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e
c
ti
v
i
t
y
o
r
r
e
d
u
c
e
t
h
e
i
n
t
e
r
f
e
r
e
n
c
e
.
I
n
t
h
is
a
r
t
i
c
l
e
,
we
u
s
e
d
t
h
r
e
e
d
i
f
f
e
r
e
n
t
t
e
c
h
n
i
q
u
e
s
,
le
a
s
t
m
e
a
n
s
q
u
a
r
e
(
LMS
)
,
g
e
n
e
t
i
c
a
l
g
o
r
i
t
h
m
(
GA
)
a
n
d
g
r
e
y
w
o
l
f
o
p
t
i
m
i
z
at
i
o
n
(
G
W
O
)
t
o
s
t
u
d
y
t
h
e
d
i
r
e
c
t
i
v
it
y
a
n
d
SS
L
i
n
a
s
m
a
r
t
a
n
t
e
n
n
a
s
y
s
t
e
m
.
Fig
u
r
e
1
.
Mu
ltip
at
h
p
r
o
p
ag
ati
o
n
s
ce
n
ar
io
T
h
e
m
ain
co
n
tr
ib
u
tio
n
s
o
f
th
is
p
ap
er
a
r
e:
-
T
h
e
av
er
a
g
e
o
f
d
ir
ec
tiv
ity
h
as
b
ee
n
in
c
r
ea
s
ed
s
ig
n
if
ican
tly
in
m
u
ltip
ath
e
n
v
ir
o
n
m
en
t
wh
e
n
a
L
MS
is
u
s
ed
f
o
r
b
ea
m
f
o
r
m
in
g
c
o
m
p
ar
e
d
to
GA
an
d
GW
O,
wh
ich
g
iv
es a
s
h
ar
p
er
an
d
m
o
r
e
p
r
ec
is
e
b
ea
m
p
atter
n
s
.
-
T
h
e
m
o
s
t
av
e
r
ag
e
SLL
r
ed
u
ct
io
n
in
m
u
ltip
ath
e
n
v
ir
o
n
m
en
t
is
ac
h
iev
ed
wh
en
u
s
in
g
GA
f
o
r
b
ea
m
f
o
r
m
i
n
g
f
o
llo
wed
b
y
L
MS
in
th
e
s
ec
o
n
d
lev
el.
-
T
h
e
r
esu
lts
s
h
o
w
th
at
co
n
v
er
g
en
ce
o
f
a
GW
O
is
ac
h
iev
ed
f
aster
th
an
GA
b
ased
m
eth
o
d
.
L
MS
m
eth
o
d
is
m
o
s
t slo
w
d
o
wn
co
n
v
er
g
e
n
ce
b
etwe
en
th
ese
th
r
ee
m
eth
o
d
s
.
T
h
e
r
em
ain
i
n
g
o
f
t
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
ws:
i
n
s
ec
tio
n
2
,
a
g
e
o
m
etr
y
co
n
f
ig
u
r
atio
n
o
f
a
lin
ea
r
an
ten
n
a
ar
r
a
y
an
d
th
e
ar
r
ay
f
ac
to
r
eq
u
atio
n
s
ar
e
d
is
cu
s
s
ed
.
A
d
escr
ip
tio
n
o
f
th
e
L
MS,
Gen
etic
an
d
GW
O
alg
o
r
ith
m
s
an
d
its
im
p
lem
en
ta
tio
n
s
tep
s
ar
e
d
is
cu
s
s
ed
in
s
am
e
s
ec
tio
n
.
A
s
im
p
le
ex
p
lan
atio
n
ab
o
u
t simu
latio
n
en
f
o
r
ce
m
e
n
t
is
in
clu
d
ed
in
s
ec
tio
n
3
,
as
well
as
th
e
r
esu
lts
o
f
s
im
u
latio
n
f
o
r
all
th
r
ee
alg
o
r
i
th
m
s
ar
e
p
r
esen
ted
an
d
d
is
cu
s
s
ed
in
s
ec
tio
n
4
.
W
h
ile
th
e
co
n
clu
s
io
n
p
r
esen
ts
in
s
ec
tio
n
5
.
2.
ANT
E
NNA
ARR
AY
CO
NF
I
G
URATI
O
N
T
h
e
-
elem
en
t
ar
r
a
y
is
co
n
s
id
er
ed
a
co
m
m
o
n
g
en
e
r
al
lin
ea
r
ar
r
ay
.
Fo
r
s
im
p
licity
,
all
elem
en
ts
in
th
is
s
tu
d
y
ar
e
ass
u
m
ed
th
at
h
av
e
eq
u
al
am
p
litu
d
es
an
d
eq
u
ally
s
p
ac
ed
.
Fig
u
r
e
2
d
e
p
icts
a
lin
ea
r
ar
r
ay
o
f
a
n
N
-
elem
en
t,
wh
ich
co
llected
is
o
tr
o
p
ic
r
ad
iatio
n
o
f
an
ten
n
a
el
em
en
ts
.
I
n
th
is
p
ap
er
,
a
u
n
if
o
r
m
lin
ea
r
ar
r
ay
alo
n
g
th
e
x
-
ax
is
co
n
s
is
tin
g
o
f
2
N
is
o
t
r
o
p
ic
elem
en
ts
is
s
tu
d
ied
.
T
h
e
ar
r
ay
f
ac
t
o
r
(
AF)
is
ca
lcu
lated
u
s
in
g
th
e
f
o
llo
win
g
eq
u
atio
n
s
;
=
1
+
(
∅
+
)
+
2
(
∅
+
)
+
3
(
∅
+
)
+
⋯
+
(
−
1
)
(
∅
+
)
(
1
)
w
h
er
e
>
>
r
ep
r
esen
ts
th
e
f
ar
-
f
ield
co
n
d
itio
n
,
a
f
ar
-
f
ield
d
is
tan
ce
is
g
iv
en
as
=
2
2
/
(
2
)
w
h
er
e
is
th
e
m
ax
im
u
m
a
n
ten
n
a
d
im
en
s
io
n
,
is
th
e
d
is
tan
ce
f
r
o
m
elem
en
t
to
f
ar
-
f
ield
p
o
in
t,
an
d
is
th
e
p
h
ase
s
h
if
t b
etwe
en
s
u
cc
es
s
iv
e
elem
en
ts
as sh
o
wn
in
(
1
)
ca
n
m
o
r
e
p
r
ec
is
ely
ex
p
r
ess
as
=
∑
(
−
1
)
(
)
=
1
(
3
)
wh
er
e
=
s
in
∅
+
.
I
t is n
o
ted
th
at
if
th
e
a
r
r
ay
is
lin
ed
u
p
alo
n
g
th
e
−
th
en
=
c
os
∅
+
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
A
b
ea
mfo
r
min
g
co
mp
a
r
a
tive
s
tu
d
y
o
f
lea
s
t m
ea
n
s
q
u
a
r
e
(
LMS
)
,
g
e
n
etic
a
lg
o
r
ith
m
...
(
A
s
s
a
d
S
.
Da
g
h
a
l
)
2913
Fig
u
r
e
2
.
Un
if
o
r
m
lin
ea
r
ar
r
a
y
g
eo
m
etr
y
3.
L
E
A
ST
M
E
AN
SQ
UAR
E
A
L
G
O
RIT
H
M
I
n
1
9
5
9
W
id
r
o
w
an
d
Ho
f
f
i
n
tr
o
d
u
ce
d
t
h
e
L
MS
alg
o
r
ith
m
to
th
e
wo
r
ld
.
I
t
is
o
n
e
o
f
th
e
ea
r
lies
t
ad
ap
tiv
e
alg
o
r
ith
m
s
th
at
d
ep
e
n
d
s
o
n
g
r
a
d
ien
t
b
ased
m
eth
o
d
o
f
a
b
r
u
p
t.
L
MS
is
a
s
ea
r
ch
alg
o
r
ith
m
th
at
u
s
e
th
e
m
o
d
if
y
i
n
g
o
f
o
b
jectiv
e
f
u
n
ctio
n
to
s
i
m
p
l
if
y
th
e
g
r
a
d
ien
t
v
ec
to
r
co
m
p
u
tatio
n
.
Fig
u
r
e
3
a
r
ticu
late
th
e
f
lo
w
ch
ar
t
o
f
L
MS
alg
o
r
ith
m
[
1
6
-
18]
.
T
h
e
L
MS
a
lg
o
r
ith
m
s
ca
n
d
ef
in
ed
m
ath
em
atica
lly
b
y
f
o
llo
win
g
eq
u
ati
o
n
s
:
(
)
=
(
)
.
(
)
(
4
)
(
)
=
(
)
−
(
)
(
5
)
(
+
1
)
=
(
)
+
(
)
.
(
)
(
6
)
=
g
ain
co
n
s
tan
t a
n
d
m
o
n
ito
r
th
e
ad
ap
tatio
n
r
ate.
(
)
=
in
p
u
t sig
n
al
.
(
)
=
er
r
o
r
b
etwe
en
d
esire
d
an
d
o
u
tp
u
t sig
n
al
(
)
=
d
esire
d
s
ig
n
al
.
w
h
er
e
is
th
e
co
r
r
elatio
n
m
atr
i
x
=
1
/
(
)
Fig
u
r
e
3
.
Flo
wch
ar
t
o
f
L
MS
al
g
o
r
ith
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
2
9
1
1
-
2
9
2
0
2914
4.
G
E
NE
T
I
C
A
L
G
O
RI
T
H
M
Gen
etic
alg
o
r
ith
m
is
co
n
s
id
er
ed
o
n
e
o
f
t
h
e
f
ir
s
t’
s
m
eta
-
h
eu
r
is
tic
alg
o
r
ith
m
s
.
I
t
is
class
if
ied
as
g
l
o
b
al
n
u
m
er
ical
o
p
tim
izatio
n
m
eth
o
d
s
,
wh
ich
g
o
t
p
o
p
u
lar
b
y
J
o
h
n
Ho
llan
d
in
1
9
7
0
s
.
T
h
e
GA
u
s
es
f
iv
e
b
asic
s
tep
s
to
s
o
lv
e
p
r
o
b
lem
s
an
d
it c
a
n
b
e
s
u
m
m
ar
ized
as f
o
llo
w
[
1
9
-
2
2
]
:
-
C
r
ea
tin
g
in
itial p
o
p
u
latio
n
o
f
ch
r
o
m
o
s
o
m
es r
an
d
o
m
ly
.
E
ac
h
ch
r
o
m
o
s
o
m
e
is
a
p
o
s
s
ib
le
s
o
lu
tio
n
.
-
Dete
r
m
in
e
th
e
f
itn
ess
v
alu
e
a
n
d
f
itn
ess
f
u
n
ctio
n
to
s
o
lv
e
th
e
p
r
o
b
lem
,
an
d
is
it
n
ee
d
to
m
in
im
ized
o
r
m
ax
im
ized
.
-
I
n
itially
,
all
th
e
cr
ea
ted
ch
r
o
m
o
s
o
m
es
ar
e
p
ass
ed
o
n
th
e
Fit
n
ess
f
u
n
ctio
n
f
o
r
ev
alu
atio
n
,
an
d
th
en
th
e
b
est
ch
r
o
m
o
s
o
m
es
th
at
g
av
e
u
s
th
e
b
est
s
o
lu
tio
n
s
ar
e
id
en
tifie
d
.
T
h
e
ch
r
o
m
o
s
o
m
es
th
at
g
av
e
u
s
th
e
wo
r
s
t
r
esu
lts
ar
e
d
is
ca
r
d
ed
a
n
d
in
ca
s
e,
we
h
av
e
a
n
ew
g
en
er
atio
n
o
f
ch
r
o
m
o
s
o
m
es.
-
C
r
o
s
s
o
v
e
r
p
r
o
c
e
s
s
t
a
k
e
s
p
l
a
c
e
b
e
t
w
e
e
n
t
h
e
b
e
s
t
c
h
r
o
m
o
s
o
m
e
s
t
o
c
r
e
a
t
e
n
e
w
c
h
r
o
m
o
s
o
m
e
s
.
M
a
y
m
u
t
a
t
i
o
n
p
r
o
c
e
s
s
a
p
p
l
i
e
s
o
n
s
o
m
e
c
h
r
o
m
o
s
o
m
e
s
w
i
t
h
a
c
e
r
t
a
i
n
p
e
r
c
e
n
t
a
g
e
t
o
g
i
v
e
u
s
n
e
w
c
h
r
o
m
o
s
o
m
e
s
a
n
d
n
e
w
s
o
l
u
t
i
o
n
s
-
T
h
is
p
r
o
ce
s
s
o
f
s
tep
3
an
d
4
is
r
ep
ea
ted
with
in
ce
r
tain
iter
atio
n
s
to
o
b
tain
th
e
r
esu
lts
r
e
q
u
ir
ed
to
s
o
lv
e
th
e
p
r
o
b
lem
.
5.
G
RE
Y
WO
L
F
O
P
T
I
M
I
Z
AT
I
O
N
AL
G
O
RI
T
H
M
T
h
e
GW
O
alg
o
r
ith
m
is
co
n
s
id
er
ed
o
n
e
o
f
th
e
m
eta
-
h
e
u
r
is
tic
alg
o
r
ith
m
s
th
at
em
u
late
th
e
s
o
cial
h
u
n
tin
g
m
an
n
er
a
n
d
h
ie
r
ar
ch
ical
o
f
th
e
g
r
ey
wo
l
v
es
g
r
o
u
p
.
T
h
e
h
u
n
ti
n
g
m
an
n
er
o
f
th
e
g
r
ey
w
o
lv
es
ca
n
b
e
s
u
m
m
a
r
ized
as
f
o
llo
ws
[
2
3
-
2
5
]
:
T
h
e
f
i
r
s
t
g
r
ad
e,
wh
ich
r
ep
r
esen
ts
th
e
ch
i
(
)
th
at
th
ey
ar
e
th
e
lead
er
s
an
d
ca
n
b
e
m
ales
o
r
f
em
ales.
T
h
e
s
ec
o
n
d
g
r
a
d
e
in
t
h
e
h
ier
ar
c
h
ical
is
co
n
s
is
ts
o
f
th
e
r
h
o
(
)
wo
lv
es,
wh
ich
f
o
llo
wed
b
y
th
e
wo
lv
es
ca
lled
g
am
m
a
(
)
,
wh
ile
th
e
d
o
wn
s
o
r
tin
g
o
f
th
e
g
r
ey
wo
lv
es
is
n
am
ed
m
u
(
)
.
T
h
e
h
ier
ar
c
h
ical
o
f
th
e
g
r
e
y
wo
lv
es
ar
e
c
o
n
s
t
itu
ted
m
ath
e
m
atica
lly
th
r
o
u
g
h
o
u
t
c
o
u
n
ti
n
g
as
th
e
b
est
s
o
lu
tio
n
th
at
f
o
llo
wed
b
y
th
e
2
nd
an
d
th
e
3
rd
f
in
est s
o
lu
tio
n
s
an
d
,
r
esp
ec
tiv
ely
.
T
h
e
r
em
ain
in
g
o
f
th
e
n
o
m
in
ate
d
s
o
lu
tio
n
s
ar
e
s
u
p
p
o
s
ed
to
b
e
.
T
h
e
m
ajo
r
s
tep
s
s
eq
u
en
ce
o
f
t
h
e
g
r
ey
wo
lf
h
u
n
tin
g
ca
n
b
e
s
u
m
m
ar
ized
as f
o
llo
ws:
-
Pu
r
s
u
it,
ch
asin
g
af
ter
a
n
d
d
r
a
w
n
ea
r
th
e
p
r
ey
.
-
Kee
p
tr
ac
k
in
g
,
s
u
r
r
o
u
n
d
in
g
,
a
n
d
b
eset th
e
p
r
ey
u
n
til it sto
p
s
m
o
v
in
g
.
-
R
aid
to
war
d
s
th
e
p
r
ey
.
Du
r
in
g
a
h
u
n
tin
g
p
r
o
ce
s
s
,
g
r
e
y
wo
lv
es h
ed
g
e
th
e
p
r
ey
th
at
i
s
m
ath
em
atica
lly
m
o
d
elled
as
[
9
]
:
⃗
⃗
=
|
.
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
−
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
|
(7
)
(
+
1
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
=
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
−
.
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
8
)
wh
er
e
an
d
ar
e
th
e
c
o
ef
f
icie
n
t
v
ec
to
r
s
,
is
r
ef
er
r
ed
to
t
h
e
c
u
r
r
en
t
iter
atio
n
,
⃗
⃗
⃗
⃗
⃗
r
ep
r
esen
ts
th
e
lo
ca
tio
n
v
ec
to
r
o
f
th
e
p
r
ey
,
a
n
d
⃗
r
ef
er
s
to
th
e
lo
ca
tio
n
v
ec
to
r
o
f
th
e
g
r
e
y
wo
lf
.
T
h
e
v
ec
to
r
s
an
d
ar
e
e
v
alu
ated
as
;
=
2
.
2
⃗
⃗
⃗
(
9
)
=
2
.
1
⃗
⃗
⃗
−
(
1
0
)
wh
er
e
co
m
p
o
n
en
ts
o
f
ar
e
d
ec
r
ea
s
ed
lin
ea
r
ly
f
r
o
m
2
to
0
d
u
r
i
n
g
th
e
iter
atio
n
s
an
d
1
⃗
⃗
⃗
,
2
⃗
⃗
⃗
ar
e
r
an
d
o
m
v
ec
to
r
s
th
at
lie
in
th
e
r
an
g
e
[
0
,
1
]
.
I
n
th
e
GW
O
alg
o
r
ith
m
p
r
o
ce
s
s
,
t
h
e
h
u
n
tin
g
a
p
p
r
o
ac
h
h
as
led
b
y
χ,
ρ
,
a
n
d
γ
,
wh
er
e
th
e
o
th
er
wo
lv
es
f
o
llo
w
th
o
s
e
th
r
ee
ty
p
es
o
f
wo
lv
es.
Fu
r
th
er
,
in
th
e
p
ac
k
o
f
th
e
wo
l
v
es,
it
is
s
u
p
p
o
s
ed
th
at
th
e
wo
lv
es’
χ,
ρ
,
an
d
γ
h
av
e
th
e
b
est
f
am
iliar
ity
ab
o
u
t
th
e
p
o
t
en
tial
p
o
s
itio
n
o
f
th
e
p
r
ey
.
C
o
n
s
eq
u
en
tly
,
th
e
first
th
r
ee
b
est
s
o
lu
tio
n
s
will
b
e
s
av
e,
an
d
th
e
o
th
e
r
s
ea
r
ch
in
g
o
p
er
ato
r
s
will
u
p
d
ate
th
eir
lo
ca
tio
n
s
ac
co
r
d
in
g
to
th
e
p
o
s
itio
n
o
f
th
e
b
est s
ea
r
ch
o
p
er
ato
r
.
Fo
r
t
h
is
p
u
r
p
o
s
e,
we
u
s
e
th
e
f
o
llo
win
g
eq
u
atio
n
s
:
⃗
⃗
⃗
⃗
⃗
=
|
1
⃗
⃗
⃗
⃗
.
⃗
⃗
⃗
⃗
⃗
−
⃗
|
⃗
⃗
⃗
⃗
⃗
=
|
2
⃗
⃗
⃗
⃗
.
⃗
⃗
⃗
⃗
⃗
−
⃗
|
⃗
⃗
⃗
⃗
⃗
=
|
3
⃗
⃗
⃗
⃗
.
⃗
⃗
⃗
⃗
−
⃗
|
1
⃗
⃗
⃗
⃗
⃗
=
⃗
⃗
⃗
⃗
⃗
−
1
⃗
⃗
⃗
⃗
⃗
.
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
2
⃗
⃗
⃗
⃗
⃗
=
⃗
⃗
⃗
⃗
⃗
−
2
⃗
⃗
⃗
⃗
⃗
.
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
3
⃗
⃗
⃗
⃗
⃗
=
⃗
⃗
⃗
⃗
−
3
⃗
⃗
⃗
⃗
⃗
.
(
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
+
1
)
=
1
⃗
⃗
⃗
⃗
⃗
+
2
⃗
⃗
⃗
⃗
⃗
+
3
⃗
⃗
⃗
⃗
⃗
3
}
(
1
1
)
B
y
u
s
in
g
(
1
1
)
,
a
n
d
ac
c
o
r
d
in
g
to
th
e
p
a
r
am
eter
s
χ,
ρ
,
an
d
γ
i
n
th
e
n
t
h
-
d
im
e
n
s
io
n
al
s
ea
r
ch
i
n
g
d
o
m
ain
,
w
h
er
e
a
s
ea
r
ch
o
p
er
ato
r
will
b
e
u
p
d
atin
g
its
lo
ca
tio
n
.
Als
o
,
th
e
fin
al
lo
ca
tio
n
o
f
th
e
s
ea
r
ch
d
o
m
ain
wo
u
ld
b
e
in
a
r
an
d
o
m
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[
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6
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.
6.
M
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I
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D
M
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I
n
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e
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b
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d
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two
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an
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itted
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Simu
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a
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(
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t
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ser)
D
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5
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D
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-
3
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D
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70
D
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f
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(
D
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a
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g
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(
2
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ser)
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A
2
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=
-
5
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D
O
A
2
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0
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D
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2
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5
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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m
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Vo
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18
,
No
.
6
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Dec
em
b
e
r
2
0
2
0
:
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1
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9
2
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2916
T
ab
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4
.
An
ten
n
a
p
a
r
am
eter
s
r
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lt
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o
r
ar
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a
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5
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s
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g
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y
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alg
o
r
ith
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Fig
u
r
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4
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SLL
,
m
ain
lo
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o
f
f
ir
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t
u
s
er
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d
its
3
DOAs f
o
r
N
=
3
2
,
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=
0
.
2
5
o
p
tim
ized
with
L
MS
Fig
u
r
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5
.
SLL
,
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ain
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o
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s
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d
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n
d
its
3
DOAs f
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=
3
2
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5
o
p
tim
ized
wi
th
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MS
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
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l
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b
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r
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tive
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r
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ith
m
...
(
A
s
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d
S
.
Da
g
h
a
l
)
2917
Fig
u
r
e
6
.
SLL
,
m
ain
lo
b
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o
f
f
ir
s
t u
s
er
an
d
its
3
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r
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=
3
2
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2
5
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tim
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GA
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u
r
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7
.
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its
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o
r
N
=
3
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,
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.
2
5
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tim
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
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m
m
u
n
C
o
m
p
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t E
l Co
n
tr
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l
,
Vo
l.
18
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
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9
1
1
-
2
9
2
0
2918
Fig
u
r
e
8
.
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,
m
ain
lo
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o
f
f
ir
s
t u
s
er
an
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its
3
DOAs f
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r
N
=
3
2
,
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2
5
o
p
tim
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with
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O
Fig
u
r
e
9
.
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,
m
ain
lo
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o
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d
u
s
er
a
n
d
its
3
DOAs f
o
r
N
=
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2
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0
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2
5
o
p
tim
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wi
th
GW
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8.
CO
NCLU
SI
O
N
I
n
th
is
p
ap
er
,
th
e
p
e
r
f
o
r
m
an
c
e
o
f
th
r
ee
t
y
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o
f
a
d
ap
tiv
e
al
g
o
r
ith
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s
f
o
r
th
e
b
ea
m
f
o
r
m
in
g
in
s
m
ar
t
an
ten
n
a
s
y
s
tem
s
,
in
ter
m
s
o
f
p
r
ef
er
en
ce
in
d
ir
ec
tiv
ity
an
d
th
e
ex
ten
t
o
f
s
id
elo
b
e
lev
el
r
ed
u
ctio
n
f
o
r
m
u
ltip
at
h
en
v
ir
o
n
m
en
t
h
as
b
ee
n
s
tu
d
ie
d
.
Desp
ite
th
e
s
im
p
le
d
if
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er
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n
ce
s
,
th
e
o
u
tc
o
m
es
o
f
all
t
h
r
ee
alg
o
r
ith
m
s
a
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
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m
p
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t E
l Co
n
tr
o
l
A
b
ea
mfo
r
min
g
co
mp
a
r
a
tive
s
tu
d
y
o
f
lea
s
t m
ea
n
s
q
u
a
r
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(
LMS
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g
e
n
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a
lg
o
r
ith
m
...
(
A
s
s
a
d
S
.
Da
g
h
a
l
)
2919
g
en
er
ally
clo
s
e;
th
e
GW
O
alg
o
r
ith
m
n
ee
d
s
th
e
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m
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e
an
s
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m
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tatio
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e
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th
e
r
est
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f
th
e
alg
o
r
ith
m
s
,
wh
ile
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e
L
MS
alg
o
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ith
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est
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m
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m
s
.
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at
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e
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o
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o
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y
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e
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MS
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o
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ith
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.
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h
e
f
i
n
d
in
g
s
d
e
m
o
n
s
tr
ates
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at
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MS
alg
o
r
ith
m
ac
h
iev
es
b
etter
r
esu
lts
in
ter
m
s
o
f
d
ir
ec
tiv
ity
in
m
u
ltip
ath
e
n
v
ir
o
n
m
en
t,
f
o
llo
wed
b
y
GW
O
alg
o
r
ith
m
in
th
e
s
ec
o
n
d
r
an
k
.
RE
F
E
R
E
NC
E
S
[1
]
An
a
n
d
,
Arj
u
n
,
a
n
d
G
u
sta
v
o
d
e
Ve
c
ian
a
,
“
Re
so
u
rc
e
a
ll
o
c
a
ti
o
n
a
n
d
HA
RQ
o
p
ti
m
iza
ti
o
n
fo
r
URL
LC
traffic
in
5
G
wire
les
s n
e
two
rk
s
,
”
IEE
E
J
o
u
r
n
a
l
o
n
S
e
lec
ted
Are
a
s i
n
C
o
mm
u
n
ica
ti
o
n
s
,
v
o
l
.
36
,
n
o
.
1
1
,
p
p
.
2
4
1
1
-
2
4
2
1
,
2
0
1
8
.
[2
]
Am
e
e
ru
d
d
e
n
,
M
o
h
a
m
m
a
d
R.
,
a
n
d
Ha
rry
C
.
S
.
Ru
g
h
o
o
p
u
t
h
,
“
S
m
a
rt
Hy
b
ri
d
G
e
n
e
ti
c
Alg
o
rit
h
m
s
i
n
th
e
b
a
n
d
wid
t
h
o
p
ti
m
iza
ti
o
n
o
f
a
P
IF
A
An
ten
n
a
,
”
2
0
1
4
IEE
E
C
o
n
g
re
ss
o
n
Evo
lu
ti
o
n
a
ry
Co
m
p
u
t
a
ti
o
n
(CEC)
,
2
0
1
4
.
[3
]
Ad
a
m
s,
Ja
c
o
b
J.
,
a
n
d
Je
n
n
ifer
T.
Be
rn
h
a
rd
,
“
A
m
o
d
a
l
a
p
p
ro
a
c
h
t
o
tu
n
in
g
a
n
d
b
a
n
d
wid
th
e
n
h
a
n
c
e
m
e
n
t
o
f
a
n
e
lec
tri
c
a
ll
y
sm
a
ll
a
n
ten
n
a
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
An
ten
n
a
s a
n
d
Pr
o
p
a
g
a
ti
o
n
,
v
o
l
.
59
,
n
o
.
4
,
p
p
.
1
0
8
5
-
1
0
9
2
,
2
0
1
1
.
[4
]
Zh
a
n
g
Ya
n
,
e
t
a
l.
,
“
M
u
lt
i
-
p
a
t
h
i
n
tere
sts
ro
u
ti
n
g
sc
h
e
m
e
fo
r
m
u
lt
i
-
p
a
th
d
a
ta
tran
sfe
r
in
c
o
n
te
n
t
c
e
n
tr
ic
n
e
two
r
k
in
g
,”
Na
ti
o
n
a
l
Do
c
to
r
a
l
Aca
d
e
mic
Fo
r
u
m o
n
In
fo
rm
a
ti
o
n
a
n
d
C
o
mm
u
n
i
c
a
ti
o
n
s T
e
c
h
n
o
lo
g
y
,
Ja
n
u
a
ry
2
0
1
3
.
[5
]
D
h
a
wa
, S
.
D.
,
e
t
a
l.
,
“
R
a
n
d
o
m
li
n
e
a
r
n
e
two
rk
c
o
d
in
g
b
a
se
d
m
u
lt
ip
a
th
traffics
o
v
e
r
h
e
ter
o
g
e
n
e
o
u
s c
l
o
u
d
ra
d
i
o
a
c
c
e
ss
n
e
two
rk
,
”
J
o
u
rn
a
l
o
f
T
h
e
o
re
ti
c
a
l
a
n
d
Ap
p
li
e
d
In
f
o
rm
a
ti
o
n
T
e
c
h
n
o
l
o
g
y
,
v
o
l
.
96
,
n
o
.
9
,
p
p
.
2
3
8
1
-
2
3
8
,
M
a
y
2
0
1
8
.
[6
]
Re
d
d
y
T.
Bh
e
e
m
a
rju
n
a
,
e
t
a
l
.
,
“
M
u
S
e
Qo
R:
M
u
lt
i
-
p
a
t
h
fa
il
u
re
-
to
lera
n
t
se
c
u
rit
y
-
a
wa
re
Qo
S
ro
u
ti
n
g
i
n
Ad
h
o
c
wire
les
s
n
e
two
rk
s
,
”
C
o
mp
u
ter
Ne
two
rk
,
v
o
l.
50
,
n
o
.
9
,
p
p
.
1
3
4
9
-
1
3
8
3
,
2
0
0
6
.
[7
]
Ko
n
o
v
a
lt
se
v
,
An
d
r
iy
,
F
e
li
x
An
tre
ich
,
a
n
d
Ac
h
im
H
o
rn
b
o
ste
l
,
“
P
e
rfo
rm
a
n
c
e
a
ss
e
ss
m
e
n
t
o
f
a
n
ten
n
a
a
rra
y
a
lg
o
rit
h
m
s
fo
r
m
u
lt
i
p
a
th
a
n
d
i
n
terfe
re
n
c
e
m
it
ig
a
ti
o
n
,
”
Pro
c
e
e
d
i
n
g
s
o
f
th
e
2
n
d
W
o
rk
sh
o
p
o
n
GNS
S
S
ig
n
a
ls &
S
i
g
n
a
l
Pro
c
e
ss
in
g
(GNS
S
S
IGNAL
S
’
0
7
)
,
2
0
0
7
.
[8
]
S
e
n
,
S
a
ty
a
b
ra
ta,
a
n
d
Ar
y
e
Ne
h
o
ra
i
,
“
Ad
a
p
ti
v
e
OFDM
ra
d
a
r
f
o
r
targ
e
t
d
e
tec
ti
o
n
in
m
u
l
ti
p
a
t
h
sc
e
n
a
rio
s
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
i
g
n
a
l
Pr
o
c
e
ss
in
g
,
v
o
l.
59
,
n
o
.
1
,
p
p
.
7
8
-
90,
F
e
b
r
u
a
ry
2
0
1
1.
[9
]
Ra
m
S
h
a
n
k
a
r
,
“
A
stu
d
y
o
f
a
d
a
p
ti
v
e
b
e
a
m
fo
rm
in
g
tec
h
n
i
q
u
e
s
u
isin
g
sm
a
rt
a
n
ten
n
a
fo
r
m
o
b
il
e
c
o
m
m
u
n
ica
ti
o
n
,”
Th
e
sis (M
tec
h
)
,
2
0
0
7
.
[1
0
]
Da
s,
S
u
sm
it
a
,
“
S
m
a
rt
a
n
ten
n
a
d
e
sig
n
fo
r
wire
les
s
c
o
m
m
u
n
ica
ti
o
n
u
si
n
g
a
d
a
p
ti
v
e
b
e
a
m
-
fo
rm
in
g
a
p
p
ro
a
c
h
,
”
T
ENCON
2
0
0
8
-
2
0
0
8
IE
EE
Reg
io
n
1
0
C
o
n
f
e
re
n
c
e
,
De
c
e
m
b
e
r
2
0
0
8
.
[1
1
]
S
e
n
a
p
a
ti
A
.
,
a
n
d
Jib
e
n
d
u
S
.
R
.
,
“
Ad
a
p
ti
v
e
b
e
a
m
fo
rm
a
ti
o
n
i
n
sm
a
rt
a
n
ten
n
a
u
sin
g
tsc
h
e
b
y
sc
h
e
ff
d
istri
b
u
ti
o
n
a
n
d
v
a
rian
ts o
f
lea
st me
a
n
sq
u
a
re
a
lg
o
rit
h
m
,
”
M
icr
o
w
a
v
e
Rev
iew
,
v
o
l.
2
2
,
n
o
.
1
,
p
p
.
1
1
-
1
6
,
A
u
g
u
st
2
0
1
6
.
[1
2
]
Ra
g
h
a
v
a
n
V
.
,
e
t
a
l.
,
“
Be
a
m
fo
rm
in
g
trad
e
o
ffs
fo
r
i
n
it
ial
UE
d
isc
o
v
e
ry
in
m
il
li
m
e
ter
-
wa
v
e
M
I
M
O
sy
ste
m
s
,
”
IEE
E
J
o
u
rn
a
l
o
f
S
e
lec
ted
T
o
p
ics
in
S
ig
n
a
l
Pro
c
e
ss
in
g
,
v
o
l.
10
,
n
o
.
3
,
p
p
.
5
4
3
-
5
5
9
,
Ja
n
u
a
ry
2
0
1
6
.
[1
3
]
As
m
a
.
I.
M
o
h
si
n
,
As
a
a
d
.
S
.
Da
g
h
a
l
a
n
d
Ad
h
e
e
d
.
H.
S
a
ll
o
m
i
,
“A
b
e
a
m
fo
rm
in
g
stu
d
y
o
f
th
e
li
n
e
a
r
a
n
t
e
n
n
a
a
rra
y
u
si
n
g
G
re
y
Wo
lf
o
p
t
imiz
a
ti
o
n
a
l
g
o
rit
h
m
,
”
In
d
o
n
e
sia
n
J
o
u
r
n
a
l
o
f
El
e
c
tr
ica
l
En
g
in
e
e
rin
g
a
n
d
Co
mp
u
ter
S
c
ien
c
e
,
v
o
l.
2
0
,
n
o
.
3
,
2
0
2
0
.
[1
4
]
S
a
x
e
n
a
P
.
,
a
n
d
A.
G
.
Ko
t
h
a
ri
, “
P
e
rfo
rm
a
n
c
e
a
n
a
ly
sis
o
f
a
d
a
p
ti
v
e
b
e
a
m
fo
rm
in
g
a
l
g
o
rit
h
m
s fo
r
sm
a
rt
a
n
ten
n
a
s
,
”
I
ER
I
Pro
c
e
d
ia
,
v
o
l.
10
,
p
p
.
1
3
1
-
1
3
7
,
2
0
1
4
.
[1
5
]
Dib
N
.,
“
De
sig
n
o
f
p
lan
a
r
c
o
n
c
e
n
tri
c
c
ircu
lar a
n
ten
n
a
a
rra
y
s wit
h
r
e
d
u
c
e
d
si
d
e
l
o
b
e
lev
e
l
u
sin
g
sy
m
b
io
ti
c
o
r
g
a
n
ism
s
se
a
rc
h
,
”
Ne
u
ra
l
Co
mp
u
ti
n
g
a
n
d
A
p
p
li
c
a
ti
o
n
,
v
o
l
.
30
,
n
o
.
12
,
p
p
.
3
8
5
9
-
3
8
6
8
,
Ap
ril
2
0
1
7
.
[1
6
]
Ra
z
ia,
S
.
,
T.
H
o
ss
a
in
,
a
n
d
M
.
A.
M
a
ti
n
,
“
P
e
rf
o
rm
a
n
c
e
a
n
a
ly
sis
o
f
a
d
a
p
ti
v
e
b
e
a
m
fo
rm
in
g
a
lg
o
r
it
h
m
f
o
r
sm
a
rt
a
n
ten
n
a
sy
ste
m
,
”
2
0
1
2
I
n
ter
n
a
t
io
n
a
l
Co
n
f
e
re
n
c
e
o
n
In
f
o
rm
a
ti
c
s,
E
lec
tro
n
ic
s &
Vi
sio
n
(ICIE
V)
,
2
0
1
2
.
[1
7
]
G
o
n
d
a
l,
M
.
A
.
,
a
n
d
Am
ir
A
.
,
“
An
a
ly
sis
o
f
o
p
ti
m
ize
d
sig
n
a
l
p
r
o
c
e
ss
in
g
a
lg
o
rit
h
m
s
fo
r
sm
a
rt
a
n
ten
n
a
sy
ste
m
,
”
Ne
u
ra
l
Co
mp
u
t
in
g
a
n
d
Ap
p
li
c
a
t
io
n
s
,
v
o
l.
23
,
n
o
.
3
-
4
,
p
p
.
1
0
8
3
-
1
0
8
7
,
2
0
1
3
.
[1
8
]
S
e
n
a
p
a
ti
A.,
Ka
u
sta
b
h
G
h
a
tak
,
a
n
d
Jib
e
n
d
u
S
e
k
h
a
r
R
o
y
,
“
A co
m
p
a
ra
ti
v
e
stu
d
y
o
f
a
d
a
p
ti
v
e
b
e
a
m
fo
r
m
in
g
tec
h
n
iq
u
e
s
in
sm
a
rt
a
n
ten
n
a
u
si
n
g
LM
S
a
lg
o
rit
h
m
a
n
d
it
s
v
a
rian
ts
,
”
2
0
1
5
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
t
a
ti
o
n
a
l
In
telli
g
e
n
c
e
a
n
d
Ne
two
rk
s
,
Ja
n
u
a
ry
2
0
1
5
.
[1
9
]
T.
S
.
G
.
Ba
sh
a
,
M
.
N.
G
.
P
ra
sa
d
,
a
n
d
P
.
V
S
ri
d
e
v
i,
“
Be
a
m
fo
r
m
in
g
i
n
sm
a
rt
a
n
ten
n
a
with
imp
ro
v
e
d
g
a
i
n
a
n
d
su
p
p
re
ss
e
d
i
n
terfe
re
n
c
e
u
sin
g
g
e
n
e
ti
c
a
lg
o
rit
h
m
,
”
v
o
l.
2
,
n
o
.
1
,
p
p
.
1
–
1
4
,
M
a
rc
h
2
0
1
2
.
[2
0
]
S
u
k
u
m
a
r
H.
,
a
n
d
S
a
n
jee
v
Ku
m
a
r
,
“
R
e
d
u
c
e
d
in
si
d
e
lo
b
e
lev
e
l
(sl
l)
u
sin
g
g
e
n
e
ti
c
a
lg
o
rit
h
m
o
f
sm
a
rt
a
n
ten
n
a
sy
ste
m
,
”
I
n
ter
n
a
t
io
n
a
l
Jo
u
rn
a
l
o
f
E
n
g
in
e
e
rin
g
S
c
ien
c
e
s
&
M
a
n
a
g
e
me
n
t
,
v
o
l
.
5
,
n
o
.
3
,
p
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2
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[2
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5
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6
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C.
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I
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.
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s d
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iza
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q
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s.
Evaluation Warning : The document was created with Spire.PDF for Python.