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c
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3
7
,
N
o
.
2
,
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e
b
r
ua
r
y
20
2
5
,
pp.
829
~
838
I
S
S
N:
2
502
-
4
7
52
,
DO
I
:
10
.
11591/i
j
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3
7
.
i
2
.
pp
829
-
838
829
Jou
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:
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s
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e
y
w
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d
s
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c
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s
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m
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k
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Ha
n
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v
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t
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s
Qua
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n
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R
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s
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l
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c
t
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Th
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s
is
an
o
p
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s
a
r
t
i
c
l
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u
n
d
e
r
the
CC
BY
-
SA
l
i
cen
s
e.
C
or
r
e
s
pon
din
g
A
u
th
or
:
Na
ga
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a
j
Ga
dde
De
pa
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of
A
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t
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f
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d
Da
t
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c
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c
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,
S
J
C
I
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s
t
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t
u
t
e
of
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hn
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g
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c
k
ba
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l
a
pur
,
K
a
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t
a
ka
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n
d
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E
m
a
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l
:
n
a
ga
r
a
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ga
dde
11
@
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c
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m
1.
I
NT
RODU
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us
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1]
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h
a
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Evaluation Warning : The document was created with Spire.PDF for Python.
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In
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a
r
c
h
wor
k
e
x
a
m
i
ne
d
a
v
a
r
i
e
t
y
of
r
e
c
e
n
t
a
l
l
o
c
a
t
i
o
n
of
r
e
s
o
u
r
c
e
m
o
de
l
s
.
Only
a
f
e
w
s
t
ud
i
e
s
[
3]
h
a
v
e
a
t
t
e
m
pt
e
d
to
a
ddr
e
s
s
SA
i
s
s
ue
s
by
c
o
n
s
i
de
r
i
ng
s
t
a
t
i
c
s
e
t
up
v
a
r
i
a
t
i
o
n
s
in
c
o
n
s
t
r
a
i
n
t
a
n
d
o
bj
e
c
t
i
ve
f
u
nc
t
i
o
n
[
4]
.
W
he
n
we
tr
y
to
b
oo
s
t
t
h
e
i
n
t
e
ns
i
t
y
a
n
d
h
e
t
e
r
o
ge
n
e
o
us
n
a
t
ur
e
of
t
h
e
n
e
t
wo
r
k
,
t
h
e
c
o
n
ve
n
t
i
o
na
l
h
e
x
a
go
n
a
l
n
e
t
wo
r
k
s
u
f
f
e
r
s
[
5]
.
To
c
a
p
t
ur
e
t
h
e
s
pa
t
i
a
l
i
n
f
o
r
m
a
t
i
o
n
b
e
ha
vi
o
r
of
de
vi
c
e
s
in
H
W
N
s
,
n
e
w
s
t
ud
i
e
s
a
r
e
f
o
c
us
i
n
g
on
de
s
i
g
ni
n
g
n
o
nli
ne
a
r
ge
o
m
e
t
r
y
us
i
ng
po
i
n
t
pr
o
c
e
s
s
e
s
.
T
h
e
ge
o
m
e
t
r
y
-
ba
s
e
d
m
o
de
l
de
t
e
r
m
i
ne
s
t
h
e
i
n
t
e
r
f
e
r
e
nc
e
s
a
n
d
m
e
e
t
s
t
h
e
pe
r
f
o
r
m
a
n
c
e
r
e
qu
i
r
e
m
e
n
t
s
of
HW
N
s
[
6]
.
P
e
r
f
o
r
m
a
n
c
e
of
t
h
e
n
e
t
wo
r
k
s
uc
h
as
t
h
r
o
ugh
put,
us
e
r
-
a
wa
r
e
pe
r
f
o
r
m
a
nc
e
,
a
n
d
ut
i
l
i
t
y
(
s
uc
h
as
r
a
t
e
of
t
r
a
n
s
m
i
s
s
i
o
n
,
a
n
d
s
i
g
na
l
-
to
-
i
n
t
e
r
f
e
r
e
n
c
e
r
a
t
i
o
)
can
be
s
t
udi
e
d
us
i
ng
t
h
e
s
e
lf
-
s
o
v
e
r
e
i
g
n
po
i
s
s
o
n
-
po
i
n
t
-
pr
o
c
e
s
s
e
s
.
C
h
a
t
to
pa
dhy
a
y
e
t
al.
[
7]
,
t
h
e
po
i
s
o
n
pr
o
c
e
s
s
y
i
e
l
d
s
goo
d
r
e
s
ul
t
s
w
h
e
n
a
ppr
o
xi
m
a
t
e
i
n
t
e
r
f
e
r
e
n
c
e
is
us
e
d.
A
c
c
o
r
di
n
g
to
De
n
g
a
n
d
Ha
e
n
gg
i
[
8]
,
i
n
t
e
r
f
e
r
e
n
c
e
d
i
s
s
e
mi
na
t
i
o
n
is
m
o
de
l
e
d
f
o
r
ge
n
e
r
a
l
ne
t
wor
ks
w
i
t
h
n
on
-
p
o
i
s
s
on
d
e
pl
o
y
m
e
n
ts
,
w
h
i
l
e
in
[
9
]
th
e
e
s
ti
m
a
t
i
on
of
HW
N
i
n
te
r
f
e
r
e
n
c
e
is
d
on
e
.
K
a
l
a
m
k
a
r
a
n
d
H
a
e
n
g
gi
[
9
]
,
t
hi
s
wa
s
e
x
t
e
n
de
d
to
b
ot
h
s
i
n
g
l
e
a
n
d
m
u
l
t
i
-
t
i
e
r
HW
Ns
by
c
o
ns
i
d
e
r
i
n
g
s
i
g
na
l
i
n
t
e
r
f
e
r
e
n
c
e
w
i
t
h
m
e
t
a
-
d
i
s
t
r
i
b
ut
i
o
n
a
n
d
r
e
f
l
e
c
t
s
t
h
e
i
de
a
li
s
t
i
c
m
o
de
l
c
o
n
s
i
d
e
r
i
n
g
b
o
t
h
l
o
n
g
-
t
e
r
m
e
v
o
l
ut
i
o
n
a
n
d
u
l
t
r
a
-
de
n
s
e
f
if
t
h
-
ge
n
e
r
a
t
i
o
n
n
e
t
wor
k
de
pl
o
y
m
e
n
t
s
.
R
e
a
l
-
t
i
m
e
s
a
f
e
t
y
a
pp
l
i
c
a
t
i
o
ns
in
t
h
e
f
ut
ur
e
w
i
ll
r
e
qu
i
r
e
hi
g
h
-
s
pe
e
d
a
n
d
r
e
li
a
bl
e
c
o
m
m
u
ni
c
a
t
i
o
n
in
t
h
e
v
e
hi
c
u
l
a
r
n
e
t
wor
k.
A
c
c
o
r
di
n
g
to
r
e
c
e
n
t
r
e
s
e
a
r
ch
[
10]
,
a
s
ub
-
c
h
a
nne
l
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
e
t
h
o
d
wa
s
pr
e
s
e
n
t
e
d
to
i
m
pr
o
v
e
t
h
e
e
f
f
e
c
t
i
v
e
n
e
s
s
of
t
h
e
v
e
hi
c
l
e
n
e
t
wo
r
k
’
s
e
n
e
r
g
y
u
s
a
ge
a
n
d
m
e
e
t
qua
l
i
t
y
-
of
-
s
e
r
vi
c
e
(
Qo
S
)
a
n
d
r
e
l
i
a
bi
li
t
y
r
e
qu
i
r
e
m
e
n
t
s
in
t
h
e
a
r
c
hi
t
e
c
t
ur
e
of
a
l
l
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
e
e
t
i
n
gs
.
T
h
e
s
t
a
n
da
r
d
w
i
r
e
l
e
s
s
a
n
d
v
e
hi
c
u
l
a
r
ad
-
h
o
c
ne
t
wor
ks
a
r
e
m
o
de
l
e
d
us
i
n
g
c
o
gni
t
i
ve
b
e
h
a
vi
o
r
[
11]
.
Hi
g
h
m
o
bil
i
t
y
w
a
s
s
h
o
wn
to
h
a
ve
a
s
i
g
nif
i
c
a
n
t
i
m
pa
c
t
on
t
h
e
a
c
c
ur
a
c
y
of
c
h
a
nne
l
e
s
t
i
m
a
t
i
o
n
in
t
hi
s
s
t
ud
y
.
A
r
e
s
o
ur
c
e
pr
ovi
s
i
o
ni
ng
m
e
t
h
o
d
m
us
t
c
o
n
s
i
de
r
i
m
pe
r
f
e
c
t
i
o
n
s
of
t
h
e
c
h
a
nn
e
l
-
s
ta
te
i
nf
or
m
a
t
i
o
n
(
C
S
I
)
to
e
n
s
u
r
e
t
h
e
r
e
l
i
a
bi
l
it
y
of
t
h
e
v
e
hi
c
l
e
n
e
tw
or
k
.
C
S
I
-
b
a
s
e
d
a
l
l
o
c
a
t
i
o
n
of
r
e
s
our
c
e
s
m
e
t
h
o
ds
f
or
v
e
hi
c
l
e
-
to
-
de
vi
c
e
c
o
m
m
uni
c
a
t
i
o
n
wa
s
c
o
n
s
i
de
r
e
d
in
[
1
2
]
.
Al
l
f
a
s
t
a
n
d
s
l
ow
di
s
i
n
t
e
gr
a
t
i
ng
c
h
a
r
a
c
te
r
i
s
t
i
c
s
we
re
c
o
n
s
i
de
r
e
d
by
[
13
]
in
th
e
i
r
m
ode
l
of
r
e
s
ou
r
c
e
a
l
l
oc
a
t
i
o
n
.
C
h
e
n
e
t
a
l
.
[
14
]
,
a
ke
r
n
e
l
-
b
a
s
e
d
m
a
c
hi
n
e
-
l
e
a
r
ni
n
g
m
o
de
l
wa
s
de
v
e
l
o
p
e
d
to
p
r
e
d
i
c
t
th
e
s
p
ots
of
de
vi
c
e
s
us
i
n
g
C
S
I
[1
5
]
in
s
ub
-
6
gi
ga
h
e
r
tz
b
a
n
ds
.
N
a
y
a
k
e
t
a
l
.
[
1
6
]
s
h
o
we
d
us
i
n
g
C
S
I
f
or
p
os
i
t
i
o
ni
n
g
s
y
s
te
m
s
wi
l
l
n
ot
i
n
c
u
r
a
d
d
i
t
i
o
n
a
l
c
os
ts
.
W
h
e
n
it
c
o
m
e
s
to
op
t
i
m
i
z
i
n
g
r
e
s
ou
r
c
e
a
l
l
oc
a
t
i
o
n
in
a
ga
m
e
t
h
e
or
y
(
G
T
)
[
1
4]
,
[
1
7
]
t
h
e
r
e
s
ou
r
c
e
a
l
l
o
c
a
t
i
o
n
m
e
t
h
ods
f
or
W
S
Ns
p
r
e
s
e
n
ted
in
[
18
]
ou
tpe
r
f
or
m
e
d
m
a
c
hi
n
e
-
l
e
a
r
ni
n
g
m
ode
l
s
[
1
9
]
.
A
hi
g
h
l
e
v
e
l
of
a
c
c
e
s
s
e
qu
i
t
y
a
n
d
hi
g
h
-
pe
r
f
o
r
m
i
n
g
e
f
f
i
c
i
e
n
c
y
c
a
nn
o
t
be
a
c
hi
e
v
e
d
w
i
t
h
t
h
e
e
xi
s
t
i
n
g
GT
-
b
a
s
e
d
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
o
de
l
s
[
20]
,
[
2
1]
.
Hi
g
h
-
e
f
f
i
c
i
e
n
c
y
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
is
t
h
e
f
o
c
us
of
t
hi
s
r
e
s
e
a
r
c
h
wo
r
k
by
m
o
de
li
ng
a
hy
br
i
d
r
e
s
o
ur
c
e
o
p
t
i
m
i
z
a
t
i
o
n
s
t
r
a
t
e
gy
(
HR
OS)
.
W
i
t
h
t
h
e
h
e
l
p
of
C
S
I
a
n
d
t
h
e
us
e
of
b
o
t
h
c
o
n
t
e
n
t
i
o
n
-
l
e
s
s
a
n
d
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
l
l
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
,
t
h
e
HR
OS
s
c
h
e
m
e
is
e
x
t
r
e
m
e
l
y
e
f
f
i
c
i
e
n
t
in
de
c
r
e
a
s
i
ng
i
n
t
e
r
f
e
r
e
n
c
e
a
n
d
m
a
i
n
t
a
ini
ng
a
hi
g
h
l
e
v
e
l
of
pe
r
f
o
r
m
a
n
c
e
.
B
e
c
a
us
e
of
i
t
s
hi
g
h
t
h
r
o
ugh
pu
t
a
n
d
l
o
w
c
o
l
li
s
i
o
n
r
a
t
e
,
HR
OS
can
a
l
l
o
c
a
t
e
r
e
s
o
ur
c
e
s
f
a
i
r
ly
a
n
d
e
f
f
i
c
i
e
n
t
l
y
to
n
e
w
l
y
j
o
i
ne
d
m
u
l
t
i
-
m
o
de
t
r
a
n
s
mi
s
s
i
o
n
s
.
T
h
e
s
i
g
ni
f
i
c
a
nc
e
of
t
hi
s
r
e
s
e
a
r
c
h
wo
r
k
is
as
f
o
l
l
o
w
s
.
T
h
e
pr
o
p
o
s
e
d
m
o
de
l
de
v
e
l
o
p
s
a
n
o
v
e
l
c
ha
nn
e
l
-
s
t
a
t
e
e
s
t
i
m
a
t
i
o
n
m
o
de
l
us
i
ng
r
e
i
nf
o
r
c
e
m
e
n
t
l
e
a
r
ni
ng
e
m
p
l
o
yi
ng
t
h
e
m
a
r
ko
v
s
t
a
t
e
m
o
de
l
.
De
v
e
l
o
p
e
d
a
HR
OS
to
r
e
duc
e
c
o
l
l
i
s
i
o
n
a
n
d
i
m
pr
o
v
e
o
v
e
r
a
ll
n
e
t
wor
k
t
h
r
o
ugh
put.
T
h
e
HR
OS
is
v
e
r
y
e
f
f
i
c
i
e
n
t
c
o
n
s
i
de
r
i
n
g
hi
g
hly
d
y
na
mi
c
m
o
bil
i
t
y
m
o
de
l
s
i
.
e
.
,
l
o
w
-
hi
g
h
de
n
s
i
t
y
a
n
d
s
p
e
e
d.
T
h
e
HR
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2.
RE
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WORK
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K
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[
22]
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h
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x
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ne
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Ha
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[
23]
,
a
ddr
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[
2
4
],
di
s
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T
a
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[
25
]
,
m
o
de
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d
r
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ur
c
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l
o
c
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W
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et
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.
[
26]
,
a
ddr
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s
s
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d
t
h
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pr
o
bl
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m
of
m
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l
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27]
,
h
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By
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s
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t
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a
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w
m
u
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t
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m
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li
s
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c
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nn
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l
a
c
c
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s
s
i
b
il
i
t
y
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t
h
a
t
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,
∈
as
a
r
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s
u
l
t
of
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m
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e
n
a
t
ur
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,
t
h
e
y
w
i
ll
be
a
bl
e
to
a
c
c
e
s
s
c
h
a
nn
e
l
s
.
F
ur
t
h
e
r
m
o
r
e
,
t
h
e
r
e
a
r
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s
o
m
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m
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ga
m
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t
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ll
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ll
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t
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w
ly
-
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o
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d
m
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m
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m
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m
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ti
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a
d
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r
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d
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a
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d
m
a
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pe
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t
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t
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i
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m
a
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xi
s
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m
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A
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in
H
R
OS,
whi
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s
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l
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w
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s
e
c
t
i
o
n
s
of
t
hi
s
wo
r
k.
3.
2.
Cont
e
n
t
ion
l
e
s
s
-
b
as
e
d
r
e
s
ou
r
c
e
op
t
im
iz
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io
n
s
t
r
at
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gy
T
h
e
c
h
a
nn
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l
is
a
s
s
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g
n
e
d
r
a
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do
m
ly
to
m
u
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m
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de
m
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e
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e
r
m
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s
w
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t
h
r
a
n
do
m
i
z
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d
t
i
m
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o
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pe
r
i
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ds
in
a
c
o
n
t
e
n
t
i
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-
l
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s
s
-
b
a
s
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d
a
ll
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c
a
t
i
o
n
of
r
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s
o
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c
e
s
a
l
go
r
i
t
hm
.
O
n
c
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t
h
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t
i
m
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o
ut
pe
r
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d
h
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a
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h
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nne
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c
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t
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be
d
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9)
.
(
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=
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⁄
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T
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10)
.
{
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(
10)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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do
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52
Hy
br
id
r
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s
ou
r
c
e
opti
miz
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on
s
tr
ategy
in
he
ter
oge
ne
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w
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le
s
s
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s
(
N
agar
aj
Gadde
)
833
W
h
e
r
e
(
)
is
e
qua
l
to
1.
C
o
n
t
e
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t
i
o
n
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l
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s
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b
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s
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d
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t
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a
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t
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s
s
u
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c
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t
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r
=
(
1
,
2
,
3
,
…
,
)
t
h
a
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s
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in
a
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)
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t
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c
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pe
c
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(
11)
f
o
r
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s
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d.
{
=
⌈
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∑
∈
⌉
+
0
=
1
,
2
,
3
,
…
,
∑
=
=
1
(
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wh
e
r
e
0
∈
{
0
,
1
,
2
,
3
,
…
,
⌈
|
|
+
(
|
|
−
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)
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∈
⁄
⌉
−
⌈
|
|
−
∑
≠
,
∈
∑
∈
⁄
⌉
−
1
}
.
3.
3.
Cont
e
n
t
ion
-
b
as
e
d
r
e
s
ou
r
c
e
op
t
im
iz
at
ion
s
t
r
at
e
gy
T
hi
s
s
e
c
t
i
o
n
d
i
s
c
us
s
e
s
t
h
e
wo
r
ki
n
g
of
a
c
o
n
t
e
n
t
i
o
n
-
ba
s
e
d
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s
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c
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o
p
t
i
m
i
z
a
t
i
o
n
s
t
r
a
t
e
g
y
.
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h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
m
o
de
l
r
e
duc
e
s
t
h
e
pr
o
b
a
bil
i
t
y
of
c
ha
nn
e
l
c
o
l
li
s
i
o
n
w
i
t
h
de
c
e
n
t
r
e
s
o
u
r
c
e
u
t
i
li
z
a
t
i
o
n
.
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h
e
m
o
de
l
a
do
pt
s
a
p
r
o
b
a
bil
i
s
t
i
c
-
b
a
s
e
d
c
o
m
put
a
t
i
o
n
m
o
de
l
.
In
a
m
a
nne
r
l
i
ke
t
h
e
c
o
n
t
e
n
t
i
o
n
-
l
e
s
s
-
ba
s
e
d
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
e
t
h
o
d,
t
h
e
f
o
l
l
o
w
i
ng
e
x
pr
e
s
s
i
o
n
can
be
us
e
d
to
de
t
e
r
m
i
ne
t
h
e
b
a
n
dw
i
dt
h
r
a
t
e
of
e
a
c
h
m
u
l
t
i
-
m
o
de
m
o
bil
e
-
t
e
r
m
i
na
l
in
t
h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
l
l
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
e
t
h
o
d.
(
)
=
(
1
−
)
−
1
(
12)
In
(
12)
r
e
pr
e
s
e
n
t
s
t
h
e
pr
o
b
a
bil
i
t
y
of
t
h
e
c
h
a
nn
e
l
.
To
i
nc
r
e
a
s
e
t
h
e
b
a
n
d
w
i
dt
h
,
we
m
a
ke
′
(
)
as
0
,
t
h
e
n
,
=
1
,
a
n
d
us
i
n
g
t
hi
s
t
h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
ll
o
c
a
t
i
o
n
of
t
h
e
r
e
s
o
ur
c
e
m
e
t
h
o
d
is
o
b
t
a
i
n
e
d.
T
h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
l
l
o
c
a
t
i
o
n
of
t
h
e
r
e
s
o
ur
c
e
is
g
i
v
e
n
us
i
ng
(
13)
.
(
)
=
1
(
1
−
1
)
−
1
(
13)
F
o
r
t
h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
ll
o
c
a
t
i
o
n
of
t
h
e
r
e
s
o
ur
c
e
m
e
t
h
o
d
(
)
=
(
1
−
1
⁄
)
−
1
s
h
o
ul
d
be
in
b
e
t
we
e
n
t
h
e
′′
(
)
>
0
a
n
d
′
(
)
<
0
.
If
t
h
e
g
o
e
s
to
an
i
nf
i
n
i
t
e
s
t
a
t
e
,
t
h
e
n
t
h
e
tot
a
l
b
a
n
dw
i
dt
h
of
t
h
e
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
a
l
l
o
c
a
t
i
o
n
of
t
h
e
r
e
s
o
ur
c
e
m
e
t
h
o
d
c
h
a
n
ge
s
to
(
14)
.
l
im
→
∞
(
)
=
1
ℱ
(
14)
T
h
e
ut
i
li
t
y
f
u
n
c
t
i
o
n
f
o
r
t
h
e
c
o
n
t
e
n
t
i
o
n
-
ba
s
e
d
a
ll
o
c
a
t
i
o
n
of
t
h
e
r
e
s
o
ur
c
e
m
e
t
h
o
d
f
o
r
t
h
e
m
u
l
t
i
-
m
o
de
m
o
bil
e
-
t
e
r
m
i
na
l
is
g
i
ve
n
us
i
ng
(
15)
.
{
}
=
1
(
1
−
1
)
−
1
(
15)
It
is
v
e
r
y
d
i
f
f
i
c
u
l
t
to
e
x
t
r
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c
t
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r
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a
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q
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c
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e
n
t
i
o
n
-
ba
s
e
d
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
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s
m
e
t
h
o
d,
unl
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ke
a
c
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t
i
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-
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ba
s
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d
r
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s
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ur
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a
l
l
o
c
a
t
i
o
n
s
t
r
a
t
e
gy
.
A
s
s
u
m
e
we
’
re
p
l
a
y
i
ng
a
ga
m
e
of
r
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s
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ur
c
e
a
c
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s
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h
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m
u
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r
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r
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-
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d
a
n
d
t
h
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c
h
a
nn
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l
s
a
r
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-
s
i
z
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d,
a
n
d
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a
c
h
m
u
l
t
i
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m
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de
m
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bil
e
t
e
r
m
i
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l
s
e
que
n
t
i
a
ll
y
s
e
l
e
c
t
s
an
a
v
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il
a
bl
e
c
h
a
nn
e
l
.
B
e
f
o
r
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c
h
a
nn
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a
c
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s
,
o
n
e
m
u
l
t
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m
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m
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r
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s
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l
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c
t
s
t
h
e
b
e
s
t
s
o
l
ut
i
o
n
(
B
S
)
.
He
n
c
e
,
in
e
a
c
h
i
t
e
r
a
t
i
o
n
of
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
n
a
l
s
,
t
h
e
r
e
is
an
o
pt
i
m
a
l
a
ns
we
r
.
P
e
r
f
e
c
t
n
a
s
h
e
qu
il
i
br
i
u
m
w
i
t
h
a
da
pt
i
v
e
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
a
y
o
nly
be
a
c
hi
e
v
e
d
in
t
hi
s
m
a
nne
r
if
t
h
e
f
o
l
l
o
w
i
ng
c
o
n
d
i
t
i
o
n
s
a
r
e
m
e
t
.
T
h
e
f
o
l
l
o
w
i
ng
c
o
n
d
i
t
i
o
n
m
u
s
t
be
m
e
t
if
t
h
e
r
e
c
e
n
t
ly
j
o
i
ne
d
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
s
h
a
v
e
t
wo
BS
,
n
a
m
e
ly
1
a
n
d
2
.
By
a
gr
e
e
i
ng
to
e
m
pt
y
r
e
s
o
ur
c
e
s
wh
e
n
no
m
u
l
t
i
-
m
o
de
m
o
bi
l
e
t
e
r
m
i
na
l
s
p
i
c
k
t
h
e
m
,
a
n
d
t
h
e
n
a
gr
e
e
i
n
g
to
a
l
r
e
a
d
y
-
c
h
o
s
e
n
r
e
s
o
ur
c
e
s
wh
e
n
2
a
g
r
e
e
s
,
1
is
e
xc
e
ll
e
n
t
.
W
h
e
n
at
l
e
a
s
t
o
n
e
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
s
e
l
e
c
t
s
a
r
e
s
o
ur
c
e
,
t
h
e
r
e
s
o
ur
c
e
w
i
t
h
t
h
e
m
o
s
t
r
e
a
l
i
s
t
i
c
n
u
m
be
r
of
r
e
s
o
ur
c
e
s
is
pr
e
f
e
r
r
e
d.
In
t
h
e
n
e
x
t
s
e
c
t
i
o
n
,
hi
g
h
-
e
f
f
i
c
i
e
nc
y
r
e
s
o
ur
c
e
a
l
l
o
c
a
t
i
o
n
pr
o
p
o
s
e
s
a
p
l
a
n
to
pr
o
m
ot
e
e
qui
t
a
bl
e
a
n
d
e
f
f
i
c
i
e
n
t
di
s
t
r
i
b
ut
i
o
n
of
a
v
a
il
a
bl
e
r
e
s
o
ur
c
e
s
.
3.
4.
Hyb
r
id
r
e
s
ou
r
c
e
op
t
im
i
z
at
ion
s
t
r
at
e
gy
T
hi
s
h
i
g
h
-
e
f
f
i
c
i
e
nc
y
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
m
e
t
h
o
d
i
n
c
o
r
po
r
a
t
e
s
b
ot
h
t
h
e
c
o
n
t
e
n
t
i
o
n
-
l
e
s
s
-
b
a
s
e
d
a
n
d
c
o
n
t
e
n
t
i
o
n
-
b
a
s
e
d
r
e
s
o
ur
c
e
a
l
l
o
c
a
t
i
o
n
m
e
t
h
o
ds
to
a
tt
a
i
n
pe
r
f
e
c
t
n
a
s
h
e
qu
i
li
br
i
u
m
(
NE
)
in
d
i
s
t
r
i
b
ut
e
d
s
y
s
t
e
m
s
w
i
t
h
e
qua
l
r
e
s
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ur
c
e
a
l
l
o
c
a
t
i
o
n
e
f
f
i
c
i
e
nc
y
.
F
ur
t
h
e
r
m
o
r
e
,
A
l
go
r
i
t
hm
1
e
x
p
l
a
i
ns
h
o
w
t
h
e
hi
g
h
-
e
f
f
i
c
i
e
n
c
y
a
l
l
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
s
t
r
a
t
e
g
y
wo
r
ks
.
E
a
c
h
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
in
a
l
go
r
i
t
hm
1
r
a
n
do
m
ly
s
e
l
e
c
t
s
a
t
i
m
e
to
b
a
c
k
o
f
f
,
a
n
d
t
h
e
n
t
h
e
t
i
m
e
o
u
t
t
i
m
e
b
e
g
i
ns
.
Dur
i
n
g
t
h
e
t
i
m
e
-
o
u
t
t
i
m
e
,
t
h
e
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
s
e
l
e
c
t
s
a
c
o
m
m
u
ni
c
a
t
i
o
n
c
h
a
nn
e
l
b
a
s
e
d
on
t
h
e
b
e
s
t
s
o
l
ut
i
o
n
it
ha
s
a
l
r
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a
d
y
f
o
un
d
w
i
t
h
t
h
a
t
c
h
a
nn
e
l
.
To
Evaluation Warning : The document was created with Spire.PDF for Python.
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e
s
t
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bli
s
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i
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m
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l
t
i
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m
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t
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in
a
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a
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a
s
t
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i
r
s
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i
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h
e
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m
u
l
t
i
-
m
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m
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e
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t
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r
m
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.
It
h
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s
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n
pr
o
v
e
n
t
h
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s
i
m
u
l
a
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e
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n
m
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a
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e
ve
s
be
tt
e
r
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e
t
wor
k
pe
r
f
o
r
m
a
n
c
e
a
n
d
f
a
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r
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ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
t
h
a
n
t
h
e
us
ua
l
a
ll
o
c
a
t
i
o
n
of
r
e
s
o
ur
c
e
s
s
c
h
e
m
e
.
Al
go
r
i
t
hm
1.
R
e
s
o
ur
c
e
o
p
t
i
mi
z
a
t
i
o
n
s
t
r
a
t
e
gy
1. Obtain
available resource/channel
through sensing operation
2.
Update
and
arrange
available
channel
[
1
,
2
,
3
,
…
,
]
in
decreasing
order
for
respective
time
.
3.
Every
MMT
that
looks
for
communication
opportunity
chooses
a
random
back
-
off
tim
e
from
(
0
,
↑
)
and the back
-
off time
is initialized.
4. While
present time
≤
(
+
↑
)
do
5. if
the backoff time of MMT
finishes
then
6. if
contentionless
-
based resource allocation scheme
then
7. Select
the BS with a free channel
8. End if
9.
if
contention
-
based resource allocation scheme
then
10. Select
the resource with higher BS.
11. End if
12.
The selected channel id is broadcasted
13. End if
14. End while
15.
Every
MMT
optimizes
its
radio
according
to
the
ideal
channel
and
initializes
communication utilizing desired resource allocation schemes
16. Return
4.
RE
S
UL
T
AND
AN
AL
YSI
S
He
r
e
t
h
e
pe
r
f
o
r
m
a
nc
e
a
c
hi
e
v
e
d
us
i
ng
t
h
e
pr
o
p
o
s
e
d
HR
OS
a
n
d
e
xi
s
t
i
n
g
r
e
s
o
ur
c
e
o
p
t
i
m
i
z
a
t
i
o
n
-
b
a
s
e
d
n
e
t
wo
r
k
s
e
l
e
c
t
i
o
n
(
R
ON
S
)
[
28]
is
e
v
a
l
ua
t
e
d.
E
x
pe
r
im
e
n
t
s
a
r
e
c
o
n
duc
t
e
d
us
i
n
g
an
ns
3
-
b
a
s
e
d
s
im
u
l
a
t
or
n
a
m
e
ly
,
s
im
i
t
s
s
im
u
l
a
t
o
r
[
28]
.
He
r
e
t
h
e
m
u
l
t
i
-
m
o
de
t
e
r
m
i
na
l
us
e
s
d
i
f
f
e
r
e
n
t
ki
n
d
s
of
s
e
r
vi
c
e
c
l
a
s
s
e
s
s
uc
h
as
r
e
a
l
-
t
i
m
e
po
l
l
i
ng
s
e
r
vi
c
e
(
R
T
P
S
)
,
n
o
n
-
r
e
a
l
-
t
i
m
e
po
l
li
ng
s
e
r
vi
c
e
(
NR
T
P
S
)
,
a
n
d
b
e
s
t
e
f
f
o
r
t
(
B
E
)
s
e
r
vi
c
e
a
n
d
m
o
v
e
s
t
h
r
o
ugh
a
ge
o
gr
a
phi
c
a
l
a
r
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a
c
o
m
po
s
e
d
of
h
e
t
e
r
o
ge
n
e
o
us
n
e
t
wor
ks
f
o
r
m
e
d
us
i
ng
uni
ve
r
s
a
l
m
o
bil
e
t
e
l
e
c
o
m
m
u
ni
c
a
t
i
o
ns
s
y
s
t
e
m
(
UM
T
S
)
,
w
o
r
l
dw
i
de
i
n
t
e
r
o
pe
r
a
bi
l
i
t
y
f
o
r
m
i
c
r
o
wa
v
e
ac
c
e
s
s
(
W
i
M
A
X)
,
l
o
n
g
-
t
e
r
m
e
v
o
l
ut
i
o
n
(
L
T
E
)
,
a
n
d
w
i
r
e
l
e
s
s
l
o
c
a
l
a
r
e
a
n
e
t
wo
r
k
(
W
L
A
N)
.
T
h
e
MMT
e
xhi
bi
t
s
d
y
na
m
i
c
m
o
bil
i
t
y
t
h
a
t
m
o
v
e
s
t
h
r
o
ugh
hi
g
h
-
de
ns
i
t
y
e
nvi
r
o
nm
e
n
t
s
w
i
t
h
l
o
w
s
pe
e
d
s
uc
h
as
c
i
t
i
e
s
a
n
d
l
e
s
s
-
de
ns
i
t
y
e
nvi
r
o
nm
e
n
t
s
w
i
t
h
hi
g
h
s
pe
e
d
s
uc
h
as
e
x
pr
e
s
s
wa
y
s
a
n
d
hi
g
h
wa
y
s
.
T
h
e
i
e
e
e
802.
11
s
t
a
n
da
r
d
m
e
d
i
u
m
a
c
c
e
s
s
c
o
n
t
r
o
l
(
M
A
C
)
,
r
a
y
l
e
i
g
h
c
h
a
nne
l
w
i
t
h
a
dd
i
c
t
i
v
e
w
hi
t
e
ga
us
s
i
a
n
n
o
i
s
e
is
us
e
d.
T
h
e
l
o
g
-
n
o
r
m
a
l
pa
t
h
l
o
s
s
m
o
de
l
w
i
t
h
m
u
l
t
i
pa
t
h
f
a
d
i
ng
is
c
o
n
s
i
de
r
e
d
w
i
t
h
ba
n
dw
i
dt
h
s
e
t
to
3
-
27
M
b
ps
f
o
r
e
x
pe
r
im
e
n
t
a
n
a
ly
s
i
s
.
T
h
e
us
e
r
s
m
o
v
e
in
t
h
e
n
e
t
wo
r
k
wi
t
h
r
a
n
do
m
n
e
s
s
f
o
l
l
o
w
i
ng
u
nif
o
r
m
d
i
s
t
r
i
b
ut
i
o
n
.
Al
l
t
h
e
s
u
b
s
c
r
i
be
r
s
i
nc
l
ud
i
ng
n
e
w
a
n
d
ha
n
do
f
f
us
e
r
s
,
f
o
l
l
o
w
P
o
i
s
s
o
n
d
i
s
t
r
i
b
ut
i
o
n
.
T
h
e
t
r
a
f
f
i
c
ge
n
e
r
a
t
e
d
in
t
h
e
n
e
t
wo
r
k
m
a
j
o
r
ly
is
n
o
n
-
r
e
a
l
-
t
i
m
e
s
e
r
vi
c
e
w
it
h
40%
,
r
e
a
l
-
t
i
m
e
w
i
t
h
30%
,
a
n
d
b
e
s
t
-
e
f
f
o
r
t
s
wi
t
h
30%
.
T
h
e
t
h
r
o
ugh
put
a
n
d
c
o
l
l
i
s
i
o
n
a
r
e
t
h
e
pe
r
f
o
r
m
a
nc
e
m
e
t
r
i
c
s
us
e
d
f
o
r
v
a
l
i
da
t
i
n
g
t
h
e
HR
OS
a
n
d
R
ON
S
s
c
h
e
m
e
.
4.
1.
T
h
r
ou
gh
p
u
t
p
e
r
f
o
r
m
an
c
e
T
h
e
t
h
r
o
ugh
put
e
x
pe
r
i
e
n
c
e
d
us
i
ng
HR
OS
a
n
d
R
ON
S
is
s
t
ud
i
e
d
by
c
o
ns
i
de
r
i
n
g
d
if
f
e
r
e
n
t
c
a
s
e
s
t
udi
e
s
.
A
tot
a
l
of
t
h
r
e
e
di
f
f
e
r
e
n
t
c
a
s
e
s
t
udi
e
s
s
uc
h
as
v
a
r
y
i
ng
de
ns
i
t
y
,
s
l
o
t
s
,
a
n
d
s
pe
e
d
is
c
o
n
s
i
de
r
e
d
f
o
r
t
h
e
s
i
m
u
l
a
t
i
o
n
s
t
ud
y
.
In
c
a
s
e
1,
t
h
r
o
ugh
put
is
m
e
a
s
ur
e
d
by
va
r
y
i
ng
t
h
e
MMT
s
i
z
e
w
i
t
h
a
f
i
xe
d
s
pe
e
d
of
5
m
/s
.
He
r
e
t
h
e
MMT
s
i
z
e
va
r
i
e
s
f
r
o
m
50,
100,
a
n
d
20
0,
a
n
d
t
h
e
t
h
r
o
ugh
put
o
u
t
c
o
m
e
a
c
hi
e
v
e
d
us
i
ng
t
h
e
HR
OS
s
c
h
e
m
e
a
n
d
R
ON
S
s
c
h
e
m
e
is
gr
a
phi
c
a
l
ly
s
h
o
wn
in
F
i
gur
e
1.
An
a
v
e
r
a
ge
t
h
r
o
ugh
put
e
n
ha
n
c
e
m
e
n
t
of
19.
52%
is
a
c
hi
e
v
e
d
us
i
ng
t
h
e
HR
OS
s
c
h
e
m
e
in
c
o
m
pa
r
i
s
o
n
wi
t
h
t
h
e
R
ON
S
s
c
h
e
m
e
f
o
r
v
a
r
i
e
d
m
u
l
t
i
-
m
o
d
e
m
o
bil
e
t
e
r
m
i
na
l
de
n
s
i
t
y
.
In
c
a
s
e
2,
t
h
r
o
ugh
put
is
m
e
a
s
ur
e
d
by
v
a
r
yi
ng
M
M
T
s
pe
e
d
w
i
t
h
f
i
xe
d
M
T
T
s
s
i
z
e
of
100.
He
r
e
t
h
e
MMT
s
pe
e
d
v
a
r
i
e
s
f
r
o
m
3,
6,
a
n
d
9
m
/
s
a
n
d
t
h
r
o
ugh
put
o
u
t
c
o
m
e
a
c
hi
e
v
e
d
u
s
i
n
g
t
h
e
H
R
O
S
s
c
he
m
e
a
n
d
R
ON
S
s
c
he
m
e
is
gr
a
phi
c
a
ll
y
s
h
o
wn
in
F
i
gu
r
e
2.
An
a
v
e
r
a
ge
t
h
r
o
ugh
put
e
n
h
a
nc
e
m
e
n
t
of
13.
12%
is
a
c
hi
e
ve
d
us
i
ng
t
h
e
HR
OS
s
c
he
m
e
in
c
o
m
p
a
r
i
s
o
n
w
i
t
h
t
h
e
R
ON
S
s
c
he
m
e
f
o
r
v
a
r
i
e
d
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
s
pe
e
ds
.
An
a
v
e
r
a
ge
t
h
r
o
ugh
put
e
n
h
a
n
c
e
m
e
n
t
of
16.
32%
is
a
c
hi
e
ve
d
us
i
n
g
t
h
e
HR
OS
s
c
h
e
m
e
in
c
o
m
pa
r
i
s
o
n
w
i
t
h
t
h
e
R
ON
S
s
c
h
e
m
e
f
o
r
v
a
r
i
e
d
t
i
m
e
s
l
o
t
s
i
z
e
s
.
T
h
e
R
ON
S
m
o
de
l
us
e
s
c
ha
nn
e
l
s
t
a
t
e
i
n
f
o
r
m
a
t
i
o
n
f
o
r
r
e
s
o
u
r
c
e
a
l
l
o
c
a
t
i
o
n
o
pt
i
mi
z
a
t
i
o
n
by
m
i
n
im
i
z
i
ng
co
-
c
h
a
nn
e
l
i
n
t
e
r
f
e
r
e
n
c
e
a
n
d
t
r
i
e
s
to
m
a
xim
i
z
e
t
h
e
r
e
s
o
u
r
c
e
u
t
i
l
i
t
y
;
h
o
we
ve
r
,
t
h
e
m
o
de
l
f
a
il
e
d
to
e
ns
ur
e
a
hi
g
h
e
r
t
h
r
o
ugh
put
wi
t
h
f
a
i
r
n
e
s
s
;
t
h
us
,
r
e
s
u
l
t
i
n
g
in
hi
g
h
e
r
c
o
l
li
s
i
o
n
a
n
d
de
gr
a
de
d
t
h
r
o
ugh
o
ut
e
s
pe
c
i
a
l
ly
f
o
r
hi
g
h
e
r
n
o
de
de
ns
i
t
y
c
o
ns
i
de
r
i
ng
d
i
f
f
e
r
e
n
t
s
c
e
n
a
r
i
o
as
s
h
o
w
n
in
F
i
gur
e
s
1
a
n
d
2.
On
t
h
e
o
t
h
e
r
s
i
de
,
th
e
H
R
OS
l
e
v
e
r
a
ge
s
b
o
t
h
c
o
n
t
e
n
t
i
o
n
a
n
d
c
o
n
t
e
nt
i
o
n
-
l
e
s
s
r
e
s
o
ur
c
e
a
l
l
o
c
a
t
i
o
n
de
s
i
g
n
to
a
s
s
ur
e
hi
g
h
e
r
r
e
s
o
ur
c
e
u
t
i
li
z
a
t
i
o
n
w
i
t
h
m
i
n
i
m
a
l
i
n
t
e
r
f
e
r
e
n
c
e
a
i
d
i
ng
in
i
m
pr
o
v
e
d
t
h
r
o
ugh
pu
t
c
o
n
s
i
d
e
r
i
n
g
d
i
f
f
e
r
e
n
t
s
c
e
n
a
r
i
o
s
as
s
h
o
w
n
in
F
i
gur
e
s
1
a
n
d
2.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
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a
n
J
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c
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4
7
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Hy
br
id
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miz
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ategy
in
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ter
oge
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ous
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(
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agar
aj
Gadde
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835
F
i
gur
e
1
.
T
h
r
o
ugh
put
vs
M
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T
s
F
i
gur
e
2
.
T
h
r
o
ugh
put
vs
s
pe
e
d
4.
2.
Col
l
is
ion
p
e
r
f
o
r
m
an
c
e
He
r
e
c
o
l
li
s
i
o
n
pe
r
f
o
r
m
a
n
c
e
of
t
h
e
HR
OS
s
c
h
e
m
e
a
n
d
R
ON
S
s
c
he
m
e
is
e
va
l
ua
t
e
d
by
v
a
r
yi
ng
n
e
t
wo
r
k
de
n
s
i
t
y
,
s
pe
e
d,
a
n
d
s
l
o
t
s
i
z
e
.
In
c
a
s
e
1,
c
o
l
li
s
i
o
n
is
m
e
a
s
ur
e
d
by
va
r
y
i
ng
t
h
e
MMT
s
i
z
e
w
i
t
h
a
f
i
xe
d
s
pe
e
d
of
5
m
/s
.
He
r
e
t
h
e
MMT
s
i
z
e
is
v
a
r
i
e
d
f
r
o
m
50,
100,
a
n
d
200,
a
n
d
c
o
l
li
s
i
o
n
o
ut
c
o
m
e
a
c
hi
e
v
e
d
us
i
ng
t
h
e
HR
OS
s
c
he
m
e
a
n
d
R
ON
S
s
c
h
e
m
e
is
gr
a
p
hi
c
a
l
ly
s
h
o
wn
in
F
i
gur
e
3.
An
a
v
e
r
a
ge
c
o
l
l
i
s
i
o
n
r
e
duc
t
i
o
n
of
24.
5%
is
a
c
hi
e
v
e
d
us
i
ng
t
h
e
HR
OS
s
c
he
m
e
in
c
o
m
pa
r
i
s
o
n
w
i
t
h
t
h
e
R
ON
S
s
c
he
m
e
f
o
r
v
a
r
i
e
d
m
u
l
t
i
-
m
o
d
e
m
o
bil
e
t
e
r
m
i
na
l
de
n
s
i
t
y
.
In
c
a
s
e
2,
c
o
l
l
i
s
i
o
n
is
m
e
a
s
ur
e
d
by
v
a
r
yi
ng
MMT
s
pe
e
d
w
i
t
h
f
i
xe
d
M
T
T
s
s
i
z
e
of
100.
He
r
e
t
h
e
M
M
T
s
s
pe
e
d
is
v
a
r
i
e
d
f
r
o
m
3,
6,
a
n
d
9
a
n
d
t
h
e
c
o
l
li
s
i
o
n
o
ut
c
o
m
e
a
c
hi
e
v
e
d
us
i
ng
t
h
e
HR
OS
s
c
h
e
m
e
a
n
d
R
ON
S
s
c
he
m
e
is
gr
a
phi
c
a
ll
y
s
h
o
wn
in
F
i
gur
e
4.
An
a
v
e
r
a
ge
c
o
l
li
s
i
o
n
r
e
duc
t
i
o
n
of
28.
27%
is
a
c
hi
e
ve
d
us
i
ng
t
h
e
HR
OS
s
c
he
m
e
in
c
o
m
p
a
r
i
s
o
n
w
i
t
h
t
h
e
R
ON
S
s
c
he
m
e
f
o
r
v
a
r
i
e
d
m
u
l
t
i
-
m
o
de
m
o
bil
e
t
e
r
m
i
na
l
s
pe
e
ds
.
An
a
v
e
r
a
ge
c
o
l
li
s
i
o
n
r
e
duc
t
i
o
n
of
26.
16%
is
a
c
hi
e
ve
d
us
i
n
g
t
h
e
HR
OS
s
c
h
e
m
e
in
c
o
m
pa
r
i
s
o
n
w
i
t
h
t
h
e
R
ON
S
s
c
h
e
m
e
f
o
r
v
a
r
i
e
d
t
i
m
e
s
l
o
t
s
i
z
e
s
.
T
h
e
R
ON
S
m
o
de
l
us
e
s
c
ha
nn
e
l
s
t
a
t
e
i
n
f
o
r
m
a
t
i
o
n
f
o
r
r
e
s
o
u
r
c
e
a
l
l
o
c
a
t
i
o
n
o
pt
i
mi
z
a
t
i
o
n
by
m
i
n
im
i
z
i
ng
co
-
c
h
a
nn
e
l
i
n
t
e
r
f
e
r
e
n
c
e
a
n
d
t
r
i
e
s
to
m
a
xim
i
z
e
t
h
e
r
e
s
o
u
r
c
e
u
t
i
l
i
t
y
;
h
o
we
ve
r
,
t
h
e
m
o
de
l
f
a
il
e
d
to
e
ns
ur
e
a
hi
g
h
e
r
t
h
r
o
ugh
put
wi
t
h
f
a
i
r
n
e
s
s
;
t
h
us
,
r
e
s
u
l
t
i
n
g
in
hi
g
h
e
r
c
o
l
li
s
i
o
n
a
n
d
de
gr
a
de
d
t
h
r
o
ugh
o
ut
e
s
pe
c
i
a
l
ly
f
o
r
hi
g
h
e
r
n
o
de
de
ns
i
t
y
c
o
ns
i
de
r
i
ng
d
i
f
f
e
r
e
n
t
s
c
e
n
a
r
i
o
as
s
h
o
w
n
in
F
i
gur
e
s
3
a
n
d
4.
On
t
h
e
o
t
h
e
r
s
i
de
,
th
e
H
R
OS
l
e
v
e
r
a
ge
s
b
o
t
h
c
o
n
t
e
n
t
i
o
n
a
n
d
c
o
n
t
e
nt
i
o
n
-
l
e
s
s
r
e
s
o
ur
c
e
a
l
l
o
c
a
t
i
o
n
de
s
i
g
n
to
a
s
s
ur
e
hi
g
h
e
r
r
e
s
o
ur
c
e
u
t
i
li
z
a
t
i
o
n
w
i
t
h
mi
nim
a
l
i
n
t
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RE
F
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R
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NC
E
S
[
1]
S
.
D
a
ng,
J
.
Z
h
o
u,
B
.
S
hi
ha
da
,
a
nd
M
.
-
S
.
A
l
o
ui
ni
,
“
T
ow
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r
d
s
pe
c
tr
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l
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n
e
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g
y
e
f
f
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c
ie
n
t
5
G
n
e
tw
o
r
ks
us
in
g
r
e
la
y
e
d
O
F
D
M
w
it
h
in
de
x
m
o
dul
a
ti
o
n
,”
F
r
ont
ie
r
s
i
n C
om
m
uni
c
at
io
ns
and N
e
tw
or
k
s
, vo
l.
2, M
a
r
. 2021, d
o
i:
10.3389/
f
r
c
mn.2021.635295.
[
2]
W
.
ur
R
a
hma
n
a
nd
E
.
-
N
.
H
uh,
“
C
o
nt
e
nt
-
a
w
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r
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o
pt
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ti
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n
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-
a
s
s
is
te
d
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o
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le
v
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e
o
s
tr
e
a
mi
ng,”
M
ul
ti
m
e
di
a
T
ool
s
and A
ppl
ic
at
io
ns
, vo
l.
82, n
o
. 27, pp. 42053
–
42085, N
ov
. 2023,
do
i:
10.1007/s
11042
-
02
3
-
15163
-
w.
[
3]
D
.
L
y
n
c
h, M
. F
e
nt
o
n,
D
. F
a
ga
n, S
.
K
uc
e
r
a
,
H
. C
la
us
s
e
n, a
nd
M
. O
’
N
e
il
l,
“
A
ut
oma
te
d
s
e
l
f
-
o
pt
im
i
z
a
ti
o
n i
n
h
e
te
r
o
g
e
ne
o
us
w
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r
e
l
e
s
s
c
o
mm
uni
c
a
ti
o
ns
ne
twor
ks
,”
I
E
E
E
/
A
C
M
T
r
ans
ac
ti
ons
on
N
e
tw
or
k
in
g
,
vo
l.
27,
n
o
.
1,
pp.
419
–
432,
F
e
b.
2019,
do
i:
10.1109
/
T
N
E
T
.2018.2890547.
[
4]
Y
.
C
he
n,
B
.
A
i,
Y
.
N
iu
,
R
.
H
e
,
Z
.
Z
h
o
ng,
a
nd
Z
.
H
a
n,
“
R
e
s
our
c
e
a
ll
o
c
a
ti
o
n
f
or
de
v
i
c
e
-
to
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de
vi
c
e
c
o
mm
uni
c
a
ti
o
ns
in
mul
ti
-
c
e
ll
mul
ti
-
ba
nd
he
t
e
r
o
g
e
n
e
o
us
c
e
ll
ul
a
r
ne
tw
or
k
s
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
V
e
hi
c
ul
ar
T
e
c
hnol
ogy
,
v
o
l.
68,
n
o
.
5,
pp.
4760
–
4773,
M
a
y
2019, do
i:
10.1109/
T
V
T
.2019.2903858.
[
5]
Z
.
N
a
,
J
.
L
v
,
M
.
Z
ha
ng,
B
.
P
e
ng,
M
.
X
i
o
ng,
a
nd
M
.
G
ua
n,
“
G
F
D
M
ba
s
e
d
w
ir
e
l
e
s
s
po
w
e
r
e
d
c
o
mm
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ic
a
ti
o
n
f
or
c
oo
p
e
r
a
ti
ve
r
e
la
y
s
y
s
te
m
,”
I
E
E
E
A
c
c
e
s
s
, v
o
l.
7, pp. 50971
–
50979, 2019, d
oi
:
10.
1109/AC
C
E
S
S
.2019.2
911176.
[
6]
N
.
D
e
ng
a
nd
M
.
H
a
e
nggi
,
“
S
I
N
R
a
nd
r
a
t
e
m
e
ta
d
is
tr
ib
ut
i
o
ns
f
o
r
H
C
N
s
w
it
h
j
o
in
t
s
p
e
c
t
r
um
a
ll
oc
a
ti
o
n
a
nd
of
f
l
o
a
di
n
g,”
I
E
E
E
T
r
ans
ac
ti
ons
on C
o
m
m
uni
c
at
io
ns
, v
o
l.
67, n
o
. 5, pp. 3709
–
372
2, M
a
y
2019, d
o
i
:
10.1109/
T
C
O
M
M
.2019.2895357.
[
7]
A
.
C
ha
tt
o
pa
dh
y
a
y
,
B
.
B
la
s
z
c
z
y
s
z
y
n,
a
nd
E
.
A
lt
ma
n,
“
T
w
o
-
t
ie
r
c
e
ll
ul
a
r
n
e
tw
o
r
ks
f
or
th
r
o
ughput
ma
x
im
i
z
a
ti
o
n
of
s
ta
ti
c
a
nd
mo
bi
l
e
us
e
r
s
,”
I
E
E
E
T
r
ans
ac
ti
ons
on
W
ir
e
le
s
s
C
om
m
u
ni
c
at
io
ns
,
vo
l.
18,
n
o
.
2,
pp.
997
–
1010,
F
e
b.
2019,
do
i:
10.1109/
T
W
C
.2018.2887386.
[
8]
N
.
D
e
ng
a
nd
M
.
H
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e
nggi
,
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g
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2D
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ks
,”
I
E
E
E
J
our
nal
on
Se
le
c
te
d A
r
e
as
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C
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S
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K
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e
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im
a
ti
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th
e
S
I
R
m
e
ta
di
s
tr
ib
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i
o
n
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ge
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e
r
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l
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e
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ul
a
r
n
e
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o
r
ks
,”
I
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T
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S
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S
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r
r
ie
r
s
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l
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nd
us
e
r
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o
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k
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M
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S
y
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t
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m,”
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2022
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E
E
E
C
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e
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e
nc
e
on
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nt
e
r
di
s
c
ip
li
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s
in
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e
c
hnol
ogy
and
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anage
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e
nt
f
or
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ia
l
I
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ur
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e
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ll
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ti
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n
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he
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L
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6G
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,”
W
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,
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o
r
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R
-
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A
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V
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s
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ig
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nd
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pe
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ti
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f
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ma
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me
-
th
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a
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o
a
c
h,”
I
E
E
E
T
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ti
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le
s
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B
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M
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X
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W
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Z
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W
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r
s
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he
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g
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nd
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r
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ll
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o
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o
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e
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r
r
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e
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M
A
s
y
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e
a
na
l
y
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R
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e
ll
ul
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le
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ve
r
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th
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g
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o
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c
a
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a
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c
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l
a
c
c
e
s
s
a
nd
s
c
he
dul
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g
o
pt
i
mi
z
a
ti
o
n
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UAV
s
w
a
r
ms
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me
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th
e
or
e
ti
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le
a
r
ni
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h
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E
E
O
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ti
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ha
nd
ov
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xe
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ut
i
on
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ma
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hi
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r
ni
ng
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lg
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or
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t
e
r
o
g
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n
e
o
us
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e
l
e
s
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,”
I
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r
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J
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I
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s
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d
ha
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r
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nd
mm
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ve
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gr
a
te
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ve
hi
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ul
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r
n
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r
ks
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E
E
T
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r
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e
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c
c
e
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s
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dg
e
c
o
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M
E
C
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E
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N
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Z
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C
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r
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c
ha
nn
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l
a
s
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nt
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nd
po
w
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r
a
ll
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ti
o
n
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or
no
n
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o
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th
o
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lt
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c
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t
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r
o
g
e
n
e
o
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tr
a
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d
e
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r
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,”
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r
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W
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s
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C
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S
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lg
o
r
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or
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te
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e
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t
e
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e
ne
o
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c
e
ll
ul
a
r
n
e
tw
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r
ks
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o
d
e
li
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.
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H
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ks
w
it
h
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te
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f
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n
c
e
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E
E
E
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Se
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T
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.2020.
3047877.
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