d
en
ce
a
n
d
a
Gau
s
s
ian
d
is
t
r
ib
u
tio
n
with
ze
r
o
m
ea
n
an
d
u
n
it
v
ar
ian
ce
.
Af
ter
war
d
,
th
e
tr
a
n
s
m
itted
s
ig
n
al
is
ex
p
r
ess
ed
as
x
=
s
.
T
h
e
tr
a
n
s
m
itter
is
s
u
b
ject
to
p
o
wer
l
im
itatio
n
ac
co
r
d
in
g
t
o
th
e
co
n
s
tr
ain
t
‖
‖
=
,
an
d
th
e
an
alo
g
b
ea
m
f
o
r
m
er
s
ar
e
u
n
itar
y
m
atr
ices
with
eq
u
al
-
n
o
r
m
elem
en
ts
,
i.e
.
,
[
[
]
:
,
[
]
:
,
]
,
=
1
⁄
.
Fo
r
a
n
ar
r
o
w
b
an
d
b
lo
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-
f
ad
in
g
ch
a
n
n
el,
t
h
e
s
ig
n
al
r
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v
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at
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e
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ten
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as c
an
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e
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as:
=
√
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(
1
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wh
er
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is
th
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×
1
r
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s
ig
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al
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atr
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d
im
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th
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ad
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ite
Gau
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ian
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AW
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,
~
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2
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.
T
h
e
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s
ig
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m
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n
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ain
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[
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d
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o
m
b
i
n
er
as
(
2
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:
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=
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+
(
2
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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N:
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8
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C
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r
a
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5445
Fig
u
r
e
1
.
Sin
g
le
u
s
er
m
m
W
av
e
M
-
MI
MO
s
y
s
tem
with
HB
T
h
e
Saleh
-
Vale
n
zu
ela
(
SV)
ch
an
n
el
m
o
d
el
[
2
4
]
ca
n
b
e
u
tili
ze
d
to
r
ep
r
esen
t
th
e
m
m
W
av
e
tr
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s
m
is
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io
n
en
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ir
o
n
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e
n
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er
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e
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n
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o
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clu
s
ter
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o
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e
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as:
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∑
∑
α
ij
Γ
R
(
Θ
R
(
ij
)
)
Γ
T
(
Θ
T
(
ij
)
)
R
(
Θ
R
(
ij
)
)
(
Θ
T
(
ij
)
)
i
=
1
i
=
1
(
3
)
T
h
e
p
ar
am
ete
r
γ
=
√
/
is
th
e
n
o
r
m
aliza
tio
n
f
ac
to
r
a
n
d
α
ij
is
th
e
co
m
p
lex
c
h
an
n
el
g
ain
co
n
n
ec
ted
to
t
h
e
ℎ
s
ca
tter
in
g
clu
s
ter
an
d
ℎ
r
ay
f
o
r
=
1
,
…
,
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d
=
1
,
…
,
.
An
g
les
o
f
ar
r
iv
al
an
d
d
ep
ar
tu
r
e
a
r
e
d
en
o
ted
b
y
Θ
R
(
ij
)
=
(
R
ij
,
R
ij
)
an
d
Θ
T
(
ij
)
=
(
T
ij
,
T
ij
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,
r
esp
ec
tiv
ely
.
W
e
r
ef
er
to
th
e
az
im
u
th
an
d
elev
atio
n
an
g
les,
b
y
t
h
e
an
g
u
l
ar
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ar
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eter
s
an
d
.
T
h
e
g
ain
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o
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th
e
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an
s
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it
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eiv
e
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ten
n
a
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en
ts
,
r
esp
ec
tiv
ely
,
ar
e
Γ
R
(
Θ
R
(
ij
)
)
an
d
Γ
T
(
Θ
T
(
ij
)
)
.
Fin
ally
,
th
e
n
o
r
m
alize
d
r
ec
eiv
e
an
d
tr
an
s
m
it
ar
r
ay
r
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o
n
s
e
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ec
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r
s
at
th
e
az
im
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th
(
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atio
n
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an
g
le
R
ij
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ij
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an
d
T
ij
(
RT
ij
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ar
e
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ep
r
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ted
b
y
th
e
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ec
to
r
s
R
(
Θ
R
(
ij
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)
an
d
T
(
Θ
T
(
ij
)
)
r
esp
ec
tiv
ely
.
[
R
(
Θ
R
(
ij
)
)
]
=
{
−
2
n
T
(
Θ
R
(
ij
)
)
}
is
th
e
ℎ
co
m
p
o
n
en
t
o
f
t
h
e
s
teer
in
g
v
ec
to
r
(
Θ
R
(
ij
)
)
,
wh
er
e
n
=
[
,
,
]
T
d
en
o
tes
th
e
p
o
s
itio
n
o
f
th
e
ℎ
r
ec
eiv
e
an
ten
n
a
in
th
e
C
ar
tesi
an
co
o
r
d
in
ate
s
y
s
tem
an
d
(
Θ
R
(
ij
)
)
=
[
(
R
ij
)
(
R
ij
)
,
(
R
ij
)
(
R
ij
)
,
(
R
ij
)
]
.
Similar
to
R
(
Θ
R
(
ij
)
)
,
th
e
tr
a
n
s
m
it
s
id
e
s
teer
in
g
v
ec
to
r
T
(
Θ
T
(
ij
)
)
ca
n
b
e
d
escr
ib
ed
.
2
.
2
.
E
nerg
y
ef
f
iciency
E
n
er
g
y
ef
f
icien
c
y
s
tan
d
s
as
a
cr
u
cial
m
etr
ic
in
e
v
alu
atin
g
th
e
p
er
f
o
r
m
an
ce
o
f
co
m
m
u
n
icatio
n
s
y
s
tem
s
.
I
t
r
ef
er
s
to
t
h
e
o
p
er
atio
n
al
s
tate
o
f
a
s
y
s
tem
in
wh
ich
en
e
r
g
y
co
n
s
u
m
p
tio
n
i
s
m
in
im
ized
w
h
ile
p
r
o
v
id
i
n
g
an
id
en
tical
s
er
v
ice.
Her
e,
E
E
m
ea
s
u
r
es
th
e
r
elatio
n
s
h
ip
b
etwe
en
th
e
SE
o
f
th
e
s
y
s
tem
an
d
its
s
tatic
p
o
wer
co
n
s
u
m
p
tio
n
[
2
5
]
in
th
e
p
r
esen
ce
o
f
R
F h
ar
d
war
e
lo
s
s
es.
2
.
2
.
1
.
P
o
wer
co
ns
um
ptio
n a
n
d lo
s
s
m
o
dels
Fo
r
th
e
d
o
w
n
lin
k
h
y
b
r
id
b
ea
m
f
o
r
m
in
g
ar
ch
itectu
r
e,
th
e
r
e
ce
iv
er
’
s
p
o
wer
c
o
n
s
u
m
p
tio
n
i
s
n
eg
lig
ib
le
co
m
p
ar
ed
to
th
e
tr
an
s
m
itter
’
s
an
d
is
th
er
ef
o
r
e
o
m
itted
.
I
n
a
f
u
lly
co
n
n
ec
ted
s
tr
u
ctu
r
e
,
th
e
tr
an
s
m
itter
em
p
lo
y
s
o
f
/
ch
ain
p
air
s
.
I
n
ad
d
itio
n
,
th
e
ar
ch
itectu
r
e
u
tili
ze
s
p
h
a
s
e
-
s
h
if
ter
s
,
r
esu
ltin
g
in
a
s
tatic
p
o
wer
co
n
s
u
m
p
tio
n
o
f
:
=
+
+
[
2
+
]
+
(
4
)
w
h
e
r
e
r
e
f
e
r
s
to
a
l
o
ca
l
o
s
ci
lla
t
o
r
s
h
a
r
e
d
b
y
a
ll
ch
ai
n
s
,
w
h
il
e
is
t
h
e
p
o
w
er
u
s
e
d
b
y
al
l
a
m
p
li
f
ie
r
s
wit
h
a
p
o
wer
-
ad
d
ed
ef
ficien
c
y
η
ex
p
r
ess
ed
as
η
⁄
[
2
6
]
.
T
h
e
tr
an
s
m
itte
d
p
o
wer
ac
co
u
n
tin
g
R
F
lo
s
s
es
as
d
ef
in
ed
in
(
Sectio
n
I
I
-
D
in
[
1
2
]
)
is
ca
lcu
lated
as
=
[
‖
‖
2
2
]
=
1
̃
,
h
er
e,
̃
=
[
‖
̃
‖
2
2
]
=
(
1
−
)
‖
‖
2
+
(
)
an
d
is
th
e
q
u
an
tizatio
n
er
r
o
r
m
atr
ix
[
1
2
]
.
s
tan
d
s
f
o
r
a
s
in
g
le
p
ass
iv
e
p
h
ase
-
s
h
if
t
elem
en
t'
s
p
o
wer
co
n
s
u
m
p
tio
n
with
b
its
o
f
r
eso
lu
tio
n
.
T
h
e
p
o
wer
co
n
s
u
m
ed
b
y
d
iv
id
e
r
s
a
n
d
c
o
m
b
in
er
s
is
g
en
er
ally
u
n
k
em
p
t.
T
h
e
p
o
we
r
co
n
s
u
m
p
tio
n
o
f
DACs
is
=
1
2
+
2
,
wh
er
e
th
e
s
am
p
lin
g
r
ate
at
th
e
tr
an
s
m
itter
is
,
q
is
th
e
r
es
o
lu
tio
n
o
f
th
e
DACs
,
th
e
f
ac
to
r
1
=
1
.
5
×
10
−
5
,
in
d
icate
s
a
co
ef
f
icien
t
o
f
th
e
s
tatic
p
o
wer
co
n
s
u
m
p
tio
n
,
wh
ile
f
ac
to
r
2
=
9
×
10
−
12
ex
p
r
ess
es
a
co
ef
f
icien
t
o
f
th
e
d
y
n
am
ic
p
o
wer
co
n
s
u
m
p
tio
n
.
Fin
ally
,
r
ep
r
esen
ts
th
e
p
o
we
r
co
n
s
u
m
p
tio
n
o
f
a
s
in
g
le
R
F
ch
ain
in
cl
u
d
in
g
two
lo
w
-
p
ass
filt
er
s
,
ea
ch
d
en
o
ted
as
(
)
,
two
m
ix
er
s
,
ea
ch
lab
eled
as
(
)
an
d
a
9
0
° h
y
b
r
id
with
b
u
f
f
er
s
(
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
4
4
3
-
5
4
5
2
5446
T
h
e
p
o
wer
is
g
iv
en
b
y
:
=
+
+
[
2
+
]
+
(
5
)
T
h
en
,
th
e
E
n
er
g
y
ef
f
icien
cy
c
an
b
e
d
e
r
iv
ed
as
(
6
)
:
=
1
2
(
|
+
−
1
×
|
)
(
6
)
wh
er
e
−
1
=
2
is
th
e
n
o
is
e
co
v
ar
ian
ce
m
atr
ix
af
ter
th
e
co
m
b
i
n
in
g
b
lo
ck
.
2
.
3
.
P
r
o
blem
f
o
rm
ula
t
io
n
T
h
e
jo
in
t o
p
tim
izatio
n
p
r
o
b
le
m
f
o
r
HB
esti
m
atio
n
ca
n
b
e
w
r
itten
as
(
7
)
:
,
,
,
:
∈
ℱ
,
∈
,
∥
∥
=
2
(
7
)
T
h
e
s
ets
o
f
an
alo
g
b
ea
m
f
o
r
m
er
s
th
at
ar
e
tech
n
ically
p
o
s
s
ib
le
u
n
d
er
an
d
co
n
s
tr
ain
ts
ar
e
d
en
o
ted
b
y
ℱ
an
d
r
esp
ec
tiv
ely
.
Fo
r
a
m
o
r
e
d
etailed
ex
p
lan
atio
n
with
s
lig
h
tly
d
if
f
er
e
n
t
n
o
tatio
n
,
p
lea
s
e
r
ef
er
to
ar
ticle
[
1
6
]
.
T
o
s
im
p
lif
y
an
d
s
o
lv
e
th
e
o
p
tim
izatio
n
p
r
o
b
lem
q
u
o
ted
in
(
7
)
,
we
d
i
v
id
e
th
e
co
m
b
in
e
d
p
r
ec
o
d
e
r
/co
m
b
in
e
r
d
esig
n
is
s
u
e
in
to
two
d
is
tin
ct
s
u
b
-
p
r
o
b
lem
s
b
y
s
ep
ar
ately
esti
m
atin
g
th
e
p
r
ec
o
d
er
s
(
̃
an
d
̃
)
an
d
c
o
m
b
in
e
r
s
(
̃
an
d
̃
)
.
I
n
itially
,
to
fin
d
th
e
es
tim
ated
p
r
ec
o
d
er
s
,
we
co
m
p
u
te
all
co
m
b
in
atio
n
s
o
f
p
ath
s
s
elec
ted
f
r
o
m
th
e
e
n
tire
s
et
o
f
tr
an
s
m
is
s
io
n
p
ath
s
,
g
en
er
atin
g
all
co
n
ce
iv
ab
le
an
ten
n
a
r
esp
o
n
s
e
v
ec
to
r
s
to
c
o
n
s
tr
u
ct
a
p
r
ec
o
d
e
r
m
atr
ix
.
Su
b
s
eq
u
en
tly
,
co
lu
m
n
s
m
u
s
t
b
e
ch
o
s
en
f
r
o
m
T
(
Θ
T
(
ij
)
)
wh
ich
ac
h
iev
es
th
e
m
ax
im
u
m
E
E
wh
en
th
e
co
m
b
in
er
is
tak
in
g
FD
o
p
tim
al
.
T
h
en
,
th
e
esti
m
ated
p
r
ec
o
d
er
s
ar
e
c
o
n
s
tr
u
cted
f
r
o
m
ℱ
(
̈
)
.
Alg
o
r
ith
m
1
.
E
n
er
g
y
-
e
f
f
icien
c
y
HB
f
o
r
M
-
MI
MO
s
y
s
tem
s
1:
Input:
,
,
,
,
,
.
2:
Output:
̃
,
̃
,
̃
,
̃
3:
Compute
=
(
)
=
(
)
4:
for
=
1
∶
5:
(
)
=
ℱ
(
)
;
̈
=
(
)
;
6:
(
)
=
(
̈
̈
)
−
1
̈
;
̈
=
(
)
7:
(
)
=
1
(
)
2
|
+
2
(
)
−
1
̈
̈
×
̈
̈
|
8:
end for
9:
[
~
,
̈
]
=
(
(
:
,
1
)
)
10:
̃
=
(
̈
)
,
̃
=
(
̈
)
11:
Use the finding
̃
and
̃
for calculate
̃
and
̃
12:
for
=
1
∶
do
13:
(
)
=
(
)
;
̈
=
(
)
14:
(
)
=
(
̈
̈
)
−
1
(
̈
)
;
̈
=
(
)
16:
=
̃
̃
̃
̃
+
2
;
17:
(
)
=
1
(
)
2
|
+
2
(
̈
̈
̈
̈
×
̈
̈
)
−
1
×
̃
̃
×
̃
̃
̈
̈
|
18:
end for
19:
[
~
,
̈
]
=
(
(
:
,
1
)
)
20:
̃
=
(
̈
)
,
̃
=
(
̈
)
;
21:
[
~
,
̈
]
=
(
(
:
,
1
)
)
T
h
e
FD
o
p
tim
al
p
r
ec
o
d
er
a
n
d
co
m
b
in
er
a
r
e
d
e
n
o
ted
an
d
,
r
esp
ec
tiv
ely
.
T
h
e
ch
a
n
n
el
m
atr
ix
is
p
r
o
ce
s
s
ed
to
s
in
g
u
l
ar
v
alu
e
d
ec
o
m
p
o
s
itio
n
(
SVD
)
,
in
o
r
d
er
to
m
ak
e
=
.
L
ev
er
a
g
in
g
th
e
m
en
tio
n
ed
d
ec
o
m
p
o
s
itio
n
,
[
3
]
in
d
icate
s
th
at
co
r
r
esp
o
n
d
s
to
th
e
f
ir
s
t
N
co
lu
m
n
s
o
f
as
=
(
1
)
wich
ca
n
b
e
u
s
ed
t
o
o
b
tain
th
e
FD
o
p
tim
al
p
r
ec
o
d
in
g
m
atr
ix
an
d
ca
n
b
e
ca
lcu
lat
ed
u
s
in
g
th
e
u
n
co
n
s
tr
ain
e
d
b
ea
m
f
o
r
m
er
as
(
8
)
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2088
-
8
7
0
8
C
o
n
vo
lu
tio
n
a
l
n
eu
r
a
l n
etw
o
r
k
-
b
a
s
ed
h
y
b
r
id
b
ea
mfo
r
min
g
…
(
Ha
n
a
n
e
A
ya
d
)
5447
=
(
1
(
+
2
)
−
1
)
(
8
)
W
e
th
en
esti
m
ate
h
y
b
r
id
co
m
b
in
er
s
u
s
in
g
th
e
f
in
d
in
g
s
o
f
th
e
h
y
b
r
i
d
p
r
ec
o
d
e
r
s
o
b
tain
e
d
th
r
o
u
g
h
th
e
s
am
e
m
eth
o
d
o
l
o
g
y
.
T
h
e
esti
m
ated
c
o
m
b
in
er
s
ar
e
c
o
n
s
tr
u
cted
f
r
o
m
(
̈
)
.
T
h
e
o
p
tim
izatio
n
s
u
b
-
p
r
o
b
lem
f
o
r
HB
is
d
escr
ib
ed
in
Alg
o
r
ith
m
1
.
2
.
4
.
CNN
-
ba
s
ed
bea
m
f
o
rm
e
rs desi
g
n
I
n
th
is
s
ec
tio
n
,
we
p
r
esen
t
th
e
ar
ch
itectu
r
e
o
f
th
e
p
r
o
p
o
s
e
d
n
etwo
r
k
f
o
r
j
o
in
t
an
alo
g
p
r
ec
o
d
er
an
d
co
m
b
in
er
o
p
tim
izatio
n
,
d
ep
ict
ed
in
Fig
u
r
e
2
.
T
h
is
f
i
g
u
r
e
illu
s
tr
ates
th
e
ar
ch
itectu
r
e
co
m
p
r
is
in
g
two
C
NNs.
On
e
o
f
t
h
em
f
o
cu
s
es
o
n
o
p
ti
m
izin
g
p
r
ec
o
d
er
s
b
y
e
x
tr
ac
ti
n
g
ess
en
tial
f
ea
tu
r
es
f
r
o
m
t
h
e
ch
an
n
el
m
atr
ix
.
I
t
g
en
er
ates
o
p
tim
al
p
h
ases
f
o
r
t
h
e
p
r
ec
o
d
e
r
,
s
teer
in
g
s
ig
n
al
p
o
wer
in
d
esire
d
d
ir
ec
tio
n
s
,
u
lt
im
ately
en
h
an
cin
g
E
E
.
T
h
e
o
th
er
C
NN
is
d
ed
icat
ed
to
co
m
b
in
er
d
esig
n
.
I
t
p
r
o
c
ess
es
ch
an
n
el
d
ata
th
r
o
u
g
h
co
n
v
o
lu
tio
n
al
lay
er
s
,
g
en
er
atin
g
p
h
ases
to
o
p
tim
ize
co
m
b
in
er
s
.
T
h
is
lead
s
to
ef
f
icien
t
s
ig
n
al
r
ec
ep
tio
n
,
co
n
tr
i
b
u
tin
g
to
o
v
er
all
E
E
im
p
r
o
v
em
e
n
t.
E
ac
h
C
NN
co
m
p
r
is
es
eig
h
t
lay
er
s
an
d
h
as
a
s
im
ilar
s
tr
u
ctu
r
e,
ex
ce
p
t f
o
r
th
e
f
in
al
lay
er
.
I
n
d
ata
g
en
er
atio
n
,
t
o
en
h
a
n
ce
th
e
p
r
o
ce
s
s
in
g
ca
p
ab
ilit
y
o
f
im
p
e
r
f
ec
t
ch
an
n
el
s
tate
in
f
o
r
m
atio
n
(
C
SI)
,
we
f
ir
s
t
co
n
s
tr
u
ct
r
a
n
d
o
m
l
y
ℎ
p
er
f
ec
t
c
h
an
n
el
m
atr
ices
(
ℎ
)
f
o
r
d
if
f
er
e
n
t
u
s
er
lo
ca
tio
n
s
.
T
h
en
,
we
a
d
o
p
t
n
o
is
y
ch
an
n
el
m
atr
ices
f
o
r
ea
ch
g
e
n
er
ated
p
er
f
ec
t
ch
a
n
n
el
m
atr
i
x
,
in
tr
o
d
u
cin
g
elem
en
t
-
wis
e
s
y
n
th
etic
n
o
is
e.
T
h
e
lev
el
o
f
s
y
n
th
etic
n
o
is
e
is
d
eter
m
in
ed
b
y
th
e
f
o
r
m
u
la
:
=
20
10
(
|
[
]
,
|
2
2
)
,
wh
er
e
2
r
ep
r
esen
ts
th
e
v
ar
ian
ce
o
f
th
e
n
o
is
e
in
th
e
tr
ain
in
g
p
h
ases
ass
o
ciate
d
with
th
e
ch
an
n
e
l
co
m
p
o
n
en
t
[
]
,
.
T
o
ac
co
u
n
t
f
o
r
v
ar
iatio
n
s
in
th
e
wir
eles
s
en
v
i
r
o
n
m
en
t,
we
u
s
e
th
r
ee
d
if
f
er
en
t
lev
els
o
f
.
T
h
ese
v
alu
es
(
1
5
,
2
0
,
a
n
d
2
5
d
B
)
r
ef
lect
r
ea
lis
tic
tr
ain
in
g
s
ce
n
ar
io
s
r
a
n
g
in
g
f
r
o
m
lo
w
to
m
o
d
er
ate
SNR
co
n
d
itio
n
s
,
as
ad
o
p
ted
i
n
[
1
6
]
.
Prio
r
to
f
ee
d
in
g
th
e
c
o
m
p
lex
-
v
alu
ed
ch
a
n
n
el
m
atr
i
x
in
to
th
e
r
ea
l
-
v
alu
ed
n
eu
r
al
n
etwo
r
k
s
,
we
f
u
r
th
er
m
o
d
if
y
it
to
f
ac
ilit
ate
f
ea
tu
r
e
ex
tr
ac
tio
n
an
d
en
h
a
n
ce
p
er
f
o
r
m
an
ce
.
T
h
u
s
,
we
g
o
f
o
r
an
i
n
p
u
t
lay
e
r
o
f
s
ize
×
×
3
.
T
h
e
f
ir
s
t
in
p
u
t
[
[
]
:
,
:
,
1
]
,
=
|
[
]
,
|
is
th
e
elem
en
t
-
wis
e
ab
s
o
lu
te
v
alu
e
o
f
t
h
e
c
h
an
n
el
m
atr
ix
.
[
[
]
:
,
:
,
2
]
,
=
{
[
]
,
}
an
d
[
[
]
:
,
:
,
3
]
,
=
{
[
]
,
}
ar
e
r
esp
ec
tiv
el
y
,
th
e
s
ec
o
n
d
a
n
d
t
h
ir
d
i
n
p
u
ts
.
T
h
ey
r
e
p
r
esen
t
th
e
r
ea
l
an
d
i
m
ag
in
ar
y
p
ar
ts
o
f
th
e
c
h
an
n
el
m
atr
ix
.
T
h
e
m
atr
i
x
[
[
]
:
,
:
,
1
:
3
]
,
is
th
e
th
r
ee
-
d
im
en
s
io
n
al
in
p
u
t
d
ata
o
f
th
e
n
etwo
r
k
.
Fig
u
r
e
2
.
Pro
p
o
s
ed
C
NN
m
o
d
el
f
o
r
jo
in
t
b
ea
m
f
o
r
m
er
s
d
esig
n
T
h
e
r
est o
f
th
e
n
etwo
r
k
s
tr
u
ct
u
r
e
in
clu
d
es c
o
n
v
o
lu
tio
n
al
lay
er
s
with
f
ilter
s
o
f
s
ize
×
u
s
ed
in
th
e
s
ec
o
n
d
a
n
d
th
ir
d
lay
e
r
s
to
ex
tr
ac
t
a
n
d
s
elec
t
d
ata
f
ea
tu
r
e
v
ec
to
r
s
.
T
h
e
ac
tiv
ati
o
n
f
u
n
ctio
n
s
in
t
h
e
co
n
v
o
l
u
tio
n
al
lay
er
s
ar
e
all
r
ec
tifie
d
lin
ea
r
u
n
it
(
R
eL
U)
f
u
n
ctio
n
s
.
T
h
e
f
o
u
r
th
an
d
s
ix
th
lay
er
s
ar
e
FC
wi
th
u
n
its
.
T
o
p
r
ev
en
t
o
v
er
f
itti
n
g
,
d
r
o
p
o
u
t
lay
er
s
with
a
p
r
o
b
ab
ilit
y
ar
e
in
clu
d
e
d
af
ter
FC
lay
er
s
,
s
p
ec
if
ically
in
th
e
f
if
th
an
d
s
ev
en
th
lay
e
r
s
.
T
h
e
o
u
tp
u
t
la
y
er
o
f
th
e
is
b
ased
o
n
t
h
e
v
ec
to
r
ized
f
o
r
m
o
f
th
e
p
h
ases
,
r
esu
ltin
g
in
a
s
ize
o
f
×
1
.
Similar
ly
,
th
e
o
u
tp
u
t
lay
er
o
f
h
as
a
s
ize
o
f
×
1
.
T
h
e
g
e
n
er
ated
d
ata
f
o
r
ℎ
=
1
an
d
=
2
f
ee
d
th
e
C
NNs
d
u
r
in
g
t
h
e
tr
ai
n
in
g
an
d
v
alid
atio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
7
0
8
I
n
t J E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
15
,
No
.
6
,
Decem
b
e
r
20
25
:
5
4
4
3
-
5
4
5
2
5448
p
h
ases
.
T
h
e
tr
ain
in
g
an
d
v
ali
d
atio
n
d
atasets
ar
e
cr
ea
ted
b
y
r
an
d
o
m
ly
d
i
v
id
in
g
th
e
to
tal
d
ata
in
to
f
o
r
tr
ain
in
g
an
d
1
−
f
o
r
v
alid
atio
n
.
T
h
e
Ad
am
o
p
tim
izatio
n
Alg
o
r
ith
m
is
u
s
ed
to
tr
ain
th
e
m
o
d
el.
Alg
o
r
ith
m
2
.
C
NN
-
EE
b
ased
HB
o
p
tim
izer
1:
Input:
,
ℎ
,
,
,
,
,
.
2:
Output:
Training data
and
3:
Generate
{
(
)
}
=
1
ℎ
4:
for
1
≤
≤
(
ℎ
)
and
1
≤
≤
do
5:
[
(
,
)
]
,
~
(
[
]
,
,
2
)
.
6: Use Algorithm 1 to get
̃
(
,
)
,
and
̃
(
,
)
as labels
7:
(
,
)
=
∠
{
̃
}
,
(
,
)
=
∠
{
̃
}
,
8: Input data:
9:
[
[
(
,
)
]
:
,
:
,
1
]
,
,
[
[
(
,
)
]
:
,
:
,
2
]
,
and
[
[
(
,
)
]
:
,
:
,
3
]
,
10: Construct the input
-
output pair (
(
,
)
,
(
,
)
) for
and
(
(
,
)
,
(
,
)
) for
11:
end for
p and l
12:
training data for
and
3.
M
E
T
H
O
DO
L
O
G
Y
3
.
1
.
Sim
ula
t
i
o
n set
up
T
h
is
wo
r
k
is
im
p
lem
en
ted
in
MA
T
L
AB
R
2
0
2
2
a,
wh
er
e
a
h
y
b
r
id
B
ea
m
f
o
r
m
in
g
u
s
in
g
DL
ap
p
r
o
ac
h
es
b
ased
o
n
t
h
e
E
E
c
r
iter
io
n
(
M
-
MI
MO
E
E
)
s
y
s
tem
d
esig
n
ed
u
s
in
g
th
e
f
o
llo
win
g
p
ar
am
eter
s
=
64
,
=
16
,
=
10
d
B
an
d
=
28
GHz
.
T
h
e
p
r
o
p
ag
a
tio
n
ch
a
n
n
el
e
n
v
ir
o
n
m
en
t
is
m
o
d
eled
with
=
4
an
d
=
4
f
o
r
ea
c
h
clu
s
ter
,
a
n
d
2
=
5°
f
o
r
all
tr
an
s
m
it
a
n
d
r
ec
ei
v
e
az
im
u
th
an
d
elev
atio
n
an
g
les,
r
an
d
o
m
ly
ch
o
s
en
with
in
th
e
in
ter
v
als
[
−
60°
,
60°
]
an
d
[
−
20°
,
20°
]
.
T
h
is
p
ap
er
u
tili
ze
s
ac
tu
al
v
alu
es
=
22
.
5
,
=
0
.
3
,
=
14
,
=
3
an
d
=
0
.
27
[
1
2
]
.
T
h
e
s
tu
d
y
e
x
a
m
in
es
th
e
ef
f
ec
t
o
f
d
if
f
e
r
en
t
R
F
ch
ain
c
o
n
f
ig
u
r
atio
n
s
,
s
p
ec
if
ically
test
in
g
s
y
s
tem
s
with
4
R
F
ch
ain
s
an
d
6
R
F
ch
ain
s
at
b
o
th
th
e
tr
an
s
m
itter
an
d
r
ec
e
iv
er
s
id
es.
T
h
e
s
im
u
latio
n
ev
alu
ates
th
e
s
y
s
tem
's
p
er
f
o
r
m
an
ce
b
ased
o
n
E
E
cr
iter
io
n
(M
-
MI
MO
E
E
)
s
y
s
tem
with
FD
p
r
ec
o
d
i
n
g
(
iFu
llOPT)
s
o
lu
tio
n
a
n
d
SE
d
ee
p
le
ar
n
in
g
b
ased
h
y
b
r
id
b
ea
m
f
o
r
m
in
g
d
esig
n
s
o
lu
tio
n
b
ased
o
n
SE
(
M
-
MI
MO
SE)
as
p
r
esen
ted
in
[
1
6
]
.
Ad
d
itio
n
ally
,
we
p
er
f
o
r
m
a
co
m
p
ar
ativ
e
an
al
y
s
is
o
f
v
ar
io
u
s
MI
MO
s
etu
p
s
,
in
clu
d
in
g
4
×4
MI
MO
,
9
×9
MI
MO
,
an
d
a
lar
g
e
-
s
ca
le
6
4
×1
6
M
-
MI
MO
s
y
s
tem
.
3
.
2
.
CNN
t
r
a
ini
ng
a
nd
im
plem
ent
a
t
io
n
T
h
e
C
NN
in
p
u
t
d
ataset,
p
r
ev
i
o
u
s
ly
in
tr
o
d
u
ce
d
in
s
ec
tio
n
2
.
4
,
is
s
p
lit
in
to
a
7
0
/3
0
t
r
ain
in
g
-
v
alid
atio
n
r
atio
.
T
h
e
m
o
d
el
is
tr
ain
ed
u
s
in
g
th
e
Ad
a
m
o
p
tim
izer
,
with
th
e
co
n
f
i
g
u
r
atio
n
s
u
m
m
ar
ized
in
T
ab
le
1
.
T
ab
le
1
.
C
NN
tr
ain
in
g
h
y
p
e
r
p
ar
am
eter
s
an
d
s
tr
u
ctu
r
al
s
ettin
g
s
S
y
mb
o
l
D
e
scri
p
t
i
o
n
V
a
l
u
e
ℎ
P
e
r
f
e
c
t
c
h
a
n
n
e
l
r
e
a
l
i
z
a
t
i
o
n
s
8
.
6
N
o
i
s
y
a
u
g
me
n
t
a
t
i
o
n
s
p
e
r
r
e
a
l
i
z
a
t
i
o
n
1
0
0
Tr
a
i
n
i
n
g
d
a
t
a
f
r
a
c
t
i
o
n
70
%
1
−
V
a
l
i
d
a
t
i
o
n
d
a
t
a
f
r
a
c
t
i
o
n
30
%
A
d
a
m
l
e
a
r
n
i
n
g
r
a
t
e
0
.
0
0
0
5
B
a
t
c
h
si
z
e
1
0
0
ℎ
Tr
a
i
n
i
n
g
e
p
o
c
h
s
2
0
0
C
o
n
v
.
f
i
l
t
e
r
s
p
e
r
l
a
y
e
r
64
×
F
i
l
t
e
r
k
e
r
n
e
l
si
z
e
3
×
3
N
e
u
r
o
n
s
i
n
f
u
l
l
y
c
o
n
n
e
c
t
e
d
l
a
y
e
r
s
1
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D
r
o
p
o
u
t
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r
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RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
s
ec
tio
n
b
eg
in
s
b
y
an
aly
z
in
g
th
e
s
ca
lab
ilit
y
o
f
M
-
MI
MO
an
d
p
r
o
v
i
d
es
in
s
ig
h
ts
in
to
th
e
tr
ad
e
-
o
f
f
s
b
etwe
en
R
F
ch
ain
o
p
tim
izatio
n
an
d
s
y
s
tem
ef
f
icien
cy
,
em
p
h
asizin
g
th
e
ad
v
an
tag
es
o
f
M
-
MI
MO
f
o
r
en
h
an
cin
g
en
er
g
y
p
e
r
f
o
r
m
an
c
e
in
m
o
d
er
n
co
m
m
u
n
icatio
n
s
y
s
tem
s
.
Fig
u
r
e
3
p
r
esen
ts
th
e
p
er
f
o
r
m
an
ce
o
f
a
M
-
MI
MO
E
E
s
y
s
tem
w
ith
=
64
an
d
=
16
u
n
d
er
two
R
F
ch
ain
co
n
f
ig
u
r
atio
n
s
:
=
4
an
d
=
6
.
Fig
u
r
e
3
(
a)
co
m
p
ar
es
th
e
SE
v
er
s
u
s
SN
R
,
wh
ile
Fig
u
r
e
3
(
b
)
d
ep
icts
th
e
E
E
v
er
s
u
s
SNR
f
o
r
b
o
th
co
n
f
ig
u
r
atio
n
s
.
Fig
u
r
e
3
(
a)
ill
u
s
tr
ates
th
at
SE
in
cr
ea
s
es
l
in
ea
r
ly
with
th
e
SNR
,
as
ex
p
e
cted
.
A
zo
o
m
ed
-
in
r
eg
io
n
h
ig
h
li
g
h
ts
th
at
t
h
e
co
n
f
ig
u
r
atio
n
with
4
R
F
ch
ain
s
ac
h
iev
es
s
lig
h
tly
h
ig
h
er
SE
co
m
p
ar
ed
to
6
R
F
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ce
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er
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s
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o
r
th
e
s
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e
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n
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ig
u
r
ati
o
n
s
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ev
ea
lin
g
th
at
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o
u
tp
er
f
o
r
m
s
=
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d
u
e
to
th
e
r
ed
u
ce
d
p
o
wer
co
n
s
u
m
p
tio
n
ass
o
ciate
d
with
f
ewe
r
R
F
ch
ain
s
.
T
h
is
an
aly
s
i
s
h
ig
h
lig
h
ts
a
tr
ad
e
-
o
f
f
:
r
ed
u
c
in
g
th
e
n
u
m
b
er
o
f
R
F
ch
ain
s
im
p
r
o
v
es
E
E
with
o
u
t
s
ig
n
if
ican
tly
co
m
p
r
o
m
is
in
g
SE,
m
a
k
in
g
=
4
a
m
o
r
e
E
E
a
n
d
p
r
ac
tical
ch
o
ice
f
o
r
M
-
MI
MO
s
y
s
tem
s
.
(
a)
(
b
)
Fig
u
r
e
1
.
Per
f
o
r
m
an
c
e
an
aly
s
i
s
o
f
d
if
f
er
e
n
t RF
ch
ain
co
n
f
ig
u
r
atio
n
s
in
M
-
MI
MO
with
=
64
,
=
16
.
(
a)
SE
v
er
s
u
s
SNR
an
d
(
b
)
E
E
v
er
s
u
s
SNR
Fig
u
r
e
4
s
h
o
ws
th
e
E
E
p
er
f
o
r
m
an
ce
v
er
s
u
s
SNR
f
o
r
M
-
M
I
MO
E
E
,
M
-
MI
MO
SE,
an
d
iFu
llOPT.
T
h
e
M
-
MI
MO
E
E
m
eth
o
d
ac
h
iev
es
th
e
b
est
E
E
ac
r
o
s
s
all
SNR
lev
els,
h
ig
h
lig
h
tin
g
its
s
u
p
er
io
r
o
p
tim
izatio
n
f
o
r
e
n
er
g
y
co
n
s
u
m
p
tio
n
.
I
n
c
o
n
tr
ast,
iFu
llOPT
d
em
o
n
s
tr
ates
v
er
y
lo
w
E
E
d
u
e
to
th
e
h
ig
h
-
p
o
wer
c
o
n
s
u
m
p
tio
n
o
f
R
F
ch
ain
s
r
eq
u
ir
e
d
f
o
r
d
ig
i
tal
p
r
ec
o
d
in
g
.
T
h
is
h
ig
h
lig
h
ts
th
e
ad
v
a
n
tag
e
o
f
M
-
MI
MO
E
E
,
wh
ich
lev
er
a
g
es
HP to
r
ed
u
ce
R
F c
h
ain
u
s
ag
e
an
d
im
p
r
o
v
e
E
E
.
Fig
u
r
e
5
co
m
p
a
r
es
th
e
E
E
p
er
f
o
r
m
a
n
ce
o
f
4
×
4
MI
MO
,
9
×
9
MI
MO
,
an
d
6
4
×
1
6
M
-
M
I
MO
.
T
h
e
r
esu
lts
clea
r
ly
s
h
o
w
th
at
M
-
MI
MO
E
E
with
a
lar
g
e
r
an
ten
n
a
a
r
r
ay
(
6
4
×
1
6
)
o
u
t
p
er
f
o
r
m
s
s
m
aller
co
n
f
ig
u
r
atio
n
s
,
ac
h
iev
i
n
g
h
ig
h
er
E
E
ac
r
o
s
s
all
SNR
v
alu
es.
T
h
is
im
p
r
o
v
em
en
t
is
p
ar
tic
u
lar
ly
s
ig
n
if
ican
t
at
lo
w
SNR
,
wh
er
e
M
-
MI
MO
E
E
b
en
ef
its
f
r
o
m
HP
to
o
p
tim
ize
en
er
g
y
u
s
ag
e
an
d
r
ed
u
ce
R
F
ch
ain
co
n
s
u
m
p
tio
n
.
As th
e
SNR
in
cr
ea
s
es,
th
e
ad
v
an
tag
e
o
f
M
-
MI
MO
E
E
b
ec
o
m
es e
v
en
m
o
r
e
a
p
p
ar
en
t,
p
r
o
v
in
g
its
ef
f
icien
cy
an
d
s
ca
lab
ilit
y
f
o
r
lar
g
e
-
s
ca
le
MI
MO
s
y
s
tem
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
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7
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15
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6
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b
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r
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4
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u
r
e
4
.
E
E
p
er
f
o
r
m
a
n
ce
v
s
SNR
f
o
r
M
-
MI
MO
E
E
,
M
-
MI
MO
SE,
an
d
iFu
llOPT
Fig
u
r
e
5
.
C
o
m
p
a
r
ativ
e
ev
alu
at
io
n
o
f
M
I
MO
s
y
s
tem
s
: 4
×4
,
9
×9
,
an
d
6
4
×1
6
co
n
f
ig
u
r
atio
n
s
5.
CO
NCLU
SI
O
N
I
n
th
is
wo
r
k
,
we
p
r
esen
ted
an
en
er
g
y
-
ef
f
icien
t
ap
p
r
o
ac
h
f
o
r
h
y
b
r
id
b
ea
m
f
o
r
m
in
g
in
m
m
W
av
e
M
-
MI
MO
s
y
s
tem
s
,
with
a
p
ar
ticu
lar
f
o
cu
s
o
n
m
ax
im
izin
g
E
E
.
Simu
latio
n
r
esu
lts
d
e
m
o
n
s
tr
ate
th
at
th
e
p
r
o
p
o
s
ed
M
-
MI
MO
E
E
co
n
f
ig
u
r
atio
n
p
ar
ticu
lar
ly
with
a
6
4
×1
6
an
ten
n
a
s
etu
p
s
ig
n
if
ican
tly
o
u
t
p
er
f
o
r
m
s
s
m
aller
MI
MO
s
y
s
tem
s
in
ter
m
s
o
f
E
E
.
M
o
r
eo
v
er
,
th
e
a
n
a
ly
s
is
s
h
o
wed
th
at
o
p
tim
izin
g
th
e
n
u
m
b
er
o
f
R
F
ch
ain
s
,
s
u
ch
as
u
s
in
g
4
R
F
ch
ain
s
in
s
tead
o
f
6
,
o
f
f
er
s
a
n
o
tab
le
im
p
r
o
v
em
en
t
in
E
E
with
o
u
t
a
s
u
b
s
tan
tial
lo
s
s
in
SE.
T
h
ese
f
i
n
d
in
g
s
u
n
d
e
r
l
in
e
th
e
p
o
ten
tial
o
f
E
E
-
b
ased
ap
p
r
o
ac
h
es
in
en
h
a
n
cin
g
t
h
e
p
er
f
o
r
m
an
ce
an
d
s
ca
lab
ilit
y
o
f
Ma
s
s
iv
e
MI
MO
s
y
s
tem
s
,
m
ak
in
g
th
em
a
p
r
o
m
is
in
g
s
o
lu
tio
n
f
o
r
f
u
t
u
r
e
en
er
g
y
-
ef
f
icien
t
co
m
m
u
n
icatio
n
n
etwo
r
k
s
.
Fo
r
f
u
tu
r
e
wo
r
k
,
th
e
co
n
s
id
er
at
io
n
o
f
m
u
ltiu
s
er
s
ce
n
ar
io
s
with
r
ec
o
n
f
ig
u
r
ab
le
in
tellig
en
t su
r
f
ac
es c
an
b
e
e
x
p
lo
r
ed
,
with
atten
tio
n
to
th
e
e
n
e
r
g
y
co
n
tr
ib
u
tio
n
o
f
ea
ch
elem
e
n
t.
ACK
NO
WL
E
DG
M
E
N
T
S
W
e
ex
p
r
ess
o
u
r
d
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p
a
p
p
r
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atio
n
to
th
e
L
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a
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o
r
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r
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llab
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ir
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en
t
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ad
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p
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Als
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th
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th
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r
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u
p
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r
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f
C
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DR
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y
(
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AE
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team
)
o
f
th
e
C
o
n
s
er
v
ato
ir
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Natio
n
al
d
es Ar
ts
et
Mé
tier
s
(
C
N
AM
)
in
Par
is
,
Fra
n
ce
.
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I
n
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&
C
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p
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n
,
“
P
o
w
e
r
-
p
e
r
f
o
r
m
a
n
c
e
a
n
a
l
y
s
i
s
o
f
a
s
i
mp
l
e
o
n
e
-
b
i
t
t
r
a
n
s
c
e
i
v
e
r
,
”
i
n
2
0
1
7
I
n
f
o
rm
a
t
i
o
n
T
h
e
o
ry
a
n
d
A
p
p
l
i
c
a
t
i
o
n
s
W
o
rks
h
o
p
(
I
T
A)
,
2
0
1
7
,
p
p
.
1
–
6
,
d
o
i
:
1
0
.
1
1
0
9
/
I
TA
.
2
0
1
7
.
8
0
2
3
4
5
4
.
B
I
O
G
RAP
H
I
E
S O
F
AUTH
O
RS
H
a
n
a
n
e
Ay
a
d
is
c
u
rre
n
tl
y
p
u
rsu
in
g
a
P
h
.
D.
in
tele
c
o
m
m
u
n
ica
ti
o
n
s
a
t
th
e
Un
iv
e
rsity
o
f
Tl
e
m
c
e
n
,
Al
g
e
ria.
S
h
e
re
c
e
iv
e
d
h
e
r
M
.
S
c
.
d
e
g
re
e
in
n
e
two
r
k
s
a
n
d
tele
c
o
m
m
u
n
ica
ti
o
n
s
in
2
0
1
8
.
He
r
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
HP,
m
m
Wav
e
m
a
ss
iv
e
M
IM
O,
a
n
d
d
e
e
p
lea
rn
i
n
g
tec
h
n
iq
u
e
s
fo
r
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s.
S
h
e
c
o
m
p
l
e
ted
a
re
se
a
rc
h
i
n
tern
sh
ip
a
t
CNA
M
,
P
a
ris,
u
n
d
e
r
th
e
TAS
S
I
LI
ATOME
5
+
p
ro
jec
t,
a
n
d
h
a
s
a
u
th
o
re
d
p
a
p
e
rs
p
re
se
n
ted
a
t
in
tern
a
ti
o
n
a
l
c
o
n
fe
re
n
c
e
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
a
y
a
d
.
h
a
n
a
n
e
@u
n
iv
-
tl
e
m
c
e
n
.
d
z
.
Mo
h
a
m
m
e
d
Ya
ss
in
e
Be
n
d
i
m
e
r
a
d
re
c
e
iv
e
d
h
is
B.
S
c
.
d
e
g
re
e
in
e
lec
tri
c
a
l
a
n
d
e
lec
tro
n
ic
e
n
g
in
e
e
rin
g
,
a
s
we
ll
a
s
h
is
M
.
S
c
.
a
n
d
P
h
.
D.
d
e
g
re
e
s
in
tele
c
o
m
m
u
n
ica
ti
o
n
a
n
d
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
tec
h
n
o
l
o
g
ie
s
fro
m
th
e
Un
i
v
e
rsity
o
f
Tl
e
m
c
e
n
,
Alg
e
ria,
in
2
0
1
0
,
2
0
1
2
,
a
n
d
2
0
1
6
,
re
sp
e
c
ti
v
e
ly
.
I
n
2
0
1
6
,
h
e
jo
i
n
e
d
t
h
e
Un
i
v
e
rsity
o
f
Be
c
h
a
r
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
.
He
is
c
u
rre
n
t
ly
a
n
a
ss
o
c
iate
p
ro
fe
ss
o
r
in
t
h
e
Tele
c
o
m
m
u
n
ica
ti
o
n
s
De
p
a
rtme
n
t
a
n
d
a
m
e
m
b
e
r
o
f
th
e
Dig
i
tal
Co
m
m
u
n
ica
ti
o
n
Tea
m
a
t
th
e
LT
T
Lab
o
ra
to
r
y
o
f
Tel
e
c
o
m
m
u
n
ica
ti
o
n
s,
Un
i
v
e
rsit
y
o
f
Tl
e
m
c
e
n
.
His
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
a
n
d
m
o
b
il
e
n
e
tw
o
rk
s,
e
n
e
rg
y
e
fficie
n
c
y
in
wire
les
s
sy
st
e
m
s,
a
n
d
a
rti
ficia
l
in
telli
g
e
n
c
e
f
o
r
o
p
t
imiz
in
g
c
o
m
m
u
n
ica
ti
o
n
p
ro
t
o
c
o
ls.
He
h
a
s
a
u
th
o
re
d
se
v
e
ra
l
p
a
p
e
rs
in
IEE
E‐i
n
d
e
x
e
d
j
o
u
rn
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s
a
n
d
p
a
rti
c
ip
a
tes
in
n
a
ti
o
n
a
l
a
n
d
in
tern
a
ti
o
n
a
l
re
se
a
rc
h
p
ro
jec
ts
(P
RF
U,
P
HC
-
TAS
S
ILI
,
ACA
DEM
Y).
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
y
a
ss
in
e
.
b
e
n
d
ime
ra
d
@
u
n
i
v
-
tl
e
m
c
e
n
.
d
z
.
Fethi
Ta
r
ik
Be
n
d
im
e
r
a
d
re
c
e
i
v
e
d
t
h
e
En
g
in
e
e
rin
g
d
e
g
re
e
in
e
le
c
tro
n
ics
fro
m
t
h
e
Un
iv
e
rsity
o
f
S
c
ien
c
e
a
n
d
Tec
h
n
o
lo
g
y
o
f
Ora
n
(UST
O),
Al
g
e
ria,
in
1
9
8
3
,
t
h
e
Dip
lô
m
e
d
’Ét
u
d
e
s
Ap
p
r
o
fo
n
d
ies
(DEA)
in
tele
c
o
m
m
u
n
ica
ti
o
n
s
fro
m
t
h
e
Un
iv
e
rsity
o
f
Nic
e
–
S
o
p
h
i
a
An
ti
p
o
li
s,
F
ra
n
c
e
,
in
1
9
8
4
,
a
n
d
th
e
P
h
.
D.
d
e
g
re
e
i
n
tele
c
o
m
m
u
n
ica
ti
o
n
s
fro
m
t
h
e
sa
m
e
u
n
i
v
e
rsity
i
n
1
9
8
9
.
His
P
h
.
D.
wa
s
o
fficia
ll
y
re
c
o
g
n
ize
d
a
s
e
q
u
iv
a
l
e
n
t
to
th
e
Do
c
t
o
ra
t
d
'
Ét
a
t
in
Ju
n
e
1
9
9
2
.
He
is
c
u
rre
n
tl
y
a
p
r
o
f
e
ss
o
r
with
th
e
F
a
c
u
lt
y
o
f
E
n
g
i
n
e
e
rin
g
,
Un
i
v
e
rsity
o
f
Tl
e
m
c
e
n
,
Alg
e
ria.
He
is
a
lso
t
h
e
Dire
c
to
r
o
f
t
h
e
Tele
c
o
m
m
u
n
ica
ti
o
n
s
R
e
se
a
rc
h
Lab
o
ra
to
r
y
a
t
t
h
e
Un
iv
e
rsity
o
f
Tl
e
m
c
e
n
,
a
n
d
p
re
v
i
o
u
sly
se
rv
e
d
a
s
t
h
e
d
irec
to
r
o
f
th
e
In
stit
u
te
o
f
E
lec
tro
n
ics
a
t
th
e
sa
m
e
u
n
i
v
e
rsity
.
His
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
s,
sig
n
a
l
p
ro
c
e
ss
in
g
,
a
n
d
a
d
v
a
n
c
e
d
tele
c
o
m
m
u
n
ica
ti
o
n
sy
ste
m
s.
He
h
a
s
su
p
e
r
v
ise
d
se
v
e
ra
l
P
h
.
D.
stu
d
e
n
ts
a
n
d
c
o
n
tri
b
u
ted
t
o
n
u
m
e
ro
u
s
n
a
ti
o
n
a
l
a
n
d
in
ter
n
a
ti
o
n
a
l
re
se
a
rc
h
p
r
o
jec
ts.
He
is
th
e
a
u
th
o
r
o
f
se
v
e
ra
l
p
u
b
li
c
a
ti
o
n
s
in
re
p
u
tab
le
jo
u
r
n
a
ls
a
n
d
c
o
n
fe
re
n
c
e
s
a
n
d
re
g
u
larly
se
rv
e
s
a
s
a
re
v
iew
e
r
fo
r
sc
ien
ti
fic j
o
u
r
n
a
ls.
He
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
fe
th
i
tare
k
.
b
e
n
d
ime
ra
d
@u
n
i
v
-
tl
e
m
c
e
n
.
d
z
.
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