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h
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d
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L
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iq
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e
co
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ld
b
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s
u
m
m
ar
iz
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in
th
r
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p
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in
ts
:
Firstl
y
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c
u
r
r
en
t
s
elec
ti
o
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o
f
h
a
n
d
o
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ased
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n
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ar
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in
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u
lt
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lls
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ad
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f
h
a
n
d
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m
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in
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f
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t
i
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th
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s
ta
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ar
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1
3
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t
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I
n
[
1
5
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1
6
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,
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m
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i
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[
1
3
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1
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tech
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w
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m
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k
[
1
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ter
m
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h
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m
ai
n
d
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h
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ap
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tr
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as
f
o
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d
is
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th
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Fig
u
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[
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Defin
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m
u
m
v
al
u
e
w
h
ic
h
in
d
icate
s
th
e
o
p
ti
m
al
ac
tio
n
f
o
r
ev
er
y
p
o
s
s
ib
le
n
ex
t p
air
(
,
)
is
d
en
o
ted
as
∗
(
,
)
.
∗
(
,
)
=
(
,
)
+
∑
,
(
)
∈
∗
(
,
)
∈
(
3
)
I
n
an
iter
ativ
e
p
r
o
ce
d
u
r
e,
Q
-
lear
n
in
g
d
eter
m
i
n
es
th
e
o
p
tim
al
∗
(
,
)
.
A
t
ea
ch
s
tag
e
d
u
r
in
g
th
e
lear
n
in
g
p
r
o
ce
d
u
r
e,
th
e
Q
-
v
al
u
e
f
u
n
ctio
n
s
h
o
u
ld
b
e
u
p
d
ated
u
s
i
n
g
t
h
e
(
4
)
:
(
,
)
=
(
1
−
α
)
−
1
(
,
)
+
α
(
(
,
)
+
−
1
(
,
)
)
(
4
)
w
h
er
e
α
r
ep
r
esen
t t
h
e
lear
n
in
g
r
ate.
3.
RE
S
E
ARCH
M
E
T
H
O
D
A
ll
p
ar
a
m
eter
s
r
elate
d
to
h
an
d
o
v
er
d
ec
is
io
n
p
h
ase
b
ased
o
n
Q
-
lear
n
i
n
g
tech
n
iq
u
e
ar
e
d
ef
in
ed
as
f
o
llo
w
s
:
a.
E
n
v
ir
o
n
m
e
n
t
: i
n
v
o
lv
e
s
a
ll c
o
m
p
o
n
en
ts
b
esid
es t
h
e
ag
e
n
t
I
n
o
u
r
f
r
a
m
e
w
o
r
k
,
it
co
n
tai
n
s
t
h
e
m
ac
r
o
ce
ll
eNB
an
d
all
f
e
m
to
ce
lls
HeN
B
s
i
n
t
h
e
UE
eNB
’
s
n
eig
h
b
o
r
in
g
ce
ll
lis
t
(
N
C
L
)
.
W
e
co
n
s
id
er
th
at
th
e
e
n
v
ir
o
n
m
en
t
is
a
d
is
cr
ete
-
ti
m
e,
f
in
i
te
-
s
tate
an
d
s
to
ch
as
tic
d
y
n
a
m
ic
s
y
s
te
m
.
b.
Ag
e
n
t
: is t
h
e
d
ec
is
io
n
m
a
k
er
I
n
o
u
r
ca
s
e,
th
e
a
g
e
n
t
i
n
v
o
lv
e
s
th
e
m
ac
r
o
ce
ll
m
o
b
ile
u
s
er
U
E
eNB
ex
ec
u
ti
n
g
a
h
a
n
d
o
v
er
p
r
o
ce
s
s
f
r
o
m
its
s
er
v
in
g
ce
ll to
an
o
t
h
er
n
ei
g
h
b
o
r
in
g
ce
ll t
h
at
p
r
o
v
id
e
b
etter
p
er
f
o
r
m
a
n
ce
.
c.
State:
is
t
h
e
en
v
ir
o
n
m
e
n
t
’
s
c
u
r
r
en
t state
I
n
o
u
r
f
r
a
m
e
w
o
r
k
,
i
t
i
n
v
o
l
v
es
th
e
c
u
r
r
en
t
UE
eNB
se
r
v
i
n
g
ce
ll
,
w
h
ic
h
i
s
th
e
m
ac
r
o
ce
ll
eNB
.
T
h
e
s
tat
e
s
et
S
is
d
e
f
in
ed
a
s
=
{
=
1
,
2
,
…
,
+
1
}
w
h
er
e
is
th
e
n
u
m
b
er
o
f
n
eig
h
b
o
r
in
g
f
e
m
t
o
ce
lls
.
(
=
1
)
r
ef
er
s
to
th
e
in
it
ial
s
tate
w
h
er
e
th
e
m
o
b
ile
u
s
er
UE
eNB
is
co
n
n
ec
ted
to
th
e
m
ac
r
o
c
ell
eNB
.
T
o
s
el
ec
t
th
e
tar
g
et
ce
l
l
i
n
a
s
h
o
r
t
ti
m
e
w
e
h
av
e
to
s
h
o
r
t
-
lis
t
th
e
n
eig
h
b
o
r
in
g
f
e
m
to
ce
ll
s
,
to
o
p
t
i
m
ize
th
e
ca
n
d
id
ate
n
eig
h
b
o
r
in
g
ce
ll li
s
t
w
e
p
r
o
p
o
s
e
Dis
ta
n
ce
a
n
d
m
o
v
i
n
g
Dir
ec
tio
n
Q
-
lear
n
i
n
g
b
ased
tech
n
iq
u
e
(
D
2
Q
tech
n
iq
u
e
)
.
T
h
e
UE
d
ir
ec
tio
n
ass
i
s
ts
t
h
e
h
an
d
o
v
er
d
ec
is
io
n
t
h
r
o
u
g
h
av
o
id
in
g
s
i
g
n
ali
n
g
m
ea
s
u
r
e
m
en
t
co
n
tr
o
ls
w
i
t
h
n
eig
h
b
o
r
ce
lls
t
h
at
ar
e
n
o
t
ah
ea
d
o
f
th
e
UE
tr
aj
ec
to
r
y
as
w
ell
as
i
n
s
elec
tin
g
t
h
e
n
ei
g
h
b
o
r
ce
ll
th
at
f
i
ts
a
s
th
e
tar
g
e
t
ce
ll.
T
h
e
d
is
tan
ce
b
et
w
ee
n
UE
a
n
d
tar
g
et
ce
l
l
is
i
m
p
o
r
ta
n
t,
w
h
ich
s
h
o
u
ld
n
o
t
e
x
ce
ed
th
e
ce
ll
r
ad
iu
s
,
in
o
r
d
er
th
at
ce
lls
w
h
ich
ar
e
f
a
r
a
w
a
y
f
r
o
m
t
h
e
m
o
b
ile
u
s
er
a
r
e
n
o
t in
v
o
l
v
ed
in
t
h
e
ca
n
d
id
at
e
n
eig
h
b
o
r
in
g
lis
t.
Neig
h
b
o
r
ce
lls
lo
ca
tio
n
an
d
ea
ch
u
s
er
eq
u
ip
m
e
n
t
UE
p
o
s
itio
n
ar
e
d
eter
m
i
n
ed
u
s
i
n
g
GP
S
[
20
]
.
|
∓
ℎ
°
|
is
th
e
r
an
g
e
th
a
t
all
n
o
m
i
n
e
e
ce
lls
s
h
o
u
ld
b
e
s
itu
ated
ah
ea
d
o
f
th
e
u
s
er
eq
u
ip
m
en
t
UE
d
ir
ec
tio
n
,
an
d
ea
ch
ce
ll
t
h
at
i
s
lo
ca
ted
in
s
id
e
t
h
is
zo
n
e
w
ill
h
a
v
e
t
h
e
p
r
io
r
ity
to
b
e
c
o
m
b
in
ed
i
n
to
th
e
ca
n
d
id
ate
ce
ll
lis
t
[
20
]
.
A
s
s
u
m
e
th
at
a
UE
is
m
o
v
i
n
g
f
r
o
m
lo
ca
tio
n
P
1
to
lo
ca
tio
n
P
2
as
s
h
o
w
n
i
n
Fi
g
u
r
e
2
,
P
3
is
th
e
n
eig
h
b
o
r
ce
ll
lo
ca
tio
n
.
E
v
er
y
n
ei
g
h
b
o
r
ce
ll
o
f
th
e
u
s
er
eq
u
ip
m
e
n
t
is
test
ed
v
ia
ca
lc
u
lati
n
g
th
e
an
g
le
o
f
∠
2
,
1
,
3
as f
o
llo
w
i
n
g
:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Q
-
lea
r
n
in
g
ve
r
tica
l h
a
n
d
o
ve
r
s
ch
eme
in
tw
o
-
tier
LT
E
-
A
n
etw
o
r
ks
(
A
mma
r
B
a
th
ich
)
5827
2
,
1
,
3
=
c
os
−
1
(
3
−
1
)
.
(
2
−
1
)
|
3
−
1
|
|
2
−
1
|
(
5
)
w
h
er
e
1
,
2
an
d
3
ar
e
1
(
1
,
1
)
,
2
(
2
,
2
)
an
d
3
(
3
,
3
)
r
esp
ec
tiv
e
l
y
.
T
h
e
d
is
tan
ce
b
et
w
ee
n
t
h
e
u
s
er
eq
u
ip
m
e
n
t
a
n
d
t
h
e
n
ei
g
h
b
o
r
ce
ll
is
ap
p
lied
,
w
h
ic
h
s
h
o
u
ld
n
o
t
ex
ce
ed
th
e
n
ei
g
h
b
o
r
ce
ll
r
ad
iu
s
,
in
o
r
d
er
th
at
ce
lls
w
h
ic
h
ar
e
f
ar
aw
a
y
f
r
o
m
t
h
e
u
s
er
eq
u
ip
m
e
n
t
ar
e
n
o
t
in
v
o
lv
ed
i
n
th
e
ca
n
d
id
ate
ce
ll
lis
t
[
2
1
-
2
3
]
.
T
h
e
d
is
tan
ce
b
et
w
ee
n
th
e
u
s
er
eq
u
ip
m
e
n
t
at
p
o
s
itio
n
2
an
d
th
e
ce
ll
at
lo
ca
tio
n
3
is
ca
lc
u
lated
b
y
(
6
)
:
3
,
2
=
√
(
3
−
2
)
2
+
(
3
−
2
)
2
(
6
)
Fo
r
UE
m
o
v
es
f
r
o
m
p
o
s
itio
n
P
2
to
w
ar
d
s
n
ei
g
h
b
o
r
ce
ll
lo
ca
ted
at
P
3
,
w
e
co
n
s
id
er
t
h
e
n
eig
h
b
o
u
r
ce
ll
to
b
e
a
ca
n
d
id
ate
ce
ll
if
(
θ
≤
|
∓
θ
th
°
|
)
an
d
(
d
p
3
,
p
2
≤
n
e
ighb
or
c
e
l
l
r
a
dius
d
th
)
.
T
h
e
n
ex
t
s
tag
e
co
n
tai
n
s
s
elec
ti
n
g
t
h
e
tar
g
et
ce
ll
f
r
o
m
th
e
n
o
m
i
n
ee
ca
n
d
id
ate
lis
t
b
y
u
tili
zi
n
g
th
e
W
eig
h
t
A
d
j
u
s
t
m
en
t
alg
o
r
it
h
m
[
20
]
.
I
n
o
u
r
w
o
r
k
,
t
h
e
s
h
o
r
test
d
is
tan
ce
to
th
e
u
s
er
eq
u
ip
m
e
n
t’
s
cu
r
r
en
t
p
o
s
itio
n
a
n
d
t
h
e
n
ar
r
o
w
es
t
θ
f
r
o
m
th
e
ca
n
d
id
ate
ce
ll
lis
t
w
o
u
ld
b
e
th
e
m
o
s
t
ap
p
r
o
p
r
iate
tar
g
et
ce
ll.
T
h
e
W
eig
h
t
A
d
j
u
s
t
m
e
n
t
a
lg
o
r
ith
m
is
s
h
o
w
n
in
A
l
g
o
r
ith
m
1
.
A
l
g
o
r
ith
m
1
.
W
eig
h
t
a
d
j
u
s
t
m
e
n
t
1: Input
2
,
1
,
3
and
3
,
2
2: Output:
3:
=
1
−
2
,
1
,
3
4:
=
1
−
3
,
2
2
5:
=
+
is
u
s
ed
f
o
r
ch
o
o
s
i
n
g
th
e
tar
g
e
t
ce
ll.
Fu
r
t
h
er
m
o
r
e,
n
o
r
m
aliza
tio
n
is
al
s
o
i
m
p
le
m
e
n
ted
f
o
r
b
o
th
d
is
tan
ce
a
n
d
a
n
g
le,
in
o
r
d
er
th
at
b
o
th
w
ill
b
e
ac
co
r
d
in
g
to
s
t
an
d
ar
d
in
te
g
r
atio
n
.
Fo
r
n
o
r
m
a
lizatio
n
w
e
u
s
e
as
th
e
an
g
le
v
al
u
e.
in
v
o
l
v
es
t
h
e
r
esu
lt
o
f
a
n
g
le
n
o
r
m
aliza
tio
n
as
all
an
g
les
o
f
th
e
ca
n
d
id
ate
ce
lls
ar
e
les
s
th
an
o
r
eq
u
al
|
∓
ℎ
°
|
,
th
i
s
an
g
le
(
)
is
u
s
ed
f
o
r
n
o
r
m
a
lizatio
n
p
r
o
ce
d
u
r
e.
in
v
o
lv
e
s
t
h
e
r
esu
lt
o
f
d
is
tan
ce
n
o
r
m
aliza
tio
n
w
h
ic
h
is
n
o
r
m
alize
d
v
ia
ce
ll
tr
an
s
m
is
s
io
n
r
an
g
e
(
)
to
en
h
an
ce
th
e
p
r
io
r
ity
o
f
th
e
an
g
le
v
alu
e,
a
s
th
e
d
is
tan
c
e
o
f
all
n
o
m
in
ee
ce
ll l
is
t i
s
les
s
o
r
eq
u
al
to
.
T
h
ese
m
et
h
o
d
o
lo
g
ies
f
o
r
ch
o
o
s
in
g
th
e
ca
n
d
id
ate
ce
ll li
s
t a
n
d
s
ele
ctin
g
th
e
tar
g
et
ce
ll a
r
e
illu
s
tr
a
ted
in
Alg
o
r
it
h
m
2
.
A
l
g
o
r
ith
m
2
.
C
h
o
o
s
in
g
t
h
e
ca
n
d
id
ate
ce
ll lis
t a
n
d
s
elec
ti
n
g
t
h
e
tar
g
et
ce
ll
Input
:
(
,
)
,
(
,
)
and
(
,
)
Output
:
N is an empty array which will include the candidate cell list
1: for
each neighbor cell
do
2:
,
,
=
−
(
−
)
.
(
−
)
|
−
|
|
−
|
3: if
(
,
,
≤
|
∓
°
|
) then
4: add cell to N
5: end if
// line 3
6: end for
// line 1
7: if N
is not empty
then
8: for each cell
⊂
do
9:
,
=
√
(
−
)
+
(
−
)
// cell position
is
(
−
)
10:
=
+
(
+
)
11: end for //
for line 8
12:
=
13:
rest
of all cells by 0
14: else // line 7
15:
=
16: end if //line 7
17: return
d.
A
ctio
n
:
is
t
h
e
a
g
en
t d
ec
is
io
n
r
esu
lt
I
n
o
u
r
f
r
a
m
e
w
o
r
k
,
it
r
ef
er
s
to
th
e
h
a
n
d
o
v
er
d
ec
is
io
n
r
esu
lts
:
th
e
UE
eNB
m
a
y
k
ee
p
its
co
n
n
ec
tio
n
w
it
h
t
h
e
s
er
v
i
n
g
m
ac
r
o
ce
ll
eNB
(
ac
tio
n
1
)
o
r
s
elec
t
o
n
e
o
f
t
h
e
f
e
m
to
ce
lls
H
eNB
s
f
r
o
m
its
N
C
L
(
ac
tio
n
2
,
…,
ac
tio
n
N
N
C
L
+
1
)
.
I
n
o
u
r
p
r
o
p
o
s
al
alg
o
r
ith
m
,
w
e
u
s
e
t
h
e
ϵ
-
Gr
ee
d
y
tec
h
n
iq
u
e
w
ith
an
ad
ap
tiv
e
ϵ
s
ch
e
m
e
b
y
p
r
ese
n
ti
n
g
R
S
R
Q
-
d
ep
en
d
en
t
e
x
p
lo
r
atio
n
i
n
s
tead
o
f
a
f
i
x
ed
o
r
a
h
an
d
-
tu
n
in
g
ϵ
p
ar
a
m
eter
(
R
SR
Q
Q
-
lear
n
i
n
g
b
ased
tec
h
n
iq
u
e
(
Q2
tech
n
iq
u
e
)
)
[
24,
25
]
.
Un
lik
e
t
h
e
tr
ad
itio
n
al
ϵ
-
Gr
ee
d
y
m
et
h
o
d
,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
5
8
2
4
-
5
8
3
1
5828
w
h
ic
h
u
s
e
a
f
i
x
ed
ϵ
p
ar
a
m
ete
r
,
th
e
r
eq
u
ir
ed
ac
tio
n
o
f
Q2
t
ec
h
n
iq
u
e
i
s
to
m
ak
e
th
e
ag
e
n
t
m
o
r
e
ex
p
lo
r
ati
v
e
in
cir
c
u
m
s
ta
n
ce
s
w
h
e
n
t
h
e
in
f
o
r
m
atio
n
ab
o
u
t
t
h
e
e
n
v
ir
o
n
m
e
n
t
i
s
u
n
c
lear
.
Q2
tech
n
iq
u
e
a
lg
o
r
it
h
m
i
s
s
h
o
w
n
in
A
l
g
o
r
it
h
m
3
.
A
l
g
o
r
ith
m
3
.
Q
2
tech
n
iq
u
e
Δϵ: the amount of decrease or increase of ϵ, 0 < ϵ < 1
Stage 1: Set Δϵ to 0.01 and ϵ to 0.1
Stage 2: At each trail, we compare
RSRQ
t
-
1
and
RSRQ
t
.
-
if
RSRQ
t
-
1
<
RSRQ
t
, then ϵ = ϵ
-
Δϵ
-
else ϵ = ϵ + Δϵ
e.
R
e
w
ar
d
:
I
t
in
d
icate
s
t
h
e
q
u
alit
y
o
r
g
o
o
d
n
ess
o
f
t
h
e
ac
tio
n
a
in
t
h
e
s
tate
s
,
co
n
s
id
er
ed
as
a
u
tili
t
y
f
u
n
ctio
n
an
d
d
en
o
ted
b
y
R
I
n
o
u
r
f
r
a
m
e
w
o
r
k
,
th
e
r
e
w
ar
d
is
th
e
ea
r
n
ed
ca
p
ac
it
y
a
f
te
r
co
n
n
ec
ti
n
g
to
t
h
e
tar
g
et
ce
ll
(
eNB
o
r
HeNB
)
.
Ou
r
o
b
j
ec
tiv
e
is
to
m
ai
n
tai
n
an
d
m
a
x
i
m
ize
t
h
e
c
ap
ac
it
y
o
f
UE
eNB
co
n
n
ec
ti
n
g
to
a
n
e
w
ce
l
l
af
ter
a
h
a
n
d
o
v
er
p
r
o
ce
s
s
(
C
ap
ac
it
y
Q
-
lear
n
i
n
g
b
ased
tec
h
n
i
q
u
e
(
C
Q
tech
n
iq
u
e)
)
.
T
h
u
s
,
if
UE
eNB
s
elec
ts
th
e
m
ac
r
o
ce
ll
eNB
a
s
a
s
er
v
in
g
ce
ll,
t
h
e
u
tili
t
y
f
u
n
ct
io
n
R
wh
ich
is
a
p
er
ce
i
v
ed
r
e
w
ar
d
(
ca
p
ac
it
y
)
o
f
t
h
e
tar
g
et
ce
ll
is
e
x
p
r
ess
ed
a
s
1
.
E
ls
e
i
f
UE
eNB
s
elec
t
s
to
co
n
n
ec
t
to
o
n
e
o
f
t
h
e
f
e
m
to
ce
lls
He
N
B
s
in
its
N
C
L
,
t
h
e
u
tili
t
y
f
u
n
ctio
n
R
is
ex
p
r
es
s
ed
as 2
[
2
6
,
2
7
]
.
L
et
b
e
th
e
tr
an
s
m
it
ted
p
o
w
er
b
y
t
h
e
m
ac
r
o
ce
ll
eNB
an
d
ℎ
,
th
e
g
ain
o
f
t
h
e
ch
a
n
n
e
l
b
et
w
ee
n
th
e
m
ac
r
o
ce
ll
eNB
an
d
it
s
s
er
v
in
g
k
th
m
ac
r
o
ce
ll
u
s
er
UE
eNB
.
Si
m
i
lar
l
y
,
h
i,
j
r
ep
r
esen
ts
th
e
g
ain
o
f
t
h
e
c
h
an
n
e
l
b
et
w
ee
n
t
h
e
i
th
f
e
m
to
ce
ll
He
NB
an
d
th
e
j
th
f
e
m
to
ce
ll
u
s
er
UE
HeNB
.
L
a
s
tl
y
,
P
i
r
ep
r
esen
t
s
t
h
e
tr
an
s
m
it
p
o
w
er
o
f
th
e
i
th
f
e
m
to
ce
ll
HeN
B
.
A
n
Ad
d
itiv
e
W
h
ite
Gau
s
s
ia
n
No
is
e
(
A
W
GN)
is
co
n
s
id
er
ed
at
m
a
cr
o
ce
ll
u
s
er
UE
eNB
w
it
h
2
p
o
w
er
.
Ma
cr
o
ce
ll u
s
er
UE
eNB
k
ca
p
ac
ity
f
r
o
m
it
s
s
er
v
in
g
m
ac
r
o
ce
ll e
NB
is
ca
lc
u
lated
by
(
7
)
:
=
l
og
2
(
1
+
|
ℎ
,
|
2
2
+
)
(
7
)
w
h
er
e
is
t
h
e
av
ailab
le
b
an
d
w
id
t
h
,
=
∑
|
ℎ
,
|
2
=
1
is
th
e
in
ter
f
er
en
ce
f
r
o
m
n
ei
g
h
b
o
r
in
g
f
e
m
to
ce
ll
s
HeN
B
s
,
an
d
is
t
h
e
n
u
m
b
er
o
f
n
ei
g
h
b
o
r
in
g
f
e
m
to
ce
ll
s
He
NB
s
.
W
e
co
n
s
id
er
th
at
th
e
b
an
d
w
id
th
i
s
eq
u
all
y
allo
ca
ted
to
all
u
s
er
s
(
UE
eNB
an
d
UE
HeNB
)
.
T
h
e
ca
p
a
cit
y
at
f
e
m
to
ce
ll
u
s
er
j
(
UE
HeNB
)
j
f
r
o
m
f
e
m
to
ce
ll
(
HeN
B
)
i
is
g
iv
e
n
b
y
(
8
)
:
=
l
og
2
(
1
+
|
ℎ
,
|
2
2
+
+
)
(
8
)
w
h
er
e
=
|
ℎ
,
|
2
is
th
e
i
n
ter
f
er
en
ce
f
r
o
m
m
ac
r
o
ce
ll
eNB
,
ℎ
,
is
th
e
g
ain
o
f
t
h
e
c
h
an
n
el
b
et
w
ee
n
m
ac
r
o
ce
ll
eNB
an
d
u
s
er
j.
A
l
s
o
,
=
∑
|
ℎ
,
|
2
≠
is
th
e
in
ter
f
er
e
n
ce
f
r
o
m
o
th
er
f
e
m
to
ce
lls
HeN
B
s
an
d
ℎ
,
is
th
e
g
ai
n
o
f
t
h
e
ch
an
n
el
b
et
w
ee
n
HeN
B
l
,
tr
an
s
m
i
tti
n
g
w
i
th
p
o
w
er
,
an
d
u
s
er
j
.
Fig
u
r
e
2
.
User
eq
u
ip
m
e
n
t d
is
t
an
ce
an
d
m
o
v
i
n
g
d
ir
ec
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Q
-
lea
r
n
in
g
ve
r
tica
l h
a
n
d
o
ve
r
s
ch
eme
in
tw
o
-
tier
LT
E
-
A
n
etw
o
r
ks
(
A
mma
r
B
a
th
ich
)
5829
4.
RE
SU
L
T
S
A
ND
D
I
SCU
SS
I
O
N
T
h
e
L
T
E
-
Si
m
s
i
m
u
lato
r
[
28
]
is
u
s
ed
to
ev
alu
ate
t
h
e
p
er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
d
ep
en
d
in
g
o
n
t
h
e
n
u
m
b
er
o
f
th
e
h
a
n
d
o
v
er
s
w
i
th
co
m
p
ar
e
to
th
e
alg
o
r
it
h
m
i
n
tr
o
d
u
ce
d
b
y
S
u
m
a
n
[
1
7
]
.
T
h
e
to
p
o
lo
g
y
co
n
s
i
s
ts
o
f
t
w
o
m
ac
r
o
ce
lls
(
eNB
)
w
it
h
a
r
ad
i
u
s
o
f
1
k
m
ea
c
h
a
n
d
v
ar
io
u
s
f
e
m
to
ce
lls
(
HeN
B
s
)
d
en
s
it
y
,
t
h
e
f
e
m
to
ce
ll
n
u
m
b
er
is
co
n
f
ig
u
r
ed
as
3
0
,
5
0
,
7
0
an
d
9
0
in
ea
ch
m
ac
r
o
ce
ll,
a
n
d
all
f
e
m
to
ce
lls
ar
e
co
v
er
ed
b
y
o
p
en
ac
ce
s
s
t
y
p
e
to
allo
w
t
h
e
u
s
er
eq
u
ip
m
en
t
U
E
to
h
an
d
o
v
er
to
ea
ch
f
em
to
ce
ll.
E
ac
h
f
e
m
to
ce
l
l
r
ad
iu
s
co
v
er
s
3
0
m
eter
s
.
T
h
e
UE
n
u
m
b
er
i
s
co
n
f
i
g
u
r
ed
as
1
5
,
3
0
,
4
5
an
d
6
0
.
T
h
e
UE
s
ar
e
d
is
tr
ib
u
ted
r
an
d
o
m
l
y
in
ea
c
h
m
ac
r
o
ce
ll
co
v
er
ag
e
ar
ea
an
d
ea
c
h
UE
s
tar
ts
m
o
v
i
n
g
f
r
o
m
th
e
ce
n
ter
o
f
its
s
er
v
i
n
g
eNB
b
ased
o
n
r
an
d
o
m
m
o
b
ilit
y
.
T
h
e
h
an
d
o
v
er
d
ec
is
io
n
i
n
th
e
p
r
o
p
o
s
ed
to
p
o
lo
g
y
w
ill
co
v
er
th
r
ee
v
er
tical
h
an
d
o
v
er
t
y
p
es:
Han
d
-
in
,
Han
d
-
b
et
w
ee
n
an
d
Han
d
-
o
u
t
h
an
d
o
v
er
s
b
ased
o
n
th
e
av
ailab
ilit
y
o
f
ea
c
h
v
er
tic
al
h
an
d
o
v
er
t
y
p
e.
E
ac
h
f
e
m
to
ce
ll
w
ill
b
e
r
an
d
o
m
l
y
lo
ca
ted
b
et
w
ee
n
5
0
m
ete
r
s
to
1
0
0
0
m
eter
s
f
r
o
m
th
e
m
ac
r
o
ce
ll
lo
ca
tio
n
in
th
r
ee
d
ep
en
d
en
t
s
ce
n
ar
io
s
:
cl
o
s
e,
m
id
d
le
an
d
at
th
e
ed
g
e
.
C
o
n
ce
r
n
in
g
f
e
m
to
ce
lls
d
i
s
t
r
ib
u
tio
n
s
ce
n
ar
io
s
:
clo
s
e,
m
id
d
le
a
n
d
at
t
h
e
ed
g
e,
f
e
m
to
ce
lls
ar
e
d
is
tr
ib
u
ted
in
f
o
u
r
d
i
f
f
er
en
t
g
r
o
u
p
s
:
3
0
,
5
0
,
7
0
an
d
9
0
in
ea
c
h
s
ce
n
ar
io
.
Fi
g
u
r
e
3
p
r
esen
t
s
th
e
av
er
ag
e
n
u
m
b
er
o
f
h
a
n
d
o
v
e
r
s
f
o
r
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
in
ea
ch
s
ce
n
ar
io
f
o
r
30
UE
s
.
A
s
s
h
o
w
n
i
n
Fig
u
r
e
3
,
th
e
r
elatio
n
s
h
ip
b
et
w
ee
n
t
h
e
av
er
ag
e
n
u
m
b
er
o
f
h
a
n
d
o
v
er
s
an
d
f
e
m
to
ce
ll
s
d
en
s
it
y
is
p
o
s
iti
v
e
r
elatio
n
s
h
ip
,
w
h
ich
m
ea
n
s
th
a
t
th
e
av
er
a
g
e
n
u
m
b
er
o
f
h
an
d
o
v
er
s
in
cr
ea
s
e
w
h
e
n
f
e
m
to
ce
lls
d
en
s
it
y
in
cr
ea
s
e.
W
h
ile
it
h
a
s
th
e
lo
w
est
a
v
er
ag
e
w
h
e
n
t
h
e
f
e
m
to
ce
ll
s
d
is
tr
ib
u
tio
n
is
at
th
e
ed
g
e.
T
h
is
is
b
ec
au
s
e
t
h
e
m
o
b
ile
u
s
er
s
s
tar
t
to
m
o
v
e
f
r
o
m
t
h
e
lo
ca
tio
n
o
f
m
ac
r
o
ce
ll
to
w
er
.
I
n
ad
d
iti
o
n
,
th
e
av
er
a
g
e
o
f
h
an
d
o
v
er
s
n
u
m
b
er
i
n
cr
ea
s
es a
s
th
e
n
u
m
b
er
o
f
f
e
m
to
ce
ll
s
in
all
d
is
t
r
ib
u
tio
n
s
ce
n
ar
io
s
i
n
cr
e
ases
.
Fu
r
t
h
er
m
o
r
e,
th
e
r
es
u
lts
o
f
t
h
e
av
er
ag
e
n
u
m
b
er
o
f
h
an
d
o
v
er
s
f
o
r
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
an
d
Su
m
a
n
h
an
d
o
v
er
alg
o
r
it
h
m
w
er
e
d
is
c
u
s
s
ed
in
ter
m
s
o
f
f
e
m
to
ce
ll
s
th
at
ar
e
d
is
tr
ib
u
ted
to
g
r
o
u
p
s
o
f
3
0
,
5
0
,
7
0
an
d
9
0
p
er
ea
ch
m
ac
r
o
ce
ll,
an
d
t
w
o
g
r
o
u
p
s
o
f
UE
s
(
1
5
an
d
3
0
)
as
p
r
esen
ted
i
n
Fi
g
u
r
e
4
.
B
a
s
ed
o
n
ea
ch
r
es
u
lt,
it
is
ev
id
en
t
t
h
at
b
y
in
cr
ea
s
in
g
th
e
f
e
m
to
ce
ll
s
n
u
m
b
er
,
b
o
th
alg
o
r
ith
m
s
s
h
o
w
a
n
in
cr
e
m
en
t
in
t
h
e
av
er
ag
e
h
an
d
o
v
er
s
n
u
m
b
er
,
b
ec
au
s
e
m
o
b
ile
u
s
er
s
m
a
k
e
ad
d
itio
n
al
h
a
n
d
o
v
er
s
w
it
h
r
esp
ec
t
to
th
ei
r
m
o
v
e
m
e
n
ts
i
n
ea
ch
m
o
b
ile
u
s
er
g
r
o
u
p
.
T
h
e
r
esu
lts
e
m
p
h
a
s
ize
th
a
t
th
e
b
est
p
er
f
o
r
m
a
n
ce
w
a
s
ac
h
ie
v
ed
b
y
o
u
r
alg
o
r
it
h
m
in
all
d
i
s
tr
ib
u
tio
n
s
o
f
f
e
m
to
ce
ll
s
an
d
al
l
d
en
s
ities
.
T
h
is
is
b
ec
au
s
e
o
f
u
til
izin
g
Q
-
lear
n
in
g
m
et
h
o
d
o
lo
g
y
w
h
ic
h
allo
w
t
h
e
m
o
b
il
e
u
s
er
to
le
ar
n
f
r
o
m
h
i
s
p
r
ev
io
u
s
h
is
to
r
y
,
i
n
ad
d
itio
n
to
o
th
er
s
u
p
p
o
r
tin
g
m
et
h
o
d
o
lo
g
ies
w
h
ic
h
d
o
n
o
t
allo
w
th
e
m
o
b
ile
u
s
er
to
co
n
n
ec
t to
f
e
m
to
ce
lls
th
a
t a
r
e
o
n
l
y
clo
s
e
t
o
th
e
it,
b
u
t to
co
n
n
ec
t to
t
h
o
s
e
lo
ca
ted
in
f
r
o
n
t o
f
o
r
ap
p
r
o
x
i
m
atel
y
a
h
ea
d
o
f
c
u
r
r
en
t
m
o
b
ile
u
s
er
p
o
s
itio
n
i
n
o
r
d
er
to
av
o
id
th
e
r
ed
u
n
d
an
t h
a
n
d
o
v
er
.
T
h
e
u
s
er
eq
u
ip
m
en
t
o
n
l
y
n
o
m
i
n
ates
t
h
e
f
e
m
to
ce
ll
w
h
o
s
e
to
w
er
lo
ca
tio
n
is
le
s
s
t
h
a
n
|
±
2
5
|
an
d
th
e
d
is
ta
n
ce
b
et
w
ee
n
t
h
e
UE
an
d
th
e
ca
n
d
id
ate
f
e
m
to
ce
ll
i
s
less
t
h
a
n
o
r
eq
u
al
2
8
m
eter
s
.
On
t
h
e
co
n
tr
ar
y
,
in
t
h
e
ca
s
e
o
f
S
u
m
a
n
h
a
n
d
o
v
e
r
d
ec
is
io
n
th
e
h
an
d
o
v
er
p
r
o
ce
d
u
r
e
is
tr
ig
g
er
ed
w
h
e
n
th
e
R
S
S
b
et
w
ee
n
t
h
e
UE
an
d
its
n
ei
g
h
b
o
r
f
e
m
to
ce
ll
s
is
h
i
g
h
er
th
a
n
th
e
R
SS
b
et
w
ee
n
th
e
UE
a
n
d
its
s
er
v
in
g
ce
ll
w
it
h
o
u
t
a
n
y
co
n
s
id
er
atio
n
o
f
h
o
w
lo
n
g
th
e
tar
g
et
f
e
m
to
ce
ll
w
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ll se
r
v
e
t
h
e
UE
an
d
its
u
s
e
f
u
l
n
es
s
to
d
o
h
an
d
o
v
er
o
r
n
o
t.
Fig
u
r
e
3.
C
o
m
p
ar
is
o
n
o
f
a
v
er
a
g
e
n
u
m
b
er
o
f
h
an
d
o
v
er
s
o
f
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
i
n
t
h
r
ee
s
ce
n
ar
io
s
:
c
lo
s
e,
m
id
d
le
an
d
at
th
e
ed
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
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n
g
,
Vo
l.
10
,
No
.
6
,
Dec
em
b
er
2
0
2
0
:
5
8
2
4
-
5
8
3
1
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Fig
u
r
e
4.
C
o
m
p
ar
is
o
n
o
f
a
v
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a
g
e
n
u
m
b
er
o
f
h
an
d
o
v
er
s
f
o
r
b
o
th
alg
o
r
it
h
m
s
Fin
all
y
,
r
eg
ar
d
in
g
th
e
to
tal
av
er
ag
e
n
u
m
b
er
o
f
h
a
n
d
o
v
er
s
f
o
r
ea
ch
UE
g
r
o
u
p
,
it
is
r
ed
u
ce
d
in
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
b
y
(
5
5
.
6
3
%)
co
m
p
ar
ed
to
Su
m
a
n
h
a
n
d
o
v
er
alg
o
r
ith
m
f
o
r
all
v
ar
io
u
s
f
e
m
to
ce
ll
s
d
en
s
itie
s
w
h
e
n
th
e
n
u
m
b
er
o
f
UE
is
1
5
.
Mo
r
eo
v
er
,
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
r
ed
u
ce
s
th
e
to
t
al
av
er
ag
e
n
u
m
b
er
o
f
h
a
n
d
o
v
er
s
b
y
(
4
1
.
7
4
%)
co
m
p
ar
ed
to
S
u
m
an
h
a
n
d
o
v
er
alg
o
r
ith
m
f
o
r
all
v
ar
io
u
s
f
e
m
to
c
ells
d
en
s
ities
w
h
e
n
th
e
UE
s
n
u
m
b
er
is
3
0
.
5.
CO
NCLU
SI
O
N
A
ND
F
U
T
U
RE
WO
RK
T
h
e
s
i
m
u
latio
n
r
es
u
lt
s
s
h
o
w
t
h
at
t
h
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
p
er
f
o
r
m
s
w
ell
i
n
e
n
h
an
ci
n
g
t
h
e
h
a
n
d
o
v
er
d
ec
is
io
n
i
n
L
T
E
-
A
n
et
w
o
r
k
s
.
T
h
e
s
i
m
u
lat
io
n
r
es
u
lt
s
e
x
a
m
i
n
ed
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
f
o
r
f
e
m
to
ce
ll
s
o
f
th
e
o
p
en
ac
ce
s
s
t
y
p
e
i
n
o
r
d
er
to
en
h
a
n
ce
t
h
e
tar
g
et
f
e
m
to
ce
ll
s
elec
t
io
n
in
t
h
e
v
er
tical
h
an
d
o
v
er
d
ec
is
io
n
.
T
h
e
s
elec
tio
n
o
f
s
u
itab
le
p
ar
am
eter
s
to
i
m
p
r
o
v
e
th
e
h
an
d
o
v
er
d
ec
is
io
n
s
till
e
n
co
m
p
as
s
es a
w
id
e
ar
ea
r
esear
ch
.
T
h
er
ef
o
r
e,
th
e
r
ec
o
m
m
e
n
d
ati
o
n
f
o
r
f
u
r
th
er
r
e
s
ea
r
ch
in
t
h
is
f
ield
ca
n
b
e
as
f
o
llo
w
s
:
F
ir
s
t
l
y
,
i
s
to
i
n
v
est
ig
ate
d
if
f
er
e
n
t
p
ar
a
m
eter
s
o
f
u
s
er
p
er
f
o
r
m
an
ce
i
n
l
ig
h
t
o
f
h
a
n
d
o
v
er
an
d
lo
ad
b
ala
n
cin
g
in
t
h
e
wir
eless
s
y
s
te
m
o
v
er
h
o
r
izo
n
tal
a
n
d
v
er
tical
n
et
w
o
r
k
s
.
Seco
n
d
l
y
,
to
i
n
v
esti
g
ate
d
if
f
er
en
t
p
ar
a
m
eter
s
o
f
u
s
er
p
e
r
f
o
r
m
an
ce
o
n
b
o
th
f
e
m
to
ce
ll
t
y
p
e
s
:
th
e
clo
s
e
an
d
h
y
b
r
id
.
Fin
a
ll
y
,
i
n
r
eg
ar
d
to
im
p
le
m
e
n
tat
io
n
,
UE
v
elo
cit
y
s
h
o
u
ld
b
e
tak
e
n
in
to
ac
co
u
n
t
i
n
t
h
e
h
an
d
o
v
er
d
ec
is
io
n
as
t
h
e
m
ai
n
b
eh
a
v
io
r
.
T
h
u
s
,
b
y
m
o
n
ito
r
i
n
g
t
h
e
t
h
r
ee
m
a
in
b
eh
a
v
io
r
s
at
U
E
w
h
ic
h
ar
e
th
e
UE
m
o
b
ilit
y
,
ac
ce
ler
atio
n
,
an
d
d
ec
e
ler
atio
n
as
th
e
f
r
eq
u
e
n
t
li
n
e
ch
a
n
g
es
,
th
e
s
u
i
tab
ilit
y
o
f
th
e
p
r
o
p
o
s
ed
alg
o
r
ith
m
f
o
r
th
e
UE
b
eh
av
io
r
ca
n
b
e
en
s
u
r
ed
.
ACK
NO
WL
E
D
G
E
M
E
NT
S
T
h
e
au
th
o
r
s
w
o
u
ld
li
k
e
to
ex
p
r
ess
t
h
e
g
r
atit
u
d
e
to
t
h
e
Min
i
s
tr
y
o
f
E
d
u
ca
tio
n
,
Ma
la
y
s
ia
an
d
Un
i
v
er
s
iti
T
ek
n
o
lo
g
i
M
AR
A
,
Sela
n
g
o
r
,
Ma
la
y
s
ia
f
o
r
th
e
f
i
n
an
cia
l
s
u
p
p
o
r
t
g
i
v
e
n
f
o
r
t
h
is
p
r
o
j
ec
t
(
Ger
an
B
estar
i P
er
d
an
a)
[
Fil
e
No
:
6
0
0
-
I
R
MI
/P
E
R
D
AN
A
5
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B
E
STARI
(
0
9
5
/2
0
1
8
)
.
RE
F
E
R
E
NC
E
S
[1
]
B.
M
a
,
e
t
a
l.
,
“
M
o
d
e
li
n
g
a
n
d
A
n
a
ly
sis
f
o
r
V
e
rti
c
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l
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n
d
o
f
f
Ba
s
e
d
o
n
th
e
De
c
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n
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re
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in
a
H
e
tero
g
e
n
e
o
u
s V
e
h
icle
Ne
tw
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rk
,
”
IEE
E
Acc
e
ss
,
v
o
l.
5
,
p
p
.
8
8
1
2
-
8
8
2
4
,
2
0
1
7
.
[2
]
T
.
Zah
ir,
e
t
a
l.
,
“
In
terf
e
re
n
c
e
M
a
n
a
g
e
m
e
n
t
in
F
e
m
to
c
e
ll
s
,
”
IEE
E
Co
mm
u
n
ica
ti
o
n
s
S
u
rv
e
y
s
&
T
u
to
ria
ls
,
v
o
l.
1
5
,
n
o
.
1
,
pp
.
2
9
3
-
3
1
1
,
2
0
1
3
.
[3
]
G
.
G
o
d
o
r,
e
t
a
l.
,
“
A
S
u
rv
e
y
o
f
Ha
n
d
o
v
e
r
M
a
n
a
g
e
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IJ
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.
1
,
pp.
3
6
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[6
]
H
.
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w
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“
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o
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5
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152
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.
[7
]
S
.
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ra
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t
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l.
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“
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sis
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[8
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su
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54
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A
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l.
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0
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S.
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t
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,
“
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n
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ize
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[1
1
]
Y.
C.
W
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g
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d
C.
A
.
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u
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,
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l.
7
9
,
pp
.
297
-
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2
]
M
.
H.
Ha
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t
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l.
,
“
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re
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.
4
,
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.
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3
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e
,
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t
a
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,
“
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,
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p
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1
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0
0
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.
[1
4
]
Y.
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t
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ms
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,
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p
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2
0
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4
.
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5
]
X
.
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h
e
n
,
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t
a
l.
,
“
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f
icie
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t
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m
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targ
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In
t
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io
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,
p
p
.
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3
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4
.
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6
]
F
.
M
.
Ch
a
n
g
,
e
t
a
l
.
,
“
A
n
e
ff
icie
n
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h
a
n
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ti
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ti
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re
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n
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a
d
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p
ti
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ti
m
e
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to
-
tri
g
g
e
r
in
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T
E
n
e
tw
o
rk
s,”
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ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
o
n
Co
mp
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t
a
ti
o
n
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l
S
c
ien
c
e
a
n
d
Its
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
7
9
7
5
,
p
p
.
2
7
0
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2
8
0
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2
0
1
3
.
[1
7
]
S.
De
sw
a
l
a
n
d
A
.
S
in
g
h
ro
v
a
,
“
A
V
e
rti
c
a
l
Ha
n
d
o
v
e
r
A
lg
o
rit
h
m
in
In
teg
ra
ted
M
a
c
ro
c
e
ll
F
e
m
to
c
e
ll
Ne
t
w
o
rk
s
,
”
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ter
n
a
t
io
n
a
l
J
o
u
rn
a
l
o
f
E
lec
trica
l
a
n
d
C
o
mp
u
ter
En
g
in
e
e
rin
g
(
IJ
ECE
)
,
v
o
l.
7
,
n
o
.
1
,
p
p
.
2
9
9
-
3
0
8
,
2
0
1
7
.
[1
8
]
E.
A
lp
a
y
d
in
,
“
I
n
tr
o
d
u
c
ti
o
n
t
o
m
a
c
h
in
e
lea
rn
i
n
g
,
”
M
IT
p
re
ss
,
2
n
d
e
d
it
io
n
,
2
0
1
0
.
[1
9
]
C.
J.
C.
H.
W
a
tk
in
s a
n
d
P
.
Da
y
a
n
,
“
T
e
c
h
n
ica
l
n
o
te:
Q
-
lea
rn
in
g
,
”
M
a
c
h
in
e
L
e
a
rn
i
n
g
,
v
o
l.
8
,
pp
.
2
7
9
-
2
9
2
,
1
9
9
2
.
[2
0
]
Y
.
S
.
Hu
a
n
g
,
e
t
a
l.
,
“
A
Ha
n
d
o
v
e
r
S
c
h
e
m
e
f
o
r
LT
E
W
irele
ss
N
e
tw
o
rk
s
u
n
d
e
r
th
e
A
ss
istan
c
e
o
f
GPS
,”
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c
e
e
d
in
g
2
0
1
3
8
th
In
ter
n
a
ti
o
n
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l
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fer
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a
d
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n
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n
d
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les
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mp
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t
in
g
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o
mm
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ti
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n
d
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ti
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n
s,
BW
CCA
,
pp
.
3
9
9
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4
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3
,
2
0
1
3
.
[2
1
]
A
.
Bo
g
d
a
n
o
v
,
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o
c
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ti
o
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id
e
n
ti
f
ica
ti
o
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n
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h
a
n
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g
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e
ra
ti
o
n
m
o
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n
e
tw
o
rk
s,
”
2
0
2
0
M
o
s
c
o
w
W
o
rk
sh
o
p
o
n
E
lec
tro
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Ne
two
rk
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o
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g
ies
(
M
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)
,
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o
sc
o
w
,
Ru
ss
ia,
p
p
.
1
-
4
,
2
0
2
0
.
[2
2
]
M
.
A
.
W
o
n
g
,
J.
A
.
J
A
lsa
y
a
y
d
e
h
,
S
.
M
.
I
d
ru
s,
N
.
Z
u
lk
if
li
,
a
n
d
M
.
El
sh
a
ik
h
,
“
Eff
icie
n
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2
P
d
a
ta
d
isse
m
in
a
ti
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n
i
n
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ra
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ti
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a
l
a
n
d
w
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ss
n
e
tw
o
rk
s
w
it
h
T
a
g
u
c
h
i
m
e
th
o
d
,
”
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ica
ti
o
n
,
Co
mp
u
ti
n
g
,
El
e
c
tro
n
ics
a
n
d
C
o
n
tro
l
,
v
o
l.
1
7
,
n
o.
4
,
pp
.
1
6
4
2
-
1
6
4
7
,
2
0
1
9
.
[2
3
]
K.
A
h
u
ja,
e
t
a
l.
,
“
Ne
t
w
o
rk
S
e
lec
ti
o
n
Ba
se
d
o
n
W
e
ig
h
t
Esti
m
a
ti
o
n
o
f
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ra
m
e
ters
in
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tero
g
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n
e
o
u
s
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irele
ss
M
u
lt
im
e
d
ia
Ne
t
w
o
rk
s,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
W
ire
les
s
Per
so
n
a
l
Co
mm
u
n
ica
ti
o
n
s
,
v
o
l
.
77
,
n
o
.
4
,
p
p
.
3
0
2
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-
3
0
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0
,
2
0
1
4
.
[2
4
]
H.
A
.
M
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[2
6
]
B.
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.
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G
.
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latin
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Veh
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y
,
v
o
l.
60
,
n
o
.
2,
pp.
498
-
5
1
3
,
2
0
1
1
.
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