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telli
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s
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ify
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it
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s
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e
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s
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o
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r
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in
a
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n
d
e
f
ficie
n
t
6
G
n
e
tw
o
rk
s
.
K
ey
w
o
r
d
s
:
6
G
n
et
w
o
r
k
s
A
r
ti
f
icial
i
n
tel
lig
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ce
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n
er
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f
icie
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y
Op
ti
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izatio
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o
r
it
h
m
R
a
y
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ig
h
f
ad
i
n
g
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o
n
f
ig
u
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ab
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telli
g
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t
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u
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e
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h
is i
s
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n
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p
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n
a
c
c
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ss
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rticle
u
n
d
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r th
e
CC B
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SA
li
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se
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o
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r
e
s
p
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uth
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r
:
A
b
b
as T
h
aj
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l Rh
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A
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a
h
la
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ee
Dep
ar
t
m
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h
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lt
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d
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a
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itie
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aq
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m
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ab
b
as.th
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tq
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ed
u
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iq
1.
I
NT
RO
D
UCT
I
O
N
6
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w
ir
eles
s
n
e
t
w
o
r
k
s
ar
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b
ein
g
d
r
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v
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b
y
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licatio
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s
t
h
at
r
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ir
e
m
o
r
e
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d
p
o
w
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u
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co
n
n
ec
ti
v
it
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.
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n
-
l
in
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-
of
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s
ig
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t
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s
ig
n
al
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g
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atio
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lts
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ec
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d
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f
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icie
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in
g
s
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d
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b
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im
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lin
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o
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ig
h
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s
in
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t
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R
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f
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tiv
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m
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[
1
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–
[
5
]
.
C
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ir
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ti
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r
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s
[
6
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.
Un
m
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r
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v
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icles
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U
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w
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i
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p
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p
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[
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s
[
8
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–
[
1
0
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.
T
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Evaluation Warning : The document was created with Spire.PDF for Python.
T
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L
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h
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K
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d
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h
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ee
f A
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fi
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23
m
o
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co
s
tl
y
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less
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f
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d
ly
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f
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s
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p
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im
ar
y
ad
v
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tag
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o
f
p
ass
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I
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S
tech
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a
m
ic
b
ea
m
f
o
r
m
in
g
a
n
d
r
eso
u
r
ce
allo
ca
tio
n
[
7
]
,
[
1
1
]
–
[
1
3
]
.
I
t
em
p
lo
y
s
co
n
v
e
n
ti
o
n
al
o
p
ti
m
izat
io
n
tech
n
iq
u
es
s
u
ch
a
s
b
lo
ck
co
o
r
d
in
ate
d
escen
t
(
B
C
D)
[
8
]
an
d
alter
n
ati
n
g
o
p
ti
m
izatio
n
(
AO)
[
1
0
]
,
[
1
4
]
.
B
asic
p
er
f
o
r
m
a
n
ce
an
al
y
s
e
s
f
o
r
co
m
p
licated
ar
ch
itect
u
r
es,
s
u
c
h
as
m
u
l
ti
-
an
te
n
n
a
m
u
lti
-
I
R
S
s
y
s
te
m
s
o
p
er
atin
g
in
g
en
er
alize
d
f
ad
i
n
g
c
h
an
n
el
s
,
h
av
e
b
ee
n
co
n
d
u
cted
to
en
h
an
c
e
th
ese
ap
p
lied
s
tu
d
ies
[
1
5
]
.
R
ec
en
t
s
t
u
d
ies
h
a
v
e
also
s
tar
ted
to
in
clu
d
e
n
o
n
-
id
ea
l
h
ar
d
w
ar
e
f
ea
t
u
r
es,
lik
e
n
o
n
li
n
ea
r
en
er
g
y
h
ar
v
e
s
ti
n
g
cir
c
u
its
a
n
d
w
o
r
k
ab
le
p
h
ase
-
s
h
i
f
t
m
o
d
els,
i
n
r
ec
o
g
n
i
tio
n
o
f
a
r
o
u
te
to
r
ea
l
-
w
o
r
ld
im
p
le
m
e
n
tat
io
n
[
1
6
]
.
Desp
ite
t
h
ese
s
i
g
n
if
ica
n
t
d
ev
e
lo
p
m
e
n
t
s
,
a
s
ig
n
i
f
ica
n
t
a
n
d
o
n
g
o
i
n
g
p
r
o
b
le
m
t
h
at
h
as
b
ee
n
id
en
ti
f
ied
in
n
u
m
er
o
u
s
s
t
u
d
ies
[
8
]
,
[
1
0
]
,
[
1
1
]
,
[
1
4
]
,
[
1
6
]
is
th
e
f
r
eq
u
e
n
t
m
is
h
an
d
li
n
g
o
f
t
h
e
b
asic
tr
ad
e
-
o
f
f
b
et
w
ee
n
E
E
an
d
th
e
N.
H
ig
h
E
E
is
f
r
eq
u
e
n
tl
y
attai
n
ed
at
t
h
e
ex
p
e
n
s
e
o
f
h
ar
d
w
ar
e
co
m
p
le
x
it
y
;
m
a
n
y
m
o
d
el
s
r
el
y
o
n
a
lo
t
o
f
ele
m
e
n
ts
[
9
]
,
[
1
7
]
–
[
1
9
]
,
o
r
o
n
l
y
w
o
r
k
w
ell
in
ce
r
tain
d
ep
lo
y
m
e
n
t
s
ce
n
ar
io
s
[
2
0
]
,
[
2
1
]
.
On
th
e
o
th
er
h
a
n
d
,
o
th
er
m
et
h
o
d
s
h
a
v
e
lo
w
E
E
,
f
r
eq
u
en
tl
y
a
s
a
r
esu
lt
o
f
t
h
eir
o
p
tim
iza
tio
n
al
g
o
r
ith
m
s
’
h
ig
h
co
m
p
u
tatio
n
al
o
v
er
h
ea
d
[
1
1
]
,
[
1
4
]
.
M
o
s
t
o
f
th
ese
s
tu
d
ie
s
ass
u
m
e
m
i
x
ed
NL
O
S
an
d
L
OS
p
r
o
p
ag
atio
n
co
n
d
itio
n
s
,
w
h
ic
h
i
s
p
er
h
ap
s
th
e
m
o
s
t
i
m
p
o
r
ta
n
t
f
a
cto
r
f
o
r
u
r
b
an
6
G
d
ep
lo
y
m
e
n
t
s
.
P
u
r
e
R
ay
le
ig
h
f
ad
i
n
g
,
t
h
e
p
r
ed
o
m
i
n
an
t
ch
an
n
el
m
o
d
el
i
n
cr
o
w
d
ed
u
r
b
an
ar
ea
s
w
it
h
r
ich
s
ca
tter
in
g
a
n
d
n
o
d
ir
ec
t
L
OS
p
at
h
,
is
i
g
n
o
r
ed
b
y
th
is
as
s
u
m
p
tio
n
.
T
h
is
o
v
er
s
ig
h
t
co
m
p
r
o
m
is
e
s
t
h
e
s
u
itab
ilit
y
o
f
c
u
r
r
en
t
s
o
lu
ti
o
n
s
i
n
th
e
v
er
y
s
etti
n
g
s
w
h
er
e
I
R
S
tech
n
o
lo
g
y
i
s
m
o
s
t
p
r
o
m
is
in
g
.
A
s
a
r
es
u
lt,
th
er
e
is
a
g
lar
in
g
n
ee
d
f
o
r
a
lo
w
-
co
m
p
le
x
it
y
o
p
ti
m
izatio
n
f
r
a
m
e
w
o
r
k
th
at,
i
n
r
ea
lis
tic
R
a
y
lei
g
h
f
ad
i
n
g
co
n
d
itio
n
s
,
m
ax
i
m
izes E
E
w
it
h
f
e
w
r
e
f
lecti
v
e
ele
m
e
n
t
s
.
A
d
y
n
a
m
ic
-
s
tatic
p
ar
ticle
s
w
ar
m
o
p
ti
m
izatio
n
(
D
S
-
P
SO)
f
r
a
m
e
w
o
r
k
f
o
r
I
R
S
-
e
n
ab
led
6
G
n
et
w
o
r
k
s
i
s
p
r
esen
ted
in
th
is
p
ap
er
to
clo
s
e
th
is
g
ap
.
P
h
ase
s
h
if
t
s
a
n
d
u
s
er
s
c
h
ed
u
lin
g
ar
e
d
y
n
a
m
ica
ll
y
m
o
d
if
ied
b
y
t
h
e
f
r
a
m
e
w
o
r
k
to
o
p
tim
ize
E
E
an
d
m
in
i
m
ize
to
tal
N.
W
e
h
av
e
th
r
ee
th
in
g
s
to
co
n
tr
ib
u
te:
em
p
ir
ical
v
alid
atio
n
s
h
o
w
i
n
g
s
u
p
er
io
r
E
E
(
3
6
6
Mb
it/J
o
u
le)
w
ith
a
m
i
n
i
m
al
N
(
7
)
co
m
p
ar
ed
to
b
en
ch
m
ar
k
s
;
a
n
ar
ti
f
icia
l
in
telli
g
e
n
ce
(
A
I
)
-
d
r
iv
en
o
p
ti
m
izatio
n
alg
o
r
ith
m
DS
-
P
SO,
w
h
ich
d
ec
o
u
p
les
ex
p
lo
r
atio
n
an
d
ex
p
lo
itatio
n
to
o
p
tim
ize
I
R
S
p
er
f
o
r
m
an
ce
w
it
h
lo
w
co
m
p
le
x
it
y
;
an
d
a
co
m
p
r
eh
en
s
iv
e
s
y
s
te
m
m
o
d
el
u
n
d
er
r
ea
lis
tic
R
a
y
lei
g
h
f
ad
in
g
.
T
h
is
s
tu
d
y
p
r
o
v
id
es
a
n
en
er
g
y
-
ef
f
icie
n
t,
s
ca
lab
le
s
o
lu
tio
n
f
o
r
u
r
b
an
co
m
m
u
n
icati
o
n
in
f
r
a
s
tr
u
ct
u
r
e
b
y
ad
d
r
ess
in
g
t
h
e
s
e
p
r
o
b
lem
s
an
d
ex
ten
d
i
n
g
t
h
e
u
s
e
o
f
I
R
S tec
h
n
o
lo
g
y
i
n
n
ex
t
-
g
e
n
er
atio
n
n
e
t
w
o
r
k
s
.
T
h
e
r
est
o
f
th
e
o
u
tl
in
e
f
o
r
th
e
p
ap
er
is
b
elo
w
.
Sectio
n
2
p
r
o
v
id
es
a
d
escr
ip
tio
n
o
f
t
h
e
p
r
o
p
o
s
ed
DS
-
P
SO
alg
o
r
ith
m
,
in
cl
u
d
in
g
it
s
f
o
r
m
u
latio
n
,
k
e
y
c
h
ar
ac
ter
is
ti
cs,
an
d
co
m
p
u
tatio
n
al
co
m
p
l
ex
it
y
.
T
h
e
r
esear
c
h
m
et
h
o
d
o
lo
g
y
i
s
p
r
ese
n
ted
i
n
s
ec
tio
n
3
,
w
h
ich
i
n
clu
d
e
s
t
h
e
s
i
m
u
latio
n
s
et
u
p
,
t
h
e
p
r
o
b
le
m
f
o
r
m
u
lat
io
n
f
o
r
EE
o
p
tim
izatio
n
,
a
n
d
t
h
e
s
y
s
te
m
an
d
ch
a
n
n
el
m
o
d
el.
A
co
m
p
a
r
is
o
n
w
it
h
p
r
ev
io
u
s
w
o
r
k
s
,
a
n
ab
latio
n
s
t
u
d
y
o
n
alg
o
r
ith
m
ic
co
n
v
er
g
en
ce
,
a
n
an
al
y
s
i
s
o
f
e
n
er
g
y
e
f
f
icie
n
c
y
,
a
d
is
c
u
s
s
io
n
o
f
p
r
ac
tical
li
m
ita
tio
n
s
,
an
d
a
n
ex
p
lan
atio
n
o
f
s
o
m
e
s
i
g
n
if
ica
n
t
is
s
u
e
s
f
o
r
p
o
s
s
ib
le
f
u
tu
r
e
r
esear
ch
ar
e
all
in
clu
d
ed
in
s
ec
tio
n
4
,
w
h
ic
h
is
d
ev
o
ted
to
th
e
r
esu
lt
s
an
d
d
is
c
u
s
s
io
n
.
I
n
s
ec
tio
n
5
,
w
e
f
i
n
all
y
p
r
o
v
id
e
th
e
f
in
d
i
n
g
s
o
f
o
u
r
in
v
esti
g
atio
n
.
2.
P
RO
P
O
SE
D
AL
G
O
R
I
T
H
M
T
h
e
A
I
-
p
o
w
er
ed
DS
-
P
SO
alg
o
r
ith
m
,
w
h
ich
m
ax
i
m
izes
th
e
u
s
e
o
f
I
R
S,
is
co
v
er
ed
in
t
h
is
s
ec
tio
n
.
I
n
o
r
d
er
to
b
etter
b
alan
ce
ex
p
lo
r
a
tio
n
an
d
ex
p
lo
itatio
n
in
in
tr
icate
,
n
o
n
-
co
n
v
e
x
s
ea
r
ch
s
p
ac
es,
DS
-
P
SO
i
m
p
r
o
v
e
s
o
n
th
e
tr
ad
itio
n
al
P
SO
b
y
u
t
ilizin
g
a
h
y
b
r
id
to
p
o
lo
g
ical
p
ar
ad
ig
m
w
ith
d
u
al
s
ta
tic
(
S)
an
d
d
y
n
a
m
ic
(
D)
i
n
f
lu
e
n
ce
f
ield
s
.
T
ab
le
1
s
u
m
m
ar
izes t
h
e
m
a
in
p
ar
a
m
eter
s
co
n
tr
o
llin
g
th
e
al
g
o
r
ith
m
’
s
o
p
e
r
atio
n
.
T
ab
le
1.
B
asic p
ar
am
eter
s
f
o
r
r
u
n
n
i
n
g
t
h
e
A
I
-
b
a
s
ed
DS
-
P
SO
alg
o
r
ith
m
P
a
r
a
me
t
e
r
V
a
l
u
e
M
a
x
i
m
u
m
I
t
e
r
a
t
i
o
n
(
)
1
0
0
0
P
a
r
t
i
c
l
e
s (
)
o
r
(
s
w
a
r
m si
z
e
)
50
P
r
o
b
a
b
i
l
i
t
y
o
f
n
e
i
g
h
b
o
r
h
o
o
d
r
e
st
r
u
c
t
u
r
i
n
g
p
e
r
i
o
d
s
0
.
5
C
o
e
f
f
i
c
i
e
n
t
o
f
I
n
t
e
r
c
e
p
t
(
sp
a
c
e
d
p
o
i
n
t
s o
f
f
r
e
q
u
e
n
c
y
)
1
T
h
e
mi
n
i
mu
m
v
a
l
u
e
s (
)
a
n
d
max
i
mu
m v
a
l
u
e
s (
)
o
f
t
h
e
se
a
r
c
h
s
p
a
c
e
’
s
(
1
,
7
0
)
2
.
1
.
Alg
o
rit
h
m
ic
f
o
r
m
ula
t
io
n
T
h
e
DS
-
P
SO
alg
o
r
it
h
m
,
w
h
ic
h
is
p
ar
a
m
eter
ized
ac
co
r
d
in
g
t
o
T
ab
le
1
,
f
u
n
c
tio
n
s
in
f
i
v
e
m
ain
s
ta
g
es:
−
P
h
ase
1
:
t
h
e
in
itializatio
n
s
ta
g
e.
=
50
is
u
s
ed
to
in
itialize
a
s
w
ar
m
o
f
p
ar
ticles.
E
v
er
y
p
ar
ticle
is
g
iv
e
n
a
v
e
lo
cit
y
(
)
th
at
i
s
u
n
i
f
o
r
m
l
y
s
a
m
p
led
f
r
o
m
t
h
e
r
an
g
e
[
,
]
=
[
1
,
70
]
an
d
a
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
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NI
K
A
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elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
22
-
33
24
r
an
d
o
m
ized
p
o
s
itio
n
(
)
(
w
it
h
i
n
th
e
s
ea
r
c
h
s
p
ac
e)
.
E
ac
h
p
ar
tic
le
’
s
in
i
tial
p
o
s
itio
n
(
)
is
u
s
ed
to
s
et
its
p
er
s
o
n
al
b
est
(
)
,
d
y
n
a
m
ic
b
est (
p
a
r
_
b
es
t
)
,
a
n
d
s
tatic
b
est (
_
)
.
−
P
h
ase
2
:
d
u
al
-
to
p
o
lo
g
y
v
e
lo
cit
y
m
o
d
u
latio
n
(
v
elo
cit
y
u
p
d
ate
)
.
P
a
r
ticle
v
elo
cities
ar
e
u
p
d
ated
ea
ch
iter
atio
n
u
s
in
g
a
tr
ip
ar
tite
attr
ac
tio
n
m
o
d
el
th
at
i
n
co
r
p
o
r
ate
s
g
u
id
an
ce
f
r
o
m
t
h
e
p
ar
ticle
’
s
o
w
n
m
e
m
o
r
y
an
d
its
to
p
o
lo
g
ic
al
n
ei
g
h
b
o
u
r
h
o
o
d
s
:
(
)
=
[
(
−
1
)
+
1
1
(
(
−
1
)
−
(
−
1
)
)
+
2
2
(
(
−
1
)
−
(
−
1
)
)
+
3
3
(
(
−
1
)
−
(
−
1
)
)
]
(
1
)
w
h
er
e
(
≈
0
.
7298
)
is
th
e
co
n
s
tr
ictio
n
co
ef
f
icien
t
p
r
ev
en
ti
n
g
d
iv
er
g
en
c
e,
(
₁
,
₂
,
₃
=
4
.
1
/
3
≈
1
.
3667
)
ar
e
ac
ce
ler
atio
n
co
ef
f
icie
n
t
s
,
an
d
₁
,
₂
,
₃
ar
e
r
an
d
o
m
n
u
m
b
er
s
u
n
i
f
o
r
m
l
y
d
is
tr
ib
u
ted
in
[
0
,
1
]
.
−
P
h
ase
3
:
p
o
s
itio
n
u
p
d
ate
an
d
f
itn
es
s
ev
al
u
atio
n
.
P
ar
ticles r
elo
ca
te
b
ased
o
n
th
eir
u
p
d
ated
v
elo
cit
y
:
(
)
=
(
−
1
)
+
(
)
(
2
)
An
e
v
al
u
atio
n
is
co
n
d
u
cted
o
n
t
h
e
o
b
j
ec
tiv
e
f
u
n
ctio
n
(
)
.
T
h
e
in
d
iv
id
u
al
b
es
t
(
(
,
u
p
d
atin
g
in
t
h
e
ev
e
n
t
th
at
a
b
etter
s
o
lu
tio
n
is
d
is
co
v
er
ed
.
A
ls
o
u
p
d
ated
ap
p
r
o
p
r
iately
ar
e
th
e
s
tatic
b
est
_
(
b
est
in
th
e
s
tat
ic
n
ei
g
h
b
o
r
h
o
o
d
)
an
d
th
e
d
y
n
a
m
ic
b
est
_
(
b
est
in
th
e
d
y
n
a
m
i
c
n
eig
h
b
o
r
h
o
o
d
)
.
−
P
h
ase
4
:
r
estru
ctu
r
i
n
g
t
h
e
n
ei
g
h
b
o
r
h
o
o
d
d
y
n
a
m
icall
y
.
T
h
e
d
y
n
a
m
ic
n
ei
g
h
b
o
u
r
h
o
o
d
s
u
n
d
er
g
o
s
to
ch
asti
c
r
ec
o
n
f
i
g
u
r
atio
n
w
i
th
p
r
o
b
ab
ili
t
y
=
0
.
5
(
as sp
ec
i
f
ied
i
n
T
ab
le
1
)
.
T
h
is
h
elp
s
b
r
ea
k
f
r
ee
f
r
o
m
lo
ca
l
o
p
tim
a
b
y
in
tr
o
d
u
ci
n
g
ex
p
lo
r
ato
r
y
n
o
is
e.
−
P
h
ase
5
:
f
in
a
lizatio
n
an
d
o
u
t
p
u
t.
T
h
e
alg
o
r
it
h
m
r
etu
r
n
s
t
h
e
o
p
ti
m
a
l
p
ar
ticle
p
o
s
itio
n
an
d
t
h
e
ass
o
ciate
d
f
it
n
es
s
v
a
lu
e
(
)
w
h
en
=
1000
iter
atio
n
s
h
a
v
e
b
ee
n
co
m
p
let
ed
.
T
h
e
p
s
eu
d
o
co
d
e
f
o
r
th
e
D
S
-
P
SO
alg
o
r
ith
m
is
a
v
ailab
le
i
n
[
2
2
]
f
o
r
a
d
etailed
d
escr
ip
t
io
n
o
f
th
e
alg
o
r
ith
m
ic
s
tep
s
.
Sev
er
al
r
e
ce
n
t
o
p
ti
m
izatio
n
ch
alle
n
g
es,
s
u
c
h
a
s
t
h
o
s
e
m
e
n
tio
n
ed
i
n
[
2
3
]
–
[
2
5
]
,
h
av
e
d
em
o
n
s
tr
ated
t
h
e
alg
o
r
it
h
m
’
s
r
esil
ien
ce
a
n
d
ef
f
ec
ti
v
en
e
s
s
.
2
.
2
.
K
ey
i
nn
o
v
a
t
i
o
ns
T
h
er
e
ar
e
th
r
ee
m
ai
n
w
a
y
s
th
at
DS
-
P
SO
is
d
i
f
f
er
en
t
f
r
o
m
s
tan
d
ar
d
P
SO:
i)
t
o
p
o
lo
g
ical
d
u
alit
y
:
b
y
s
ep
ar
atin
g
e
x
p
lo
r
atio
n
(
led
b
y
_
)
an
d
ex
p
lo
itatio
n
(
led
b
y
_
)
in
to
d
if
f
er
en
t
i
n
f
lu
e
n
ce
f
ield
s
,
it
ca
n
u
s
e
a
w
ell
-
r
o
u
n
d
ed
an
d
s
u
cc
es
s
f
u
l
s
ea
r
ch
s
tr
ateg
y
;
ii)
s
to
ch
ast
ic
r
ec
o
n
f
i
g
u
r
atio
n
:
b
y
u
s
in
g
p
r
o
b
ab
ilis
tic
r
estru
ct
u
r
in
g
o
f
d
y
n
a
m
ic
n
ei
g
h
b
o
r
h
o
o
d
s
,
p
o
p
u
latio
n
-
b
ase
d
o
p
tim
izer
s
ca
n
av
o
id
th
e
c
o
m
m
o
n
m
i
s
ta
k
e
o
f
co
n
v
er
g
in
g
to
o
q
u
ic
k
l
y
;
an
d
i
ii)
tr
iad
ic
a
cc
eler
atio
n
:
t
h
e
th
r
ee
-
co
ef
f
icie
n
t
s
y
s
te
m
(
₁
,
₂
,
₃
)
m
a
k
e
s
p
ar
ticle
gu
id
a
n
ce
b
etter
t
h
an
th
e
u
s
u
al
s
o
cial/co
g
n
iti
v
e
b
i
n
ar
y
m
o
d
el.
T
h
is
m
ak
e
s
co
n
v
er
g
e
n
ce
p
r
o
p
er
ties
m
o
r
e
r
eliab
le.
2
.
3
.
Co
m
ple
x
it
y
a
na
ly
s
is
T
h
e
d
if
f
icu
lt
y
o
f
co
m
p
u
ti
n
g
DS
-
P
SO
is
o
n
e
o
f
th
e
p
r
i
m
ar
y
r
ea
s
o
n
s
it
ca
n
n
o
t
b
e
ap
p
lied
t
o
r
ea
l
-
ti
m
e
s
y
s
te
m
s
.
E
ac
h
iter
atio
n
’
s
co
m
p
lex
it
y
is
(
)
,
w
h
er
e
is
th
e
n
u
m
b
er
o
f
p
ar
ticles
(
5
0
,
ac
co
r
d
in
g
to
T
ab
le
1
)
.
Sin
ce
w
e
n
ee
d
to
ad
j
u
s
t
ea
ch
p
ar
ticle
’
s
p
o
s
itio
n
an
d
s
p
ee
d
as
w
ell
as
ch
ec
k
t
h
e
f
it
n
es
s
f
u
n
ct
io
n
,
th
i
s
lin
ea
r
s
ca
lin
g
is
r
eq
u
ir
ed
.
W
h
ile
m
ain
tai
n
i
n
g
th
e
(
)
co
m
p
lex
it
y
,
DS
-
P
SO
o
n
l
y
ad
d
s
a
co
n
s
ta
n
t
n
u
m
b
er
o
f
p
ar
ticles,
b
u
t it
tak
e
s
m
o
r
e
ef
f
o
r
t to
r
em
e
m
b
er
t
h
e
t
w
o
b
est
v
alu
es
f
o
r
ea
ch
p
ar
ticle
(
_
an
d
_
).
B
ec
au
s
e
o
f
th
is
,
th
e
o
p
ti
m
iza
t
io
n
’
s
o
v
er
all
co
s
t
is
(
)
,
w
h
er
e
i
s
th
e
n
u
m
b
er
o
f
iter
atio
n
s
(
1
0
0
0
,
ac
co
r
d
in
g
to
T
ab
le
1
)
.
T
h
e
m
a
in
b
en
ef
it
o
f
DS
-
P
SO
is
h
o
w
q
u
ick
l
y
it
co
n
v
er
g
es.
C
o
m
p
ar
ed
to
class
ical
P
SO,
DS
-
P
SO
u
s
u
a
ll
y
y
ield
s
a
h
i
g
h
-
q
u
alit
y
s
o
lu
t
io
n
in
a
m
u
ch
s
m
aller
n
u
m
b
er
o
f
iter
atio
n
s
(
a
s
m
a
ller
)
b
y
s
k
il
lf
u
ll
y
b
alan
ci
n
g
ex
p
lo
r
ati
o
n
an
d
ex
p
lo
itatio
n
.
I
t
is
p
er
f
ec
t
f
o
r
r
ea
l
-
t
i
m
e
I
R
S
Op
ti
m
izatio
n
p
r
o
b
lem
s
b
ec
au
s
e,
in
p
r
ac
tice,
th
is
r
ed
u
ctio
n
in
th
e
n
u
m
b
er
o
f
iter
atio
n
s
r
eq
u
ir
ed
ca
n
r
ed
u
ce
th
e
o
v
er
all
co
m
p
u
ta
tio
n
al
co
s
t to
(
)
w
h
ile
k
ee
p
i
n
g
t
h
e
s
a
m
e
p
er
f
o
r
m
a
n
ce
t
h
r
esh
o
ld
.
3.
M
E
T
H
O
D
T
h
is
s
ec
tio
n
d
escr
ib
es
th
e
r
esear
ch
m
et
h
o
d
o
lo
g
y
,
w
h
ic
h
in
cl
u
d
es
th
e
f
o
r
m
u
la
tio
n
o
f
th
e
o
p
ti
m
izat
io
n
p
r
o
b
lem
,
th
e
s
y
s
te
m
a
n
d
ch
a
n
n
el
m
o
d
els,
a
n
d
th
e
co
m
p
r
eh
e
n
s
i
v
e
s
i
m
u
latio
n
s
et
u
p
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
E
n
h
a
n
cin
g
r
eflec
tive
elem
en
ts
o
f in
tellig
en
t reflective
s
u
r
fa
ce
s
in
6
G
…
(
Je
h
a
n
K
a
d
h
im
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h
a
r
ee
f A
l
-
S
a
fi
)
25
3
.
1
.
Sy
s
t
em
a
n
d
cha
nn
el
m
o
del
T
h
e
s
y
s
te
m
m
o
d
el
co
n
s
id
er
s
a
s
o
u
r
ce
(
S)
co
m
m
u
n
icati
n
g
w
it
h
a
d
esti
n
atio
n
(
D)
,
aid
e
d
b
y
eit
h
er
a
s
tan
d
ar
d
o
r
an
o
p
ti
m
ized
I
R
S.
T
h
e
o
v
er
all
tr
an
s
m
is
s
i
o
n
ar
ch
itect
u
r
e
i
s
d
ep
icted
in
Fi
g
u
r
e
1
.
T
h
e
co
m
m
u
n
icatio
n
s
ce
n
ar
io
illu
s
t
r
atin
g
t
h
e
s
p
ec
if
ic
ch
an
n
el
g
a
in
s
i
n
b
o
th
s
tan
d
ar
d
an
d
o
p
tim
ized
I
R
S
-
as
s
is
ted
lin
k
s
i
s
s
h
o
w
n
in
Fig
u
r
e
2
.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
b
o
th
I
R
S
m
o
d
els
i
s
ev
a
lu
ated
a
n
d
co
m
p
ar
ed
ag
ai
n
s
t
a
b
aselin
e
s
i
n
g
le
-
i
n
p
u
t si
n
g
le
-
o
u
tp
u
t (
SISO)
s
y
s
te
m
.
Fig
u
r
e
1
.
Data
tr
an
s
m
is
s
io
n
s
u
p
p
o
r
ted
b
y
th
e
(
I
R
S
s
ta
n
d
ar
d
/I
R
S
e
n
h
a
n
ce
d
)
m
o
d
els
Fig
u
r
e
2
.
Destin
at
io
n
v
ar
iab
le
s
f
o
r
th
e
co
m
m
u
n
icatio
n
s
s
y
s
t
e
m
s
i
m
u
latio
n
s
et
u
p
w
i
th
s
tan
d
ar
d
/en
h
an
ce
d
I
R
S
F
o
r
t
h
e
b
a
s
el
in
e
S
I
S
O
ch
an
n
el
,
t
h
e
r
ec
e
iv
e
d
s
ig
n
a
l
an
d
i
ts
c
o
r
r
e
s
p
o
n
d
in
g
a
ch
i
ev
a
b
l
e
r
a
te
a
r
e
g
iv
en
b
y
:
=
ℎ
sd
√
+
(
3
)
=
l
og
2
(
1
+
|
ℎ
|
2
2
)
(
4
)
w
h
er
e
ℎ
is
t
h
e
ch
a
n
n
el
co
ef
f
i
cien
t,
r
ep
r
esen
ts
th
e
i
n
f
o
r
m
atio
n
s
i
g
n
a
l
o
f
u
n
i
t
-
p
o
w
er
,
r
ep
r
esen
ts
t
h
e
p
o
w
er
o
f
tr
a
n
s
m
is
s
io
n
,
a
n
d
∼
(
0
,
2
)
,
r
ep
r
esen
ts
t
h
e
n
o
is
e
at
th
e
r
e
ce
iv
er
(
ad
d
itiv
e
w
h
ite
Gau
s
s
i
an
n
o
is
e
(
A
W
GN)
)
.
T
h
e
s
tan
d
ar
d
I
R
S
m
o
d
el
co
m
p
r
is
es
(
)
p
ass
iv
e
r
ef
lecti
n
g
elem
en
ts
.
ℎ
∈
ℂ
r
ep
r
esen
ts
th
e
s
o
u
r
ce
-
to
-
I
R
S
c
h
an
n
el,
an
d
ℎ
∈
ℂ
r
ep
r
esen
ts
t
h
e
I
R
S
-
to
-
d
es
tin
at
io
n
c
h
an
n
el.
T
h
e
I
R
S
r
ef
lectio
n
m
atr
i
x
is
=
.
(
1
,
…
,
)
,
w
h
er
e
is
th
e
r
ef
lectio
n
co
e
f
f
icien
t
an
d
(
)
ar
e
th
e
p
h
ase
s
h
i
f
ts
.
T
h
e
r
ec
eiv
ed
s
ig
n
al
w
it
h
I
R
S a
s
s
i
s
ta
n
ce
is
:
=
(
ℎ
+
ℎ
ℎ
)
√
+
(
5
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
22
-
33
26
T
h
e
o
p
tim
al
p
h
a
s
e
s
h
i
f
ts
t
h
at
alig
n
th
e
r
e
f
lecte
d
s
i
g
n
als
co
h
er
en
tl
y
at
t
h
e
d
esti
n
atio
n
d
eter
m
in
e
t
h
e
p
o
s
s
ib
le
r
ate,
w
h
ich
i
s
g
i
v
e
n
b
y
(
6
)
.
IRS
(
)
=
l
og
2
(
1
+
(
|
ℎ
|
+
∑
=
1
|
[
ℎ
]
[
ℎ
]
|
)
2
2
)
(
6
)
T
h
e
o
p
tim
ized
I
R
S
m
o
d
el
(
)
p
r
o
p
o
s
ed
in
th
is
w
o
r
k
en
h
a
n
ce
s
p
er
f
o
r
m
a
n
ce
b
y
r
ed
u
ci
n
g
th
e
to
tal
N
to
,
w
h
ile
i
m
p
r
o
v
i
n
g
p
h
ase
-
s
h
i
f
t
o
p
ti
m
izatio
n
.
I
ts
r
e
f
lectio
n
m
atr
ix
is
=
(
1
,
…
,
)
.
T
h
e
r
ec
eiv
ed
s
ig
n
al
a
n
d
ac
h
ie
v
ab
le
r
ate
f
o
r
th
is
m
o
d
el
ar
e:
=
(
ℎ
+
ℎ
Θ
ℎ
)
√
+
(
7
)
(
)
=
l
og
2
(
1
+
(
|
ℎ
|
+
∑
=
1
|
[
ℎ
]
[
ℎ
]
|
)
2
2
)
(
8
)
w
h
er
e
is
th
e
i
n
f
o
r
m
atio
n
s
i
g
n
al
o
f
u
n
it
-
p
o
w
er
,
(
)
is
th
e
p
o
wer
o
f
tr
an
s
m
i
s
s
io
n
,
an
d
∼
(
0
,
2
)
,
is
th
e
n
o
i
s
e
f
o
r
th
e
o
p
ti
m
ized
s
y
s
te
m
.
3
.
2
.
O
ptim
iza
t
io
n
p
ro
ble
m
T
h
e
co
r
e
o
b
j
ec
tiv
e
is
to
o
p
tim
ize
I
R
S
-
as
s
is
ted
co
m
m
u
n
ica
tio
n
w
i
th
t
w
o
k
e
y
g
o
als:
m
a
x
i
m
izi
n
g
E
E
an
d
m
i
n
i
m
izi
n
g
t
h
e
n
u
m
b
er
o
f
I
R
S e
le
m
e
n
t
s
.
T
h
is
is
f
o
r
m
ali
ze
d
as a
m
u
lt
i
-
o
b
j
ec
tiv
e
o
p
ti
m
izatio
n
p
r
o
b
lem
:
,
[
−
(
,
)
,
]
(
9
)
S
u
b
j
ec
t to
:
r
ate
co
n
s
tr
ain
t:
(
,
)
≥
; Po
w
er
co
n
s
tr
ai
n
t:
∥
∥
2
≤
; E
le
m
e
n
t li
m
it:
≤
.
In
(
1
0
)
ca
n
b
e
u
s
ed
to
d
eter
m
i
n
e
E
E
b
y
co
m
p
ar
i
n
g
th
e
r
ate
t
o
th
e
to
tal
en
er
g
y
co
n
s
u
m
p
tio
n
.
=
/
(
1
0
)
w
h
er
e
th
e
to
tal
p
o
w
er
co
n
s
u
m
p
tio
n
(
)
in
clu
d
es:
T
r
an
s
m
i
t
p
o
w
er
p
,
s
tatic
p
o
w
er
co
n
s
u
m
p
ti
o
n
(
s
o
u
r
ce
)
an
d
(
d
esti
n
atio
n
)
,
I
R
S e
le
m
e
n
t p
o
w
er
d
is
s
ip
atio
n
T
h
u
s
,
th
e
o
p
tim
izatio
n
p
r
o
b
le
m
b
ec
o
m
e
s
:
,
Θ
(
,
Θ
)
(
)
(
1
1
)
S
u
b
j
ec
t to
:
m
in
i
m
u
m
r
ate
r
eq
u
ir
e
m
en
t:
(
,
)
≥
;
m
a
x
i
m
u
m
I
R
S e
le
m
en
ts
:
≤
.
3
.
3
.
Si
m
ula
t
io
n
s
et
up
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
p
er
f
o
r
m
an
ce
i
s
ev
al
u
ated
th
r
o
u
g
h
s
i
m
u
latio
n
s
u
s
in
g
th
e
3
r
d
g
en
er
atio
n
p
ar
tn
er
s
h
ip
p
r
o
j
ec
t
(
3
GPP
)
u
r
b
an
m
icr
o
(
UM
i)
ch
an
n
el
m
o
d
el
at
a
ca
r
r
ier
f
r
eq
u
en
c
y
o
f
3
GHz
,
in
co
r
p
o
r
atin
g
b
o
th
NL
OS
a
n
d
L
O
S
co
n
d
iti
o
n
s
f
o
r
d
is
tan
ce
s
≥
10
m
.
T
h
e
an
ten
n
a
g
ai
n
s
f
o
r
th
e
tr
an
s
m
it
ter
(
)
an
d
r
ec
eiv
er
(
)
ar
e
s
et
to
5
d
B
i,
a
n
d
th
e
d
esti
n
atio
n
d
ev
ice
u
s
e
s
an
o
m
n
id
ir
ec
tio
n
a
l
an
te
n
n
a
(
0
d
B
i
)
.
Sh
ad
o
w
f
ad
in
g
is
n
eg
lecte
d
.
T
h
e
ch
an
n
e
l g
ai
n
v
a
lu
e
s
f
o
r
U
Mi
-
L
OS
a
n
d
UM
i
-
N
L
OS
ar
e
co
m
p
u
ted
u
s
in
g
:
(
)
[
]
=
+
+
{
−
37
.
5
−
22
l
og
10
(
1
m
)
if
L
OS
−
35
.
1
−
36
.
7
l
og
10
(
1
m
)
if
NL
OS
(
1
2
)
Op
ti
m
ized
UM
i
-
L
OS c
h
an
n
el
g
ain
,
r
es
u
lti
n
g
f
r
o
m
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
ap
p
licatio
n
,
is
d
er
iv
ed
as:
(
)
[
]
=
Op
tim
ize
d
[
+
+
{
−
37
.
5
−
22
l
og
10
(
/
1
m
)
}
]
opt
imize
d
L
OS
(
1
3
)
T
h
e
s
i
m
u
la
tio
n
p
r
esu
p
p
o
s
es
t
h
at
th
e
I
R
S
an
d
th
e
s
o
u
r
ce
ar
e
7
0
m
eter
s
ap
ar
t.
T
h
er
e
is
a
1
0
m
r
an
g
e
b
et
w
ee
n
th
e
s
o
u
r
ce
an
d
th
e
d
esti
n
atio
n
/
u
s
er
.
T
h
e
to
tal
p
o
w
er
co
n
s
u
m
p
t
io
n
(
)
is
ca
lcu
lated
f
o
r
th
e
s
tan
d
ar
d
I
R
S
m
o
d
el,
th
e
o
p
ti
m
ized
I
R
S
m
o
d
el,
an
d
th
e
SIS
O
ca
s
e
u
s
i
n
g
(
1
4
)
-
(
1
6
)
,
r
esp
ec
tiv
el
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
E
n
h
a
n
cin
g
r
eflec
tive
elem
en
ts
o
f in
tellig
en
t reflective
s
u
r
fa
ce
s
in
6
G
…
(
Je
h
a
n
K
a
d
h
im
S
h
a
r
ee
f A
l
-
S
a
fi
)
27
otal
(
)
=
(
)
+
+
+
(
1
4
)
otal
(
)
=
(
)
+
+
+
(
1
5
)
otal
=
+
+
(
1
6
)
T
h
e
o
p
ti
m
al
n
u
m
b
er
o
f
ele
m
e
n
ts
f
o
r
th
e
s
ta
n
d
ar
d
an
d
o
p
ti
m
ized
I
R
S
m
o
d
els
is
d
eter
m
i
n
ed
in
(
1
7
)
,
(
1
8
)
,
r
esp
ec
tiv
el
y
.
(
)
=
√
(
2
−
1
)
2
2
3
−
1
√
(
1
7
)
(
)
=
√
(
2
−
1
)
2
2
3
−
1
√
(
1
8
)
T
h
e
b
asic
p
a
r
am
eter
s
ar
e:
n
o
i
s
e
p
o
w
er
s
p
ec
tr
al
d
en
s
i
t
y
o
f
-
1
7
4
d
B
m
/Hz,
n
o
is
e
p
o
w
er
o
f
-
9
4
d
B
m
,
a
n
o
is
e
f
i
g
u
r
e
o
f
1
0
d
B
,
a
b
an
d
w
id
t
h
o
f
1
0
MH
z,
a
f
ix
ed
s
o
u
r
ce
-
d
es
tin
at
io
n
d
is
ta
n
ce
o
f
1
0
m
,
an
d
p
o
w
er
d
is
s
ip
atio
n
p
er
I
R
S
ele
m
e
n
t.
=
5
,
s
o
u
r
ce
an
d
d
esti
n
ati
o
n
h
ar
d
w
ar
e
p
o
w
er
co
n
s
u
m
p
tio
n
=
=
100
a
m
a
x
i
m
u
m
d
ata
r
ate
≤
10
/
/
,
r
ef
lectio
n
co
ef
f
icie
n
t
=
1
,
an
d
p
o
w
er
a
m
p
li
f
ier
ef
f
icie
n
c
y
=
0
.
5
.
T
h
e
E
E
f
o
r
ea
ch
ca
s
e
-
SISO
,
s
tan
d
ar
d
I
R
S,
an
d
o
p
tim
iz
ed
I
R
S
v
ia
(
1
9
)
-
(
2
1
)
,
r
esp
e
ctiv
el
y
is
ev
alu
a
ted
as a
f
u
n
ct
io
n
o
f
N,
w
it
h
t
h
e
SISO
ca
s
e
(
=
0
)
s
er
v
in
g
as th
e
b
aseli
n
e.
=
/
otal
(
1
9
)
=
/
otal
(
2
0
)
=
/
otal
(
2
1
)
4.
RE
SU
L
T
S
AND
D
I
SCU
SS
I
O
N
T
h
e
s
i
m
u
latio
n
a
n
al
y
s
is
an
d
i
t
s
r
es
u
lts
ar
e
d
escr
ib
ed
in
t
h
i
s
s
ec
tio
n
.
Ho
w
ev
er
,
t
h
e
p
r
o
p
o
s
ed
DS
-
P
SO
w
a
s
u
s
ed
to
ev
alu
ate
t
h
e
i
m
p
r
o
v
ed
I
R
S
m
o
d
el
b
y
co
m
p
ar
i
n
g
it
w
it
h
p
r
ev
io
u
s
s
t
u
d
ies
a
n
d
b
en
ch
m
ar
k
s
y
s
te
m
s
.
T
h
ese
r
esu
lt
s
ar
e
i
n
ter
p
r
eted
,
th
ei
r
i
m
p
o
r
ta
n
ce
is
e
m
p
h
asized
,
an
d
th
e
s
t
u
d
y
’
s
li
m
itatio
n
s
ar
e
ac
k
n
o
w
led
g
ed
i
n
th
e
d
is
c
u
s
s
io
n
.
4
.
1
.
Ana
ly
s
is
o
f
EE
a
nd
re
f
lect
iv
e
ele
m
ent
s
T
h
e
s
u
cc
ess
f
u
l
o
p
ti
m
iza
tio
n
o
f
th
e
cr
u
cial
tr
ad
e
-
o
f
f
b
et
w
ee
n
E
E
an
d
N
is
th
e
m
ain
d
is
co
v
er
y
o
f
th
i
s
w
o
r
k
.
T
h
e
s
u
g
g
es
ted
DS
-
PSO
-
o
p
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I
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b
i
t
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le
w
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th
6
4
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l
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5
0
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k
m
.
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h
i
s
is
a
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ly
d
if
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w
h
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is
l
es
s
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ig
n
if
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th
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.
W
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h
1
-
6
6
e
l
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en
t
s
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th
e
s
t
an
d
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m
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f
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(
s
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,
s
am
p
l
e
1
9
)
a
ch
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v
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o
f
9
9
.
1
9
-
1
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0
.
2
M
b
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t
/
J
o
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l
e
,
w
h
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ch
is
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m
p
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r
a
b
l
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t
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th
e
r
h
ig
h
-
p
e
r
f
o
r
m
in
g
w
o
r
k
s
l
ik
e
[
6
]
,
[
1
9
]
.
H
o
w
ev
e
r
,
b
y
c
o
m
b
in
in
g
an
u
n
p
r
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c
e
d
en
t
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d
E
E
(
3
6
6
M
b
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t
/
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o
u
l
e
)
w
it
h
m
in
im
a
l
h
a
r
d
w
a
r
e
(
7
e
l
em
e
n
t
s
)
,
th
e
D
S
-
PS
O
-
o
p
t
im
iz
e
d
I
R
S
(
s
e
e
T
a
b
l
e
3
,
s
am
p
l
es
2
0
-
2
1
)
s
e
ts
a
n
e
w
s
ta
n
d
a
r
d
f
o
r
t
e
r
r
e
s
t
r
i
a
l
n
e
tw
o
r
k
s
.
Ov
e
r
a
7
0
-
m
e
t
e
r
r
an
g
e
,
th
is
p
e
r
f
o
r
m
an
c
e
le
v
e
l
c
an
b
e
s
u
s
ta
i
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e
d
.
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lt
h
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g
h
th
e
m
o
d
el
s
in
[
1
0
]
an
d
[
2
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c
o
n
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en
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te
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Vs
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s
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,
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el
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cy
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d
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m
p
l
ex
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ty
f
o
r
i
n
t
e
r
n
e
t
o
f
th
in
g
s
(
I
o
T
)
a
n
d
d
e
n
s
e
u
r
b
an
a
p
p
l
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c
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w
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r
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w
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s
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c
r
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l
.
4
.
3
.
Abla
t
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n a
lg
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it
h
m
ic
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4
d
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o
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ates
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d
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f
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ce
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f
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D
S
-
P
SO
a
lg
o
r
ith
m
b
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v
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n
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m
ax
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m
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m
n
u
m
b
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(
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w
h
ile
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ta
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=
50
p
ar
ticles.
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ab
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A
b
latio
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D
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M
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A
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M
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4
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~
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4
%
o
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p
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man
c
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~
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%
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p
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c
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1
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b
a
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i
n
e
)
3
6
6
P
e
a
k
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r
f
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man
c
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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T
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p
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tr
o
l
,
Vo
l.
24
,
No
.
1
,
Feb
r
u
ar
y
20
26
:
22
-
33
30
T
h
e
r
esu
lts
,
w
h
ic
h
ar
e
co
m
p
il
ed
in
T
a
b
le
4
,
s
h
o
w
t
h
at
DS
-
P
SO
co
n
ti
n
u
e
s
to
p
er
f
o
r
m
ad
m
i
r
ab
ly
e
v
en
w
h
e
n
its
iter
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n
b
u
d
g
et
is
li
m
ited
.
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h
e
p
er
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r
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a
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ce
w
as
g
r
ea
t a
t 2
5
0
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e
n
b
et
ter
at
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b
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h
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h
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m
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m
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t
h
an
th
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S
s
tan
d
ar
d
m
o
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el.
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h
is
s
h
o
w
s
t
h
at
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S
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P
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ca
n
f
in
d
a
s
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l
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tio
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at
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s
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m
o
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t
p
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f
ec
t
in
a
s
h
o
r
t
a
m
o
u
n
t
o
f
ti
m
e.
T
h
is
is
a
k
e
y
f
ea
t
u
r
e
f
o
r
r
ea
l
-
ti
m
e
ap
p
s
th
at
d
o
n
’
t
h
a
v
e
a
lo
t
o
f
p
r
o
ce
s
s
i
n
g
p
o
w
er
.
T
h
e
alg
o
r
ith
m
ca
n
e
f
f
ec
tiv
e
l
y
b
ala
n
ce
ex
p
lo
r
atio
n
an
d
ex
p
lo
itat
io
n
th
an
k
s
to
its
d
u
al
-
to
p
o
lo
g
y
d
esi
g
n
,
w
h
ic
h
f
ac
i
litate
s
r
ap
id
an
d
s
tab
le
co
n
v
er
g
e
n
ce
.
4
.
4
.
Dis
cus
s
io
n
Dis
en
tan
g
li
n
g
h
ar
d
w
ar
e
co
m
p
lex
it
y
f
r
o
m
E
E
is
th
e
p
r
im
ar
y
o
b
j
ec
tiv
e,
as
s
h
o
w
n
in
s
ec
tio
n
4
.
1
.
T
h
e
n
u
m
b
er
o
f
co
m
p
o
n
en
t
s
in
co
n
v
e
n
tio
n
al
m
o
d
els
d
ec
r
ea
s
es
as
th
eir
u
til
it
y
i
n
cr
ea
s
es.
Ho
w
e
v
er
,
th
e
o
p
ti
m
ized
I
R
S
is
m
o
s
t
e
f
f
ec
tiv
e
w
h
e
n
u
s
ed
w
it
h
f
e
w
co
m
p
o
n
en
ts
.
Fo
r
6
G
n
et
w
o
r
k
s
to
f
u
n
ctio
n
w
ell
in
t
h
e
lo
n
g
r
u
n
,
A
I
-
d
r
iv
en
o
p
ti
m
iza
tio
n
is
cr
u
cial.
Ma
n
y
f
ee
l t
h
is
i
s
n
o
t t
h
e
p
r
o
p
e
r
w
a
y
f
o
r
th
e
I
R
S to
o
p
er
ate.
W
ith
th
is
f
r
es
h
p
er
s
p
ec
tiv
e,
it
o
u
ts
h
in
e
s
ev
e
n
t
h
e
m
o
s
t
w
ell
-
th
o
u
g
h
t
-
o
u
t
s
tr
ate
g
ie
s
.
A
cc
o
r
d
in
g
to
th
e
co
m
p
ar
is
o
n
in
s
ec
tio
n
4
.
2
,
th
e
m
aj
o
r
it
y
o
f
p
r
io
r
r
esear
ch
h
as
co
n
ce
n
tr
ated
o
n
eith
er
o
p
er
atio
n
al
ef
f
icie
n
c
y
,
w
h
ic
h
o
f
te
n
r
eq
u
ir
es
a
lar
g
e
n
u
m
b
er
o
f
co
m
p
o
n
e
n
ts
[
1
3
]
,
[
2
6
]
,
o
r
in
cr
ea
s
ed
E
E
,
w
h
ich
t
y
p
icall
y
r
eq
u
ir
es
a
s
m
al
ler
n
u
m
b
er
o
f
ele
m
e
n
ts
[
6
]
,
[
1
9
]
.
C
o
n
tr
ar
il
y
,
o
u
r
r
esear
ch
d
e
m
o
n
s
tr
ates
a
m
o
r
e
r
ef
in
e
d
eq
u
ilib
r
iu
m
.
T
h
e
f
i
n
est
an
d
m
o
s
t
ef
f
icien
t
I
R
S
is
th
e
o
n
e
th
at
h
a
s
b
ee
n
en
h
an
ce
d
w
it
h
DS
-
P
SO.
I
n
h
i
g
h
l
y
cr
o
w
d
e
d
m
etr
o
p
o
litan
r
eg
io
n
s
,
t
h
is
co
m
b
in
atio
n
tac
k
les
s
ca
lab
ilit
y
an
d
co
s
t h
ea
d
-
o
n
.
Sectio
n
4
.
3
p
r
o
o
f
o
f
al
g
o
r
ith
m
ic
r
o
b
u
s
t
n
e
s
s
d
e
m
o
n
s
tr
ates
th
e
m
et
h
o
d
’
s
g
e
n
er
aliza
b
ilit
y
.
T
h
an
k
s
to
its
f
ast
an
d
n
ea
r
-
o
p
ti
m
al
s
o
l
u
tio
n
f
in
d
i
n
g
ca
p
ab
ilit
ies,
t
h
e
DS
-
P
SO
m
eth
o
d
i
s
f
an
ta
s
tic
.
A
p
p
licatio
n
s
t
h
at
o
p
er
ate
in
r
ea
l
-
ti
m
e
an
d
ad
ap
t
to
d
y
n
a
m
ic
w
ir
ele
s
s
s
ett
in
g
s
r
el
y
o
n
th
i
s
.
Als
o
,
it
is
a
m
ea
s
u
r
e
o
f
p
er
f
o
r
m
an
ce
.
I
n
co
n
cl
u
s
io
n
,
th
is
s
t
u
d
y
ill
u
s
t
r
ates
th
at
an
ad
v
an
ce
d
,
lo
w
-
co
m
p
lex
i
t
y
A
I
alg
o
r
it
h
m
li
k
e
D
S
-
P
SO
i
s
cr
u
cia
l
to
s
u
r
p
as
s
m
i
n
o
r
i
m
p
r
o
v
e
m
en
ts
an
d
en
ab
le
th
e
d
e
v
elo
p
m
e
n
t
o
f
a
n
e
w
cla
s
s
o
f
h
i
g
h
-
e
f
f
icie
n
c
y
,
lo
w
-
co
m
p
le
x
it
y
I
R
S i
m
p
le
m
e
n
tatio
n
s
f
o
r
f
u
t
u
r
e
n
et
w
o
r
k
s
.
4
.
5
.
P
r
a
ct
ica
l
li
m
it
a
t
io
ns
a
nd
f
uture
w
o
rk
I
n
th
is
s
tu
d
y
,
o
u
r
r
esu
lts
s
h
o
w
s
i
g
n
i
f
ican
t
s
u
p
er
io
r
it
y
.
T
h
is
s
tu
d
y
m
u
s
t
ac
k
n
o
w
led
g
e
it
s
p
r
ac
tical
li
m
ita
tio
n
s
,
th
o
u
g
h
.
R
ea
l
-
w
o
r
ld
d
y
n
a
m
ic
w
ir
ele
s
s
en
v
ir
o
n
m
en
ts
m
a
k
e
it
c
h
alle
n
g
i
n
g
to
o
b
tain
t
h
e
o
p
ti
m
a
l
ch
an
n
el
s
tate
i
n
f
o
r
m
atio
n
(
C
SI)
th
at
th
e
p
r
o
p
o
s
ed
m
o
d
el
ass
u
m
es.
T
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
p
r
o
p
o
s
ed
o
p
tim
izatio
n
al
g
o
r
ith
m
i
s
d
e
ter
io
r
ated
b
y
esti
m
atio
n
er
r
o
r
s
,
f
ee
d
b
ac
k
d
elay
s
,
an
d
o
u
t
-
of
-
d
ate
ch
a
n
n
el
in
f
o
r
m
atio
n
.
T
h
e
ac
cu
r
ac
y
o
f
th
e
ch
a
n
n
el
p
ar
a
m
eter
s
ℎ
a
n
d
ℎ
is
th
e
o
n
l
y
f
ac
to
r
th
at
af
f
ec
t
s
th
e
alg
o
r
ith
m
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ase
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31
5.
CO
NCLU
SI
O
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T
h
e
c
r
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t
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c
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,
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p
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