Cross-Layer Design Approach for Multicast Scheduling over Satellite Networks

Abstract

In this paper, we propose a cross-layer design approach using perfect prediction-based wireless channel conditions to improve the performance of a multicast packet scheduler over satellite network environments in the downlink transmission. The satellite channels are modeled in single and multi-environments with different values of Rician K factors and its corresponding elevation angle, mean and standard deviation values. From simulation results, the channel state information (CSI) of each user in the multicast group is considered and becomes the condition for the transmission of the multicast packet. We assume the users suffer slow-varying channels such that the CSI update is within the time interval for slot allocation. The result indicates that a positive performance improvement is gained by adopting a cross-layer-design approach in a fading environment. By obtaining the CSI before transmitting multicast flows, the approach reduces unnecessary transmission of background traffic and hence reduces unnecessary resource allocation and retransmission requests

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IWSSCO5, SIENNA, ITALY, 8-9 SEPTEMBER 2005
Cross-Layer Design Approach for Multicast
Scheduling over Satellite Networks
A.Sali, A.Widiawan, S. Thilakawardana, R.Tafazolli, B.G.Evans
Mobile Communications Research Group, Centre for Communications Systems Research,
University of Surrey,
GU2 7XH Guildford, Surrey,
UK
Abstract- In this paper, we propose a cross-layer design
approach
using
perfectprediction-based
channel
conditions to improve the performance of a multicast packet
scheduler over satellite network environments in the downlink
transmission. The satellite channels are modeled in single and
multi-environments with different values of Rician K factors and
its corresponding elevation angle, mean and standard deviation
values. From simulation results, the channel state information
(CSI) of each user in the multicast group is considered and
becomes the condition for the transmission of the multicast
packet. We assume the users suffer slow-varying channels such
that the CSI update is within the time interval for slot allocation.
The result indicates that a positive performance improvement is
gained by adopting a cross-layer-design approach in a fading
environment. By obtaining the CSI before transmitting multicast
flows,
the
approach
reduces
unnecessary
transmission
background
allocation and retransmission requests.
Index Terms- cross-layer design, channel-state-information,
multicast scheduler, geo-stationary satellite networks
wireless
of
traffic and hence reduces unnecessary resource
I. INTRODUCTION
R
esearch on cross-layer design has recently attracted
significant interest. It is concemed with adapting or
haringof information among
specified in the open system interconnection (OSI) protocol
layers. There are a number of papers
addressing specific cross-layer issues. In [1], channel variation
and traffic burstiness is exploited to improve the performance
of resource allocation. The merging of the two parameters has
shown tremendous increase in network performance. Cross-
layer design mechanism can be easy and simple to implement,
as discussed
information in medium access control (MAC) layer with rate
selection in physical/link (PHY/LINK) layer. The mechanism
is implemented by using one single bit ACK/NACK signal
indicating the
downlink transmission. Another instance of a cross-layer
scheduler that enjoys low-complexity implementation can be
found
multiuser scheduling at the data link layer with each user
employing adaptive modulation and coding (AMC) at the PHY
layer. The scheduler classifies users in two categories: QoS
guaranteed and best-effort. The scheduler has the capability to
variouslayers
as
in the
literature
in
[8]. The authors merge the scheduling
correct
reception/failure of the packetin
in
[5]. The paper develops cross-layer design for
enable prescribed QoS guarantees and efficient bandwidth
utilization simultaneously.
Satellite networks offer the advantage of wide geographical
coverage. To model the satellite propagation channel, [2] uses
two-level Markov state method. The propagation fading is
treated as slow fading (shadowing) and fast fading (multipath).
Two Markov state transition matrices are used to simulate the
variation in the strength of the received signal caused by
shadowing or multipath fading. In [7], channel characterization
for mobile satellite communications is presented. The channel
model
implemented
defined
as
channels
environmental conditions.
In
information in the satellite interface differentiates the packet
scheduler in the satellite interface to its terrestrial counterpart.
The authors adapt weighted fair queuing (WFQ) and multi-
level priority queuing (MLPQ) mechanisms for the time-
scheduling function. The work has achieved significant results
in terms of resource utilization achieved by the scheduler and
performance obtained by the flows at the packet level.
In multicast transmission, the delivery mechanism sends a
packet to all multicast group users using one resource unit
instead ofN resource units to N users in unicast transmission.
However, in multicast transmission even if one of the users in
a multicast group requests a retransmission due to bad channel
condition, the retransmission process has to be executed to all
users in the group. This scenario will exhaust the network
resource. To reduce retransmission requests, we propose to
adapt channel state information (CSI) as a conditional decision
to resume packet transmission.
In this paper, we propose an adaptation of physical layer
channel state information onto the multicast packet scheduler
at the MAC layer to improve network performance in a geo-
stationary satellite system. We consider downlink transmission
from the satellite to the multicast group users.
This paper is organized as follows. In Section II we present
the system architecture of the proposed multicast scheduler
with cross-layer design approach.
results are discussed in Section III. Finally we summarize our
current conclusion and propose future research direction in
Section IV.
is suitable for quasi-stationary channels, which
in this paper. Quasi-stationary channels are
characterized
is
by
slowly
varying
[3],
it
is indicated that the lack of channel
state
The initial simulation
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IWSSCO5, SIENNA, ITALY, 8-9 SEPTEMBER 2005
11. SYSTEM ARCHITECTURE
A. Multicastpacket schedulingwith channel state
information
The adaptation of the proposed cross-layer design approach
is based on sharing the channel
physical layer with the multicast packet scheduler at the MAC
layer. Having access to this information the scheduler can
make the decision as to whether the channel is relatively
'good' or 'bad' prior to resuming transmission.
channel is predicted, the associated packet will be paused until
it sees a 'good' channel status.
The system that we address in this paper handles fixed packet
size, 1,which is set to 125bytes in a TDMA environment and
each TDMA slot is 20ms. We characterize two distinct classes
of multicast service: streaming and file delivery.
Streaming traffic, being non-delay tolerant, will always be
allocated slots and transmitted to the users regardless of the
channel information. Thus only file delivery services, which
are not delay-restricted, will be considered for channel-state-
dependent scheduling. We summarize our scheduling policy as
follows:
Scheduling andAdmission Policy
1) For each streaming
allocated at all time.
2) For the remaining slots, if file delivery traffic packets
arrive, channel state information (CSI) for the intended
multicast group is calculated.
3) If CSI is above a certain threshold, the slot is allocated
to that packet. If not, then the slot is allocated to another
service such as unicast.
status information from
If a 'bad'
traffic packet, time slots are
The scheduling policy is illustrated in Figure 1. Referring
to the figure, the channel state information for each receiver is
obtainedfrom
the physical
allocation. The algorithm to obtain the channel information is
as follows:
layer every 20msfor slot
Multicast
traffic
services
f
j 7
Channel state
information
ulticast
_
r__
scheduler
20ms
125bytes
Fig. I Multicast scheduler with channel-state-dependence
1) Channel level,si(t), for each multicast group user i is
calculated according to lognormal fading as defined in (1).
2) This channel information corresponds to packet error
rate (PERj) from lookup table obtained from a PER
performance reference curves [6].
3) Threshold (PERthresh) for each receiver is established on
the
strength of the fading
according to packet error rate (PER) stated in Table 1. In
at that particular
instant,
the simulation,
assumed.
4) Depending on comparison of PER; andPERhre.,h.ld, a
decision is made as to whether the channel is in 'good' or
'bad' condition.
5) The percentage of users in a multicast group,
achieving PER higher than the threshold, 5T, is calculated.
6)
threshold, 45T, then the packet is selected for transmission
7)
users with 'good' channel condition is achieved. Whilst the
packets are delayed, the resource can be utilized by other
services such as unicast.
file delivery service with PER
10-2
is
;5,
If the percentage,
5j,
is greater than the specified
If not, then the packet is delayed until percentage of
Notice that our scheduler depends on both the queue state in
terms of available slots for resource allocation at MAC layer,
and the channel state at the physical layer. Hence, it offers
cross-layer scheduler facilities.
Traffic Modeling
The services considered for scheduling analysis are given in
Table 1. For video streaming model, packet generation rate is
64kbps for file size of 50kB. For background services, which
is categorized as best effort traffic, generation rate is 32kbps
and the file size is 100kB. The packet error rate (PER) for both
services are set to 10-2.
B.
TABLE I
SERVICES AND TRAFFIC MODELS CONSIDERED IN SIMULATIONS
Packet
error
rate
(PER)
Service
category
Service type
Traffic
model
File
transfer
delay
Streaming
Video
streaming
Generation
rate 64kbps,
file size
5okB
0.01 s
I0-2
File
Software
distribution
Generation
rate 32kbps,
file size
IookB
Is
lo02
delivery
C.
The reference link level simulation curve, relating PER vs
Eb/No is as given in [6]. The reference curve uses Turbo
coding, where the physical layer adaptation is observed and the
performance is reflected in PER employing a Rician channel
with K factor of between 0 and 7dB. The system employs
time-division
multiple
access (TDMA)
downlink transmission, although our results can be extended to
the uplink transmission as well. Notably, the Rician channel is
representative
of
or
transmitting file delivery services, the desired PER, which is
10-2, can be achieved by Eb/No values below the desired PER.
The entire Eb/No values are partitioned into non-overlapping
consecutive intervals such that the range is fixed every 0.25dB.
Channel Model
and
focuses on
rural
suburban
environments.
In
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IWSSCO5, SIENNA, ITALY, 8-9 SEPTEMBER 2005
The signal measured at the user is defined as s(t) which is
calculated as
s(t) = Srandom(t) + j
(1)
where ,u is the mean value and Srandom(t) is defined as
Srandom(t) = A xSrandom(t-l)+ v11A
X xSrandm_uncor(t) (2)
assume that all users experiences the same environment, that is
Rician distribution with K factor of7dB.
It
retransmission requests by users which is an advantage for
multicast service delivery as well as preserving the network
resources. Since the scheduler consideration is for non-delay
sensitive service, this does not affect the performance ofdelay-
sensitive traffic, such as streaming traffic.
is seen that high success rate
is achieved by low
where A is the auto-correlation factor and Srandom_uncor is defined
as
Srandom_uncor = ofxwgn(t)
(3)
where u is sigma and wgn(t) is white Gaussian noise random
coefficient.
Referring to [11], single environment model uses values for
,u and u calculated for urban areas with K factor of 7 and
elevation angle a of 80°. The values are 1.7480 for ,u and 0.8
for
single and multi-environment. Whereas for multi-environment,
different values of K are reflected on specified mean and
standard deviation values, as will be discussed further in
Section 111(b).
Notably, it is assumed that the channel remains invariant per
packet for each user. However the channel is allowed to vary
from packet to packet. We also assume that perfect channel
state information (CSI)
information is fed back to the satellite without latency and
error.
. The auto-correlation factor, A, is set to 0.8 for both
is available
at the receiver. This
I1I. PERFORMANCE EVALUATIONS AND CONCLUSION
Single Environment
Our goal here is to reduce the number of retransmission due
to bad channel conditions. The initial result shown in Fig. 2
depicts the scheduler's performance for delay-tolerant traffic
from
delivery
employment of cross-layer design. The figure is meant for
validation and verification of the simulator as to whether the
simulator giving the correct result or not.
Here, failure rate is calculated in terms of fraction of
multicast users in a multicast group requesting retransmission.
The failure rate is directly related to packet loss rate (PLR) due
to bad channel condition. The packet loss rate is plotted over
the number of users in a multicast group. For cross layer
scheduling
transmission, 5T, is 0.9 and 0.8. It is observed that by deploying
channel state information on a multicast packet scheduler, the
packet loss rate is reduced significantly. This is because in the
scheduler with channel information, the chosen packet has
relatively high percentage of 'good' channel condition. Thus
packet loss due to bad channel condition is higher in scheduler
without channel
information compared to scheduler with
channel information. The behavior of PLR is almost constant
with multicast group size because the fraction of multicast
users in a group having bad channels is the same ratio and
directly related to the target packet error rate. Furthermore, we
A.
file
serviceboth withand withoutthe
thethreshold
fraction of
users to resume
0014
0013
0.012
0-
0.011
X,
CA
001
a. 0.009
m 0 008
-EC- With CSI, zeta=0 8
Without CSI
A
With CSI, zeta=0.9
---- Expected PLR
---
0 007 k
0.0061
100 200300400500 600
700800
Multicast group size
9001000
Fig. 2 Packet Loss Rate vs. multicast group size
To verify the expected packet loss rate (PLR), equation (13)
in [9] is used:
PLR=]-(1-Pa)(J-Po)
(4)
Where Pd denotes packet dropping due to overflow or blocking
probability and P0 is the target PER which is set to 10-2 in this
paper. Since we focus on the impact of shadowing in physical
layer, we assume there is no packet to be dropped due to
overflow, i.e., Pd is 0.
Now extending the investigations in the scope of cross-layer
scheduling, the percentage threshold is varied to observe the
performance of the scheduler. Again in Fig. 2, it is observed
that lower value of ST (zeta), experiences higher packet loss
rate.
Increasing the threshold
transmission is more guaranteed in this group than in groups
with low percentage threshold. As we take the probability of at
least one user request retransmission due to bad channel
condition, it is observed in Fig.3 that larger multicast group
sizes suffer high probability of retransmission. The probability
is defined as
-
=I)=nbad
is not harmful
since the
fnRtx>
(5)
where nbad is the number of bad channel events and N is the
total number of events.
The increase of probability of retransmission with respect to
multicast group size occurs because the fraction ofusers in bad
channel conditions is linearly related to a multicast group size.
From Fig. 3, it is shown that by increasing XT (zeta), the
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IWSSCO5, SIENNA, ITALY, 8-9 SEPTEMBER 2005
probability can be substantially reduced. Thus 5 becomes a
control parameter to monitor failure rate in certain multicast
group size.
However, by increasing 5T (zeta), the average packet delay is
increased.
This
percentage, the longer a multicast group has to wait for packets
to be transmitted to them.
delay due to both delays waiting for slots as well as delay due
to waiting for good channel condition,
is depicted
in
Fig.
4. The higher the
Fig. 4 considers average packet
T= TCh+Tq
(6)
However, we assume the number of slots is infinite as we
want to show the impact exclusively on delay due to waiting
for achieving high fraction of users with good channel
condition only.
B. Multi-channel Environment
Next, we model different channel conditions to represent
different environments experienced by users in a multicast
group.
For each considered environment, by means of
empirical models from [11], the optimum values for Rician
factor, K, mean,p,and standard deviation, a, as functions of
elevation angle, a, are
K=Ko+K1a+K2a2+K3a3
,=, O+/ 1a+,u2a2+t3a3
o =uo+ ula+ u2.2±+ Sf
(7)
(8)
(9)
The coefficients are provided in Table II for considered
environmental conditions. The polynomial empirical formulas
are valid in the range 20° < a <900.
11
0-
200
300
400
500600
700800
900
Multicast group size
Fig. 3 Probability ofat least one user in a multicast group request
retransmission (failure rate) vs. Eb/No threshold
0.4295
,,
0.429
/
0.4285
0.428
0.4275
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Target number of subscribers (normalized) in good channel condition
Fig. 4 Average packet delay vs. target number ofusers (normalized) in good
channel cond ition, ST (zeta)
TABLE II
SIMULATION PARAMETERS FORAVERAGE PACKET DELAY
Multicast group size
Maximumre-attempt
1000
50*20ms/slot
TABLE II
EMIPIRICAL COEFFICIENTS FOR DIFFERENTENVIRONMENTS [11 ]
Coefficient value
Ko
K,
K2
K3
1 o
Suburban area
-13.600
9.650*10-'
-1.663*10-2
1.187*10-4
-1.998
-9.919*10-3
1.520*10-3
-1.266*10-
8.000
-3.741*I 0-l
6.125*10-3
-3.333* 10-5
Urban area
1.750
6.700*10-2
0.0
0.0
-52.12
2.758
4.777*10-2
2.714*104
7.800
-3.542* 10-l
6.500*10-3
-3.958*105
~
.2
j3
o0
a,
2
013
In this scenario, the percentage of users in suburban and
urban area is stated in Table 1I. The Rician K factor is
measured in dB and defined as the ration ofpower in constant
part and power in random part of Rician distribution. The
mean, ,u, and standard deviation, a, are parameters of Rician
probability
function
parameters
are presented
empirical models in equations (7)-(9).
density
(pdf). The values of each
in Table
calculated from
III,
TABLE III
SIMULATION PARAMETERS FOR MULTI-ENVIRONMENT SCENARIO
Area
Rician K
factor
0 dB
3 dB
7 dB
10 dB
aC)
I
%
Subs
20
40
30
10
Suburban
Urban
Urban
Suburban
20
20
80
60
-1.69
-13.90
1.75
0.14
2.70
3.06
0.80
0.40
The operating point is a defined Eb/No value for each user,
which is given by
Y i (t)=Yref
if(t)
(10)
where yref is the reference Eb/No from the AWGN channel
reference model to achieve target PER of 10-2 and F1(t) is the
calculated received signal, s(t), for each user i in the multicast
group using ,u and a values stated in Table III.
704
R
E
>1
a,M
-o
z
u-..IC
co
O-
a)
cm
coT
10,3
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IWSSC05, SIENNA, ITALY, 8-9 SEPTEMBER 2005
In this scenario, the packet size is set to 125 bytes and
multicast group size is 1000 users. The algorithm for multi-
environment is the same as in single environment except that
instead of comparing the achieved PER with the target PER,
we now compare the achieved operating point, y i(t), with
Eb/No threshold, y T-
With the mixture of environments in the scenario, the
probability of at least one user request retransmission is plotted
vs.
relatively good channel, ST (zeta). From Fig. 5, as YT increases,
the probability of retransmission decreases due to the high
threshold that each user has to meet in order to be considered
for transmission. Furthermore, higher 6T (zeta) experiences
lower failure rate, reflecting a second threshold that the
multicast group has to meet prior to obtaining a slot from
multicast scheduler for downlink transmission.
As opposed to low probability of retransmission enjoyed in
higher values of YT, high average packet delay is expected as
the price to pay for waiting for a good fraction of users in the
group, X;, having good channels, as depicted in Fig. 6. This is
because, the packet intended for multicast group which does
not meet ;T in the first attempt, is retracted (back-off) and
repeated
mechanism increases the waiting time ofthe multicast packet.
x 10'3
26
YT. The result is compared over fraction of users in
until
it reaches
gi higher than
;T.. The repeat
.~~~~~~~~~-9 zeta=-0 .9
zeta=0.8
2,6 :\O
2.4
2.2
18
1.4
1.2
1
1.5
2
25
Eb/No threshold (dB)
3354
4.5
5
Fig. 5 Probability ofat least one user in a multicast group request
retransmission (failure rate) vs. Eb/No threshold, Y T
CONCLUSIONS
This paper assumes single and multi-environment satellite
channels for downlink transmission of multicast packets with
cross layer design mechanism. Channel state information (CSI)
of each user in the multicast group is considered and becomes
the condition for the transmission ofthe multicast packets. The
observations are sustained in best effort traffic though the
scenario simulates real-time traffic as well. The result indicates
that a positive performance improvement is gained by adopting
a cross-layer-design approach in fading environments.
By obtaining the CSI before transmitting multicast flows, the
approach reduces unnecessary transmission of background
traffic and hence reduces unnecessary resource allocation and
retransmission requests. However, to achieve a relatively good
channel condition for a multicast group, higher average packet
delay is expected. For further work, more accurate channel
estimation to depict real-environments will be investigated for
scheduling and transmission ofmulticast packets.
1.
1.6 6
,E 1.4
>1
CL 12
0.
0.6
0.4
I'll
1
2
3
4
5
Eb/No threshold (dB)
6
7
Fig. 6 Average packet delay vs. Eb/No threshold, y T
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zeta=0.9
zeta=0.8
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