Int. J. Communications, Network and System Sciences, 2011, 4, 648-655
doi:10.4236/ijcns.2011.410079 Published Online October 2011 (
Copyright © 2011 SciRes. IJCNS
Reliable Battery-Aware Cooperative Multicasting for MBS
WiMAX Traffic
Sara Moftah Elrabiei1, Moohamed Hadi Habaebi1,2
1Department of Computer Engineering, University of Tripoli (Formally Known as Alfateh), Tripoli, Libya
2Department of Electrical and Computer Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
Received August 22, 2011; revised September 14, 2011; accepted September 26, 2011
Recently, relay agents selection schemes were introduced to support downlink multicasting in WiMAX sin-
gle frequency networks. Such schemes were devised to work cooperatively in order to facilitate reliable Mul-
ticast Broadcast Multimedia Services (MBMS) traffic delivery over wireless channels without any consid-
eration to mobile relay agents’ battery energy levels. In this paper, we introduce a battery-aware balancing
algorithm to operate in conjunction with these relay agents selection schemes proposed in the open literature.
A simulation model, used to present the effect of “before” and “after” the battery-awareness selection crite-
rion, highlighted the benefit of using such algorithms in prolonging network lifetime with emphasis on reli-
able delivery.
Keywords: Reliability, Multicasting, WiMAX, MBMS, Battery
1. Introduction
Cooperative communication, has gained momentum
nowadays, due to its effectiveness and importance in
assisting the communication among wireless subscrib-
ers, and its ability to enhance capacity of the mobile
wireless broadband through multi-hop forward capabil-
ity. Different users can act as cooperative partners or
relay agents (RAs) to assist each other in information
transmission, to combat fading in wireless networks,
leading to improvement in overall system performance
[1-6]. Power consumption is a significant issue and key
performance indicator in such a cooperative mobile
environment, because of the limited battery power
available for mobile termnals that constrains the con-
tinuous operation time of wireless devices [7]. Fur-
thermore, multimedia mobile application software
nowadays is more processor intensive, and most of the
WiMAX MBMS applications last for long durations of
time. Therefore, a critical challenge is how to make
efficient use of energy, and to extend the battery life-
time for those cooperative relays without jeopardizing
their QoS such as throughput and system reliability. In
[5], three different re-multicasting schemes were pro-
posed and studied, for IPTV traffic, in conjunction with
other schemes available in the open literature. All pro-
posed schemes divide the downlink frame into 2-phases
(e.g., the first phase is used to receive multicast data
from the base station (BS) and the second phase is used
to re-multicast it to neighboring mobile subscriber sta-
tions (SSs)) but they differ in how to select the RAs to
perform such process. Furthermore, none of the selec-
tion schemes considered the effect of battery drainage
on selected RAs throughout the re-multicasting duration.
The contribution of this paper is two-folds. At first, we
study the effect of using such re-multicasting schemes
without any consideration to the SS battery state and
highlight the effect of the selection process on average
node energy consumption levels in the network. Sec-
ondly, we propose a selection criteria energy level bal-
ancing algorithm and implement it into the three dif-
ferent RAs selection schemes and evaluate its effect on
system reliability. The rest of this paper is organized as
follows. Section 2 introduces the effect of battery
drainage of RAs in battery unaware network using our
MATLAB built simulator. Section 3 discusses the bat-
tery aware RA selection optimization schemes. Section
4 introduces the simulation model used. Section 5
evaluates the performance of the proposed schemes and
discusses the simulation results, and Section 6 con-
cludes the paper.
2. Battery Drain of RAs in Battery Unaware
To achieve a reliable downlink transmission for MBMS
traffic and to extend the coverage outside the trans-
mission area, three energy efficient re-multicasting
techniques were proposed in [5] for properly selecting
RAs in a two phase cooperative transmission model. At
phase I, the BS multicasts data to all SSs at high
transmission rate R1, where only subscribers in a good
channel state (SSGCS), e.g., as defined by their CINR
threshold, can successfully receive the data, and the
remaining group of subscribers in a bad channel state
(SSsBCS) fail to receive the data. BS preselects some of
SSGCS to be RAs using one of the selection algorithms in
Elrabiei et al. [5]. Upon receiving signals from BS, each
RA decodes the received signals and then forwards them
to SSsBCS at a proper rate R2 in phase II. By exploiting the
channel state information (CSI) and the location based
service (LBS), a Nearest-Neighbor Discovery Protocol
(NNP), and two other optimized versions of it, based on
RA’s transmission range and instantaneous CSI, were
simulated and studied in the context of WiMAX single
frequency networks. These protocols are described brief-
ly below as follow:
2.1. NNP RA Selection Scheme
The proposed relay agent selection protocol is based on
geographical positioning of users and on the instanta-
neous channel condition of the SSs. Since we have as-
sumed that BS knows the location of each SS, the as-
signed RA is chosen to be the nearest located SSGCS
(neighbor) to the SSBCS of interest, independently of
other SSsBCS. The NNP can be executed periodically
according to the mobility of the users and how often
they change their locations.
2.2. Transmission Radius RA Selection Scheme
The proposed NNP selects a RA for each SSBCS inde-
pendently of other RAs selection, by finding the nearest
SSGCS to the SSBCS from the same MGroup. In worst case
scenario, there is independent RA for each SSBCS, (i.e.,
number of RAs equal number of SSsBCS). Therefore, to
reduce the number of RAs selected, a scheme optimiza-
tion is proposed. If there are two or more SSsBCS, which
are in close proximity to each other, the BS can select
one RA for all of them if they are located within the
RA’s transmission radius. In other words, BS chooses
the RA with Transmission Radius arg min
the SSsBCS. For instance, if we consider da1, da2, are the
distances between RAa and SS1BCS, SS2BCS respectively,
and db1, db2, are the distances between RAb and SS1BCS,
SS2BCS respectively, then we select the RA which has:
min(da1 + da2, db1 + db2). The objective of the optimiza-
tion is to minimize the number of RAs as much as possi-
ble, by selecting the SSsGCS that can support re-mul- ti-
casting to the largest number of SSsBCS within its cover-
age transmission range, and not necessary be the nearest
SSGCS neighbor to each SSBCS. The template is used to
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2.3. SS-SS Inter-Link CSI RA Selection Algrithm
The proposed Transmission Radius RA selection algo-
rithm proposed in 2.2 section above reduces further the
number of RAs involved in the re-multicasting process
but at the expense of algorithm reliability. This is be-
cause some of the SSs previously enjoying their own
nearby RA are now located at the edge of transmission
range of the new RA. Adding that to the varying channel
state, it would result into less reliable delivery. In order
to compensate for that and to further reduce the number
of RAs involved, each SS in the cell is assumed to report
also its instantaneous channel quality between itself and
its neighboring SSs (i.e., SS-SS inter-link CSI) that can
overhear in the cell. Such SS-SS inter-link CSI aggrega-
tion can help the BS to select the proper SSsGCS, that have
maximum number of SSsBCS within in its transmis-
sion range and have good inter-link CSI, to support the
re-multicasting. Such technique requires further modify-
cations to the Wimax Channel Quality Indicator Channel
(CQICH) but the implementation is out of the scope of
this paper.
The RAs are selected so that channel condition
between them and all SSsBCS has to be in a good state.
Using SS-SS instantaneous Inter-link CSI, R2 in phase II
is determined based on computing the sum of the instan-
taneous received power from all corresponding RAs
using the large-scale and small-scale fading channel
model, by selecting the Adaptive Modulation and Coding
(AMC) level that support the worst CSI between SSsBCS
and the RAs. Further details can be found in [5].
The above presented schemes were found to consid-
erably reduce the amount of energy consumed [5], provi-
ding a lower cost coverage solution with no dereliction in
achieved throughput for all multicast group members. In
this study, we have assumed throughout our proposed
Copyright © 2011 SciRes. IJCNS
work that in each frame, the BS selects the proper RAs
for re-multicasting depending on one of the selection
techniques discussed above and that the RA’s battery
capacity reduces due to re-multicasting process only. But
no prior formulation was made to estimate the energy
consumption levels or to predict the network energy map.
There have been several attempts to address the above
issue mentioned especially in the context of wireless
sensor networks, where the main source of energy is bat-
teries. Nevertheless, tackling the process of estimating
the energy levels at all SSs in the network required in a
quantitative closed formula is, somewhat, cumbersome.
However, we decided to use qualitative simulation to
highlight the effect of our proposed re-multicasting tech-
niques or energy consumption levels. The notations used
throughout the paper are listed in Table 1.
3. Proposed Battery-Aware RA Selection
The power consumption and the duration of how long the
SS should work as a RA can be adjusted dynamically by
the BS. The accurate knowledge of the available energy
levels in each SS in the network (i.e., energy map of the
whole network) is important information for the BS to
make its selection. This can be implemented either by
allowing the SS to report its battery energy level as extra
information sent to the BS, or estimated by default as the
BS runs the RA selection algorithms and has a priori
knowledge of the RAs selection frequency rate. We have
resorted to the first assumption in our simulation study.
All SSs are involved in aggregating the CSI map of the
network to the BS. Therefore, it is similarly possible to
Table 1. Notations definitions (true unless stated otherwise).
Notation Description
RA Relay agent
SSBCS Subscriber station with bad channel state
SSGCS Subscriber station with good channel state
SSi,j The j-th group member in MGroupi
The rate of the BS in Phase I for MGroupi
The rate of each cooperative transmitter in Phase II for
T The transmission time of Phase I for MGroupi
T The transmission time of Phase II for MGroupi
Service probability for MGroupi
E The received signal power for SSi,j in Phase I
p The power
di Distance between SSi and BS
bn The lower boundaries of signal to noise ratio (SNR) for
MCS level n
N0 The noise power
map the network mobile SSs battery draining levels at
least in a coarse grained fashion. Selected RAs can be
considered based on their CSI, geographic locations in
the cell, and their battery draining levels. SSsGCS that
have low battery levels can be spared from the down-link
phase II re-multicasting process until recharged. SSs with
stationery position and infinite power supply can act as
RAs similarly to stationary RSs proposed in 16j standard,
although they don’t have to re-multicast continuously on
every frame. Similar schemes are used extensively in
routing network traffic over wireless links. Therefore we
proposed the following battery aware RA selection algo-
rithm described in the following pseudo-code:
Pseudo Code for Battery-Aware RA Selection Algorithm
chedule the appropriate MGroupi using algorithm described in [6]
ocate all the SSsBCS
ocate all the SSsGCS
or each frame, collect the battery capacity levels in all SSsGCS
Compute the average battery available in all mobile SSsGCS.
liminate all SSGCS that have battery capacity less than the computed
average battery capacity (e.g., average battery capacity of the cell is
computed by taking the average of battery capacity counter values of
all mobile SSsGCS involved in the RA selection process only) from RAs
elect the RAs from the new set of S
GCS based on one of the proposed
A selection algorithms described in [5]
etermine T1, R1, T2, R2 described in [5]
t phase
: BS transmits with R1 at T1
t phase I
: All RAs retransmit with R2 at T2
the capacity counter value of the battery (drain_level) for
each RA selected based on the formula drain_level = T2/(T1 + T2)
Once battery capacity levels of all SSsGCS are aggre-
gated to the BS, the algorithm simply excludes certain
SSsGCS, which have battery energy levels below the av-
erage battery capacity of all mobile SSsGCS in the cell,
from the RA’s selection process. Such approach relieves
these SSsGCS from draining their batteries and averages
out the RA selection process, thus, prolonging the net-
work lifetime and, consequently, improving network
reliability in general.
Once battery capacity levels of all SSsGCS are aggre-
gated to the BS, the algorithm simply excludes certain
SSsGCS, which have battery energy levels below the av-
erage battery capacity of all mobile SSsGCS in the cell,
from the RA’s selection process. Such approach relieves
these SSsGCS from draining their batteries and averages
out the RA selection process, thus, prolonging the net-
work lifetime and, consequently, improving network
reliability in general.
We have formulated a general term for the probability
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Copyright © 2011 SciRes. IJCNS
Table 2. System parameters.
of any randomly picked SSj becoming a RA in the
MGroupi that is updated in the battery-aware network [8],
and can be estimated using the following formula:
PRA(SSij) =·
ij n
PE b
min, , ,
P (battery power > threshold) (1)
where PRA(SSij) is the probability of a SSi to be a RA in
MGroupi, i is the Service Probability for MGroupi
given by [6, Equation (6)],
,0ij n
j = SSGC
is the prob-
ability that SS has a good CSI (i.e. SS ), the term
is the probability that
SSj is nearest neighbor to the SSBCS, and the term P(battery
power > threshold) is probability that the battery capacity
at the SSj is above a threshold value set by the BS to ensure
the ability of re-multicasting. Further details on the
mathematical formulation are presented in [8].
n, ,Pdd d mi
Number of cells 1
Carrier frequency & BW 2.5 GHz, 10 MHz
Cell radius 2.56 Km
The total number of SSs in the system 200
The number of MGroups M 16
The number of group members in each MGroup Ni Varying
BS maximum transmission power 20 W
SS maximum transmission power 3 W
Noise power 99 dBm
Path loss exponent α 4.375
Close-in reference distance d0 100 m
Lognormal Shadowing 8.9 dB std
Coverage ratio C 50%
Transmitter antenna gain Gt 1
Receiver antenna gain Gr 1
System loss L 1
DL/UL sub-frame duration 1.25 ms/1.25 ms
OFDMA symbol duration τ 102.9 μs
Frame duration 5 ms
Time ratio for multicasting 25%
4. Simulation Model Table 3. Modulation and coding schemes for IEEE 802.16.
Level Modulation and
coding bn (dB) rn (Mbps) Surface
ratio %
0 No Tx 0 0 27.0782 %
1 BPSK (1/2) 3 2.52 19.7459 %
2 QPSK (1/2) 6 5.04 12.3036 %
3 QPSK (3/4) 8.5 7.56 11.0675 %
4 16 QAM (1/2) 11.5 10.08 9.1849 %
5 16 QAM (3/4) 15 15.12 6.3544 %
6 64 QAM (2/3) 18.5 20.16 3.3007 %
7 64 QAM (3/4) 21 22.68 10.9648 %
We have used in this work a channel model that
incorporates both large-scale and small-scale fadings that
are represented by propagation path-loss, shadowing, and
random Rayleigh fading channel [9,10], and no mutual
interference is assumed because the SSs use the
orthogonal channel. As for the adaptive modulation and
coding, the combinations of modulation and coding set
(MCS) levels for the IEEE 802.16 air-interface are
shown in Table 3 in the first four columns, where bn
represents the lower boundaries of SNR (receiver
minimum sensitivity level), and rn is the corresponding
discrete peak transmission rate for the state n used in this
work for 10 MHz system. State 0 represents the state that
no transmission is allowed, which occurs when there is
poor channel condition.
5. Performance Evaluation
5.1. Battery Energy-Unaware System
When analyzing our results it appeared that some mobile
SSs are selected as RAs more often than their counter-
parts especially when these SSs were located at places
with line of sight connection from the BS and/or have
always good channel state. This frequent selection re-
sulted in the depletion of their limited energy sources
faster than the average depletion rate of the network.
We compare the performance for the three systems,
before and after battery-awareness situations, using
extensive simulations with Matlab. In our simulated
system, the network is composed of one BS and a
number of SSs which are randomly and uniformly
distributed over the coverage area of the BS (i.e., a circle
centered at the BS with a radius of 2.5 km). There are
different MGroups in the network and each group
includes some members which are randomly selected
from the whole SSs set. Channel model described in
above is applied, and the scheduling scheme for
MGroups proposed in [4] is utilized so that the
throughput performance and fair scheduling is not
jeopardized. The main system simulation parameters are
listed in Table 2 and their descriptions are discussed in
[5]. We have repeated the simulation several times with
different random seeds to calculate the average results.
In Figure 1, the histogram illustrates the number of times
a 50 SSs sample were selected as RAs through 600 frames
duration time. Increasing the SS density in the cell, resulted
in reduction of their severed energy consumption levels but
the general trend persisted. Over time, the discharging
process drained their batteries and they were automatically
eliminated from the network population. Thus, reducing the
system reliability in general and bringing the network
lifetime to a lesser reach.
In order to investigate the effect of battery levels on
Figure 1. Histogram of number of times the SSs are selected as RAs through the 600 frames.
the network reliability, we have set the number of SSs in
the cell to 50 SSs, 10% of them are stationary with
unlimited power supply (e.g., their battery levels are un-
affected by the re-multicasting process), while the other
90% are mobile, slowly cruising around the cell. In order
to highlight the performance of the three different
schemes introduced in [5], we have isolated the effect of
the re-multicasting process on battery state by assuming
an initial value of battery state and each re-multicasting
frame results in a amount of energy consumed, where, T1
and T2 are the first and the second phase of the transmis-
sion burst T assigned for downlink multicast transmis-
sion duration respectively, where T = T1 + T2. All other
energy consumption processes such as UL transmission,
DL unicast transmission, processing, etc., were ignored
in order to isolate the effect of battery drainage on sys-
tem performance. We have repeated the simulation sev-
eral thousand times in order to calculate the average re-
sults. The normalized average capacity per mobile SSGCS
is calculated for the mobile SSGCS only and not for all SSs
in the cell.
The effect of the re-multicasting process at each frame
on the average battery capacity per mobile SSGCS in the
network is plotted in Figure 2. The average battery ca-
pacity was normalized relative to the initial battery ca-
pacity value of the SS capacity. The results were plotted
for the NNP RA scheme, transmission radius RA selec-
tion scheme, and SS-SS inter-link CSI RA selection
scheme. The average normalized battery capacity is de-
creasing with increasing the time elapsed. Obviously,
decreasing the capacity in each re-multicasting process
depends on the duration of the phase II duration (T2).
After 1000 frames the battery capacity per mobile SSGCS
reduces to half of the initial value using the optimized
selection techniques, whereas the NNP the capacity re-
duces only 20% for its initial value. This is due to the
fact that our optimized schemes selected RAs based on
distance and CSI criteria that fit fewer SSsGCS. These
SSsGCS were used more often depleting their energy lev-
els quickly in comparison to other SSs bringing in the
process the network average battery state to lower levels
faster. On the other hand, the NNP algorithm used the
simpler selection criterion by allowing more RAs in the
network to be replaced by other RAs more frequently,
thus resulting in smoother average battery energy con-
sumption throughout the cell.
The same results were confirmed by the average
network reliability performance over time as shown in
Figure 3. The general trend is that the reliability decreased
with increasing the time elapsed, and overtime, some RAs
discharged all their battery capacity, hence reducing the
number of RAs in the network. The optimized schemes are
often deeply affected in comparison to the NNP, because
they have fewer RAs, when the stationary RAs are not
affected at all. After 1000 frames, the reliability of the
optimized schemes decreased to about 40%, while the
NNP reliability reduced only to about 70%.
5.2. Battery Energy-Aware System Performance
In this section, the performance of our proposed scheme
was calculated in terms of relative power consumption
levels and system reliability. Extensive simulations with
Matlab were conducted using the implemented bat-
tery-aware RA selection schemes. The same simulation
set-up was maintained in order to facilitate the “before”
opyright © 2011 SciRes. IJCNS
Figure 2. Average normalized battery capacity per mobile SSGCS versus frame number.
Figure 3. Reliability of the proposed schemes versus frame number.
and “after” comparison. The number of RAs is basically
dependent on the number of SSs in the cell, the location
distribution of SSsBCS within the cell and on the wireless
channel conditions. The normalized average battery ca-
pacity per mobile SSGCS in the network at each frame for
all proposed schemes, was computed and plotted in Fig-
ure 4. The implementation of the battery-awareness on
the three RA selection schemes demonstrated more bal-
anced performance over time achieving almost a steady
Similarly, the more balanced performance achieved for all
three RA selection schemes is translated into slower
degradation in system reliability as illustrated in Figure 5.
The battery-aware SS-SS inter-link CSI selection scheme
achieved the best performance in comparison to the other
two schemes, but the overall results are much better than
those shown in Figure 3. This is because the BS always
monitors the battery state of the whole network allowing it
to fine tune the process of RA selection continuously,
hence, prolonging the network life time.
opyright © 2011 SciRes. IJCNS
Figure 4. Average normalized battery capacity per mobile SSGCS versus frame number for the battery aware optimized
Figure 5. Reliability of the proposed schemes versus frame number for the battery aware optimized scheme.
6. Conclusions
The relay agent selection balancing algorithm, intro-
duced in this paper for downlink multicasting traffic over
WiMAX channels, demonstrated the effectiveness of
considering the battery energy levels of mobile sub-
scriber stations as an additional RA selection criterion
when supporting re-multicasting over 2-phased down-
link frames. Such added criterion has prolonged the net-
work lifetime by reducing the average node energy con-
sumption levels with emphasis on reliable MBMS traffic
delivery. Amongst the algorithms, the battery-aware
SS-SS inter-link CSI RA selection scheme achieved the
best performance in comparison to the other two NNP
and Transmission-Radius RA selection schemes. Never-
theless, the overall results demonstrated more reliable
opyright © 2011 SciRes. IJCNS
delivery over longer periods of time.
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