This document discusses how mobility affects the performance of multi-hop cognitive radio networks under different propagation models. It summarizes the results of simulations run with the Network Simulator 2 (NS2) that tested free space, two-ray ground, and shadowing propagation models carrying MPEG4 video traffic over a cognitive radio network with mobile nodes. The key findings are: 1) Free space propagation provided the best throughput and lowest loss ratio compared to two-ray ground and shadowing models; 2) All propagation models performed significantly worse with mobile nodes compared to stationary nodes; 3) Mobility reduced average performance by disrupting signal strength and increasing noise as nodes moved.

1. Mobility and Propagation Models in Multi-hop
Cognitive Radio Networks
Salman Saadat
Communication & Network Engineering Department
King Khalid University, Abha, Saudi Arabia
ssadat@kku.edu.sa
Abstract—Cognitive radio networks allow unlicensed
(secondary) users to opportunistically utilize the idle
resource of a licensed network for communication
without affecting the quality of service being offered to
the primary or licensed users. This paper investigates
the effect of mobility on performance of multi-hop
cognitive radio network under various propagation
models. MPEG4 video; a bandwidth intensive traffic, is
tested over these network conditions for secondary
users and results are validated using NS2 simulations.
Performance metrics used for evaluation include
throughput, delay variations etc.
Keywords— Mobility; Cognitive Radio; NS2; MPEG4
I. INTRODUCTION
Increasing demand of wireless applications has made
spectrum a scarce resource. On other hand the
allocated spectrum is mostly under-utilized.
Cognitive Radio Network (CRN) was designed to
combat these spectrum challenges. CRN has the
ability to use idle periods - called the spectrum
opportunities, of primary users (licensed users) for
transmission of secondary user (unlicensed users)
data without affecting the quality of service already
being offered to the primary user.
Most of the research to date has focused single hop
cognitive radio networks. Multi-hop CRN
environment is more complicated as compared to
single hop CRN due to following three key
differences [1]:
First routes are not limited to a single frequency
channel like single hop but also include routes though
various relay nodes towards the destination. Second
multi-hop network node will also need to model the
behavior of other node (other secondary node). Third
to learn and efficiently adapt their decisions over
time, the wireless node needs to possess accurate
information about the channel condition, interference
pattern and other node transmissions.
Mobility in CRN differs from traditional wireless as
there is no centralized control channel. Traditional
wireless networks have to handle tasks such as
location and handover management but in CRN [2]
the task becomes even harder as it has to deal with
the changes in nodes operational frequency due to
primary user activity with incidental adjustment of
the wireless access technology and extensive protocol
reconfiguration.
II. RELATED WORK
Mobility management at three different layers
(physical, link and network layers) is discussed in [1],
case studies shows that the mobility can be improved
by the cooperation between networks. It suggests that
MAC protocol should be redesigned to accommodate
common control channel requirement for CRN. In [3]
mobility management at link, network and cross
layers is studied. The highlighted research issues for
mobility are spectrum hand off and performance,
location areas management, location update and
paging, and network handoff and quality of service
(QoS).
Different prediction schemes are used in [4] to
monitor mobility, it tries to find best mobility
prediction technique to select most stable route for
communication to improve overall performance
(reliability/throughput). It shows that Markov model
has higher accuracy as compared to the other
prediction schemes. Effect of mobility on spectrum
sensing is discussed in [5] and effects of mobility on
uplink interference for short-range cognitive radio
networks are discussed in [6].
Mobility is multi-hop CRN is more complicated as
compared to single hop due to above mentioned
Proceeding of the 2013 IEEE International Conference on Space Science and Communication (IconSpace), 1-3 July 2013, Melaka, Malaysia
978-1-4673-5233-8/13/$31.00 ©2013 IEEE 375

2. challenges. To the best of our knowledge mobility in
multi-hop CRN is not explored extensively. In this
paper we have studied the effect of mobility on the
performance of cognitive radio networks in multi-hop
environment under various propagation models for
bandwidth intensive data such as MPEG4 video.
A radio propagation model is used to determine the
path loss along a link. It can give effective coverage
area of a transmitter. However a single propagation
model may not work for all types of communication
due to different terrain, path, and obstruction and
atmosphere conditions. The simplest path loss model
is the free-space propagation used for line-of-sight
(LOS) communication. It uses Friis equation to
calculate end to end path loss but does not consider
any obstructions, reflections or scattering from
transmitter to the receiver. Since LOS is not possible
in most of the cases free-space model may not work
for most of the wireless environments. Another
propagation model, two-ray ground model; considers
both LOS and ground reflected path. Two-ray ground
model is commonly used propagation model for long
distances as it accounts for a ground-reflected ray
between transmitter and receiver in addition to the
LOS component and includes the effect of
oscillations caused by the constructive and
destructive combination of the two rays. However
free-space propagation model is still in use for the
short distances. The two-ray model has been shown
to produce more accurate path-loss estimates at long
distances than Friis free-space equation; as it
accounts for antenna height differences at the
transmitter and receiver, which is not considered in
the Friis free-space equation. The third propagation
model that is used in this paper is shadowing model.
Unlike free space and two ray ground model in case
of shadowing model received signal strength is not
just a function of distance; it includes the fading
effects in its path loss calculations [7] [8].
III.NETWORK TOPOLOGY AND PERFORMANCE
METRICS
A 10 nodes topology including 2 primary users
operating in 802.11 WLAN based cognitive radio
environment is assumed for Network Simulator -
NS2 [9] simulations. CRCN [10] and MPEG4 [10]
patches are used in NS2 environment in order to
realize MPEG4 traffic in cognitive radio network.
These patches have been used since network
simulator version 2.31 which has been used for this
study does not support cognitive radio network and
MPEG4 video traffic by default.
For simulation all nodes are assumed to be mobile
moving randomly in 1000 x 1000 flat grid. Each
simulation scenario is run for 300 seconds. Routing
protocol used for this study is DSR and the queuing
technology implemented is drop-tail with a queue
length of 50. Number of channels per radio is set at 3
and each channel is used by the node in a sequential
order for communication. Omni directional antenna is
employed for all scenarios.
Performance of the MPEG4 traffic is evaluated under
varying propagation models. Two ray ground
reflection model, free space model and shadowing
models are tested for this purpose.
Two secondary nodes running MPEG4 video traffic
are the subject of this study. All other nodes carry
constant bit rate (CBR) traffic. Two primary nodes in
the topology transmit CBR data at 400Kbps while
secondary nodes serve a 100Kbps data traffic. The
network is implemented in such a way that primary
nodes are not affected by the secondary node activity.
Whenever a primary user starts communication all
the secondary users which have been using its
network resource previously stop transmitting.
Moreover all the undelivered secondary user packets
in the network are dropped on the resurfacing of
primary traffic to avoid any variations in delay on the
primary user data. However, among the secondary
nodes; the subject nodes carrying MPEG4 video
traffic are given preference in utilization of the idle
resource.
Since one of the main parameter of this study is to
evaluate performance under multi hop secondary user
transmission, the subject secondary nodes carrying
MPEG4 video traffic are forced to employ 2 hops in
order to communicate among themselves.
Each propagation model is tested over 9 different
node movement scenarios. For mobility starting point
of each node is kept constant in all the scenarios.
Moreover; movement pattern for each scenario is
unique and each propagation model is tested over all
these scenarios for comparison purpose.
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3. Fig. 1. 10 nodes network topology with 2 primary users and 8
secondary users.
IV.PROPAGATION MODELS
Free space propagation model assumes that there is a
line of sight path between transmitter and receiver
and there are no reflections or multipath. It is usually
not implemented for long range in ground mobile
environments. It calculates the received power using
following formula [8]:
2
4 2 2 (1)
Where; Pr and Pt are received and transmitted powers
respectively, Gt and Gr are transmitter and receiver
antenna gains, d is the distance between transmitter
and the receiver, λ is wavelength and L is system
loss.
For Two-ray ground propagation model received
power is calculated using following formula [8]:
2 2
4 (2)
Here two paths are assumed between transmitter and
the receiver; one direct line of sight and other
reflected. Two-ray ground model gives better
predictions for long ranges compared to free space
model. In the above equation, ht and hr are the
antenna heights of transmitter and the receiver
respectively.
In case of shadowing model received power is not
only a function of distance from the transmitter but
also fading effects. Therefore the received power has
random value with log normal distribution.
Shadowing model used for simulation results in ns
uses following equation to predict received power
[8].
10 log (3)
Where, Pr(d) is the received power at a distance 'd'
and Pr(d0) is the received power at a reference
distance 'd0'. Here β is the path loss exponent and
XdB is the shadowing deviation. The values of both β
and XdB are derived empirically.
V. RESULTS
For the following simulation results of multi-hop
mobile cognitive radio network, network parameters
for all the propagation models are maintained the
same for comparison purpose. System loss factor 'L',
antenna gains Gt and Gr are all kept constant at 1. The
value of path loss exponent β in shadowing model is
selected as 2.
TABLE I. FREE SPACE PROPAGATION MODEL (SIMULATION
TIME 300 SEC)
TABLE II. TWO RAY GROUND PROPAGATION MODEL
(SIMULATION TIME 300 SEC)
TABLE III. SHADOWING PROPAGATION MODEL (PATH LOSS
EXP = 2 AND STD_DEV = 8) (SIMULATION TIME 300 SEC)
Scenario
No. of MPEG4
Pkts Tx
No of MPEG4 Pkts Rx
with Mobility
Throughput
with
Mobility(Kbps)
Loss Ratio Average Delay Average Jitter Variance in Delay
1 10798 3379 6.758 68.70716799 3.564809 -0.010694 0.01452
2 10803 3342 6.684 69.06414885 3.615438 -0.00987 0.014853
3 10746 3328 6.656 69.03033687 3.501582 -0.007626 0.013988
4 10802 3242 6.484 69.98703944 3.508602 -0.00861 0.01395
5 10807 3359 6.718 68.9182937 3.566123 -0.007398 0.014492
6 10734 3373 6.746 68.57648593 3.49572 -0.009823 0.013964
7 10778 3350 6.7 68.91816664 3.549636 -0.008958 0.014352
8 10774 3368 6.736 68.7395582 3.527364 -0.010243 0.014209
9 10806 3306 6.612 69.40588562 3.55934 -0.005188 0.014427
Without
Mobility
10725 3360 6.72 68.67132867 3.492287 -0.00722 0.013901
Free Space Propagation Model (Simulation Time 300 sec)
Scenario
No. of MPEG4
Pkts Tx
No of MPEG4 Pkts Rx
with Mobility
Throughput
with
Mobility(Kbps)
Loss Ratio Average Delay Average Jitter Variance in Delay
1 10789 157 0.314 98.54481416 5.7354 -0.004179 0.071403
2 10806 848 1.696 92.15250787 8.69895 0.010765 0.080566
3 10774 117 0.234 98.91405235 6.160026 0.009552 0.041102
4 10684 86 0.172 99.19505803 8.90748 0.000425 0.097461
5 10736 3 0.006 99.97205663 10.624333 -0.000313 0.116156
6 10788 93 0.186 99.13793103 8.890882 0.018603 0.086053
7 10697 74 0.148 99.30821726 8.090838 0.000842 0.070456
8 10828 191 0.382 98.23605467 4.76803 0.006712 0.075341
9 10778 256 0.512 97.62479124 3.476012 -0.0015 0.013234
Without
Mobility
10737 1676 3.352 84.39042563 9.42687 0.05106 0.094117
Two Ray Ground Propagation Model (Simulation Time 300 sec)
Scenario
No. of MPEG4
Pkts Tx
No of MPEG4 Pkts Rx
with Mobility
Throughput
with
Mobility(Kbps)
Loss Ratio Average Delay Average Jitter Variance in Delay
1 10722 1310 2.62 87.7821302 6.136553 0.014332 0.040763
2 10749 1780 3.56 83.44032003 6.320716 0.015093 0.04337
3 10725 2448 4.896 77.17482517 3.499811 -0.002737 0.014014
4 10767 1084 2.168 89.93220024 6.268907 0.01027 0.042633
5 10836 1404 2.808 87.04318937 6.235941 0.011871 0.04223
6 10750 1749 3.498 83.73023256 6.195844 0.019104 0.041743
7 10773 1232 2.464 88.5640026 6.250694 0.010256 0.042299
8 10765 2855 5.71 73.4788667 3.59984 -0.006186 0.014761
9 10773 2454 4.908 77.22082985 3.576862 -0.004804 0.014586
Without
Mobility
10754 2421 4.842 77.48744653 3.507117 -0.005876 0.013994
Shadowing Propagation Model - Free Space (Path Loss Exp = 2 and Std_Dev = 8) (Simulation Time 300 sec)
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4. As indicated by the above results-free space
propagation model presents the best results followed
by shadowing propagation model for the given
parameters. However the throughput and loss ratio
provided by all propagation models for mobile
cognitive nodes with multi-hop is way below the
required values to support MPEG4 video. In case of
two-ray ground average delay, average jitter and
variance in delay is also on the higher side.
Comparing results of scenario-2 and scenario-9 in
particular for the three propagation models; it can be
noticed that the loss ratio for free-space is much
lower than the other two models. This is because the
free-space model considers transmission to be line of
sight and hence there are less channel losses. Average
delay, jitter and variance in delay are also on the
lower side for free space model. Moreover, it can be
observed that results without mobility for each
propagation model are significantly better than those
with mobility. This is understandable since with
mobility average performance will deteriorate due to
the random motion of the nodes. This random motion
can affect the node's performance in several ways
such as; reduction in signal to noise ratio (SNR) due
to its movement away from the transmitting node,
complete loss of reception because of it traversal out
of transmitters transmission range, or increase in the
received noise and deterioration of SNR by
displacing into the vicinity of other transmitting
primary or secondary nodes.
TABLE IV. FREE SPACE PROPAGATION MODEL (SIMULATION
TIME 300 SEC)
Scenario
Average Packet
Delivery Ratio
Average Delay
With Mobility 0.309609678 3.543179333
Without Mobility 0.313286713 3.492287
TABLE V. TWO RAY GROUND PROPAGATION MODEL
(SIMULATION TIME 300 SEC)
Scenario
Average Packet
Delivery Ratio
Average Delay
With Mobility 0.018837737 7.261327889
Without Mobility 0.156095744 9.42687
TABLE VI. SHADOWING PROPAGATION MODEL (PATH LOSS
EXP = 2 AND STD_DEV = 8) (SIMULATION TIME 300 SEC)
Scenario
Average Packet
Delivery Ratio
Average Delay
With Mobility 0.168449308 5.342796444
Without Mobility 0.225125535 3.507117
Delay and packet delivery ratio are most important
parameter for delay sensitive data such MPEG4
video. Tables IV, V and VI specifically present the
results for these two parameters in order to analyze
the performance of propagation models under
consideration. Clearly free space model presents the
better results but it cannot be applied to most of the
mobile network scenarios as its predicted results are
restricted to short range and line of sight
environments. Moreover; even the results presented
by free space model are not good enough to ensure
good MPEG4 video communication in mobile multi-
hop cognitive radio network.
VI.CONCLUSION AND FURTHER WORK
This paper studied the effect of mobility on traffic in
cognitive radio networks under multi-hop
transmission. The results gathered through
performance analysis of MPEG4 traffic for free-
space, two ray ground and shadowing propagation
models indicate the best result in case of free-space
propagation model. This means for the scenario under
study, the bandwidth intensive traffic such as MPEG4
is only suitable over short ranges and flat topography.
A further study can be carried out regarding various
channel and source modulation techniques that can
effect and enhance the performance over larger
distances and different environments.
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