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al
ities
o
f
clo
u
d
co
m
p
u
t
in
g
tec
h
n
o
lo
g
y
ca
n
h
elp
in
f
i
n
d
in
g
n
o
v
el
tech
n
o
lo
g
ica
l
s
o
lu
tio
n
s
f
o
r
m
o
b
ile
co
m
m
u
n
icat
io
n
n
e
t
w
o
r
k
i
n
a
co
m
m
u
n
icati
o
n
a
n
d
in
f
o
r
m
atio
n
tech
n
o
lo
g
y
f
ield
[
4
]
.
T
h
u
s
,
u
s
e
o
f
clo
u
d
co
m
p
u
ti
n
g
tech
n
iq
u
e
an
d
m
as
s
iv
e
MI
MO
s
y
s
te
m
ca
n
en
s
u
r
e
ef
f
icie
n
c
y
e
n
h
a
n
ce
m
en
t
o
f
h
ig
h
b
an
d
w
id
t
h
5
G
m
o
b
ile
ce
llu
lar
n
et
w
o
r
k
.
F
u
r
th
er
m
o
r
e,
th
ese
tech
n
iq
u
e
s
ca
n
en
h
an
ce
f
le
x
ib
ilit
y
an
d
s
c
alab
ilit
y
o
f
m
o
b
ile
ce
llu
lar
n
et
w
o
r
k
s
to
f
u
l
f
il t
h
e
m
ar
k
et
ex
p
ec
tatio
n
s
.
Fu
r
t
h
er
m
o
r
e,
in
ca
s
e
o
f
u
p
lin
k
C
SI
m
ed
i
u
m
,
th
e
e
s
ti
m
atio
n
o
f
C
SI
at
B
S
i
s
q
u
ite
e
f
f
icie
n
t
d
u
e
to
th
e
ac
cu
r
ate
tr
an
s
m
i
s
s
io
n
o
f
p
ilo
t
co
d
e
w
o
r
d
s
f
r
o
m
t
h
e
UE
.
H
o
w
e
v
er
,
cu
r
r
en
t
ce
ll
u
lar
s
y
s
te
m
s
m
o
s
tl
y
u
tili
ze
u
p
lin
k
C
SI
esti
m
a
tio
n
.
B
esid
es
,
th
e
esti
m
atio
n
o
f
C
SI
in
d
o
w
n
l
in
k
C
SI
m
ed
iu
m
is
q
u
i
te
ch
allen
g
i
n
g
task
,
s
p
ec
if
icall
y
in
ca
s
e
o
f
f
r
eq
u
e
n
c
y
d
iv
is
io
n
d
u
p
lex
(
FDD)
m
u
ltip
lex
i
n
g
.
W
h
er
ea
s
,
d
o
w
n
li
n
k
C
SI
esti
m
atio
n
is
ac
h
iev
ed
w
i
th
t
h
e
h
e
lp
o
f
u
p
lin
k
C
SI
m
ed
iu
m
i
n
ti
m
e
d
iv
is
io
n
d
u
p
le
x
m
u
lt
ip
le
x
i
n
g
u
s
i
n
g
r
ec
ip
r
o
cit
y
[
5
]
.
Ho
w
e
v
er
,
d
o
w
n
l
in
k
C
SI
esti
m
atio
n
in
F
DD
m
u
ltip
le
x
i
n
g
u
s
i
n
g
u
p
lin
k
C
SI
m
ed
i
u
m
i
s
q
u
it
e
co
m
p
le
x
tas
k
d
u
e
to
w
ea
k
r
ec
ip
r
o
cit
y
.
F
u
r
th
er
m
o
r
e,
t
h
e
ap
p
r
o
ac
h
u
ti
lized
in
clas
s
ical
MI
MO
s
y
s
te
m
s
p
r
o
d
u
ce
s
m
as
s
iv
e
o
v
er
h
ea
d
in
w
h
ic
h
d
o
w
n
li
n
k
C
SI
is
esti
m
ated
at
UE
w
it
h
t
h
e
h
elp
o
f
p
ilo
t
co
d
e
w
o
r
d
s
in
FDD
s
y
s
te
m
s
a
n
d
af
ter
esti
m
ated
C
SI
is
f
ed
b
ac
k
to
th
e
B
S.
Ho
w
e
v
er
,
in
th
is
ap
p
r
o
ac
h
,
m
a
s
s
i
v
e
o
v
er
h
ea
d
is
in
d
u
ce
d
d
u
e
to
u
tili
za
t
io
n
o
f
m
u
ltip
le
an
te
n
n
as
e
n
h
a
n
ce
s
d
i
m
e
n
s
io
n
s
o
f
C
SI
m
a
tr
ices.
T
h
u
s
,
th
is
ap
p
r
o
ac
h
is
in
f
ea
s
ib
le
in
m
as
s
i
v
e
MI
MO
s
y
s
te
m
s
.
F
u
r
t
h
er
m
o
r
e,
ch
a
n
n
el
o
v
er
h
ea
d
c
an
b
e
m
iti
g
ated
b
y
e
f
f
icie
n
t
c
o
m
p
r
es
s
io
n
o
f
C
SI
m
atr
ices.
T
h
is
ca
n
b
e
ac
h
iev
e
d
u
s
in
g
co
m
p
r
es
s
io
n
s
e
n
s
in
g
m
et
h
o
d
s
o
r
d
ee
p
lear
n
in
g
tech
n
iq
u
e
s
[
6
]
,
[
7
]
.
Nev
er
th
e
less
,
t
h
e
s
p
ar
s
it
y
o
f
C
SI
f
ee
d
b
ac
k
m
ed
iu
m
is
e
x
p
lo
ited
o
f
a
m
as
s
i
v
e
MI
MO
s
y
s
te
m
u
s
i
n
g
co
m
p
r
es
s
io
n
s
e
n
s
i
n
g
b
ased
m
eth
o
d
s
in
a
f
ix
ed
d
o
m
ai
n
[
8
]
.
Gen
er
all
y
,
co
m
p
r
es
s
io
n
s
e
n
s
i
n
g
b
ased
ap
p
r
o
ac
h
es
u
tili
ze
s
s
p
atial
-
f
r
eq
u
e
n
c
y
d
o
m
ai
n
to
ac
q
u
ir
e
C
SI
ef
f
icie
n
tl
y
a
n
d
C
SI
esti
m
at
io
n
is
d
o
n
e
b
ased
o
n
th
e
ex
p
lo
itatio
n
o
f
s
p
atial
C
SI
co
r
r
elatio
n
.
B
esid
es,
s
p
atial
co
r
r
elatio
n
is
ac
h
ie
v
ed
b
y
p
lacin
g
an
ten
n
as
clo
s
er
to
ea
ch
o
th
er
at
B
S
in
a
m
as
s
iv
e
MI
MO
s
y
s
te
m
.
Ho
w
e
v
er
,
in
s
o
m
e
l
iter
atu
r
es
[
9
]
,
jo
in
t
s
p
ar
s
it
y
o
f
c
h
an
n
e
l
m
atr
ices
i
s
ex
p
l
o
ited
b
ased
o
n
th
e
co
m
m
o
n
lo
ca
l
s
ca
tter
s
.
As
a
r
esu
lt,
e
f
f
icie
n
t
co
m
p
r
ess
i
o
n
o
f
C
SI
m
atr
ice
s
is
ac
h
iev
ed
.
Ho
w
e
v
er
,
o
p
tim
izatio
n
p
r
o
b
le
m
o
cc
u
r
s
a
t
th
e
t
i
m
e
o
f
d
ec
o
m
p
r
es
s
io
n
w
ith
co
m
p
u
tatio
n
al
co
m
p
le
x
it
y
[
7
]
.
T
h
u
s
,
i
m
p
le
m
en
tatio
n
o
f
co
m
p
r
ess
io
n
s
e
n
s
i
n
g
b
ased
m
eth
o
d
s
i
n
r
ea
l ti
m
e
an
d
v
ar
ied
p
r
ac
tical
co
m
m
u
n
icatio
n
s
y
s
te
m
s
is
q
u
ite
ch
alle
n
g
in
g
a
n
d
d
if
f
ic
u
lt.
Ho
w
e
v
er
,
d
ee
p
lear
n
in
g
-
b
ase
d
ap
p
r
o
ac
h
es
h
a
v
e
o
b
s
er
v
ed
tr
e
m
en
d
o
u
s
g
r
o
w
t
h
i
n
v
ar
ied
f
ield
s
li
k
e
w
ir
ele
s
s
c
o
m
m
u
n
icatio
n
,
co
m
p
u
ti
n
g
,
an
d
s
ig
n
al
p
r
o
ce
s
s
in
g
[
1
0
]
,
[
1
1
]
.
Fu
r
th
er
m
o
r
e,
d
ee
p
lear
n
in
g
b
ased
ap
p
r
o
ac
h
es
h
a
v
e
s
h
o
w
n
tr
e
m
e
n
d
o
u
s
p
er
f
o
r
m
an
ce
in
th
e
f
ield
o
f
i
m
a
g
e
co
m
p
r
ess
io
n
as
w
ell
as
b
etter
s
u
ited
f
o
r
C
SI
f
ee
d
b
ac
k
m
ec
h
an
i
s
m
.
Fu
r
t
h
er
,
th
e
o
p
ti
m
izatio
n
p
r
o
b
lem
o
f
i
m
a
g
e
r
ec
o
n
s
tr
u
c
tio
n
is
ef
f
i
cien
tl
y
h
a
n
d
led
u
s
i
n
g
co
n
v
o
l
u
tio
n
al
la
y
er
s
i
n
d
ee
p
lear
n
in
g
b
ased
ap
p
r
o
ac
h
es.
B
esid
es,
a
m
ap
p
in
g
m
ec
h
a
n
i
s
m
w
it
h
r
esid
u
al
n
et
w
o
r
k
is
u
tili
ze
d
to
r
ef
in
e
esti
m
ated
C
SI
m
atr
ices
at
th
e
d
ec
o
d
er
s
id
e.
Fu
r
th
er
m
o
r
e,
h
i
g
h
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
ac
y
is
ac
h
iev
ed
u
s
i
n
g
d
ee
p
lear
n
in
g
b
ased
ap
p
r
o
ac
h
es
in
ter
m
s
o
f
C
SI
f
ee
d
b
ac
k
esti
m
atio
n
.
Ho
w
ev
er
,
tr
ad
itio
n
al
ap
p
r
o
ac
h
es
o
v
er
lo
o
k
th
e
ef
f
ec
t
o
f
q
u
an
tizat
io
n
p
r
o
ce
s
s
.
A
s
a
r
es
u
lt,
co
n
s
id
er
ab
le
er
r
o
r
s
ca
n
b
e
g
en
er
ated
in
a
p
r
ac
tical
co
m
m
u
n
icat
io
n
s
y
s
te
m
.
T
h
er
ef
o
r
e,
in
th
i
s
ar
ticle,
a
n
i
m
p
r
o
v
is
ed
d
ee
p
lear
n
i
n
g
-
b
ase
d
ch
an
n
el
e
s
ti
m
atio
n
(
I
D
L
C
E
)
m
o
d
el
is
p
r
o
p
o
s
ed
to
esti
m
a
te
ch
a
n
n
el
f
ee
d
b
ac
k
an
d
to
m
iti
g
ate
C
SI
f
ee
d
b
ac
k
o
v
er
h
ea
d
in
a
m
as
s
i
v
e
MI
MO
s
y
s
te
m
.
Fu
r
t
h
er
,
C
SI
e
s
ti
m
atio
n
a
n
d
c
h
an
n
el
o
v
er
h
ea
d
r
ed
u
ctio
n
ar
e
d
o
n
e
ef
f
icie
n
tl
y
u
s
i
n
g
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
e
f
f
icien
t
l
y
est
i
m
a
tes
C
SI
at
UE
an
d
t
h
e
n
f
ed
b
ac
k
to
B
S.
Mo
r
eo
v
er
,
a
clo
u
d
p
latf
o
r
m
is
u
tili
ze
d
to
esti
m
ate
C
SI
m
atr
i
ce
s
an
d
th
eir
ac
cu
r
ac
y
i
n
ter
m
s
o
f
n
o
r
m
alize
d
m
ea
n
s
q
u
ar
e
er
r
o
r
(
NM
SE)
an
d
co
r
r
elatio
n
r
esu
lt
s
b
et
w
ee
n
u
p
lin
k
a
n
d
d
o
w
n
li
n
k
ch
a
n
n
els
.
T
h
e
p
r
o
p
o
s
ed
I
D
L
C
E
m
o
d
el
p
r
o
v
id
e
m
as
s
iv
e
s
tr
en
g
th
to
w
ar
d
s
i
m
p
le
m
e
n
tat
io
n
o
f
5
G
w
ir
eless
m
o
b
ile
n
et
w
o
r
k
.
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
i
m
p
r
o
v
e
s
b
an
d
w
id
t
h
u
tili
za
tio
n
as
w
el
l
as
s
p
ec
tr
al
ef
f
icie
n
c
y
.
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
is
u
tili
ze
d
to
ex
p
lo
it
d
o
w
n
l
in
k
C
SI
f
ee
d
b
ac
k
m
ed
iu
m
b
a
s
ed
o
n
t
h
e
C
SI
esti
m
ated
at
th
e
u
p
lin
k
m
ed
iu
m
at
s
o
u
r
ce
a
n
d
UE
,
r
esp
ec
tiv
el
y
.
F
u
r
t
h
er
,
lar
g
e
tr
ain
in
g
d
atab
ase
is
u
t
ilized
to
s
tu
d
y
ch
a
n
n
el
f
ee
d
b
ac
k
m
atr
ices
u
s
i
n
g
d
ee
p
lear
n
in
g
-
b
ased
ap
p
r
o
ac
h
.
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
p
r
o
v
id
es
b
etter
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
a
c
y
an
d
er
r
o
r
r
ed
u
ctio
n
a
n
d
co
m
p
ar
ed
ag
ai
n
s
t
d
i
f
f
er
en
t
C
SI
f
ee
d
b
ac
k
ap
p
r
o
ac
h
es
b
ased
o
n
v
ar
ied
p
er
f
o
r
m
a
n
ce
m
atr
ices
s
u
c
h
as NM
SE
a
n
d
co
s
in
e
s
i
m
ilar
it
y
.
T
h
is
p
ap
er
is
o
r
g
an
i
s
ed
i
n
f
o
ll
o
w
i
n
g
s
t
y
le.
Sec
tio
n
2
,
d
escr
i
b
es
ab
o
u
t
t
h
e
r
elate
d
w
o
r
k
d
e
m
o
n
s
tr
ated
r
eg
a
r
d
in
g
m
a
s
s
i
v
e
MI
MO
s
y
s
te
m
a
n
d
th
e
p
r
o
b
le
m
s
a
s
s
o
cia
te
to
it
an
d
s
o
l
u
tio
n
s
to
s
o
r
t
t
h
o
s
e
p
r
o
b
le
m
s
w
i
th
th
e
h
elp
o
f
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el.
Sectio
n
3
,
d
is
cu
s
s
es
ab
o
u
t
th
e
m
at
h
e
m
atica
l
m
o
d
ell
in
g
u
t
ilized
in
p
r
o
p
o
s
ed
I
DL
C
E
f
o
r
C
SI
f
e
ed
b
ac
k
esti
m
at
io
n
.
Sectio
n
4
d
escr
ib
es
ab
o
u
t
ex
p
er
im
e
n
ta
l
r
esu
lts
a
n
d
th
eir
co
m
p
ar
is
o
n
ag
ai
n
s
t tr
ad
itio
n
al
C
SI
f
ee
d
b
ac
k
ap
p
r
o
ac
h
es a
n
d
s
ec
tio
n
5
co
n
clu
d
es t
h
e
p
ap
er
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
-
4864
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
,
Vo
l.
13
,
No
.
3
,
No
v
e
m
b
er
20
24
:
786
-
798
788
2.
RE
L
AT
E
D
WO
RK
Ma
s
s
i
v
e
MI
MO
s
y
s
te
m
i
s
m
ain
l
y
u
til
ized
in
5
G
m
o
b
ile
c
ellu
lar
s
y
s
t
e
m
an
d
b
e
y
o
n
d
ap
p
licatio
n
s
.
Her
e,
B
S
in
th
e
m
as
s
iv
e
M
I
MO
s
y
s
te
m
is
eq
u
ip
p
ed
w
i
th
m
u
l
tip
le
an
te
n
n
as
a
n
d
C
S
I
r
ec
o
n
s
tr
u
ctio
n
i
s
ac
q
u
ir
ed
at
UE
an
d
f
ed
b
ac
k
to
th
e
B
S.
T
h
u
s
,
d
ee
p
lear
n
in
g
-
b
a
s
ed
m
o
d
el
is
th
e
b
est
s
u
i
tab
le
ap
p
r
o
ac
h
f
o
r
ef
f
icien
t
C
SI
r
ec
o
n
s
tr
u
cti
o
n
.
As
a
r
esu
lt,
s
ev
er
al
r
esear
ch
er
s
h
a
v
e
p
r
o
v
id
ed
th
e
ir
m
as
s
i
v
e
ef
f
o
r
ts
to
en
s
u
r
e
an
ef
f
icien
t
C
SI
r
ec
o
n
s
tr
u
ctio
n
tech
n
iq
u
e
u
s
i
n
g
m
ac
h
in
e
lear
n
in
g
,
clo
u
d
co
m
p
u
ti
n
g
o
r
d
ee
p
lear
n
in
g
m
eth
o
d
s
.
Ho
w
e
v
er
,
d
u
e
to
p
r
esen
ce
o
f
ch
an
n
el
er
r
o
r
s
,
th
e
r
ec
o
n
s
tr
u
c
tio
n
ac
cu
r
ac
y
g
e
ts
af
f
ec
ted
as
w
ell
as
tr
ad
itio
n
al
ch
an
n
el
es
t
i
m
at
io
n
tech
n
iq
u
e
s
h
av
e
h
i
g
h
c
h
an
n
el
o
v
er
h
ea
d
.
T
h
u
s
,
s
ev
er
al
r
esear
c
h
er
s
h
av
e
p
r
o
v
id
ed
th
eir
ef
f
o
r
t
s
in
t
h
is
d
o
m
a
in
to
s
t
u
d
y
th
e
m
e
n
tio
n
ed
ch
alle
n
g
es a
n
d
p
r
o
v
id
e
s
o
lu
tio
n
s
to
co
u
n
ter
th
o
s
e
p
r
o
b
lem
s
.
Ac
co
r
d
in
g
to
W
ei
et
a
l.
[
1
2
]
,
a
lo
w
r
an
k
ap
p
r
o
x
i
m
atio
n
-
b
a
s
ed
co
m
p
r
es
s
io
n
m
o
d
el
is
p
r
esen
ted
to
en
h
a
n
ce
C
SI
esti
m
atio
n
ac
cu
r
ac
y
i
n
m
as
s
iv
e
MI
MO
s
y
s
te
m
s
.
Her
e,
a
C
SI
f
ee
d
b
ac
k
m
eth
o
d
is
ad
o
p
ted
to
i
m
p
r
o
v
e
C
SI
p
r
ec
is
io
n
a
n
d
ch
an
n
el
o
v
er
h
ea
d
r
ed
u
ct
io
n
ef
f
icie
n
c
y
.
F
u
r
th
er
m
o
r
e,
ch
an
n
el
m
atr
i
x
ch
ar
ac
ter
is
tic
s
ar
e
u
tili
ze
d
to
r
ec
o
n
s
tr
u
ct
C
SI
f
ee
d
b
ac
k
an
d
co
m
p
r
es
s
io
n
r
esu
lts
ar
e
h
ea
v
il
y
i
m
p
r
o
v
ed
.
A
cc
o
r
d
in
g
to
C
h
o
i
et
a
l.
[
1
3
]
,
a
d
etailed
in
v
es
tig
a
tio
n
o
n
ch
an
n
el
m
atr
ice
s
i
s
co
n
d
u
cte
d
in
Z
er
o
-
Feed
b
ac
k
FDD
s
y
s
te
m
s
.
T
h
e
f
ea
s
ib
il
it
y
o
f
f
r
eq
u
e
n
c
y
d
o
m
ai
n
ch
a
n
n
el
m
atr
ices
i
s
u
tili
ze
d
to
esti
m
at
e
p
ar
am
eter
s
.
Her
e,
v
ec
to
r
s
p
atial
s
ig
n
at
u
r
e
(
VSS)
m
o
d
el
is
u
tili
ze
d
to
ac
q
u
ir
e
ch
an
n
el
d
ata
an
d
to
i
m
p
r
o
v
e
s
p
ec
tr
al
ef
f
icie
n
c
y
b
y
r
ed
u
cin
g
NM
SE
er
r
o
r
s
.
T
h
e
p
er
f
o
r
m
an
ce
is
m
ea
s
u
r
ed
i
n
te
r
m
s
o
f
b
ea
m
f
o
r
m
i
n
g
ef
f
ic
ien
c
y
an
d
NM
SE.
A
cc
o
r
d
in
g
to
Ma
et
a
l.
[
1
4
]
,
a
ch
an
n
el
es
ti
m
atio
n
a
n
d
f
ee
d
b
ac
k
m
ec
h
a
n
is
m
i
s
in
tr
o
d
u
ce
d
u
s
in
g
d
ee
p
lear
n
in
g
-
b
ased
m
o
d
el
to
r
ed
u
ce
th
e
u
p
l
in
k
p
ilo
t
o
v
er
h
ea
d
i
n
a
m
illi
m
etr
e
-
w
av
e
(
m
m
-
W
a
v
e)
s
y
s
te
m
s
.
T
h
is
m
ec
h
a
n
i
s
m
is
u
t
i
lized
to
j
o
in
tl
y
tr
ai
n
th
e
p
h
ase
s
h
i
f
t
n
et
w
o
r
k
a
n
d
s
tr
u
c
tu
r
ed
s
p
ar
s
it
y
o
f
c
h
an
n
el
i
s
e
x
p
lo
ited
to
i
m
p
r
o
v
e
d
o
w
n
l
in
k
ch
a
n
n
el
esti
m
atio
n
ef
f
ic
ien
c
y
.
A
cc
o
r
d
in
g
to
L
e
e
et
a
l.
[
1
5
]
,
a
d
o
w
n
li
n
k
c
h
a
n
n
el
r
ec
o
n
s
tr
u
ct
io
n
tech
n
iq
u
e
i
s
p
r
esen
ted
to
h
an
d
le
Sp
atial
Mu
lt
i
p
lex
i
n
g
in
a
m
as
s
i
v
e
MI
MO
s
y
s
te
m
to
i
m
p
r
o
v
e
u
p
li
n
k
an
d
d
o
w
n
l
in
k
ch
a
n
n
el
r
ec
ip
r
o
cit
y
.
Her
e,
d
o
w
n
li
n
k
-
b
ased
C
SI
r
ef
er
en
ce
s
ig
n
al
s
ar
e
u
tili
ze
d
to
s
p
ec
tr
al
ef
f
icie
n
c
y
an
d
co
m
p
lex
it
y
is
s
u
e
s
.
Si
m
u
latio
n
r
es
u
lts
ar
e
d
e
m
o
n
s
tr
at
ed
in
ter
m
s
o
f
s
p
ec
tr
al
e
f
f
ic
ien
c
y
a
n
d
c
h
an
n
el
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
ac
y
.
A
cc
o
r
d
in
g
to
Ki
m
an
d
C
h
o
i
[
1
6
]
,
a
ch
an
n
el
esti
m
atio
n
tech
n
i
q
u
e
is
p
r
esen
ted
to
co
u
n
ter
c
h
an
n
el
er
r
o
r
s
i
n
t
h
e
m
m
-
W
a
v
e
m
as
s
iv
e
MI
MO
s
y
s
te
m
s
.
Her
e,
an
a
lo
g
-
to
-
d
i
g
ital
co
n
v
er
ter
s
(
A
D
C
s
)
ar
e
u
s
ed
to
esti
m
ate
w
id
e
-
b
an
d
ch
an
n
e
l
ef
f
icie
n
c
y
.
Her
e,
in
ter
-
u
s
er
an
d
in
ter
-
f
r
a
m
e
i
n
ter
f
er
en
ce
is
m
i
tig
a
ted
b
ased
o
n
th
e
ch
a
n
n
el
p
ar
a
m
et
er
s
an
d
m
a
x
i
m
u
m
a
p
o
s
ter
io
r
i
(
MA
P
)
cr
iter
io
n
.
A
cc
o
r
d
in
g
t
o
Gu
o
et
a
l.
[
1
7
]
,
a
co
m
p
r
es
s
io
n
s
e
n
s
i
n
g
b
ased
m
o
d
el
is
in
tr
o
d
u
ce
d
to
co
m
p
r
ess
an
d
q
u
an
tize
C
SI
m
atr
ices
in
a
co
n
v
o
lu
tio
n
a
l
n
eu
r
al
n
et
w
o
r
k
(
C
NN)
b
ased
ar
ch
itect
u
r
e
in
a
m
a
s
s
iv
e
M
I
MO
s
y
s
te
m
.
Her
e,
a
n
o
v
el
t
r
ain
in
g
ap
p
r
o
ac
h
i
s
ad
o
p
ted
to
r
ed
u
ce
p
a
r
am
eter
s
b
y
3
8
.
0
%
an
d
4
6
.
7
%
an
d
i
m
p
r
o
v
e
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
a
c
y
.
T
h
is
tech
n
iq
u
e
o
u
tp
er
f
o
r
m
s
tr
ad
itio
n
al
ap
p
r
o
ac
h
es
in
ter
m
s
o
f
p
ar
a
m
eter
r
ed
u
ctio
n
an
d
co
m
p
r
ess
io
n
ef
f
ici
en
c
y
.
A
cc
o
r
d
in
g
to
Ye
et
a
l.
[
1
8
]
,
a
d
ee
p
lea
r
n
in
g
-
b
ased
m
o
d
el
is
ad
o
p
t
ed
to
d
e
-
n
o
is
e
ch
a
n
n
el
m
a
t
r
ices
an
d
i
m
p
r
o
v
e
r
ec
o
n
s
tr
u
ctio
n
ac
c
u
r
ac
y
i
n
m
ass
i
v
e
MI
MO
s
y
s
te
m
s
.
T
h
is
t
ec
h
n
iq
u
e
e
n
h
a
n
ce
s
C
SI
f
ee
d
b
a
ck
e
f
f
ic
ie
n
c
y
i
n
a
FDD
d
o
m
ai
n
.
A
p
er
f
ec
t
C
SI
f
ee
d
b
ac
k
m
ec
h
an
is
m
i
s
ad
o
p
ted
to
co
m
p
r
ess
C
SI
i
n
to
a
co
d
e
w
o
r
d
.
A
cc
o
r
d
in
g
to
Gk
o
n
i
s
et
a
l.
[
1
9
]
,
a
d
etailed
s
u
r
v
e
y
i
s
p
r
esen
ted
o
n
th
e
ch
alle
n
g
e
s
an
d
f
ea
s
ib
ili
t
y
o
f
5
G
w
ir
eles
s
m
o
b
ile
n
et
w
o
r
k
s
b
y
est
i
m
a
tin
g
C
SI
m
atr
ices
in
a
m
a
s
s
i
v
e
MI
MO
s
y
s
te
m
.
T
h
is
s
u
r
v
e
y
p
r
o
v
id
es
k
n
o
w
led
g
e
o
f
h
y
b
r
id
an
alo
g
-
d
ig
ital p
r
ec
o
d
in
g
m
ec
h
an
is
m
,
m
u
l
tip
le
ac
ce
s
s
s
y
s
te
m
,
an
d
w
ir
eles
s
i
n
f
o
r
m
atio
n
.
Dif
f
er
en
t
v
ie
w
s
ar
e
p
r
esen
te
d
in
th
e
m
e
n
tio
n
ed
liter
atu
r
es
f
r
o
m
v
ar
io
u
s
r
esear
ch
er
s
ab
o
u
t
C
SI
f
ee
d
b
ac
k
r
ec
o
n
s
tr
u
ctio
n
u
s
i
n
g
v
ar
ied
tech
n
iq
u
es
li
k
e
d
ee
p
lear
n
in
g
;
C
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ased
m
o
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el
an
d
m
as
s
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e
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MO
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d
mm
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te
m
s
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ter
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t,
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ter
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h
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s
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el
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C
E
m
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el
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O
DE
L
L
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N
G
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I
DL
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T
h
is
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tio
n
d
e
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cr
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es
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o
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th
e
s
y
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te
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m
o
d
ellin
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p
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p
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ed
I
DL
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m
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el
to
est
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m
ate
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S
I
f
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d
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h
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ased
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m
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el
w
it
h
a
q
u
an
tizat
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lo
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h
is
tech
n
iq
u
e
e
n
h
a
n
ce
s
C
SI
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ee
d
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k
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cu
r
ac
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in
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u
ltip
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o
m
ai
n
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lear
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m
o
d
el
is
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tili
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d
to
ac
q
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ir
e
ch
an
n
el
d
ata
an
d
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im
p
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e
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tr
al
ef
f
icie
n
c
y
b
y
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ed
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c
in
g
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er
r
o
r
s
.
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v
io
u
s
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k
s
h
o
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s
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h
at
a
d
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i
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ite
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r
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r
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et
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ee
n
u
p
li
n
k
a
n
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d
o
w
n
l
in
k
m
ed
i
u
m
.
T
h
u
s
,
u
tili
za
t
io
n
o
f
u
p
li
n
k
C
SI
m
atr
i
ce
s
ac
q
u
ir
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o
m
a
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ca
n
p
o
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ib
l
y
e
n
h
an
ce
t
h
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e
s
ti
m
atio
n
e
f
f
ic
ien
c
y
o
f
d
o
w
n
li
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C
SI
m
atr
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s
.
Her
e,
Fig
u
r
e
1
d
em
o
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s
tr
ates
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lo
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iag
r
a
m
o
f
p
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ed
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DL
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el.
Fu
r
t
h
er
,
a
d
etailed
m
a
th
e
m
atica
l r
ep
r
ese
n
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ed
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DL
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o
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el
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r
esen
ted
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a
f
o
llo
w
i
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p
ar
ag
r
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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f
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ed
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A
m
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n
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eq
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ip
p
ed
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e
v
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s
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it
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n
te
n
n
a
s
as
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b
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1
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h
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s
UE
is
eq
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ip
p
ed
w
it
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s
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le
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er
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te
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n
a.
Her
e,
o
r
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a
l
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r
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en
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d
i
v
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u
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ed
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er
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,
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r
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n
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ch
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S
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at
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m
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h
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d
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li
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SI
m
atr
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r
e
r
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r
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ted
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t
h
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atr
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a
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e
m
atr
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x
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o
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m
in
a
s
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r
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e
n
c
y
d
o
m
ai
n
u
s
in
g
f
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w
in
g
(
4
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.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
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I
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d
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4
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p
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tio
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×
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b
,
r
esp
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y
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e,
d
o
w
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li
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h
a
n
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ti
m
ated
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y
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d
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ated
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ed
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ac
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S.
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it
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m
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ee
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ar
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eter
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tili
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ed
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a
s
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t
e
m
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n
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er
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n
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b
R
g
.
Ho
w
e
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er
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ese
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ee
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ir
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n
al
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th
e
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u
m
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er
o
f
a
n
te
n
n
as.
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h
e
u
til
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o
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s
s
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e
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ee
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ac
k
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ar
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eter
s
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e
b
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s
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m
i
n
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m
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e
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y
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m
.
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a
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lt,
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n
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el
f
ee
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n
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h
u
s
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n
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e
r
h
ea
d
r
ed
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ctio
n
is
ac
h
ie
v
ed
b
y
u
tili
zi
n
g
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SI
s
p
ar
s
it
y
i
n
th
e
d
ela
y
d
o
m
ain
.
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r
t
h
er
m
o
r
e
,
in
v
er
s
e
d
is
cr
ete
Fo
u
r
ier
tr
an
s
f
o
r
m
(
I
DFT
)
is
u
s
ed
to
g
et
C
SI
m
atr
ices
in
an
g
u
lar
-
d
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y
d
o
m
ai
n
f
r
o
m
t
h
e
C
SI
m
atr
ices o
f
s
p
ati
al
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r
eq
u
en
c
y
d
o
m
ai
n
,
w
h
ich
c
an
b
e
ex
p
r
ess
ed
u
s
in
g
f
o
llo
w
i
n
g
(
5
)
.
=
(
5
)
W
h
er
e,
G
is
a
DFT
m
atr
i
x
w
it
h
a
s
in
g
u
lar
r
o
w
an
d
is
p
r
o
p
o
r
tio
n
al
to
G
∈
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g
×
R
g
w
h
er
ea
s
G
N
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an
R
g
×
R
g
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n
itar
y
m
atr
i
x
,
r
esp
ec
tiv
el
y
.
Af
ter
I
DFT
ap
p
lied
in
(
3
)
,
m
o
s
t
o
f
th
e
ele
m
e
n
t
s
h
a
v
e
al
m
o
s
t
ze
r
o
-
v
al
u
e
d
u
e
to
t
h
e
h
ig
h
s
p
ar
s
it
y
o
f
m
ass
iv
e
MI
MO
s
y
s
t
e
m
i
n
R
b
×
R
g
ch
an
n
el
m
atr
i
x
m
a
tr
ix
N
m
o
f
t
h
e
an
g
u
lar
d
ela
y
d
o
m
ai
n
ex
ce
p
t
f
ir
s
t
r
o
w
V
g
ele
m
e
n
ts
.
Her
e,
th
e
ele
m
e
n
ts
o
f
f
ir
s
t
r
o
w
V
g
h
as
u
n
i
q
u
e
n
o
n
-
ze
r
o
v
alu
e
s
.
T
h
u
s
,
ch
a
n
n
el
m
atr
i
x
r
o
w
s
g
et
tr
u
n
ca
ted
i
n
to
a
s
in
g
le
-
r
o
w
m
atr
ix
d
ir
ec
tl
y
.
T
h
u
s
,
th
e
d
o
w
n
li
n
k
ch
an
n
el
m
atr
i
x
N
q
an
d
u
p
lin
k
ch
a
n
n
el
m
atr
ix
N
w
ar
e
d
en
o
ted
as
N
̃
q
an
d
N
̃
w
af
ter
I
DFT
p
r
o
ce
s
s
w
ith
a
s
in
g
le
r
o
w
V
g
d
is
tin
c
t
n
o
n
-
ze
r
o
v
al
u
es,
r
esp
ec
tiv
el
y
.
Fu
r
t
h
er
m
o
r
e
,
th
e
co
d
e
w
o
r
d
s
ar
e
en
co
d
ed
to
co
m
p
r
ess
d
i
m
en
s
io
n
s
o
f
d
o
w
n
li
n
k
C
SI
m
atr
ices
a
n
d
lo
w
-
b
it
en
co
d
i
n
g
is
p
er
f
o
r
m
ed
.
A
s
a
r
es
u
lt,
r
ed
u
n
d
an
c
y
ca
n
b
e
m
iti
g
ated
.
B
esid
es,
th
e
class
i
ca
l
C
SI
m
e
th
o
d
s
u
til
izes
o
n
l
y
e
n
co
d
er
-
d
ec
o
d
er
m
ec
h
a
n
i
s
m
,
en
co
d
er
is
u
s
ed
to
co
m
p
r
es
s
co
d
e
w
o
r
d
s
,
an
d
d
e
co
d
er
is
u
tili
ze
d
to
r
ec
o
v
er
th
o
s
e
co
d
e
w
o
r
d
s
,
r
esp
ec
tiv
el
y
.
T
h
u
s
,
a
q
u
an
tizer
b
lo
ck
is
ad
d
ed
to
th
e
en
co
d
er
s
y
s
te
m
to
j
o
in
tl
y
co
m
p
r
ess
an
d
q
u
an
tize
co
d
e
w
o
r
d
s
ef
f
i
cien
tl
y
.
T
h
er
ef
o
r
e,
d
o
w
n
l
in
k
C
SI
f
ee
d
b
ac
k
m
ed
i
u
m
i
n
FDD
s
y
s
te
m
co
n
tain
s
t
h
r
ee
b
lo
ck
s
to
r
ec
o
n
s
tr
u
c
t c
h
an
n
el
m
atr
ice
s
s
u
c
h
a
s
en
co
d
er
b
lo
ck
,
q
u
an
tizer
b
lo
ck
an
d
d
ec
o
d
er
b
l
o
ck
.
E
x
ac
tl
y
,
a
DL
-
b
ased
ch
a
n
n
el
e
s
ti
m
atio
n
f
r
a
m
e
w
o
r
k
is
d
esig
n
ed
to
j
o
in
tl
y
co
m
p
r
ess
d
i
m
en
s
io
n
s
ef
f
icie
n
tl
y
a
n
d
q
u
an
tize
C
SI
m
atr
ices
at
t
h
e
r
ec
eiv
er
s
id
e.
T
h
e
r
ec
o
n
s
tr
u
ctio
n
o
f
co
d
e
w
o
r
d
s
is
p
er
f
o
r
m
ed
at
t
h
e
tr
a
n
s
m
itter
s
id
e
to
o
p
ti
m
ize
C
SI
c
h
an
n
el
m
atr
i
x
r
ec
o
n
s
tr
u
ctio
n
.
T
h
er
ef
o
r
e,
j
o
i
n
t
o
p
ti
m
iza
tio
n
o
f
co
m
p
r
e
s
s
i
o
n
an
d
q
u
a
n
tizatio
n
is
p
er
f
o
r
m
ed
u
s
in
g
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
in
a
m
as
s
i
v
e
MI
MO
s
y
s
te
m
.
Her
e,
r
ec
o
v
er
ed
d
o
w
n
l
in
k
ch
a
n
n
el
m
atr
i
x
is
ex
p
r
ess
ed
b
y
N
̂
q
an
d
g
i
(
∙
)
d
en
o
tes
q
u
an
tizatio
n
f
u
n
ct
i
o
n
.
T
h
en
,
en
co
d
er
b
lo
ck
,
q
u
an
t
izer
b
lo
ck
an
d
d
ec
o
d
er
b
lo
ck
u
s
i
n
g
a
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
ar
e
ex
p
r
ess
ed
b
y
f
o
llo
w
in
g
(
6
)
to
(
8
)
.
1
=
,
1
(
)
(
6
)
̂
1
=
,
1
(
1
)
(
7
)
̂
=
,
1
(
̂
1
)
(8
)
Si
m
i
lar
l
y
,
e
n
co
d
er
b
lo
ck
,
q
u
a
n
tizer
b
lo
ck
a
n
d
d
ec
o
d
er
b
lo
ck
u
s
i
n
g
a
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
co
n
s
id
er
i
n
g
Dua
l
N
e
t
−
IM
a
g
ar
ch
itectu
r
e
is
ex
p
r
ess
ed
b
y
f
o
llo
w
i
n
g
(
9
)
to
(
1
1
)
.
2
=
,
2
(
)
(
9
)
̂
2
=
,
2
(
2
)
(
1
0
)
̂
=
,
2
(
̂
2
,
)
(
1
1
)
Fu
r
t
h
er
m
o
r
e,
th
e
d
o
w
n
li
n
k
C
S
I
f
ee
d
b
ac
k
c
h
an
n
el
m
atr
i
x
i
s
o
p
ti
m
ized
b
y
m
i
n
i
m
izi
n
g
‖
N
q
−
N
̂
q
‖
2
an
d
f
u
n
ctio
n
‖
∙
‖
is
r
ep
r
esen
ted
as Fr
o
b
en
iu
s
n
o
r
m
al
izatio
n
.
3
.
1
.
Dee
p
lea
rning
-
ba
s
ed
cha
nn
el
s
t
a
t
e
info
r
m
a
t
io
n f
ee
d
ba
ck
m
o
del
T
h
e
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
-
b
ased
C
SI
f
ee
d
b
ac
k
m
o
d
el
ac
h
i
ev
es
co
n
s
id
er
ab
le
p
er
f
o
r
m
an
c
e
g
ain
an
d
y
ield
b
y
r
ed
u
cin
g
ch
a
n
n
e
l
f
e
ed
b
ac
k
o
v
er
h
ea
d
an
d
en
h
a
n
c
e
m
en
t
o
f
d
o
w
n
li
n
k
C
SI
f
ee
d
b
ac
k
r
ec
o
n
s
tr
u
ctio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
C
h
a
n
n
el
r
ec
o
n
s
tr
u
ctio
n
th
r
o
u
g
h
imp
r
o
vised
d
ee
p
le
a
r
n
in
g
a
r
ch
itectu
r
e
…
(
P
a
r
in
ith
a
Ja
ya
s
h
a
n
ka
r
)
791
ac
cu
r
ac
y
.
Fu
r
t
h
er
m
o
r
e,
p
o
ten
t
ial
b
en
e
f
its
o
f
e
n
co
d
er
b
lo
ck
ar
e
ex
p
lo
ited
to
co
m
p
r
ess
ch
an
n
el
m
atr
ices
a
n
d
alo
n
g
w
it
h
t
h
at,
a
q
u
an
tizatio
n
b
lo
ck
is
ad
o
p
ted
to
o
b
s
er
v
e
a
d
r
asti
c
i
m
p
r
o
v
e
m
e
n
t
i
n
ter
m
s
o
f
co
m
p
r
ess
io
n
ac
cu
r
ac
y
i
n
a
m
ass
iv
e
MI
MO
s
y
s
te
m
.
T
h
er
ef
o
r
e,
f
ir
s
tl
y
,
an
a
l
y
s
i
s
o
f
q
u
a
n
tizatio
n
e
f
f
ec
t
o
n
th
e
len
g
th
o
f
co
d
e
w
o
r
d
is
d
o
n
e
to
ac
h
iev
e
h
ig
h
C
SI
r
ec
o
n
s
tr
u
ct
io
n
ac
cu
r
ac
y
b
y
u
s
i
n
g
a
g
en
er
alize
d
u
n
i
f
o
r
m
q
u
an
tizer
.
Fu
r
th
er
,
a
u
n
if
o
r
m
q
u
a
n
tizer
i
s
ad
d
ed
w
it
h
th
e
co
m
p
r
ess
io
n
b
lo
ck
a
n
d
th
e
q
u
a
n
tized
C
SI
m
atr
ice
s
ar
e
tr
an
s
m
itted
to
th
e
d
ec
o
d
er
b
lo
ck
to
o
b
s
er
v
e
q
u
an
t
izati
o
n
e
f
f
ec
t.
Her
e,
Fig
u
r
e
2
d
em
o
n
s
tr
ates a
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
-
b
ased
C
SI
f
ee
d
b
ac
k
m
o
d
el
w
i
th
a
q
u
an
t
izatio
n
b
lo
c
k
.
T
h
e
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
-
b
ased
C
SI
f
e
ed
b
ac
k
ar
ch
itect
u
r
e
co
n
tain
s
co
n
v
o
lu
tio
n
la
y
er
s
a
n
d
f
u
ll
y
li
n
k
ed
la
y
er
s
in
a
n
e
n
co
d
er
n
et
w
o
r
k
to
co
m
p
r
ess
d
i
m
en
s
io
n
s
o
f
C
SI
m
atr
ices
w
h
er
ea
s
d
ec
o
d
er
n
et
w
o
r
k
co
n
tain
s
f
u
l
l
y
li
n
k
ed
la
y
er
s
an
d
r
esid
u
al
b
lo
ck
s
to
d
ec
o
m
p
r
es
s
co
d
e
w
o
r
d
s
an
d
r
ec
o
n
s
tr
u
ct
d
o
w
n
li
n
k
C
S
I
ch
an
n
el
m
atr
ices,
r
esp
ec
ti
v
el
y
.
Fu
r
t
h
er
,
ev
er
y
r
esid
u
al
b
lo
ck
co
n
tain
s
to
tal
n
u
m
b
er
o
f
th
r
ee
co
n
v
o
lu
tio
n
al
la
y
er
s
.
Her
e,
p
r
o
p
o
s
e
d
m
u
l
tila
y
er
(
ML
Net)
ar
ch
it
ec
tu
r
e
ex
p
lo
its
t
h
e
co
r
r
elatio
n
o
f
m
ag
n
it
u
d
e
co
ef
f
icie
n
ts
e
v
alu
a
ted
b
et
w
ee
n
u
p
lin
k
an
d
d
o
w
n
li
n
k
m
ed
iu
m
to
en
h
a
n
ce
d
o
w
n
li
n
k
C
SI
f
ee
d
b
ac
k
ac
cu
r
ac
y
a
s
d
em
o
n
s
tr
ated
in
Fi
g
u
r
e
3
.
Fu
r
th
er
m
o
r
e,
th
e
p
r
o
p
o
s
e
d
ML
Net
ar
ch
itect
u
r
e
p
er
f
o
r
m
ph
ase
an
d
m
ag
n
it
u
d
e
s
ep
ar
atio
n
d
is
tin
ct
l
y
.
On
ce
p
h
a
s
e
an
d
m
ag
n
it
u
d
e
s
ep
ar
atio
n
ar
e
p
er
f
o
r
m
ed
,
th
en
co
r
r
elate
d
m
a
g
n
it
u
d
e
co
ef
f
icie
n
ts
ar
e
tr
an
s
m
i
tted
to
t
h
e
e
n
co
d
er
n
et
w
o
r
k
at
UE
,
w
h
ich
co
n
tain
s
co
n
v
o
lu
tio
n
al
la
y
er
s
an
d
f
u
ll
y
li
n
k
ed
la
y
er
s
.
T
h
en
,
th
e
d
ec
o
d
er
n
et
w
o
r
k
is
u
tili
ze
d
to
r
ec
eiv
e
co
m
p
r
ess
ed
co
d
e
w
o
r
d
s
at
th
e
BS
an
d
C
SI
m
a
g
n
itu
d
e
co
ef
f
icien
t
s
o
f
u
p
lin
k
m
ed
i
u
m
is
u
tili
ze
d
to
g
et
h
er
w
it
h
th
e
s
e
r
ec
eiv
ed
co
m
p
r
es
s
ed
co
d
e
w
o
r
d
s
.
T
h
en
,
f
u
ll
y
lin
k
e
d
la
y
er
is
u
t
ilized
to
co
n
v
er
t
c
o
m
p
r
es
s
ed
co
d
e
w
o
r
d
s
i
n
to
t
h
e
o
r
ig
in
al
le
n
g
t
h
o
f
co
d
e
w
o
r
d
s
.
T
h
en
,
b
o
th
C
SI
m
ag
n
it
u
d
e
co
ef
f
icie
n
ts
o
f
u
p
l
in
k
m
ed
i
u
m
a
n
d
d
o
w
n
lin
k
C
SI
co
ef
f
icie
n
t
s
ar
e
co
m
b
i
n
ed
to
g
et
h
er
u
s
i
n
g
co
n
v
o
lu
tio
n
al
la
y
er
s
to
co
m
p
u
te
o
u
tp
u
t.
T
h
e
n
,
th
e
o
b
tain
ed
o
u
tp
u
t
i
s
r
ef
o
r
m
ed
i
n
to
f
ea
t
u
r
e
m
ap
s
.
T
h
u
s
,
t
h
e
r
es
u
lt
a
n
t
f
ea
tu
r
e
m
ap
s
ar
e
f
ed
i
n
to
th
e
r
esid
u
al
b
lo
ck
s
to
r
ec
o
n
s
t
r
u
ct
d
o
w
n
li
n
k
C
SI
m
atr
ices.
F
u
r
t
h
er
m
o
r
e,
a
m
ag
n
i
tu
d
e
-
p
h
ase
q
u
an
tizatio
n
m
et
h
o
d
is
in
tr
o
d
u
ce
d
to
m
i
n
i
m
ize
b
an
d
w
id
t
h
u
tili
za
t
io
n
w
ith
leas
t
q
u
an
tiza
tio
n
er
r
o
r
s
.
T
h
u
s
,
t
h
e
C
SI
m
a
g
n
i
tu
d
e
co
ef
f
icien
ts
w
it
h
h
ig
h
er
v
alu
e
s
p
r
o
v
id
e
g
r
ea
ter
p
h
ase
q
u
a
n
tizatio
n
.
Si
m
ilar
l
y
,
h
i
g
h
er
p
h
ase
co
ef
f
icie
n
ts
r
ec
ei
v
e
lar
g
er
C
SI
m
a
g
n
i
tu
d
e
v
alu
es.
Fu
r
t
h
er
m
o
r
e,
u
n
i
f
o
r
m
q
u
an
ti
za
tio
n
p
r
o
ce
s
s
i
s
a
s
i
m
p
le
a
n
d
g
e
n
er
alize
d
r
o
u
n
d
in
g
p
r
o
ce
d
u
r
e
an
d
u
tili
ze
d
in
v
ar
ied
en
v
ir
o
n
m
e
n
t
s
.
Her
e,
ev
er
y
v
al
u
e
o
f
ch
a
n
n
el
co
ef
f
icie
n
t
is
r
o
u
n
d
ed
to
clo
s
est
o
n
e
u
s
i
n
g
th
e
r
o
u
n
d
in
g
m
e
th
o
d
w
ith
v
ar
ied
q
u
an
tizatio
n
lev
el
s
.
T
h
en
,
th
e
m
a
g
n
itu
d
e
o
f
C
SI
co
ef
f
icien
ts
is
n
o
r
m
al
ized
b
et
w
ee
n
ar
e
n
o
r
m
alize
d
b
et
w
e
en
t
h
e
i
n
ter
v
a
ls
[
b
m
ax
,
b
m
i
n
]
.
C
o
n
s
id
er
th
at
,
n
u
m
b
er
o
f
b
its
ar
e
d
en
o
ted
a
s
d
in
th
e
p
r
o
ce
s
s
o
f
m
ag
n
it
u
d
e
n
o
r
m
aliza
tio
n
.
E
v
er
y
m
a
g
n
it
u
d
e
v
alu
e
o
f
C
SI
co
ef
f
icien
t
is
q
u
a
n
tized
in
to
2
d
lev
el
s
in
u
n
i
f
o
r
m
m
an
n
er
u
s
i
n
g
f
o
llo
w
i
n
g
(
1
2
)
an
d
(
1
3
)
.
̂
=
∆
.
⌊
∆
⌉
(
1
2
)
W
h
er
e
,
∆
=
(
b
m
ax
−
b
m
i
n
)
.
(
2
d
−
1
)
−
1
(
1
3
)
I
n
th
is
w
a
y
,
t
h
e
u
n
if
o
r
m
q
u
a
n
t
izer
is
in
teg
r
ated
in
to
th
e
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
-
b
ased
C
SI
f
ee
d
b
ac
k
m
o
d
el.
I
n
v
ar
ied
s
tate
-
of
-
ar
t
-
ch
an
n
el,
es
ti
m
a
tio
n
tec
h
n
iq
u
e
s
p
r
o
v
id
e
ch
an
n
el
f
ee
d
b
ac
k
m
ec
h
a
n
i
s
m
w
it
h
o
u
t
an
y
q
u
an
tiza
tio
n
ap
p
r
o
ac
h
.
Ho
w
e
v
er
,
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
o
b
tain
co
m
p
r
es
s
ed
C
SI
co
ef
f
icien
t
s
a
n
d
th
e
n
tr
an
s
m
it
t
h
e
co
m
p
r
e
s
s
ed
a
n
d
q
u
an
tized
C
SI
co
ef
f
icie
n
t
s
to
th
e
d
ec
o
d
er
n
et
w
o
r
k
a
f
ter
q
u
a
n
tizatio
n
.
T
h
u
s
,
t
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
is
co
m
p
ar
at
iv
el
y
m
o
r
e
r
o
b
u
s
t t
h
a
n
tr
ad
itio
n
al
ch
a
n
n
el
es
ti
m
atio
n
m
et
h
o
d
s
in
ter
m
s
o
f
q
u
an
tizat
io
n
er
r
o
r
s
.
Fin
al
l
y
,
a
cc
u
r
ac
y
o
f
C
SI
r
ec
o
n
s
tr
u
ct
io
n
p
r
o
ce
s
s
is
m
u
c
h
h
i
g
h
er
w
it
h
q
u
an
tizat
io
n
.
A
s
a
r
esu
lt,
f
ee
d
b
ac
k
b
an
d
w
id
th
e
f
f
icien
c
y
g
et
s
en
h
a
n
ce
d
.
Fig
u
r
e
2
.
Qu
an
t
izatio
n
b
ased
p
r
o
p
o
s
ed
d
ee
p
lea
r
n
in
g
-
b
ased
C
SI
f
ee
d
b
ac
k
m
o
d
el
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
9
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4864
I
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&
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b
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l.
13
,
No
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3
,
No
v
e
m
b
er
20
24
:
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Fig
u
r
e
3.
A
r
ch
itectu
r
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NE
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m
o
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el
3
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2
.
Co
rr
el
a
t
io
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m
a
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io
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us
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pro
po
s
e
d
i
m
pro
v
is
e
d dee
p lea
rning
-
ba
s
ed
c
ha
nn
el
esti
m
a
t
io
n
m
o
de
l
T
h
e
d
o
w
n
li
n
k
C
SI
f
ee
d
b
ac
k
m
atr
ices
ar
e
esti
m
ated
an
d
ch
an
n
el
f
ee
d
b
ac
k
o
v
er
h
ea
d
is
ef
f
icien
tl
y
r
ed
u
ce
d
u
s
in
g
p
r
o
p
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s
ed
I
DL
C
E
m
o
d
el.
Her
e,
d
ee
p
lear
n
in
g
-
b
ased
m
o
d
el
is
in
tr
o
d
u
ce
d
to
en
h
a
n
ce
p
r
o
ce
s
s
in
g
s
p
ee
d
an
d
p
ar
am
eter
r
ed
u
cti
o
n
.
Her
e,
r
ea
l
an
d
i
m
ag
in
ar
y
p
a
r
a
m
eter
s
ac
q
u
ir
ed
f
r
o
m
th
e
co
r
r
elatio
n
o
f
d
o
w
n
s
tr
ea
m
a
n
d
u
p
s
tr
ea
m
C
SI
m
atr
ices
ar
e
co
n
tr
o
lled
u
s
in
g
p
r
o
p
o
s
ed
d
e
ep
lear
n
in
g
m
o
d
el.
F
u
r
th
er
,
th
e
co
r
r
elatio
n
co
ef
f
icien
ts
ar
e
ac
q
u
ir
ed
in
a
n
g
u
lar
d
ela
y
d
o
m
a
in
w
i
th
th
e
h
e
lp
o
f
c
h
a
n
n
el
f
ee
d
b
ac
k
m
atr
ice
s
.
B
esid
es
,
d
u
e
to
th
e
co
m
p
lex
n
atu
r
e
o
f
co
r
r
elatio
n
co
ef
f
ici
en
ts
,
th
e
y
ar
e
s
ep
ar
ated
in
to
r
ea
l
an
d
im
a
g
i
n
ar
y
co
ef
f
icie
n
t
s
.
Ho
w
e
v
er
,
o
b
tain
ed
co
r
r
elatio
n
co
ef
f
ic
ien
t
s
ar
e
er
r
atic
f
o
r
d
o
w
n
li
n
k
an
d
u
p
li
n
k
c
h
an
n
el
m
atr
ice
s
.
C
o
n
s
id
er
th
at
p
h
ase
ex
h
ib
it
lo
w
er
co
r
r
elatio
n
r
esu
lt
s
th
a
n
co
m
p
ar
e
to
th
e
m
ag
n
it
u
d
e
in
a
n
g
u
lar
-
d
ela
y
d
o
m
ain
.
T
h
er
ef
o
r
e,
s
ep
ar
ate
co
r
r
elatio
n
o
f
m
ag
n
it
u
d
e
an
d
p
h
ase
co
e
f
f
icie
n
ts
ar
e
ac
q
u
ir
ed
.
T
h
u
s
,
t
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
f
ed
b
ac
k
r
ea
l
a
n
d
i
m
a
g
in
ar
y
co
e
f
f
icien
t
s
s
ep
ar
atel
y
alo
n
g
w
it
h
th
eir
s
i
g
n
s
.
F
u
r
th
er
,
p
r
o
p
o
s
e
d
d
ee
p
lear
n
in
g
f
r
a
m
e
w
o
r
k
ef
f
icien
tl
y
r
ed
u
ce
c
h
an
n
el
o
v
er
h
ea
d
b
y
s
e
n
d
in
g
u
n
co
r
r
elate
d
d
ata
w
it
h
t
h
eir
s
ig
n
s
f
o
r
u
p
li
n
k
an
d
d
o
w
n
lin
k
m
ed
i
u
m
.
An
e
s
s
e
n
tial
f
ea
tu
r
e
o
f
t
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
is
ad
o
p
tio
n
o
f
f
u
ll
y
lin
k
er
la
y
er
s
,
r
esid
u
al
b
lo
ck
s
,
c
o
n
v
o
lu
t
io
n
al
la
y
er
s
an
d
co
n
v
er
s
io
n
in
to
th
e
f
ea
t
u
r
e
m
ap
s
.
Ho
w
e
v
er
,
d
u
e
to
less
er
v
a
lu
e
s
o
f
co
r
r
elate
d
p
h
ase
co
e
f
f
icie
n
ts
,
co
m
p
r
ess
io
n
o
f
p
h
ase
co
ef
f
icie
n
t
s
w
it
h
th
e
ir
s
ig
n
s
a
r
e
p
er
f
o
r
m
ed
at
UE
w
h
er
ea
s
m
ag
n
it
u
d
e
co
ef
f
icie
n
ts
o
f
d
o
w
n
li
n
k
m
ed
i
u
m
a
n
d
u
p
lin
k
C
S
I
m
atr
ices
ar
e
j
o
in
tl
y
o
p
ti
m
ized
at
t
h
e
B
S.
T
h
is
s
h
o
w
s
t
h
at
t
h
e
p
r
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p
o
s
ed
I
DL
C
E
m
o
d
el
en
s
u
r
e
h
ig
h
p
er
f
o
r
m
a
n
ce
ac
c
u
r
ac
y
an
d
s
ig
n
al
r
esto
r
atio
n
e
f
f
icien
c
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
R
ec
o
n
f
i
g
u
r
ab
le
&
E
m
b
ed
d
ed
Sy
s
t
I
SS
N:
2089
-
4864
C
h
a
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n
el
r
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o
n
s
tr
u
ctio
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th
r
o
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imp
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d
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p
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a
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in
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ch
itectu
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…
(
P
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r
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Ja
ya
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ka
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)
793
4.
P
E
RF
O
RM
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AT
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th
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tio
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a
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ased
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ate
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ee
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Her
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d
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ain
th
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u
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a
n
d
s
o
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ch
a
n
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el
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ar
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t
er
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d
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r
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elatio
n
co
ef
f
icie
n
t
s
e
f
f
ic
ien
t
l
y
.
T
h
e
p
r
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ed
I
DL
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m
o
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lo
w
er
s
co
m
p
u
ta
tio
n
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co
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p
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ctio
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.
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h
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ac
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o
r
r
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n
co
ef
f
icie
n
t
s
ar
e
ex
p
lo
ited
b
y
s
ep
ar
atin
g
t
h
e
m
i
n
t
o
r
ea
l
an
d
i
m
a
g
i
n
ar
y
p
ar
ts
co
n
s
id
er
in
g
C
SI
m
atr
ices
o
f
u
p
lin
k
an
d
d
o
w
n
lin
k
m
ed
i
u
m
.
Fu
r
th
er
m
o
r
e,
Fi
g
u
r
e
3
d
em
o
n
s
tr
ate
s
t
h
e
ar
ch
itec
tu
r
e
d
iag
r
a
m
o
f
p
r
o
p
o
s
ed
ML
Net
m
o
d
el
.
I
t
i
s
clea
r
l
y
v
i
s
ib
le
f
r
o
m
t
h
e
Fi
g
u
r
e
3
th
at
C
SI
m
a
tr
ices
ar
e
co
m
p
lex
i
n
n
atu
r
e
i
n
w
h
ich
p
h
ase
a
n
d
m
ag
n
i
tu
d
e
b
o
th
p
ar
a
m
eter
s
ar
e
s
ep
ar
atel
y
f
ed
a
n
d
co
n
v
o
lu
tio
n
al
la
y
er
s
ar
e
u
tili
ze
d
at
en
co
d
e
r
s
id
e
to
co
m
p
r
es
s
m
ag
n
it
u
d
e
co
ef
f
icie
n
t
s
o
f
C
SI
m
atr
ice
s
as
w
el
l
as
d
i
m
e
n
s
io
n
s
ar
e
r
es
h
ap
ed
u
s
in
g
d
i
m
en
s
io
n
r
es
h
ap
e
at
en
co
d
er
as
w
ell
as
d
ec
o
d
er
s
id
e.
Fu
r
th
er
,
b
o
th
C
SI
m
atr
ices
o
f
u
p
lin
k
m
ed
i
u
m
an
d
m
ag
n
it
u
d
e
co
ef
f
icie
n
t
s
o
f
C
SI
m
atr
ice
s
ar
e
co
n
ca
te
n
ated
a
f
ter
d
i
m
en
s
io
n
s
ar
e
r
esh
ap
ed
.
T
h
en
,
co
n
v
o
lu
tio
n
a
l
la
y
er
s
an
d
r
esid
u
al
b
lo
ck
s
ar
e
u
tili
ze
d
to
g
et
o
u
tp
u
t
at
th
e
d
ec
o
d
er
s
id
e.
Fu
r
t
h
er
m
o
r
e,
m
a
g
n
i
tu
d
e
-
p
h
as
e
-
q
u
a
n
tizatio
n
m
et
h
o
d
is
u
ti
li
ze
d
to
o
p
tim
ize
p
h
ase
co
ef
f
ici
en
ts
i
n
to
an
o
u
tp
u
t.
T
h
en
,
b
o
th
t
h
e
o
u
tp
u
ts
ar
e
tr
an
s
f
o
r
m
ed
i
n
to
t
h
e
r
ec
o
n
s
tr
u
c
ted
C
SI
m
atr
ices
an
d
th
e
s
e
r
e
co
n
s
tr
u
cted
o
u
tp
u
t
r
e
m
ain
s
co
m
p
lex
i
n
n
atu
r
e.
I
n
th
is
w
a
y
,
M
L
Net
m
o
d
el
is
p
r
o
p
o
s
ed
to
r
ec
o
n
s
tr
u
ct
C
SI
m
a
tr
ices.
A
l
s
o
,
co
r
r
elatio
n
co
ef
f
icie
n
t
s
ar
e
ev
alu
ated
b
y
s
ep
ar
ati
n
g
r
ea
l
a
n
d
i
m
a
g
in
ar
y
co
ef
f
icie
n
ts
w
it
h
th
eir
s
ig
n
s
i
n
an
g
u
lar
d
elay
d
o
m
ai
n
.
F
u
r
th
er
m
o
r
e,
p
er
f
o
r
m
a
n
ce
i
s
e
v
alu
a
ted
in
t
er
m
s
o
f
NM
SE
in
d
B
an
d
co
r
r
elatio
n
ef
f
icie
n
c
y
ag
ain
s
t c
o
m
p
r
es
s
io
n
r
atio
.
4
.
1
.
Da
t
a
s
et
d
et
a
ils
Her
e,
a
C
OST
2
,
1
0
0
m
o
d
el
is
u
tili
ze
d
to
ass
e
s
s
p
er
f
o
r
m
a
n
c
e
ef
f
icien
c
y
o
f
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
in
th
e
m
as
s
iv
e
MI
MO
s
y
s
te
m
[
2
0
]
.
Fu
r
th
er
m
o
r
e,
d
o
w
n
l
in
k
C
S
I
f
ee
d
b
ac
k
r
ec
o
n
s
tr
u
c
tio
n
ac
cu
r
ac
y
a
n
d
co
r
r
elatio
n
b
et
w
ee
n
u
p
lin
k
a
n
d
d
o
w
n
li
n
k
m
ed
iu
m
i
s
al
s
o
ev
alu
ated
b
ased
o
n
t
h
e
d
ata
e
x
tr
ac
ted
f
o
r
in
d
o
o
r
an
d
o
u
td
o
o
r
en
v
ir
o
n
m
e
n
t
s
.
Her
e,
d
ata
o
f
C
OST
2
,
1
0
0
m
o
d
el
is
s
eg
r
e
g
ated
in
to
in
d
o
o
r
an
d
o
u
td
o
o
r
en
v
ir
o
n
m
e
n
t.
Fu
r
t
h
er
,
in
d
o
o
r
an
d
o
u
td
o
o
r
en
v
ir
o
n
m
e
n
t
s
ar
e
co
n
s
id
er
ed
to
ev
al
u
ate
p
er
f
o
r
m
a
n
ce
o
f
p
r
o
p
o
s
ed
C
SI
f
ee
d
b
ac
k
m
o
d
el.
Her
e,
f
r
eq
u
en
c
y
is
f
ix
ed
at
5
.
1
GHz
an
d
5
.
3
G
Hz
f
o
r
u
p
lin
k
m
ed
i
u
m
a
n
d
d
o
w
n
l
in
k
m
ed
i
u
m
co
n
s
id
er
in
g
in
d
o
o
r
en
v
ir
o
n
m
e
n
t.
W
h
er
ea
s
,
f
r
eq
u
e
n
c
y
i
s
f
i
x
e
d
at
2
6
0
MH
z
an
d
3
0
0
MH
z
f
o
r
u
p
lin
k
m
ed
iu
m
an
d
d
o
w
n
l
in
k
m
ed
i
u
m
co
n
s
id
er
in
g
o
u
td
o
o
r
en
v
ir
o
n
m
e
n
t.
Her
e,
m
u
ltip
le
u
n
i
f
o
r
m
li
n
ea
r
ar
r
ay
an
te
n
n
as
ar
e
u
tili
ze
d
in
t
h
e
m
a
s
s
i
v
e
MI
MO
s
y
s
te
m
.
F
u
r
th
er
m
o
r
e,
BS
is
eq
u
ip
p
ed
w
i
th
u
n
i
f
o
r
m
li
n
ea
r
ar
r
ay
an
ten
n
as
R
b
=
32
an
d
nu
m
b
er
o
f
s
u
b
ca
r
r
ier
s
u
s
ed
ar
e
R
g
=
1
,
024
.
T
r
ain
in
g
o
f
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
i
s
e
f
f
ic
ien
t
l
y
d
o
n
e
u
s
i
n
g
v
ar
ied
s
y
s
te
m
p
ar
a
m
ete
r
s
.
Her
e,
ep
o
ch
s
ize
is
7
0
0
w
h
er
ea
s
b
atch
s
ize
is
2
0
0
.
R
est
o
f
th
e
s
i
m
u
latio
n
p
ar
am
eter
s
ar
e
r
etain
ed
s
am
e
as
[
2
0
]
,
[
2
1
]
.
Her
e,
th
e
p
er
f
o
r
m
an
ce
o
f
C
OST
2
1
0
0
ch
an
n
el
m
o
d
el
is
co
m
p
ar
ed
ag
ain
s
t
v
ar
ied
class
ical
C
SI
esti
m
atio
n
tec
h
n
iq
u
es.
T
h
e
p
r
o
p
o
s
ed
d
ee
p
lear
n
in
g
m
o
d
e
l
s
h
o
w
s
ex
ce
l
len
t
p
er
f
o
r
m
a
n
ce
in
ter
m
s
o
f
ch
a
n
n
el
p
ar
a
m
eter
r
ed
u
ctio
n
as
w
e
ll
as
r
ec
o
n
s
tr
u
ctio
n
e
f
f
ic
ien
c
y
.
Fu
r
th
er
m
o
r
e,
co
de
-
w
o
r
d
r
ec
o
n
s
tr
u
ctio
n
ac
c
u
r
ac
y
is
ev
al
u
a
ted
i
n
ter
m
s
o
f
NM
SE
an
d
co
r
r
elatio
n
s
i
m
il
ar
it
y
.
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
en
h
an
ce
s
b
an
d
w
id
t
h
u
tili
za
tio
n
in
th
e
m
a
s
s
i
v
e
MI
MO
s
y
s
te
m
to
i
m
p
l
e
m
en
t
5G
ce
llu
lar
n
et
w
o
r
k
e
f
f
icien
tl
y
.
4
.
2
.
Co
m
pa
ra
t
iv
e
s
t
ud
y
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
ev
alu
a
te
p
er
f
o
r
m
a
n
ce
in
ter
m
s
o
f
NM
SE,
co
r
r
elatio
n
ef
f
ici
en
c
y
an
d
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
ac
y
a
n
d
co
m
p
ar
ed
ag
ai
n
s
t
v
ar
ied
s
tat
e
-
of
-
ar
t
-
ch
a
n
n
el
esti
m
atio
n
te
ch
n
iq
u
es.
T
ab
le
1
d
em
o
n
s
tr
ate
s
NM
SE
(
d
B
)
p
er
f
o
r
m
an
ce
u
s
in
g
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
a
g
ain
s
t
v
ar
ied
s
tate
-
of
-
ar
t
-
c
h
a
n
n
e
l
esti
m
atio
n
tech
n
iq
u
es
s
u
ch
a
s
C
s
iNet
[
2
2
]
,
B
C
s
iNet
[
2
3
]
,
B
A
C
R
Ne
t
-
1
,
B
AC
R
Net
-
1
0
,
B
A
C
R
Net
-
1
an
d
B
A
C
R
Net
-
1
0
[
2
4
]
to
esti
m
ate
C
SI
f
ee
d
b
ac
k
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
ac
y
.
T
ab
le
1
r
e
s
u
lt
s
d
e
m
o
n
s
tr
ates
s
u
p
er
io
r
it
y
o
f
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
f
o
r
in
d
o
o
r
s
ce
n
ar
i
o
s
an
d
o
u
td
o
o
r
s
ce
n
ar
io
s
.
T
h
e
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
o
u
tp
er
f
o
r
m
s
all
t
h
e
C
s
iNet
an
d
th
e
ir
v
er
s
io
n
s
in
te
r
m
s
o
f
er
r
o
r
r
ed
u
ctio
n
ef
f
ic
ie
n
c
y
a
g
ain
s
t
v
ar
ied
co
m
p
r
es
s
io
n
r
atio
s
.
I
t
is
e
v
id
en
t
f
r
o
m
T
ab
le
1
r
esu
lts
th
at
lo
w
er
t
h
e
co
m
p
r
es
s
io
n
r
atio
,
th
e
er
r
o
r
r
ed
u
ctio
n
ef
f
icien
c
y
b
ec
o
m
es
h
i
g
h
er
.
T
h
at
m
ea
n
s
er
r
o
r
r
e
d
u
ctio
n
ef
f
ic
ien
c
y
i
s
in
v
er
s
el
y
p
r
o
p
o
r
tio
n
al
to
th
e
co
m
p
r
es
s
io
n
r
atio
.
Ho
w
e
v
er
,
ch
an
n
el
e
s
ti
m
atio
n
e
f
f
icien
c
y
is
s
i
g
n
i
f
ica
n
tl
y
i
m
p
r
o
v
ed
u
s
in
g
p
r
o
p
o
s
ed
I
DL
C
E
m
o
d
el
i
n
a
m
a
s
s
i
v
e
MI
MO
s
y
s
te
m
.
F
u
r
th
er
m
o
r
e,
av
er
ag
e
i
m
p
r
o
v
e
m
en
t
i
n
ch
a
n
n
el
r
e
co
n
s
tr
u
ctio
n
ac
c
u
r
ac
y
co
n
s
id
er
in
g
i
n
d
o
o
r
s
ce
n
ar
io
is
o
b
s
er
v
ed
as
3
4
.
9
0
%
ag
ai
n
s
t
p
r
ev
io
u
s
b
es
t
C
SI
f
ee
d
b
ac
k
m
et
h
o
d
(
B
A
C
R
Net
-
1
0
)
.
Si
m
ilar
l
y
,
f
o
r
o
u
td
o
o
r
s
ce
n
ar
io
s
,
r
ec
o
n
s
tr
u
ctio
n
ac
cu
r
a
c
y
i
m
p
r
o
v
e
m
en
t
is
o
b
s
er
v
ed
as
5
3
.
9
0
%
ag
ain
s
t
p
r
ev
io
u
s
b
est
C
SI
f
ee
d
b
ac
k
m
et
h
o
d
(
B
A
C
R
Net
-
1
0
)
.
A
s
d
em
o
n
s
tr
ated
i
n
T
ab
le
1
,
th
e
b
etter
p
er
f
o
r
m
a
n
ce
r
esu
lt
s
ar
e
h
ig
h
li
g
h
ted
in
b
o
ld
f
o
n
t
.
Fig
u
r
e
4
s
h
o
w
s
a
g
r
ap
h
ical
r
e
p
r
esen
tatio
n
o
f
co
r
r
elatio
n
p
er
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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Fig
u
r
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4.
C
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Fig
u
r
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5
d
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ates
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ap
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16
32
C
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Sim
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Var
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C
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m
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R
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gai
nst
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pr
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si
on
r
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or
and
out
doo
r
s
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nar
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in
d
o
o
r
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d
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r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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t J
R
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o
n
f
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g
u
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ab
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&
E
m
b
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I
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4864
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Fig
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5.
NM
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ates
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6
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2
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0
NM
SE
(
DB
)
N
MSE
per
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m
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gai
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pr
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si
on r
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sc
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32
64
-8
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0
NMS
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(
DB
)
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MSE
per
f
or
m
ance a
gai
nst
v
ar
i
ed com
pr
es
si
on r
at
i
os f
or
out
door
sc
enar
i
os
32
64
Evaluation Warning : The document was created with Spire.PDF for Python.