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Jo
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li
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g
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p
ti
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n
d
p
re
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g
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ti
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k
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m
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Mu
ltio
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p
tim
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T
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rticle
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uth
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ak
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ajam
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Dep
ar
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aiah
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d
u
ca
tio
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Fo
u
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d
atio
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awa
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a,
Vad
d
eswar
am
,
A
n
d
h
r
a
Pra
d
esh
,
I
n
d
ia
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m
ail: r
lak
s
h
m
i1
5
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@
g
m
ail.
co
m
1.
I
NT
RO
D
UCT
I
O
N
Mu
ltip
le
-
in
p
u
t
m
u
ltip
le
-
o
u
tp
u
t
(
MI
MO
)
is
an
ess
en
tial
tech
n
o
lo
g
y
th
at
m
ee
ts
th
e
r
ap
id
ly
in
cr
ea
s
in
g
d
em
an
d
f
o
r
d
ata
-
in
ten
s
iv
e
ap
p
licatio
n
s
in
b
o
th
p
r
esen
t
an
d
f
u
tu
r
e
m
o
b
ile
n
etwo
r
k
s
.
A
MI
MO
b
ase
s
tatio
n
(
B
S)
em
p
lo
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s
a
s
ig
n
i
f
ican
t
n
u
m
b
er
o
f
an
ten
n
as
t
o
s
im
u
lta
n
eo
u
s
ly
tr
a
n
s
m
it
m
u
ltip
le
in
f
o
r
m
atio
n
s
tr
ea
m
s
to
d
if
f
er
en
t
u
s
er
s
,
all
wh
ile
en
s
u
r
in
g
m
in
im
al
in
ter
f
er
e
n
ce
b
et
wee
n
u
s
er
s
.
T
h
e
ad
v
an
tag
es
o
f
MI
MO
s
y
s
tem
s
,
wh
en
im
p
lem
e
n
ted
with
s
u
itab
le
b
ea
m
f
o
r
m
in
g
tech
n
iq
u
e
s
,
en
co
m
p
ass
im
p
r
o
v
e
d
s
p
ec
tr
al
ef
f
icien
cy
an
d
in
cr
ea
s
ed
en
er
g
y
ef
f
icien
c
y
[
1
]
.
W
ith
th
e
s
u
b
s
tan
tial
in
cr
e
ase
in
th
e
n
u
m
b
e
r
o
f
a
n
ten
n
as
in
MI
MO
s
y
s
tem
s
,
th
e
im
p
lem
en
tatio
n
o
f
a
n
ten
n
a
s
elec
tio
n
(
AS)
ca
n
im
p
r
o
v
e
p
er
f
o
r
m
an
ce
in
ter
m
s
o
f
b
o
th
h
ar
d
war
e
c
o
s
ts
an
d
tech
n
o
lo
g
ical
f
ac
to
r
s
[
2
]
.
T
h
is
h
ap
p
en
s
d
u
e
to
t
h
e
f
ac
t
th
at
th
e
r
a
d
io
f
r
eq
u
en
cy
(
R
F)
ch
ai
n
s
g
en
er
ally
in
v
o
lv
e
m
u
ch
g
r
ea
ter
ex
p
en
s
es
in
co
m
p
ar
is
o
n
to
an
ten
n
a
elem
en
t
s
.
An
ef
f
ec
tiv
e
AS
s
tr
ateg
y
ca
n
n
o
tab
ly
ac
h
iev
e
co
m
p
lete
s
p
atial
d
iv
er
s
ity
wh
ile
s
ig
n
if
ican
tly
lo
wer
in
g
th
e
e
n
er
g
y
c
o
n
s
u
m
p
tio
n
o
f
R
F
ch
ai
n
s
,
th
u
s
im
p
r
o
v
i
n
g
th
e
o
v
er
all
en
er
g
y
ef
f
icien
cy
o
f
th
e
s
y
s
tem
[
3
]
.
AS is
ca
teg
o
r
ized
as a
n
o
n
d
eter
m
in
is
tic
p
o
ly
n
o
m
ial
(
NP
)
-
h
ar
d
p
r
o
b
lem
,
an
d
t
h
e
s
o
le
m
eth
o
d
to
g
u
a
r
an
tee
an
o
p
tim
al
s
o
lu
tio
n
is
v
ia
ex
h
au
s
tiv
e
s
ea
r
ch
,
wh
ich
e
n
tails
ev
alu
atin
g
all
p
o
ten
tial
co
m
b
i
n
atio
n
s
o
f
an
ten
n
as.
T
h
e
s
ig
n
if
ican
t
co
m
p
lex
ity
o
f
AS
m
ay
lim
it
its
p
r
ac
tical
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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tif
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tell
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15
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3
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3
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2370
u
s
e,
esp
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ially
in
5
G
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er
v
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s
th
at
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ally
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eq
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ir
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s
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laten
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an
d
r
ea
l
-
tim
e
d
ec
is
io
n
-
m
ak
in
g
ab
ilit
ies
[
4
]
.
E
f
f
ec
tiv
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s
o
lu
tio
n
s
ar
e
cr
u
cial
f
o
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m
ak
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ac
tically
attain
ab
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esp
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ially
f
o
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th
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ar
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o
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with
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ay
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ten
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as.
An
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o
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ith
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g
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lo
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o
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is
p
r
esen
ted
in
[
5
]
f
o
r
m
u
ltiu
s
er
MI
MO
s
y
s
tem
s
,
co
n
ce
n
tr
atin
g
o
n
th
e
s
e
lectio
n
o
f
o
p
tim
al
a
n
ten
n
as
to
eith
er
m
in
im
ize
t
h
e
s
y
m
b
o
l
er
r
o
r
r
ate
(
SER)
u
p
p
er
b
o
u
n
d
o
r
im
p
r
o
v
e
th
e
m
in
im
u
m
ca
p
ac
ity
.
T
h
is
m
e
th
o
d
m
et
h
o
d
ically
elim
in
ates
o
n
e
an
ten
n
a
at
a
tim
e,
co
n
ce
n
t
r
atin
g
o
n
th
e
o
n
e
th
at
h
as
th
e
g
r
ea
test
im
p
ac
t
o
n
e
n
er
g
y
co
n
s
u
m
p
tio
n
with
in
th
e
p
er
tin
en
t
o
r
th
o
g
o
n
al
b
ea
m
f
o
r
m
e
r
s
.
W
an
g
et
a
l.
[
6
]
in
tr
o
d
u
ce
a
co
llab
o
r
ativ
e
m
eth
o
d
f
o
r
b
ea
m
f
o
r
m
in
g
d
esig
n
an
d
an
AS
alg
o
r
ith
m
f
o
cu
s
ed
o
n
m
in
im
izin
g
t
r
an
s
m
it
p
o
we
r
f
o
r
m
u
lticast
in
g
.
E
m
p
lo
y
in
g
g
r
o
u
p
s
p
ar
s
ity
-
p
r
o
m
o
tin
g
1
1
,
2
n
o
r
m
s
in
s
tead
o
f
th
e
1
0
n
o
r
m
f
ac
ilit
ates
th
e
ex
tr
ac
tio
n
o
f
s
p
ec
if
ic
an
ten
n
as
an
d
b
ea
m
f
o
r
m
e
r
s
v
ia
an
iter
ativ
e
alg
o
r
ith
m
.
T
h
e
ap
p
licatio
n
o
f
1
1
,
2
n
o
r
m
s
is
e
m
p
lo
y
ed
in
m
ass
iv
e
MI
MO
to
m
in
im
ize
tr
an
s
m
it
p
o
wer
[
7
]
a
n
d
in
ce
ll
-
f
r
ee
M
I
MO
d
o
wn
lin
k
s
etu
p
s
f
o
r
t
h
e
co
m
b
in
ed
s
elec
tio
n
o
f
ac
ce
s
s
p
o
in
ts
an
d
p
o
wer
al
lo
ca
tio
n
[
8
]
.
An
AS
alg
o
r
it
h
m
em
p
l
o
y
in
g
m
ir
r
o
r
-
p
r
o
x
s
u
cc
ess
iv
e
co
n
v
ex
ap
p
r
o
x
im
atio
n
(
SC
A)
is
p
r
es
en
ted
in
[
9
]
aim
e
d
at
m
ax
im
i
zin
g
th
e
m
in
im
u
m
r
ate
in
m
u
ltip
le
-
in
p
u
t
s
in
g
le
-
o
u
tp
u
t
(
MI
SO)
b
r
o
ad
ca
s
tin
g
s
y
s
tem
s
.
A
s
im
ilar
m
eth
o
d
o
lo
g
y
g
r
o
u
n
d
ed
in
SC
A
is
p
r
o
p
o
s
ed
in
[
1
0
]
,
[
1
1
]
to
en
h
an
ce
e
n
er
g
y
ef
f
icien
c
y
.
R
ec
en
tly
,
th
e
u
s
e
o
f
m
ac
h
in
e
lear
n
in
g
in
co
m
m
u
n
icati
o
n
s
y
s
tem
s
h
as
attr
ac
ted
co
n
s
id
er
ab
le
atten
tio
n
[
1
2
]
–
[
1
4
]
.
T
h
e
m
ai
n
ad
v
a
n
tag
e
o
f
em
p
lo
y
in
g
m
ac
h
in
e
lear
n
in
g
in
c
o
m
m
u
n
icatio
n
s
lies
in
its
ca
p
ac
ity
to
d
is
ce
r
n
r
elatio
n
s
h
ip
s
b
etwe
en
s
y
s
tem
p
a
r
am
ete
r
s
an
d
d
esire
d
o
u
tco
m
es,
f
ac
i
litatin
g
th
e
s
h
if
t
o
f
co
m
p
u
tatio
n
al
r
eq
u
ir
em
e
n
ts
f
r
o
m
r
ea
l
-
tim
e
p
r
o
ce
s
s
in
g
to
th
e
o
f
f
lin
e
tr
ain
in
g
p
h
ase.
Xia
e
t
a
l.
[
1
5
]
p
r
esen
t
a
b
ea
m
f
o
r
m
in
g
n
eu
r
al
n
etwo
r
k
(
B
NN)
d
esig
n
ed
to
m
i
n
im
ize
tr
an
s
m
it
p
o
wer
in
m
u
ltiu
s
er
MI
SO
s
y
s
tem
s
.
T
h
is
m
eth
o
d
em
p
lo
y
s
co
n
v
o
lu
tio
n
a
l
n
eu
r
al
n
etwo
r
k
s
(
C
NN)
in
c
o
n
ju
n
ctio
n
with
a
s
u
p
e
r
v
is
ed
-
lear
n
in
g
tech
n
iq
u
e
to
p
r
ed
ict
b
o
th
t
h
e
m
ag
n
itu
d
e
an
d
d
ir
ec
tio
n
o
f
th
e
b
ea
m
f
o
r
m
in
g
v
ec
to
r
s
.
T
h
is
m
eth
o
d
is
elab
o
r
ated
u
p
o
n
in
r
ef
er
en
ce
s
[
1
6
]
–
[
1
8
]
f
o
r
u
n
s
u
p
er
v
is
ed
lear
n
in
g
aim
ed
at
o
p
tim
izin
g
th
e
weig
h
ted
s
u
m
-
r
at
e
o
f
th
e
s
y
s
tem
.
A
tr
an
s
m
is
s
io
n
s
tr
ateg
y
en
h
an
c
ed
b
y
d
ee
p
lea
r
n
in
g
is
in
tr
o
d
u
ce
d
f
o
r
a
s
in
g
le
-
u
s
er
M
I
MO
s
y
s
tem
with
co
n
s
tr
ain
ed
f
ee
d
b
ac
k
,
f
o
cu
s
in
g
o
n
p
ilo
t
-
aid
e
d
tr
ain
i
n
g
a
n
d
t
h
e
s
elec
tio
n
o
f
ch
a
n
n
el
c
o
d
es.
L
in
an
d
Z
h
u
[
1
9
]
in
tr
o
d
u
ce
a
d
ee
p
lear
n
i
n
g
a
p
p
r
o
ac
h
to
b
ea
m
f
o
r
m
in
g
d
e
s
ig
n
th
at
f
o
c
u
s
s
es
o
n
m
ax
i
m
izin
g
th
e
s
p
ec
tr
a
l
ef
f
icien
cy
o
f
a
s
in
g
le
-
u
s
er
m
illi
m
eter
wav
e
(
m
m
W
av
e)
MI
SO
s
y
s
tem
,
s
h
o
win
g
en
h
an
ce
d
s
p
ec
tr
al
ef
f
icien
cy
in
co
m
p
ar
is
o
n
to
co
n
v
en
tio
n
al
h
y
b
r
id
b
ea
m
f
o
r
m
in
g
d
esig
n
s
.
T
h
e
ap
p
licatio
n
o
f
Q
-
lea
r
n
in
g
is
elab
o
r
ate
d
in
[
2
0
]
to
tack
le
th
e
c
o
m
b
in
at
o
r
ial
co
m
p
lex
ity
ass
o
ciate
d
with
ch
o
o
s
in
g
t
h
e
o
p
tim
al
c
h
an
n
el
im
p
u
ls
e
r
esp
o
n
s
e
f
o
r
v
eh
icle
to
in
f
r
astru
ctu
r
e
c
o
m
m
u
n
icatio
n
s
.
A
s
im
ilar
ap
p
r
o
ac
h
u
tili
zin
g
Q
-
lear
n
i
n
g
is
in
tr
o
d
u
ce
d
i
n
[
2
1
]
to
tack
le
th
e
in
teg
r
ated
d
esig
n
o
f
b
ea
m
f
o
r
m
i
n
g
,
p
o
wer
co
n
tr
o
l,
an
d
in
ter
f
er
en
ce
c
o
o
r
d
i
n
atio
n
with
in
ce
llu
lar
n
etwo
r
k
s
.
Mism
ar
et
a
l.
[
2
2
]
in
tr
o
d
u
ce
a
d
ee
p
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
f
r
am
ewo
r
k
aim
e
d
at
au
to
n
o
m
o
u
s
ly
o
p
tim
izin
g
b
r
o
ad
ca
s
t
b
ea
m
s
in
MI
MO
b
r
o
ad
ca
s
t
s
y
s
tem
s
,
lev
er
ag
in
g
m
ea
s
u
r
em
en
ts
f
r
o
m
u
s
er
s
.
A
co
m
m
o
n
ly
u
tili
ze
d
d
ata
s
et
f
o
r
tr
ain
in
g
m
m
W
av
e
MI
MO
n
etwo
r
k
s
is
d
etailed
in
[
2
3
]
–
[
2
5
]
,
c
o
n
s
id
er
in
g
v
a
r
io
u
s
p
er
f
o
r
m
an
ce
m
et
r
ics.
T
h
e
in
c
o
r
p
o
r
ati
o
n
o
f
m
ac
h
in
e
lear
n
i
n
g
in
to
th
e
d
esig
n
o
f
th
e
p
h
y
s
ical
lay
er
o
f
f
er
s
a
co
m
p
ellin
g
s
tr
ateg
y
to
tack
le
th
e
ch
allen
g
es
lin
k
ed
t
o
ad
a
p
t
iv
e
s
y
s
tem
s
.
A
co
llab
o
r
ativ
e
d
esig
n
f
o
r
AS
an
d
h
y
b
r
id
b
ea
m
f
o
r
m
e
r
s
f
o
r
s
in
g
le
-
u
s
er
m
m
W
av
e
MI
MO
is
p
r
esen
ted
in
[
2
6
]
,
em
p
lo
y
in
g
tw
o
s
eq
u
en
tial
C
NNs.
On
e
C
NN
is
f
o
cu
s
ed
o
n
p
r
ed
ictin
g
th
e
ch
o
s
en
an
te
n
n
as,
w
h
er
ea
s
th
e
o
th
er
C
NN
is
aim
ed
at
esti
m
atin
g
th
e
h
y
b
r
id
b
ea
m
f
o
r
m
er
s
.
J
ad
h
a
v
a
n
d
Ku
m
ar
av
elu
[
2
7
]
p
r
o
p
o
s
e
a
m
u
lti
-
class
class
if
icatio
n
ap
p
r
o
ac
h
to
tack
le
th
e
AS
p
r
o
b
lem
in
s
in
g
le
-
u
s
er
MI
MO
s
y
s
tem
s
,
em
p
lo
y
in
g
two
class
if
icatio
n
m
eth
o
d
s
:
m
u
lticlas
s
k
-
n
ea
r
est
n
eig
h
b
o
r
s
an
d
s
u
p
p
o
r
t
v
ec
to
r
m
ac
h
in
e
(
SVM)
.
A
n
e
u
r
al
n
et
wo
r
k
(
NN)
-
b
ased
m
eth
o
d
is
p
r
esen
ted
in
[
2
8
]
to
r
ed
u
ce
th
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
o
f
AS
f
o
r
b
r
o
ad
ca
s
tin
g
.
T
h
e
NN
is
em
p
lo
y
e
d
to
d
ir
ec
tly
p
r
ed
ict
th
e
s
elec
ted
an
ten
n
as
th
at
en
h
a
n
c
e
th
e
m
in
im
u
m
s
ig
n
al
to
n
o
is
e
r
atio
am
o
n
g
th
e
u
s
er
s
.
Kh
u
r
an
a
[
2
9
]
p
r
esen
t
a
lear
n
in
g
-
b
ased
m
eth
o
d
f
o
r
s
el
ec
tin
g
tr
an
s
m
it
an
ten
n
as
d
esig
n
ed
to
im
p
r
o
v
e
s
ec
u
r
ity
in
t
h
e
wir
etap
ch
an
n
el.
T
h
is
s
tu
d
y
ex
p
l
o
r
es
two
lea
r
n
in
g
-
b
ased
m
eth
o
d
o
lo
g
ies,
n
am
ely
SVM
an
d
n
aïv
e
B
a
y
es
s
ch
em
es.
T
h
e
p
o
ten
tial
to
im
p
r
o
v
e
s
ec
r
ec
y
p
er
f
o
r
m
an
ce
wh
ile
r
e
d
u
ci
n
g
f
ee
d
b
ac
k
o
v
er
h
ea
d
is
clea
r
;
h
o
wev
er
,
t
h
e
co
n
f
ig
u
r
atio
n
a
n
aly
ze
d
in
[
2
9
]
is
lim
ited
to
a
s
in
g
le
AS
.
2.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
p
r
esen
ts
a
n
o
v
el
ap
p
r
o
ac
h
th
at
lev
er
ag
es
r
ec
e
n
t
ad
v
an
ce
m
e
n
ts
in
m
ac
h
in
e
l
ea
r
n
in
g
to
ad
d
r
ess
th
e
s
ig
n
if
ican
t
h
ig
h
-
c
o
m
p
lex
ity
c
h
allen
g
e
ass
o
ciate
d
with
th
e
s
elec
tio
n
p
r
o
ce
s
s
.
A
lear
n
in
g
-
b
ased
AS
an
d
p
r
ec
o
d
in
g
d
esig
n
(
L
-
ASPD)
alg
o
r
ith
m
was
p
r
o
p
o
s
ed
to
en
h
a
n
ce
ef
f
icien
c
y
.
T
h
e
p
r
o
p
o
s
ed
L
-
ASPD
alg
o
r
ith
m
is
d
esig
n
ed
t
o
u
tili
z
e
m
ac
h
in
e
lear
n
in
g
p
r
ed
ictio
n
s
to
ass
is
t
th
e
o
p
tim
al
alg
o
r
ith
m
in
a
d
d
r
ess
in
g
th
e
m
o
s
t
ch
allen
g
in
g
a
n
d
tim
e
-
in
ten
s
iv
e
asp
ec
ts
o
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r
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ased
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e
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u
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RE
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ticip
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M
cr
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er
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ased
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ith
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ted
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ig
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2
d
e
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icts
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ed
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o
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ith
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s
as
a
f
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n
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o
f
th
e
iter
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n
t.
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th
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o
n
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ate
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er
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s
,
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s
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ig
h
lig
h
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g
th
e
ef
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o
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th
e
s
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g
g
ested
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m
eth
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.
Fig
u
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e
3
illu
s
tr
ates th
e
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s
h
ip
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m
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d
s
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ith
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Fig
u
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4
d
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ates
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er
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n
ce
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f
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o
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t
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e
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ith
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em
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R
F
ch
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n
d
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T
h
e
L
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alg
o
r
ith
m
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v
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er
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o
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m
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ate
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m
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o
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th
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h
e
L
-
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alg
o
r
ith
m
ac
h
iev
es
8
6
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m
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ce
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ely
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e
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m
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e,
th
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s
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n
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ir
m
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g
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.
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h
e
L
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o
r
ith
m
d
em
o
n
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ates
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ed
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o
f
m
o
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th
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al
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wh
en
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m
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h
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m
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ain
o
f
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Fig
u
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5
d
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o
n
s
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ates
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m
p
ar
ativ
e
ef
f
ec
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ess
o
f
th
e
L
-
ASPD
alg
o
r
ith
m
in
r
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l
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tim
e
p
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ed
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b
ased
o
n
th
e
q
u
a
n
tity
o
f
t
r
ain
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g
s
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les
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tili
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d
.
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h
e
r
ela
tiv
e
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f
o
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m
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ce
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ex
p
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ess
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th
e
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atio
o
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th
e
s
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m
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ate
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y
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e
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SP
D
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ith
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m
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ar
e
d
t
o
t
h
at
o
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n
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o
d
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g
d
esig
n
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ASPD)
alg
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r
ith
m
.
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v
er
y
tr
ain
in
g
s
am
p
le
is
p
r
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d
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ce
d
r
a
n
d
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m
ly
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p
t
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r
in
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th
e
d
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ty
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ch
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s
m
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s
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ad
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er
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g
.
T
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p
ically
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g
m
en
tin
g
th
e
n
u
m
b
er
o
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tr
ai
n
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g
s
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im
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r
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e
L
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ith
m
g
ain
s
a
m
o
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e
p
r
o
f
o
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d
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en
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h
e
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ith
m
s
h
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2
0
0
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ain
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g
s
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les ar
e
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f
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t
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m
o
r
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th
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n
9
4
%
o
f
o
p
tim
al
p
er
f
o
r
m
an
ce
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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2
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20
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Fig
u
r
e
2
.
Per
f
o
r
m
an
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e
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
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alg
o
r
ith
m
Fig
u
r
e
3
.
Per
f
o
r
m
an
c
e
co
m
p
ar
is
o
n
o
f
th
e
p
r
o
p
o
s
ed
a
lg
o
r
ith
m
,
b
o
th
alg
o
r
ith
m
s
co
n
v
er
g
e
in
less
th
an
1
0
iter
atio
n
s
Fig
u
r
e
4
.
T
h
e
o
f
f
er
ed
L
-
ASPD's
p
er
f
o
r
m
an
ce
-
co
m
p
lex
ity
tr
a
d
eo
f
f
Fig
u
r
e
5
.
L
ea
r
n
in
g
p
er
f
o
r
m
a
n
ce
as a
f
u
n
ctio
n
o
f
tr
ain
in
g
s
am
p
le
s
ize
F
i
g
u
r
e
6
d
em
o
n
s
t
r
a
t
e
s
t
h
e
a
ch
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e
v
ab
l
e
s
u
m
r
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t
e
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l
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t
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t
o
K
S
,
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p
h
a
s
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e
n
t
if
i
e
d
t
h
r
o
u
g
h
th
e
p
r
o
p
o
s
ed
L
-
A
S
P
D
a
lg
o
r
i
th
m
.
T
o
i
l
lu
s
t
r
a
t
e
t
h
e
b
e
n
ef
i
ts
o
f
t
h
e
p
r
o
p
o
s
ed
b
e
a
m
f
o
r
m
i
n
g
d
e
s
i
g
n
,
a
cu
r
v
e
t
h
a
t
em
p
lo
y
s
z
er
o
-
f
o
r
c
in
g
b
a
s
e
d
p
o
w
er
co
n
t
r
o
l
o
n
th
e
a
n
t
en
n
a
s
u
b
s
e
t
s
i
d
e
n
t
if
i
e
d
b
y
th
e
a
lg
o
r
i
t
h
m
w
a
s
p
r
e
s
e
n
te
d
.
T
h
e
c
u
r
v
e
i
s
l
a
b
e
l
l
ed
a
s
p
r
o
p
o
s
e
d
-
z
er
o
f
o
r
c
i
n
g
in
t
h
e
f
ig
u
r
e
s
.
T
h
e
L
-
A
S
P
D
a
l
g
o
r
i
th
m
s
h
o
ws
e
n
h
an
c
ed
p
e
r
f
o
r
m
a
n
c
e
r
e
l
a
ti
v
e
t
o
a
l
l
o
t
h
er
s
c
h
e
m
e
s
ac
r
o
s
s
t
h
e
v
ar
i
o
u
s
K
S
v
a
l
u
e
s
e
x
am
i
n
ed
.
Fig
u
r
e
7
p
r
esen
ts
th
e
ef
f
ec
ti
v
e
s
u
m
r
ate
ass
o
ciate
d
with
d
if
f
er
en
t
to
tal
an
ten
n
a
c
o
u
n
ts
,
N.
T
o
g
u
ar
an
tee
a
f
air
ev
alu
atio
n
,
th
e
to
tal
tr
an
s
m
it
p
o
wer
is
s
et
at
3
0
d
B
m
,
tak
in
g
in
to
ac
co
u
n
t
th
e
en
tire
o
v
er
h
ea
d
r
elate
d
to
ch
an
n
el
esti
m
atio
n
an
d
c
o
m
p
u
tatio
n
.
I
n
t
h
e
f
o
r
m
er
s
ce
n
a
r
io
,
o
b
tain
in
g
th
e
c
h
an
n
el
s
tate
in
f
o
r
m
atio
n
(
C
SI)
d
em
an
d
s
8
c.
u
.
with
a
to
tal
o
f
6
,
7
,
o
r
8
an
ten
n
as,
wh
ile
it
r
eq
u
ir
e
s
1
2
c
.
u
.
wh
en
th
e
an
ten
n
a
co
u
n
t
in
cr
ea
s
es
to
9
o
r
1
0
.
Up
o
n
an
aly
s
is
,
it
b
ec
o
m
es
clea
r
th
at
th
e
L
-
ASPD
a
lg
o
r
ith
m
lim
its
its
s
ea
r
ch
to
th
e
1
0
m
o
s
t
p
r
o
m
is
in
g
ca
n
d
i
d
ates,
wh
ile
th
e
J
ASPD
alg
o
r
ith
m
ass
ess
es
al
l
(
NM
)
an
ten
n
a
s
u
b
s
ets.
T
h
e
f
in
d
in
g
s
in
d
icate
th
at
a
h
ig
h
er
n
u
m
b
er
o
f
an
ten
n
as
r
esu
lts
in
a
b
etter
ef
f
ec
tiv
e
s
u
m
r
ate
ac
r
o
s
s
all
s
ch
em
es,
th
u
s
co
n
f
ir
m
in
g
th
e
b
en
ef
its
o
f
AS
.
T
h
e
p
r
o
p
o
s
ed
L
-
ASPD
alg
o
r
ith
m
ex
h
ib
its
ex
ce
p
tio
n
al
p
er
f
o
r
m
an
ce
,
esp
ec
ially
f
o
r
lar
g
e
N,
ch
allen
g
in
g
th
e
tr
a
d
itio
n
al
n
o
tio
n
th
at
ex
h
a
u
s
tiv
e
s
ea
r
ch
m
eth
o
d
s
p
r
o
v
id
e
t
h
e
o
p
tim
al
o
u
tco
m
es
.
T
h
e
co
m
p
a
r
is
o
n
tak
es
in
to
a
cc
o
u
n
t
th
e
co
m
p
u
tatio
n
tim
e
,
as
illu
s
tr
ated
.
T
h
e
co
m
p
r
eh
e
n
s
iv
e
s
ea
r
ch
ap
p
r
o
a
ch
th
u
s
d
ed
icate
s
co
n
s
id
er
a
b
l
e
tim
e
to
p
in
p
o
in
tin
g
th
e
b
est
s
u
b
s
et,
p
ar
ticu
lar
ly
wh
en
N
is
s
u
b
s
tan
tial,
r
esu
lti
n
g
in
r
ed
u
ce
d
ef
f
ec
tiv
e
r
ates.
W
h
en
N
eq
u
als
1
0
,
th
e
ex
h
a
u
s
tiv
e
s
ea
r
ch
m
eth
o
d
r
eq
u
ir
es a
co
m
p
u
tatio
n
tim
e
th
at
is
2
1
tim
es m
o
r
e
th
an
th
at
o
f
th
e
L
-
ASPD a
lg
o
r
ith
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Ma
ch
in
e
lea
r
n
in
g
-
e
n
a
b
le
d
jo
i
n
t a
n
ten
n
a
s
elec
tio
n
a
n
d
p
r
ec
o
d
in
g
(
Mo
n
ica
N
iles
h
K
a
lb
a
n
d
e)
2373
Fig
u
r
e
6
.
T
h
e
r
elatio
n
s
h
ip
b
et
wee
n
th
e
n
u
m
b
er
o
f
an
ticip
ated
s
u
b
g
r
o
u
p
s
an
d
th
e
s
u
m
r
ate
p
er
f
o
r
m
an
ce
o
f
th
e
m
eth
o
d
s
th
at
wer
e
p
r
o
p
o
s
ed
Fig
u
r
e
7
.
C
o
m
p
a
r
is
o
n
o
f
ef
f
ec
tiv
e
s
u
m
r
ates f
o
r
d
if
f
er
en
t to
tal
a
n
ten
n
a
n
u
m
b
er
s
T
h
e
o
b
tain
ed
e
x
p
er
im
e
n
tal
r
es
u
lts
ar
e
co
n
s
is
ten
t
with
an
d
w
ell
s
u
p
p
o
r
ted
b
y
ex
is
tin
g
s
tu
d
ies
o
n
jo
in
t
AS
an
d
p
r
ec
o
d
i
n
g
in
m
u
lti
-
an
t
en
n
a
wir
eless
s
y
s
tem
s
.
Prio
r
o
p
tim
izatio
n
-
b
ased
wo
r
k
s
h
av
e
d
em
o
n
s
tr
ated
th
at
jo
in
tly
s
elec
tin
g
an
ten
n
as
an
d
d
esig
n
in
g
p
r
ec
o
d
er
s
s
ig
n
if
i
ca
n
tly
im
p
r
o
v
es
s
p
ec
tr
al
ef
f
i
cien
cy
co
m
p
ar
e
d
to
s
ep
ar
ate
o
r
h
eu
r
is
tic
d
esig
n
s
,
b
u
t
at
th
e
co
s
t
o
f
p
r
o
h
i
b
itiv
e
co
m
p
u
tatio
n
al
co
m
p
lex
ity
t
h
at
lim
its
r
ea
l
-
tim
e
ap
p
licab
ilit
y
.
R
ec
en
t
m
ac
h
in
e
lear
n
in
g
-
ass
is
ted
ap
p
r
o
ac
h
es
r
ep
o
r
ted
i
n
th
e
liter
atu
r
e
s
h
o
w
th
at
lear
n
in
g
-
b
ased
m
o
d
els
ca
n
ef
f
ec
tiv
ely
ap
p
r
o
x
im
ate
o
p
tim
al
AS
d
ec
is
io
n
s
b
y
ca
p
tu
r
in
g
th
e
u
n
d
er
ly
in
g
r
elatio
n
s
h
ip
b
etwe
en
ch
an
n
el
co
n
d
itio
n
s
an
d
tr
an
s
m
is
s
io
n
s
tr
ateg
ies.
I
n
lin
e
with
th
ese
f
in
d
in
g
s
,
th
e
p
r
o
p
o
s
ed
m
eth
o
d
ac
h
iev
es
p
er
f
o
r
m
an
ce
clo
s
e
to
o
p
tim
al
b
en
ch
m
ar
k
s
o
lu
tio
n
s
wh
ile
d
r
asti
ca
lly
r
ed
u
cin
g
ex
ec
u
tio
n
tim
e.
Similar
tr
en
d
s
h
av
e
b
ee
n
o
b
s
er
v
ed
i
n
lear
n
i
n
g
-
ass
is
ted
b
ea
m
f
o
r
m
i
n
g
a
n
d
AS
s
tu
d
ies,
wh
er
e
o
f
f
lin
e
tr
ain
in
g
en
a
b
les
f
ast
o
n
lin
e
in
f
er
en
ce
with
o
u
t
n
o
ti
ce
ab
le
p
er
f
o
r
m
a
n
ce
d
e
g
r
ad
ati
o
n
.
T
h
er
ef
o
r
e,
t
h
e
r
esu
lts
p
r
e
s
en
ted
in
th
is
wo
r
k
n
o
t
o
n
ly
v
alid
ate
t
h
e
e
f
f
ec
tiv
en
ess
o
f
th
e
p
r
o
p
o
s
ed
a
p
p
r
o
a
ch
b
u
t
also
r
ein
f
o
r
ce
t
h
e
g
r
o
win
g
co
n
s
en
s
u
s
th
at
m
ac
h
in
e
lear
n
in
g
p
r
o
v
id
es
a
p
r
ac
tical
an
d
s
ca
lab
le
alter
n
ativ
e
to
e
x
h
au
s
tiv
e
o
p
tim
izatio
n
f
o
r
n
ex
t
-
g
e
n
er
atio
n
wir
eless
co
m
m
u
n
icatio
n
s
y
s
tem
s
.
4.
CO
NCLU
SI
O
N
T
h
is
s
tu
d
y
e
x
am
in
es
th
e
in
teg
r
ated
d
esig
n
o
f
AS
a
n
d
p
r
ec
o
d
in
g
v
ec
to
r
s
in
m
u
lti
-
u
s
er
,
m
u
lti
-
an
ten
n
a
s
y
s
tem
s
to
o
p
tim
ize
s
p
atial
d
iv
er
s
ity
u
tili
za
tio
n
.
A
(
n
ea
r
)
o
p
tim
al
jo
in
t
AS
a
n
d
p
r
ec
o
d
in
g
alg
o
r
ith
m
is
in
itially
in
tr
o
d
u
ce
d
to
m
a
x
im
izin
g
th
e
s
y
s
tem
s
u
m
r
ate,
wh
ile
ad
h
er
in
g
to
u
s
er
s
'
q
u
alit
y
o
f
s
er
v
ice
(
Qo
S
)
r
eq
u
ir
em
e
n
ts
an
d
co
n
s
tr
ain
ts
o
n
tr
an
s
m
it
p
o
wer
.
T
h
e
p
r
o
p
o
s
ed
jo
in
t
d
esig
n
o
p
tim
izes
th
e
p
r
ec
o
d
in
g
v
ec
to
r
s
th
r
o
u
g
h
two
iter
ativ
e
o
p
tim
izatio
n
alg
o
r
ith
m
s
,
u
tili
zin
g
s
em
id
ef
in
ite
r
elax
atio
n
an
d
SC
A
m
eth
o
d
s
.
T
o
en
h
an
ce
o
p
tim
izatio
n
ef
f
icien
cy
,
a
m
ac
h
i
n
e
lear
n
i
n
g
-
b
ased
s
o
lu
tio
n
is
d
ev
elo
p
ed
f
o
r
ti
m
ely
an
d
ac
cu
r
ate
an
ten
n
a
p
r
e
d
ictio
n
s
.
T
h
e
p
r
o
p
o
s
ed
lear
n
in
g
-
b
ased
alg
o
r
ith
m
d
em
o
n
s
tr
ates
r
o
b
u
s
tn
es
s
co
n
ce
r
n
in
g
u
s
er
q
u
an
tity
an
d
d
is
tr
ib
u
tio
n
,
BS
tr
an
s
m
it
p
o
wer
,
an
d
c
h
an
n
el
f
a
d
in
g
ef
f
ec
ts
.
Simu
latio
n
r
esu
lt
s
d
em
o
n
s
tr
ate
th
at
th
e
p
r
o
p
o
s
ed
lear
n
in
g
-
b
ased
s
o
lu
tio
n
s
ig
n
if
ican
tly
s
u
r
p
ass
es
cu
r
r
en
t
s
elec
tio
n
s
ch
em
es
an
d
th
e
e
x
h
au
s
tiv
e
s
ea
r
ch
-
b
ased
s
o
lu
tio
n
.
T
h
is
w
o
r
k
s
u
g
g
ests
s
ev
er
al
p
o
ten
tial
r
esear
ch
d
ir
ec
tio
n
s
.
I
m
p
r
o
v
in
g
th
e
ef
f
icien
cy
o
f
th
e
tr
ain
in
g
p
h
ase
p
r
esen
ts
a
s
ig
n
if
ican
t
ch
alle
n
g
e,
p
ar
ticu
lar
ly
wh
en
d
ea
lin
g
with
a
lar
g
e
n
u
m
b
er
o
f
av
ailab
le
an
ten
n
as.
A
lo
w
-
c
o
m
p
lex
ity
p
r
ec
o
d
in
g
d
esig
n
,
s
u
ch
as
ze
r
o
-
f
o
r
ci
n
g
,
ca
n
b
e
em
p
lo
y
ed
t
o
ef
f
icien
tly
ac
q
u
ir
e
ad
eq
u
ate
tr
ai
n
in
g
s
am
p
les.
T
h
e
s
ec
o
n
d
is
s
u
e
p
er
tain
s
to
m
an
ag
in
g
n
etwo
r
k
d
y
n
am
ics,
n
ec
ess
itatin
g
th
e
lear
n
in
g
m
o
d
el
to
b
e
f
r
e
q
u
en
t
ly
an
d
p
r
o
m
p
tly
ad
ap
te
d
.
T
r
a
n
s
f
er
lear
n
in
g
a
n
d
r
ein
f
o
r
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d
a
ta
sc
ien
c
e
a
n
d
b
ig
d
a
ta,
c
o
m
p
u
ter
v
isio
n
,
d
istri
b
u
ted
sy
ste
m
s
,
a
n
d
c
lo
u
d
c
o
m
p
u
ti
n
g
.
H
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
v
e
n
k
a
t2
m
2
@g
m
a
il
.
c
o
m
.
S
a
r
a
d
h
a
Ra
n
i
S
a
b
b
a
v
a
r
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p
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is
a
wa
rd
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d
P
h
.
D
.
i
n
De
p
a
rtme
n
t
o
f
E
lec
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
fro
m
Ja
wa
h
a
rlal
Ne
h
ru
Tec
h
n
o
lo
g
ica
l
Un
iv
e
rsit
y
,
Ka
k
in
a
d
a
,
M
.
Tec
h
.
in
Ra
d
a
r
a
n
d
M
icro
wa
v
e
En
g
in
e
e
rin
g
fro
m
An
d
h
ra
Un
i
v
e
rsity
,
Visa
k
h
a
p
a
t
n
a
m
,
a
n
d
B.
Tec
h
.
in
El
e
c
tro
n
ics
a
n
d
Co
m
m
u
n
ica
ti
o
n
En
g
in
e
e
rin
g
fro
m
Ja
wa
h
a
rlal
Ne
h
ru
Tec
h
n
o
l
o
g
ica
l
Un
i
v
e
rsity
,
Hy
d
e
ra
b
a
d
,
in
2
0
0
5
re
sp
e
c
ti
v
e
ly
.
S
h
e
is
n
o
w
wo
r
k
i
n
g
a
s
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
in
G
ITAM
(De
e
m
e
d
to
b
e
Un
iv
e
rsity
),
Visa
k
h
a
p
a
tn
a
m
,
In
d
ia.
S
h
e
h
a
s
2
0
y
e
a
rs
o
f
e
x
p
e
rien
c
e
.
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r
re
se
a
rc
h
in
tere
sts
in
c
l
u
d
e
ima
g
e
p
ro
c
e
ss
in
g
a
n
d
c
o
m
m
u
n
ica
ti
o
n
s.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
ss
a
b
b
a
v
a
@g
it
a
m
.
e
d
u
.
Dr
.
Ra
jy
a
l
a
k
shm
i
Up
p
a
d
a
re
c
e
iv
e
d
h
e
r
P
h
.
D
.
fro
m
Ja
wa
h
a
rlal
Ne
h
ru
Tec
h
n
o
l
o
g
ica
l
Un
iv
e
rsity
,
Ka
k
i
n
a
d
a
in
2
0
1
8
.
S
h
e
c
o
m
p
lete
d
m
a
ste
r
o
f
Tec
h
n
o
l
o
g
y
fro
m
An
d
h
ra
Un
i
v
e
rsity
,
Visa
k
h
a
p
a
tn
a
m
in
2
0
0
9
.
C
u
rre
n
tl
y
s
h
e
is
wo
rk
i
n
g
a
s
a
ss
o
c
iate
p
r
o
fe
ss
o
r
o
f
ECE
De
p
a
rtme
n
t
i
n
Ad
i
ty
a
Un
iv
e
rsity
,
S
u
ra
m
p
a
lem
fro
m
th
e
y
e
a
r
2
0
1
8
.
Un
d
e
r
h
e
r
c
re
d
i
t,
sh
e
re
c
e
iv
e
d
re
se
a
rc
h
fu
n
d
i
n
g
wo
rt
h
1
7
.
7
6
lak
h
s
fro
m
WOS
-
A,
DS
T,
Ne
w
De
lh
i
a
t
Ja
wa
h
a
rlal
Ne
h
ru
Tec
h
n
o
l
o
g
ica
l
U
n
iv
e
rsit
y
,
Ka
k
in
a
d
a
in
2
0
1
5
wh
i
le
p
u
rsu
i
n
g
P
h
.
D
.
S
h
e
a
lso
re
c
e
iv
e
d
o
n
e
M
OD
ROB
p
ro
jec
t
wo
rt
h
8
.
7
3
lak
h
s
fr
o
m
AICTE,
In
d
ia
i
n
2
0
2
0
to
c
a
rry
o
u
t
h
e
r
re
se
a
rc
h
wo
rk
a
t
Ad
it
y
a
Un
iv
e
rsity
.
S
h
e
a
lso
re
c
e
iv
e
d
o
n
e
se
m
in
a
r
g
ra
n
t
fr
o
m
NCW,
Ne
w
De
lh
i
wo
rth
2
.
5
lak
h
s.
He
r
re
se
a
rc
h
in
tere
sts
in
c
lu
d
e
d
i
g
it
a
l
ima
g
e
p
ro
c
e
ss
in
g
,
m
a
c
h
i
n
e
lea
rn
in
g
,
a
n
d
c
o
m
m
u
n
ica
ti
o
n
s
.
S
h
e
c
a
n
b
e
c
o
n
t
a
c
ted
a
t
e
m
a
il
:
ra
jy
a
lak
sh
m
iu
@a
d
it
y
a
u
n
iv
e
rsit
y
.
in
.
La
k
shm
i
Durg
a
R
a
ja
m
a
h
e
n
d
r
a
v
a
r
a
p
u
re
c
e
iv
e
d
th
e
B.
Tec
h
.
d
e
g
re
e
in
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
E
n
g
i
n
e
e
rin
g
fro
m
DJ
R
Co
ll
e
g
e
o
f
E
n
g
in
e
e
rin
g
a
n
d
Tec
h
n
o
lo
g
y
,
Vijay
a
wa
d
a
,
In
d
ia,
in
2
0
1
4
a
n
d
th
e
M
.
Tec
h
.
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
s
p
e
c
il
iza
ti
o
n
fro
m
Lak
i
re
d
d
y
Ba
li
re
d
d
y
C
o
ll
e
g
e
o
f
E
n
g
in
e
e
rin
g
a
ffil
iate
d
to
Ja
wa
h
a
rlal
Ne
h
r
u
Tec
h
n
o
l
o
g
ica
l
Un
i
v
e
rsity
Ka
k
i
n
a
d
a
,
In
d
ia
in
2
0
1
6
,
re
sp
e
c
ti
v
e
ly
.
C
u
rre
n
tl
y
,
sh
e
is
a
n
a
ss
istan
t
p
ro
fe
ss
o
r
a
t
th
e
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
K
o
n
e
ru
La
k
sh
m
a
iah
Ed
u
c
a
ti
o
n
F
o
u
n
d
a
ti
o
n
,
Va
d
d
e
sw
a
ra
m
,
In
d
ia.
He
r
re
se
a
rc
h
i
n
tere
sts
in
c
lu
d
e
a
rti
ficia
l
in
telli
g
e
n
c
e
a
n
d
m
a
c
h
in
e
lea
rn
i
n
g
,
c
y
b
e
rse
c
u
rit
y
,
d
a
ta
sc
ien
c
e
a
n
d
b
ig
d
a
ta,
c
o
m
p
u
ter
v
isio
n
,
d
istri
b
u
ted
sy
ste
m
s
,
a
n
d
c
l
o
u
d
c
o
m
p
u
t
in
g
.
S
h
e
c
a
n
b
e
c
o
n
tac
ted
a
t
e
m
a
il
:
rlak
sh
m
i1
5
9
2
@g
m
a
il
.
c
o
m
.
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