Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
9,
No.
4,
August
2019,
pp.
3221
3227
ISSN:
2088-8708,
DOI:
10.11591/ijece.v9i4.pp3221-3227
r
3221
P
arallelising
r
eception
and
transmission
in
queues
of
secondary
users
Said
Lakhal,
Zouhair
Guennoun
Mohammadia
School
of
Engineering,
Mohammed
5
Uni
v
ersity
in
Rabat,
ERSC
formerly
kno
wn
as
LEC,
Research
Center
E3S,
Morocco
Article
Inf
o
Article
history:
Recei
v
ed
Jun
29,
2018
Re
vised
Mar
13,
2019
Accepted
Mar
21,
2019
K
eyw
ords:
Cogniti
v
e
radio
netw
ork
Queue
management
T
ransmission
delay
Data
shifting
Input
flo
w
Output
flo
w
ABSTRA
CT
In
a
cogniti
v
e
radio
netw
ork,
the
secondary
users
place
the
pack
ets
to
be
transmitted
on
a
queue,
for
controlling
the
order
of
arri
v
al,
and
adapting
to
the
netw
ork
state.
The
pre
vious
conceptions
assigned
to
each
secondary
user
a
single
queue,
which
contains
both:
recei
v
ed
and
forw
arded
pack
ets.
Our
present
article
di
vides
the
main
queue
into
tw
o
sub
queues:
one
to
recei
v
e
the
arri
v
ed
pack
ets,
and
the
other
to
transmit
the
a
v
ailable
pack
ets.
This
approach
reduces
the
transmission
delay
dues
to,
the
shift-
ing
of
data,
placed
on
the
single
queue,
and
to
the
sequential
process
ing
of
reception
and
transmission.
All,
without
increasing
the
memory
capacity
of
the
queue.
Copyright
c
2019
Insitute
of
Advanced
Engineeering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Said
Lakhal,
Mohammadia
School
of
Engineering,
Mohammed
5
Uni
v
ersity
in
Rabat,
ERSC
formerly
kno
wn
as
LEC,
Research
Center
E3S,
Ibn
Sina
A
v
enue,
B.P
765,
Agdal,
Rabat,
Morocco.
212697620603
Email:
said.lakhal.rech@gmail.com
1.
INTR
ODUCTION
The
demand
of
spectra
is
increased
in
last
tw
o
decades,
grace
of
the
intense
transmission
of
v
oices
and
videos
via
the
netw
ork.
Therefore,
the
classical
conceptions
became
inable
to
support
these
ne
w
challenges.
As
a
result,
the
Federal
Communications
Commission
[1,
2]
decided
to
modify
its
spectrum
allocation
strate
gy
,
with
the
aim
of
adopting
a
more
fle
xible
polic
y
.
These
ef
forts
led
to
the
birth
of
the
f
amous
cogniti
v
e
radio
netw
ork
(CRN),
by
Mitola
[3,4].
In
this
netw
ork,
the
primary
users
(PUs)
and
the
secondary
users
(SUs)
alternate
for
e
xploiting
the
spectra.
The
PUs
ha
v
e
priority
to
access
spectra.
By
cons,
the
SUs
w
ait
for
the
release
of
a
fe
w
spectra
in
order
to
transmit
data
[5,
6].
During
this
w
aiting
period,
a
particular
SU
can
accumulate
multiple
pack
ets
to
send.
These
pack
ets
are
or
g
anized
on
a
queue
to:
k
eep
them,
mark
the
order
of
arri
v
al
of
each
one,
and
adapt
to
the
netw
ork
fluctuations.
The
time
elapsed
between
the
arri
v
al
of
a
pack
et
and
its
transmission
is
called
the
w
aiting
time
or
delay
,
which
is
in
v
ersely
proportional
to
the
throughput.
In
the
classic
approach,
each
SU
is
assigned
a
unique
queue,
reserv
ed
for
both
recei
ving
and
forw
ard-
ing
pack
ets.
A
L
yapuno
v
optimization
technique
is
used
in
[7-9],
to
stabilize
the
queue
and
design
an
online
flo
w
control.
Both
[10]
and
[11]
ha
v
e
tar
geted
the
maximization
of
the
SU’s
throughput.
In
[10],
a
h
ybrid
queue
management
policies
is
proposed,
and
in
[11],
the
authors
introduced
mean
throughput
maximization
scheduling
protocol
which
schedules
the
a
v
ailable
SUs.
The
authors
of
[12]
proposed
a
repeat
queuing.
Each
pack
et
goes
through
three
phas
es:
reception,
shift
and
transmission
[13,
14].
This
design
sequentially
treats
the
reception
and
trans
mission
process,
so
the
phase
of
shifting
pack
ets
on
the
queue
tak
es
a
certain
amount
of
J
ournal
homepage:
http://iaescor
e
.com/journals/inde
x.php/IJECE
Evaluation Warning : The document was created with Spire.PDF for Python.
3222
r
ISSN:
2088-8708
time,
which
will
increase
the
delay
of
pack
ets
on
the
queue.
C
on
s
equently
,
it
decreases
the
throughput
through
the
CRN.
In
this
article,
instead
of
considering
a
single
queue,
we
design
tw
o
subqueues:
one
for
the
reception
of
the
pack
ets
and
the
other
for
the
transmission.
When
this
last
is
emptied,
the
sub
queues
e
xchange
their
roles.
The
si
ze
of
each
one
equal
to
half
the
size
of
the
queue,
considered
in
the
classical
approach.
Thus,
the
memory
capacity
to
st
ore
the
pack
ets
will
not
increase,
the
reception
and
transmission
processes
are
parallely
treated,
as
well
as,
the
phase
of
shifting
pack
ets
on
the
queue,
will
not
tak
e
place.
W
ith
this
approach,
we
concei
v
e
a
ne
w
management
procedure
of
the
tw
o
sub
queues.
Therefore,
we
are
reducing
the
delay
,
relati
v
e
to
the
con
v
entional
approaches.
2.
RALA
TED
W
ORKS
Se
v
eral
researchers
ha
v
e
addressed
the
SUs’
management
queues.
A
L
yapuno
v
optimization
technique
is
used
in
[7],
for
controlling
the
partition
of
users
into
groups,
which
are
modelled
by
the
graph
collaring,
in
order
to
share
channels
and
stabilize
queue
according.
The
simulations
demonstrated
the
lo
w-comple
xity
of
the
proposed
model.
Based
on
the
same
optimization
technique
[8],
according
to
the
collision
queues,
the
authors
designed
an
online
flo
w
control
and
resource
allocation
algorithm;
in
the
aim
to
maximize
the
SUs’
throughput,
subject
to
maximum
collision
constraints
with
the
PUs.
As
a
result,
the
desired
objecti
v
e
is
reached.
Al
w
ays
for
meeting
the
same
objecti
v
e,
a
pack
et
of
w
orks
are
de
v
eloped
in
[10,
11,
15].
The
authors
of
[10]
proposed
a
h
ybrid
queue
management
policies
(QMP)
interwea
v
e/o
v
erlay
,
and
an
adapti
v
e
QMP
.
The
e
v
aluations
sho
wed
that
the
h
ybrid
approach,
leaded
to
the
best
SUs’
throughput,
compared
to
the
con
v
entional
schemes.
A
priority
queue
scheduling
algorithm
is
formulated
in
[11];
to
a
v
oid
collision
between
heterogeneous
nodes,
during
data
transmission,
and
impro
v
e
the
entire
netw
ork
t
hroughpu
t
.
Another
scheduling
technique
is
presented
in
[15],
for
increasing
the
basic
QoS
parameters.
The
principle
of
such
technique,
consists
of
di
viding
the
netw
ork
into
tw
o
re
gions,
each
one
is
controlled
by
a
particular
base
station,
and
the
spectrum
is
allocated
on
a
priority
basis
according,
to
real-time
and
non-real-time
data.
The
e
xperimentations
sho
wed
a
decrease
in
terms
of
delay
,
collision
probability
,
end-t
o
-
end
delay
and
o
v
erhead
ratio;
as
well
as,
an
increase
in
terms
of
the
netw
ork
ef
ficienc
y
and
throughput.
The
pre-emption
and
non-preemption
priorities
attracted
man
y
attentions
in
the
pre
vious
w
orks.
In
this
topic,
we
will
in
v
estig
ate
the
authors’
contrib
utions
in
[16-18].
A
h
ybrid
approach
is
e
xposed
in
[16],
at
which
lo
w
priority
SUs
are
no
longer
pre-empted
by
high
priority
SUs,
when
their
number
of
interruptions
reaches
a
certain
threshold
v
alue.
Therefore,
the
authors
sho
wed
that
the
threshold
adjustment
according
to
the
performance
metric
pro
vided
a
promising
performance.
In
[17],
the
authors
presented
a
queuing
model,
pro
viding
the
accurate
a
v
erage
system
time,
for
general
pack
ets
service
time,
and
service
interruption
peri-
ods,
with
an
opportunistic
spectrum
access
(OSA)
netw
orks.
The
y
sho
wed
that,
for
the
same
a
v
erage
CR
transmission
link
a
v
ailability
,
the
pack
et
system
time
significantly
increases
in
a
semi-static
netw
ork,
with
long
operating
and
interruption
periods,
compared
to
an
OSA
netw
ork
with
f
ast
alternating
operating,
and
interruption
periods.
The
pack
ets
are
grouped
with
dif
ferent
prioriti
es
in
a
queue
[18],
represented
by
tw
o
dimensional
state
transition
graph.
The
simulations
demonstrated
t
w
o
results:
First,
the
decrease
of
the
a
v
erage
w
aiting
time
of
high
priority
pack
ets,
with
the
gro
wth
interference
po
wer
threshold.
Second,
the
proportionality
between
the
lo
w
priority
pack
et
a
v
erage
w
aiting
time,
and
the
arri
v
al
rate
of
the
high
priority
pack
et.
F
or
modelling
the
characterize
spectrum
handof
f
beha
viours
with
general
service,
the
authors
of
[12]
proposed
a
repeat
queuing.
After
that
,
the
y
deri
v
ed
the
close-e
xpression
of
the
e
xtended
data
deli
v
ery
,
and
the
system
sojourn
time
in
both:
staying
and
changing
scenarios.
The
analysis
of
spectrum
handof
f
beha
viours
resulting
from
multiple
inter
-
ruptions,
clarified
the
traf
fic-adapti
v
e
polic
y
and
the
admissible
re
gion.
As
it
is
kno
wn,
the
SUs
dynamically
allocate
the
free
channels.
F
or
this
purpose,
the
authors
of
[19]
proposed
a
dynamic
channel-selection
solu-
tion,
and
a
priority
virt
ual
queue
interf
ace
that
determines
the
requir
ed
information
e
xchanges,
and
e
v
aluates
the
e
xpected
delays
e
xperienced,
by
v
arious
priority
traf
fics
and
competing
users’
beha
viours.
Based
on
a
dy-
namic
strate
gy
learning
algorithm
deplo
yed
at
each
user
,
the
y
significantly
reduced
the
pack
et
loss
rate,
and
outperformed
the
con
v
entional
single-channel
dynamic
resource
allocation.
T
w
o
types
of
retrial
customers
and
a
paired
service
are
serv
ed
by
a
single
system
in
[20].
After
solving
a
Riemann
boundary
v
alue
problem,
the
authors
determined
the
joint
stationary
orbit
queue
length
distrib
ution
at
service
completion
epochs.
After
the
emer
gence
of
the
cogniti
v
e
radio
technology
,
se
v
eral
applications
ha
v
e
Int
J
Elec
&
Comp
Eng,
V
ol.
9,
No.
4,
August
2019
:
3221
–
3227
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3223
emer
ged;
among
them
we
cite,
b
ut
not
limited
to:
smart
grid
[21],
safety
[22],
military
[23],
wireless
body
area
netw
orks
[24]
and
surv
eillance
[25].
3.
OPERA
TIONS
OF
THE
TW
O
MODELS
In
the
con
v
entional
approach,
each
SU
has
a
single
queue,
on
which
are
placed
the
pack
ets
to
send
and
those
to
transmit.
Based
on
the
principle:
first
come,
first
serv
ed;
the
netw
ork
manager
shifts
the
data
to
the
output
of
the
queue
aft
er
each
transmission,
to
f
acilitate
transmission
and
reception
at
the
same
time.
Unlik
e
our
approach,
which
considers
the
same
size
of
the
queue,
b
ut
se
gmenting
it
into
tw
o
sub-queues:
one
for
the
reception
and
the
other
for
the
transmission.
By
using
this
technique,
the
data
shift
operation
on
the
queue
will
not
tak
e
place,
and
as
soon
as
the
transmission
sub-queue
is
emptied,
the
controller
of
the
transmission
will
be
mo
v
ed
to
the
last
reception
sub
queue,
and
so
the
sub
queues
e
xchange
their
roles.
The
Figure
1
illustrates
the
operating
of
our
approach
(O
A)
and
that
of
the
classical
approach
(CA),
i.e.,
by
Q
t
and
Q
r
,
we
designate
the
transmission
and
reception
queues,
respecti
v
ely
.
Figure
1.
Operating
of
our
model
and
that
of
the
classical
4.
MODELIZA
TION
OF
THE
TW
O
APPR
O
A
CHES
When
the
output
flo
w
is
greater
than
t
he
input
flo
w
,
each
arri
v
ed
pack
et
is
transmitted
before
the
arr
i
v
al
of
the
ne
xt;
therefore,
the
saturation
problem
does
not
arise.
Otherwise,
since
a
gi
v
en
moment,
the
queue
becomes
unable
to
recei
v
e
the
arri
v
ed
pack
ets,
and
so,
the
saturation
state
appears.
This
study
is
interested
in
the
second
case.
In
the
follo
wing,
we
will
model
the
saturation
problem
in
our
approach
and
that
of
the
con
v
entional,
i.e.
T
able
1
contains
all
used
symbols
in
this
modelization.
P
ar
allelising
r
eception
and
tr
ansmission
in
queues
of
secondary
user
s
(Said
Lakhal)
Evaluation Warning : The document was created with Spire.PDF for Python.
3224
r
ISSN:
2088-8708
T
able
1.
Symbols
and
their
meanings
Symbols
Meanings
os;
S
Q;
I
;
O
Necessary
time
for
shifting
one
byte
in
the
CA,
size
of
queue,
input
flo
w
,
output
flo
w
,
resp.
v
0
i
;
w
0
i
T
ime
spent,
current
size
of
queue
after
the
i
th
transmission
and
before
the
1
st
saturation,
resp,
in
the
CA.
z
0
n
T
ime
spent
after
the
n
th
transmission
and
before
the
1
st
saturation,
in
O
A.
d
T
ime
to
mo
v
e
to
the
ne
xt
pack
et
on
the
queue,
in
O
A.
v
i
m
T
ime
spent,
after
the
i
th
saturation
and
the
m
th
transmission,
in
the
CA.
w
i
m
T
ransmitted
quantity
after
the
i
th
saturation
and
the
m
th
transmission,
in
the
tw
o
approaches.
z
i
m
T
ime
spent
after
the
i
th
saturation
and
the
m
th
transmission,
in
O
A.
4.1.
Classical
appr
oach
(a)
Before
the
1
st
satruration
v
0
1
=
2
,
one
unit
for
w
aiting
the
reception
and
the
other
for
transmitting.
v
0
2
=
v
0
1
+
os
(2
I
O
)
+
1
.
Iterati
v
elty:
v
0
n
=
v
0
n
1
+
os
(
nI
(
n
1)
O
)
+
1
=
v
0
n
1
+
n
os
(
I
O
)
+
os
O
+
1
W
e
put
a
=
os
(
I
O
)
;
b
=
os
O
+
1
.
v
0
n
=
(
n
1)(
n
+
2)
2
a
+
(
n
1)
b
+
2
(1)
w
0
n
=
(
n
+
1)
I
n
O
=
n
(
I
O
)
+
I
(2)
The
saturation
of
queue
arri
v
es
when
w
0
n
S
Q
,
i.e,
n
S
Q
I
I
O
.
The
saturation
threshold
is
indicated
by:
n
0
=
int
(
S
Q
I
I
O
)
(b)
After
the
1
st
saturation
v
1
1
=
v
0
n
0
+
1
v
1
m
=
v
1
m
1
+
os
(
S
Q
O
(
m
1))
+
1
(3)
W
e
put:
c
=
os
S
Q
+
1
(4)
v
1
m
=
v
1
m
1
(
b
1)(
m
1)
+
c
v
1
m
=
m
(
m
1)
2
(
b
1)
+
c
(
m
1)
+
v
0
n
0
+
1
(5)
Since
the
input
flo
w
is
greater
than
that
of
the
output,
the
queue
will
accept
the
pack
ets
only
after
checking
the
follo
wing
condition:
w
1
m
I
,
i.e.
m
O
I
,
i.e.
m
I
O
.
Then,
the
threshold
acceptation
is:
m
0
=
int
(
I
O
)
.
The
queue
accepts
the
arri
v
ed
pack
et
at
the
m
th
0
transmission,
after
it
rejects
all
arri
v
ed
pack
ets
at:
m
0
+
1
;
::::;
2
m
0
1
transmissions.
(c)
After
the
i
th
saturation
v
i
m
=
m
(
m
1)
2
(
b
1)
+
c
(
m
1)
+
v
(
i
1)
m
+
1
v
i
m
=
i
(
m
1)
h
m
2
(
b
1)
+
c
i
+
v
0
n
0
+
i
(6)
The
queue
accepts
the
arri
v
ed
pack
et
at
the
im
th
0
transmission,
after
it
rejects
all
arri
v
ed
pack
ets
at:
im
0
+
1
;
::::;
(
i
+
1)
m
0
1
transmissions.
Int
J
Elec
&
Comp
Eng,
V
ol.
9,
No.
4,
August
2019
:
3221
–
3227
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3225
4.2.
Our
appr
oach
T
able
2.
sho
ws
the
approachment
in
stages
T
able
2.
Our
approach
Before
the
1
st
satruration
After
the
1
st
saturation
After
the
i
th
saturation
z
0
1
=
2
z
1
1
=
z
0
n
0
+
1
z
i
m
=
(
m
1)
+
z
i
1
m
+
1
z
0
n
=
z
0
n
1
+
d
+
1
z
1
m
=
z
1
m
1
+
1
z
0
n
=
(
n
1)(
d
+
1)
+
2
(5)
z
1
m
=
(
m
1)
+
z
0
n
0
+
1
(6)
z
i
m
=
i
(
m
1)
+
z
0
n
0
+
i
(7)
The
queue
accepts
the
arri
v
ed
pack
et
at
the
im
th
0
transmission,
after
it
rejects
all
arri
v
ed
pack
ets
at:
im
0
+
1
;
::::;
(
i
+
1)
m
0
1
transmissions.
4.3.
Comparison
between
the
tw
o
appr
oaches
Based
on
relation
(1),
v
0
is
quadratic
according
the
transmission
i
teration
n
.
Then,
its
curv
e
is
a
parable.
Besides
a
>
0
,
therefore,
this
parable
is
con
v
e
x.
In
the
other
hand,
z
0
is
e
xpressed
in
relation
(5)
as
a
linear
fonction,
al
w
ays
by
referring
to
the
transmission
iteration
and
assuming
that
(
d
+
1)
>
0
,
thus,
the
curv
e
of
z
0
is
an
increasing
line.
Relation
(4)
e
xpresses
the
time
of
the
m
th
transmission
after
the
i
th
saturation,
in
the
classical
ap-
proach.
This
time
is
linear
according
to
the
saturation
iteration.
In
the
other
side,
relation
(7)
presents
the
v
ariation
of
time
depending
on
the
saturation
iteration,
in
our
approach.
5.
SIMULA
TION
By
choosing
positi
v
e
v
alues
of
a,
b
and
d,
we
obtain
the
curv
es
of
v
0
and
z
0
,
illustrated
in
the
left
part
of
Figure
2.
Kno
wing
that:
m
2
(
b
1)
+
c
>>
1
and
v
0
n
0
>
z
0
n
0
,
as
a
result
we
obtain
the
right
part
of
Figure
2,
illustrating
the
spent
time
according
to
the
saturation
iteration,
for
the
tw
o
approaches.
(a)
(b)
Figure
2.
Comparison
between
the
tw
o
models
in
terms
of
delay
before
and
after
the
first
saturation.
In
Figure
2,
we
remark
that
for
the
same
transmission
iterations,
the
delay
in
the
classical
appaoch
is
greater
than
that
in
our
approach.
This
result,
can
be
e
xplained
by
tw
o
f
actors:
1)
The
delay
increase
dues
to
the
shift
of
the
queue
data
in
the
cl
assical
model.
2)
The
arrangement
of
tw
o
queues,
one
for
the
reception
and
the
other
for
the
transmission,
mak
es
it
possible
to
carry
out
a
parallel
processing
between
reception
and
transmission,
and
so,
we
g
ain
more
time
in
our
model.
P
ar
allelising
r
eception
and
tr
ansmission
in
queues
of
secondary
user
s
(Said
Lakhal)
Evaluation Warning : The document was created with Spire.PDF for Python.
3226
r
ISSN:
2088-8708
6.
CONCLUSION
In
this
w
ork,
we
ha
v
e
de
v
eloped
a
queues
management
mechanism,
based
on
the
di
vision
of
the
main
queue
into
tw
o
sub
queues:
one
for
the
reception
of
the
arri
v
ed
pack
ets
and
the
other
for
the
transmission
of
the
a
v
ailable
pack
ets.
W
ith
this
design,
we
ha
v
e
reduced
the
transmission
delay
dues
to
the
shift
of
the
data
on
the
single
queue
in
the
classic
design.
Also,
the
consideration
of
tw
o
queues
parallely
ensures
the
transmission
and
reception.
As
a
result,
the
time
of
the
sequential
treatment
is
g
ained.
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Comp
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V
ol.
9,
No.
4,
August
2019
:
3221
–
3227
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
3227
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BIOGRAPHIES
OF
A
UTHORS
S.
Lakhal
obtained
the
diploma
of
application
engineer
in
computer
sciences
in
1998,
from
the
Uni
v
ersity
Sidi
Mohamed
Ben
Abdelah,
Fes,
Morocco,
M.Sc.
de
gree
in
modelization
in
2006,
from
Mohammadia
School
of
Engineering.
He
is
currently
a
researc
her
at
the
Laboratory
of
Electronics
and
T
elecommunications,
Mohammadia
School
of
Engineers
(EMI),
Rabat,
Morocco.
His
current
research
interests
are
Computing,
Radio
cogniti
v
e,
Algorithmic
and
comple
xity
,
Modelization.
Z.
Guennoun
recei
v
ed
his
engineering
de
gree
in
Electronics
and
T
elecommunications
from
the
Electronics
a
nd
Electrical
Montefiore
Institute,
ULG
Lie
ge,
Belgium
in
1987;
his
M.Sc.
de
gree
in
Communication
Systems
from
the
EMI
School
of
Engineering,
Rabat,
Morocco
in
1993;
and
his
PhD
de
gree
from
the
same
school
in
1996.
He
visited
the
Centre
for
Communication
Research
(CCR)
in
Bristol
Uni
v
ersity
,
UK,
during
the
period
of
1990-1994
to
prepare
a
split
PhD.
During
1988-1996
he
w
ork
ed
as
an
Assistant
Lecturer
in
the
EMI
School
of
engineering,
and
from
1996
he
is
w
orking
in
the
same
school
as
a
Professor
Lecturer
.
His
fields
of
interest
are
digital
signal
processing,
error
control
coding,
speech
and
image
processing.
Currently
in
char
ge
of
the
laboratory
of
Electronics
and
T
ele
communications
(LEC)
at
EMI.
IEEE
member
since
1990;
and
member
of
the
Moroccan
IEEE
section
e
x
ecuti
v
e
committee.
P
ar
allelising
r
eception
and
tr
ansmission
in
queues
of
secondary
user
s
(Said
Lakhal)
Evaluation Warning : The document was created with Spire.PDF for Python.