A Delay Aware Power Saving Scheme (DAPSS) Base on Load in Traffic IEEE 802.16e Networks

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DELAY AWARE POWER SAVING SCHEME (DAPSS) BASE ON TRAFFIC LOAD IN
IEEE 802.16e NETWORKS.
Daniel Dauda Wisdom
Department of Mathematics
Usmanu Danfodiyo University
Sokoto, Nigeria
danieldaudawisdom! @gmail.com
Ahmed Audu
Department of Mathematics
UUDS, Nigeria
ahmed. audu@udusok.edu . ng
Ahmed Yusuf Tambuwal
Department of ICT
Usmanu Danfodiyo University
Sokoto (UDUS), Nigeria
ahmed_tambuwal@yahoo.com
Michael Bamidele Soroyewun
Department of Computer Science
Ahmadu Bello University, Zaria
delemike@gmail.com
Aminu Mohammed
Department of Mathematics
Computer Unit, UDUS
Nigeria. maminuus@yahoo.com
Samson Isaac
Department of Computer Science
Kaduna State University
Kaduna, Nigeria
samson.isaac@kasu.edu.ng
ABSTRACT
IEEE 802.16 Standard also known as worldwide
interoperability for Microwave Access (WiMAX) is
designed to support wider coverage, higher
bandwidth, less cost of deployment with different
traffic classes' support for power savings. The IEEE
added mobility characteristics which made battery-
life of Mobile Station (MS) a critical challenge, since
MS are battery powered with an impose
rechargeable life. An Efficient Battery Lifetime
Aware Power Saving Scheme was proposed. The
Scheme minimizes frequent transition of MS in
order-to reduce power consumption but increases
average response delay due to a longer sleep interval
used. Thus, a Delay Aware Power Saving Scheme
(DAPSS) Based on Traffic Load is proposed to
reduce the excessive response delay. The Scheme
introduces a modified minimum and maximum sleep
interval in order to reduce the longer sleep time of a
MS, and dynamically tunes the sleep parameters
more appropriately according to the traffic load. It
employed a delay aware algorithm to save power.
The Scheme was evaluated using discrete event
simulator, the results showed that the proposed
DAPSS achieves superior performance compared to
the existing Scheme in terms of the average power
consumption and response delay.
KEYWORDS: Battery-Life, Delay-Aware, IEEE,
Power-Saving-Scheme.
I. Introduction
The IEEE 802.16 also known as Worldwide
Interoperability for Microwave Access (WiMAX) is
designed to support wider coverage, higher bandwidth,
less cost of deployment, quality of service (QoS), with
vast traffic classes support to users as well as smaller
scale business. As one of the emerging broadband
wireless access systems for mobile stations (MS).
Formally, the IEEE 802.16 is designed for a fixed
MS [1], while subsequent version of the IEEE 802.16e
is an extension of the former standard with mobile
features so that MS could be move-able (Mobile) [2].
And because of the significance of the mobility
characteristics added in the subsequent 802.16e
standard, efficiency subsequently became a critical
challenge for battery-powered devices since MS are
battery powered with a rechargeable supper impose
lifetime.
Power Saving Classes (PSCs) of type I, II, and III are
designed to address the challenges mentioned above.
Type I is designed for best effort (BE) and non-real-
time variable rate (NRT-VR) traffics, it consists of
listening intervals as well as sleep intervals which are
interleaved respectively (Figure 1). The length of the
listening intervals in this power saving class is fixed. A
MS with PSC I subsequently checks if there are some
buffered packets for it in the listening intervals (Figure
4 and 5). If there are buffered packets, the MS will
revert to normal operation mode to receive the packet
(s). Else, the sleep window is activated in order to
further save power. This procedure is repeated and the
length of the sleep intervals is doubled until it reaches
the maximum length of the sleep window called Tmax
and maintains Power Savings [18]. PSC of Type II is
for unsolicited grant service (UGS) and real time
variable rate (RT-VR) traffics, similarly type II consists
of listening intervals and sleep intervals.
Unlike type I, the length of listening and sleep intervals
are both fixed for PSC of type II and the sum of the
sleep windows is called, the sleep cycle. PSC II is also
capable of transmitting data packets without returning to
normal operation. Thus, the length of listening intervals
is long enough to receive all packets arriving during a
single sleep cycle in PSC II [19] (Figure 1). PSC of type
III is use for managing operations and multicast
connections. These three PSC differ from each other by
their parameter sets, methods of activation/deactivation,
as well as the policies of MS availability for data
transmission [2]. Unlike PSC of type I and II, PSC III
comprises of a single sleep intervals and is mainly use
for multicast connection as well as management of
operations as seen in Figure 1 below. By activating the
PSC, a single sleep interval with defined length in

WiMAX Networks begins and subsequently the MS
reverts to normal operation mode [20]. Note that sleep
window and sleep intervals are same in this paper.
load has reached the threshold of the buffer with likely
experience of congestion, packet loss or
buffer
overrun.
a sss™ •
Figurel: Types of
PCS
in WiMAX Networks
These PSCs used three parameters to improve on power
savings, namely, idle threshold, initial sleep interval and
final sleep interval [12] [13]. The idle threshold is the
time interval that the MS is in a waiting state, it has no
messages to send or receive before moving to inactive
state. The MS before moving to inactive state negotiates
with it BS for approval in order to switch to a period of
inactivity. The BS allocates the sleep parameters
namely: initial sleep interval (Tmin), final sleep interval
(Tmax) and listening interval (L) to the MS, the MS
transmits to it period of inactivity after it receives these
parameters [13]. The Tmin is the range of
the
first-sleep
session (T1) (Figure 4),that an MS will go to sleep.
After which it wakes up for the first T to listen to the
traffic indication messages from the BS within the
duration of the L. When the traffic indication messages
indicate negative, the MS continues to sleep mode after
the L duration. Else, the traffic indication message is
positive, and the MS return to an active session. The T
together with it L is the (T+L) Sleep Cycle. Whenever
MS remain in a period of inactivity, the next sleep cycle
start as well as the T is doubled. These procedure is
repeated till the Tmax is achieved which is the extreme
length of
the
sleep intervals. When a MS achieves Tmax
the sleep time is maintained until a Positive MOB-TRF-
IND messages arrives from the BS where the MS wakes
up to receive/transmit intending packets (Figure 4) in
the third sleep interval (T3).
, MM. •
L
Figure4: IEEE 802.16e Sleep Parameters with variant
modes.
Note that at T3 the MS is experience the third sleep
window which is larger/longer than the previous
windows, and each sleep window is to double the
previous one in order to effectively save power.
However, it is also observed in this paper (Figure 4),
that in cases of a higher traffic load arrival, the longer
sleep window will also incur response delay which will
results to congestion, as well as packets loss/buffer
overrun respectively (Figure 4).
Figure 5: shows the effects of longer sleep period as
seen above in the Third buffer (L3) where the traffic
a Mode | L L
Figure 6: above shows the actual variant buffer sizes of
the intermediate devices as in the case of TCP where in
between the MS and the BS these devices exist. it can
be seen that L2 and L3 have less buffer size compared
to L1 and L4 which can take in more data. In situations
where there are higher traffics (Load) arrival,
congestion and buffer overrun is likely to be experience
at L2 and L3 since both have reached their threshold
and the MS at this time is in sleep mode and cannot
process data until at the listening mode stage where a
MON-TRF-IND message is received as Positive as seen
in Figure 4 which subsequently results to packet loss as
well as performance degradation of the overall network.
The proposed DAPSS scheme have significantly
minimize this excessive response delay accordingly, by
appropriately adjusting the sleep intervals (time) using
the three sleep parameters and employed a delay aware
algorithm to improve power savings.
Several Schemes have been proposed in order to
improve on power efficiency of MS in [3][4][5] [13]
[6][7] [12]. However, the schemes in [3], [4] wastes
energy due to their excessive listening operations and or
frequent switching frequency from sleep/wake mode
while [7][13] use half of it last sleep interval, to adjust
the Tmin when it exits from the preceding sleep mode
operation as the initiates sleep interval in the next sleep
mode operation to reduce the excessive listening
operations of mobile station (MS). However, the
scheme has excessive response delay due to its longer
sleep interval, which also results to congestion, buffer
overflow as well as overall performance degradation of
a MS. Thus, this paper proposes a new Scheme called a
Delay Aware Power Saving Scheme(DAPSS) Based on
Traffic load for mobile broadband Network Services in
order to enhance the parameters of the existing Scheme
as well as resolve the critical challenge of the excessive
response delays of packets, which subsequently affect
performance of a mobile device. The performance of the
propose scheme was evaluated and compared with that
of
the
existing Scheme using discrete event simulator.
The rest of
this
paper is organized as follows: Section II.
Presents Related Literatures, Section III. The Propose
Scheme, Section IV. Procedure of Parameter
Adjustments, Section V. Performance Evaluation,
Section VI Concludes this research.
II. Literature Review
This section presents a related work on existing
schemes. These schemes are review by highlighting
their Operational, Strength and Weaknesses of each
scheme as follows: Energy-Saving Mechanism (ESM)
was proposed by [21], to extend the battery-life of
mobile stations (MS). The ESM considers the MS to be

in sleep-mode during listening intervals but it is active.
It increases the sleep interval exponentially when there
is no traffic arrival. The mechanism significantly
minimized the frame response time and energy
consumption at the expense of excessive listening
operations, which may lead to waste of energy. An
Enhanced Energy Saving Mechanism (EESM) was
proposed in [22], to reduce the excessive listening
operations of Mobile Station (MS) in the ESM. The
mechanism uses half of the last sleep interval in the next
sleep-mode operation. When the initiate sleep interval is
less than Tmin, then the initiate sleep interval is set to
Tmin. The Base Station (BS) is notified of the initiate
sleep interval request message sent by the MS. When
the traffic is low the inter-Service Data Unit (SDU)
arrival interval is large enabling the EESM to
effectively decrease the number of listening intervals in
one sleep-mode operation. The mechanism improved
energy conservation by extending the lifetime of MSS.
However, the mechanism has higher response delay due
to the longer sleep interval.
A Delay-Aware Auto Sleep Mode Operation was
proposed in [23], to minimize delay and conserve
energy of mobile station. The algorithm dynamically
tunes the initial sleep window based on the traffic load
and the delay requirements, after serving all buffered
packets; the MS reverts to the initial-sleep window
which depends on the number of packets served. It
successfully bound the delay to a certain range.
However, it suffers little increase in energy-
consumption. Remaining Energy-Aware Power
Management Mechanism (REAPM) was proposed by
[24], to extend the battery-life of mobile stations (MS)
and minimize the response delay.
The REAPM updates the sleep parameters dynamically
taking into consideration the remaining energy and the
inter-arrival of each frame. The Tmax is updated using
smoothing technique with current inter-arrival time of a
MAC SDU at each frame arrival and adjust Tmin by
considering the energy remaining and the Tmax. More
so, after the mechanism initialize parameters such as,
the Tmin and Tmax and the current inter-arrival time of
MAC SDU. It commences from normal operation mode
and terminates this mode when its receives request
message to enter sleep mode operation. This mechanism
can achieve low-response delay if there is sufficient
energy and prolong the battery-life. However, it the
scheme has an average increase in energy consumption
due to the switching frequency and a constant Listening
interval (L) that is between the sleep intervals. A Real-
Time Heuristic Algorithm was proposed in [25], to
minimize the switching frequency of mobile station
(MS). The algorithm operates based on three criteria,
the probability of buffer overflow, expected delay
violation, and battery lifetime expiry. It uses the
probability of the finite buffer overflow to ensures that
when the packet arrives during the next sleeping
window the expected delay may not exceed the delay
violation tolerable bound, and checks the battery
lifetime in order to ensure that the power is adequate to
extend the sleep time by at least a period, and that the
MS still has enough resources to handle the
transmission so as to obtain all the packets coming to
the buffer during the expected period of time. The
algorithm minimizes energy consumption with an
increase in the average waiting time.
A Dynamic Traffic Load-Aware Sleep Mode Operation
Algorithm was proposed in [26], to enhance the
performance of battery powered mobile devices. The
algorithm employs a dynamic scheme to tune the idle
check time of a MS which is the waiting period after the
packet arrival in the wake-mode before entering the
next sleep-mode. The idle check time is adjusted
dynamically after the entire buffered packet is served
based on the number of packets served and the previous
sleep window interval. The waiting time is then set to
zero when the last sleep window is bigger than the Tmin
which makes MS to go to sleep immediately but when
the last sleep window is the same as Tmin it is set to a
certain value, which makes MS to wait for some time
before transiting to sleep-mode. The algorithm improves
power savings with a small increase in the algorithm
complexity, more so, the scheme ignores aligning both
the downlink and uplink traffics. A Power Saving
Mechanism with periodic traffic indications was
proposed in [3], to minimize power utilization and
complexity of the existing algorithm in [26]. The
mechanism uses traffic indication (TRFI D) messages to
initiate transmission at every constant time. The TRF-
IND messages consist of a listening interval, awake
interval and a sleep interval. During listening intervals,
a MS synchronizes with the current base station (BS)
and decides whether to switch to awake-mode or remain
in a sleep-mode. If there are data traffics in the buffer
for the marked MS, the BS sends a positive TRF-IND
message and the MS switch to awake-mode. The BS
sends information during the awake-mode and the
awake-mode terminates if no traffic arrives during a
time-out/fixed time of a constant length T. If any data
traffic arrives during inactive time T, the MS switches
to awake mode and transmits the data. Otherwise, goes
to a sleep-mode from the awake-mode without
switching MOB-SLP-REQ/RSP messages. The
mechanism reduced average response delay because of
its frequent switching from sleep mode or awake mode
at the expense of an increase in energy-consumption.
EBLAPS is recommended to solve the above
mentioned. An Efficient Battery Life-Aware Power
Saving Scheme (EBLAPS) was recommended in ref [4]
to reduce average delay and also minimize the energy
consumption of mobile station (MS).
The EBLAPS adaptively amend the three parameters:
idle threshold, initial sleep interval, and final sleep
interval based on traffic arrival pattern. It employs an
upgraded sleep mode control algorithm to consider type
I (Non Real Time Services) power saving classes (PSC)
in the downlink Operation of the 802.16e in order to
minimize the frequent transition to listening mode under
low traffic arrival. The scheme reduces the average

response delay and the average energy utilization.
However, it ignores to consider type II (Real-Time
Services) PSC which courses an effect in energy saving,
as well as a little increase in the average waiting delay
due to it longer sleep period.
III. Proposed DAPSS Scheme
A Delay Aware Power-Saving Scheme (DAPSS) for
mobile broadband network services (MBNS) Based on
Traffic Load is propose, which is a modification of the
Efficient Battery Lifetime-Aware Power Saving Scheme
described in ref [4] with the corresponding
shortcomings of the scheme as at the first presentation
in view. The existing Scheme successfully minimized
the frequent transition of MS to sleep mode, at the
expense of an increase in a longer sleeping interval
(session) which has an intolerable delay bound. In the
existing scheme a MS trades off power savings at the
expense of longer sleep interval in order to minimize its
power consumption, but this also causes an increase in
the average response delay due to the longer sleep
interval called response delay (time). The longer sleep
time also resulted in an increase in both delay and
power consumption due to the switching time taken for
a Mobile device to revert (return) from sleep to active
mode respectively.
Hence, the existing scheme delay bound has a severe
effect on the battery life and the overall performance of
a Mobile device as well. Therefore, the existing scheme
control-sleep algorithm frequently restrains MS from
receiving/transmitting, intending packets within an
appropriate time or jus t in time.
Whenever packets arrive to the buffer queue; they are
not receive/transmitted appropriately within their life
time (Figure 5 and 6). Due to the impose control sleep
bound, as such arriving packets are being
delayed/Queued unnecessarily, resulting to packets
congestion which further result to loss of packets due to
the longer delay (sleep)bound called response delay and
the short lived lifetime of packets on transits. The
longer response delay is due to the larger sleep intervals
used during adjustment of the sleep parameters. The
longer response delay subsequently has effects on the
quality of service (QoS) of MS and may result to user
dissatisfaction of an interactive session. Thus,
improving on these will be a significant edge in Power
Savings. Hence, we developed a scheme that addressed
these challenges highlighted aptly.
In this paper a new Scheme called a Delay Aware
Power Saving Scheme (DAPSS) Base on traffic load is
proposed. The Scheme proffers an adequate solution by
minimizing the larger sleep window used called
intervals by the existing scheme. The longer sleep time
of the existing scheme is minimized, analytically and
dynamically adjusting the sleep interval of the sleep
parameters used in PSC more appropriately or just in
time as well as minimizing the response time and power
savings efficiency as well. The propose DAPSS
dynamically captures the traffics arrival pattern more
appropriately by adaptively tuning the three operating
parameters namely: idle time, initial (Tmin) and final
(Tmax) sleep window. A modified sleep mode
algorithm called a Delay Aware algorithm is employed
minimizing the average response delay and improving
performance of a mobile device accordingly.
More-so, Figure 6 above shows the actual variant buffer
sizes of the intermediate devices as in the case of TCP
where in between the MS and the BS there are devices
called the intermediate devices that are of different
buffers sizes. And from the Figure 6 above we can see
that L2 and L3 have less buffer size compared to L1 and
L4 which can take in more data. In cases where there is
higher traffic Load arrival, congestion and buffer
overrun is likely to be experience at L2 and L3 since
both have reached their threshold and the MS at this
time is in sleep mode and cannot process data until at
the listening mode stage where a MON-TRF-IND
message is received as Positive (Figure 4, T3) which
subsequently results to packet loss as well as
performance degradation of the network. The propose
DAPSS scheme have minimize the longer delay
accordingly, by appropriately adjusting the sleep time
rightly or just in time and have also proposed an
algorithm called a delay aware algorithm (algorithm 1)
that have successfully minimized the excess longer
delay of the existing scheme. We also validated our
results analytically to justify the proposed DAPSS
scheme.
Finally, we used, discrete event simulator for the
simulation; the Simulation results evaluated showed that
the proposed scheme outperforms the existing one in
terms of both average response delay and power
savings.
The main difference between the existing Scheme and
the propose DAPSS Scheme is the modification of
Tmin and Tmax sleep intervals (Equation 1 and 4 ), the
appropriate choice of sleep parameters set used in the
proposed DAPSS and the way of adjustment of the
sleep parameters respectively. The Propose DAPSS
Scheme is analytically modified as follows:
Firstly, we define the sleep parameters which are
namely: sleep mode, listening mode, wake mode, idle
mode, serving mode. Minimum sleep interval (Tmin),
maximum sleep interval (Tmax).
Note that sleep window and sleep interval are same in
this paper.
We call the duration of first sleep interval T1=Tmin,
then the duration of kth sleep interval is given below:
A DAPSS Based on Traffic Load
To address the problems highlighted above; a DAPSS
Scheme Base on Traffic load is proposed. Unlike the
existing scheme where the Tmin and Tmax is fixed in
Equation 2 of
the
existing scheme in ref [4].
First, in the proposed DAPSS Scheme, we introduced
an average based Tmax (Equation 1), in order to reduce
the longer sleep intervals as follows:
T
=
[ 1+K|2kJT -
1 X
I
Tk-1+Tm (1)
Where Tk is the kth sleep window, T is the
1+— !2k-*T„„<'r
X
I
""
2

minimum sleep window,
T
is the maximum sleep
A
" max
A
window, k is a positive integer.
T
is determined by
examining the inter arrival time of a downlink frame(s)
in order
to
reduce the average response delay the
downlink frames may had incurred while waiting for the
MS to wake up. The minimum sleep window is T
is
r r
min
give in Equation 4 below:
T
average = 0
—
X)
T
min + 7
T
d
(2)
Where Taverage is the weighted average inter arrival
time in between the downlink frame from the BS for the
MS.
c =
(1 —
y)c
—1
2y
\T
V ' } n / I a
—
T \
average d |
(3)
c is the weighted average variance of the inter arrival
time of the
T - = T
min
average
downlink
—
k<c„
frame
(4)
Finally, the Tmin which is the minimum sleep interval
is derived as seen in Equation 4 above. From the above
Equations
y
and k are constants given as 0<
y
<1
and
k>1, Td is the time taken after which the downlink (DL)
frames arrive
at
the base station (BS) for
a
mobile
Station (MS) since it went into sleep mode last. Unlike
the existing scheme which has
a
Tmin value that
is
fixed and the sleep window is also made to be constant
to Tmax respectively.
The existing scheme Tmax sleep interval is maintained,
therefore, when the sleep interval approaches Tmax as
shown in Equation (2) of the existing Scheme in ref [4]
the sleep interval becomes larger resulting to a longer
sleep window, which is
a
key concern as the traffic
arrival increases with an exponential increase sleep
intervals seen in Figure 4 above. In the propose DAPSS
Scheme we have modified the Tmin and Tmax sleep
parameters
as
seen above
in
Equation
1
are used
dynamically to adjust the sleep intervals aptly, as the
sleep window increase the sleep parameters take
a
suitable average value so as to significantly minimize
the existing schemes longer sleep intervals. More so, the
sleep parameters are examined based on traffic load
arrival pattern dynamically in order to predict the next
actual arrival
of the
downlink frames which
significantly improved performance as well as minimize
the average number of listening intervals in the sleep
window as well as power consumption. In addition,
when the sleep window subsequently approaches Tmax,
the sleep window
is
increased incrementally
as an
average of kth sleep window and Tmax as an average
sleep interval as seen in Equation (1) to (4) respectively,
thereby minimizing the response delay the downlink
frame may had incurred in the process while waiting for
the MS to switch to wake mode.
Unlike the existing scheme in ref [4], that take the full
length of the of the Tmin and Tmax sleep intervals that
is constantly increasing and subsequently resulst to
a
longer sleep window. The proposed DAPSS Tmin and
Tmax sleep intervals are modified and given as seen in
T +T
Equation 4 above for Tmin and the Tmax as k—12 max
thus as the MS approaches Tmax where a longer sleep
intervals
is
subsequently experienced in the existing
scheme, the propose DAPSS considers
an
average
suitable sleep intervals in order to quickly reverts to
active mode
in
case
of
positive MOB-TRF-IND
Messages as well as higher
traffic
load arrival from BS.
Let (n and P) denote the number of sleep intervals and a
period of successive series of sleep intervals in sleep
mode.
(L) is the
listening interval, while power
consumption in sleep mode is (PS), power consumption
in the listening interval is (PL). D represents the frame
response delay that
a
MS requires
to
successfully
deliver packets.
We assume that MS follow a Poisson distribution with
arrival rate as (X). This implies that, the inter arrival
time is distributed according to an exponential law with
parameter (1/
X).
Let ek denote the event that there is at
least one (1) packet arrival during the monitoring
period.
Pr
[
= true] = —e -ATt + L ) (5)
The term Pr[n=k] represents the probability of success
in the exact k-th iteration, which is also the probability
of failure in iteration 1 to k -1 and success in the k-th.
The number of sleep cycles is an independent random
variable.
Pr (n = 1) = Pr (e = true) =
1 —
eMn+L)
(6)
for n > 2, (n = k)
Pr (ei = false,
; ek—j
= false
:
ek = true)
= nk=11Pr (e1
= false)
Pr
(e
k
= true)
k—1
= ee
T2L
(1
—
L)
) (7)
—
*iL(T
2L)
k=1 k—1
(8)
From Equation (6) and (8), the average numbers of
sleep intervals are calculated. We present
n
as the
number of sleep intervals and the probable value of n is
represented by P[n].

Note that the value of n is between 0 to Thus, the
possible value
of n is
given
as
follows:
Pr [n] = ± Pr (n = k)
Pr [n] =
±e
k=l
L)
k=1
(9)
Individual sleep cycle has a length of Tk + L. Thus, the
possible duration
of a
sequence
of
sleep cycles
is
dynamically calculated as follows:
E
[
D] = ±± Pr ( n = k )( k-th cycle duration ) (10)
;Pr (n
=
k )±iT;
+
L )
= (Ze
-A±( T + L) -A±( T + L))±(
Tj +L)
(11)
k
k-th sleep cycle is P
k
= ± (TP +
FL
l ) (12)
j=1
Assuming the frame response delay resulting to the
outflow/overrun from the sequence of sleep cycles will
arrive at any moment during the last sleep cycle with
uniform probability. The length of k-th cycle is (Tk +L).
The possible response Delay of frame is presented as
follows:
E
[
D] = ±
Pr(
n = k )±(T + L
) 1
(13)
k=1
j=1
2
Pr[d] =
1
±fe-A(Tkk-eeT + L))±T +L)
2 k=1 y j=1
Finally, the expected frame response delay is expressed
as
E [ D ] = = ± e
2 k=1
-A(T+i)±(Tk +L)_
1
^^^^^ +L)±(rt +L)
-
2
e J
=
1 ( )
Hence, the average power consumption during the
sleep, listening and idle mode are also presented
as
follows:
P
=± P
•M-
sleep /
/
J_
•
P
=T\ P
J- awake
' '
\
J-
f=
W+±\ Poownlink-f,
.+±
ap
.1 +P
'-
1
IsA
d1
D
+ V P
+ P
Downlink-subframe
^^
apdf
P ap^T p
(15)
(16)
P iddle
= ± P
iddle.
i
+
±\P ]
D ownlink-subframe ^±aPp
+ Pa
(17)
Therefore, the sum of the average power consumption
A(P) of the proposed DAPSS Scheme is also expressed
as follows:
A(P)
= T
iddle
+
Piddle±i
P
k^±\Tiddle
+T
awake
+
Psleep
) (18)
k=0
L=0
Where
++ p is
the idle, sleep,
T iddle T awake P sleep
wake mode (state).
3.2 Procedure of Parameters Adjustment
The procedures of parameters adjustment of DAPSS
scheme: The MS begins in
a
normal mode operation.
And subsequently request for sleep if the mobile sleep
request (MOB-SLP-REQ) is granted, the MS transits to
sleep mode else a positive (+) mobile traffic indication
message (MOB-TRF-IND) is sent to the MS from the
BS and the MS wakes up and process data packets on
the queue. This process is repeated until a Negative (-)
Mobile Sleep Response (MOB-SLP-RSP)) is granted by
the BS as seen in figure
2
above. Otherwise the MS
reverts
to
normal mode operation and continue the
process else end the process. To be more precise see
Figure 7 below.
Figure: 7 Procedure of parameter s adjustment of the DAPSS Scheme.
Algorithm 1: DAPSS Based on Traffic load
Input: rimi, ._ k._
l.
L
_
k — 1
6.
".
S.
9.
10.
11.
12
Calculate = 2'T,mjn
_ /.
1/(3"™, <!•„„);
k= £+1 and repeat step 1
Else
calculate
rigii;t
= 2 Tsmiii _ X
jf(Po^^&{X^T:^miJ)<vm_Po\vg, Cars ! option ) < min_; -espouse
Dda\ •)
T
I T T
1 iami_i--£ jsDin.1 ima^i--1 laai
min_ power Consumption
— Poyver(Trmi„
Tvm„,)
min_ responseDelay
—
D eiay(T^9Trm„;)
Else
Input
k
do step
1
End
IV. Performance Evaluation
This section presents the performance evaluation of the
propose DAPSS against that of the EBLAPS using
a
Network simulator 3 (NS3). The evaluation is based on
the average energy savings and average response delay.
The simulation topology consists of a base station (BS)
with Mobile Station connected around
it
as seen
in
Figure
8
below. The simulation parameters are also
presented in table 1 below:
Table 1: Simulation Parameters
Pcsm power consumption in sleep mode 1
Pclm power consumptio n in listening mode 30
L Listening mode 1
Tmin Minimum Sleep Intervals 1
Tmax Maximum Sleep Interval 1024
Ar Arrival Rates 0.001; 0.4
K Constant
Id Idle Time 0.5-4
k=1
k=1
s=1
=1 p=1

Figure 2: Illustrates the Average Power Consumption
against the Mean Arrival Rate of packets.
Propos e DAPS S
The above Figure 3: Illustrates Average Response
Delay against the Mean Packet Arrival Rate.
V. Conclusion
We have proposed a new Scheme called a Delay Aware
Power Saving Scheme (DAPSS) in the downlink
operation of the IEEE 802.6e WiMAX Networks for
MBNS based on Traffic Load. The DAPSS Scheme
minimized the excessive longer sleep interval of the
existing scheme by appropriately adjusting the sleep
parameters, with the aid of a delay aware algorithm and
resolve the response delay of MS Significantly. In
addition, we analytically modified the Tmin and Tmax
sleep parameters and conducted a simulation
experiment using a discrete event Network Simulator.
Our simulation results proved that the proposed DAPSS
Scheme has a superior performance significantly in
terms of both the average response delay and power
savings.
Acknowledgements
The Authors wish to thank anonymous reviewers who
took out their time and constructively made comments
that have improved on this manuscript, and also thank
the Usmanu Danfodiyo University for their supports as
at the time of
this
research studies.
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