modifying the bit-error rate curve used by OPNET. The second issue is to model the pathloss in wireless environments. The present OPNET Wireless Suite uses ...
P2-14
Modeling and Simulation of Fading and Pathloss in OPNET for Range Communications Joseph Dorleusa, Ralph Holwecka, Zhi Renb. Hongbin Lib, Hong-Liang Cuib, and John Medinac aUS Army PEO STRI, Orlando, Florida 32826, USA bL.C. Pegasus Corporation, Basking Ridge, New Jersey, 07920, USA CWSMR, New Mexico, 88002, USA Abstract - In this paper, we address two important issues in OPNET-based modeling and simulation for range wireless communications. The first issue is to model the fading effect in wireless communications. The present OPNET Wireless Suite views all wireless channels as Gaussian channels and ignores the fading effect. We add a Rayleigh fading channel model to OPNET and implement the fading effect in simulation by modifying the bit-error rate curve used by OPNET. The second issue is to model the pathloss in wireless environments. The present OPNET Wireless Suite uses a fixed value of the pathloss exponent without considering that different environments have different pathloss exponents. We solve this problem by providing a user with the ability to choose a suitable pathloss exponent from 2 to 5 according to the environment. Numerical results are presented to verify our enhanced wireless models and demonstrate their applications in OPNET wireless network simulation. Index Terms - Wireless modeling and simulation, fading, pathloss, OPNET.
based on the External Model Access (EMA) library of OPNET. The simulation architecture can efficiently partition modeling and simulation tasks, and synchronously access the system functions regardless of the location. Undoubtedly, OPNET is the industry's leading environment for network modeling and simulation. However, as a packet-oriented simulation tool, it is not well suited for simulation at the physical layer which involves bits and signals in communication. Moreover, it is difficult to simulate some prominent wireless communication effects, such as pathloss, fading, shadowing [5] in OPNET. In this paper, we address some of the above problems of OPNET for wireless network simulation. First, we note that the OPNET Modeler Wireless Suite [6] ignores the fading effect, treating all wireless channels as Gaussian channels. As a result, the simulation results obtained by OPNET are usually overly optimistic and do not reflect what really occurs in a fading environment. To solve this problem, we add a Rayleigh fading channel model to OPNET and implement the fading effect in simulation by modifying the bit-error rate curve used by OPNET. Second, it is found that the OPNET Wireless Suite uses a fixed value of the pathloss exponent without considering that different environments have different pathloss exponents. In our enhanced wireless model, we add a "pathloss exponent" option which can be set from 2 to 5, and this function is implemented at thee OPNET' s wireless pipeline stage. Numerical results are presented to verify our physical-layer enhanced wireless models and demonstrate their application in OPNET simulation of wireless systems. The rest of this paper is organized as follows. Section 2 discusses our implementation of fading in OPNET. Section 3 narrates our improvement on pathloss modeling in OPNET. Finally, Sections 4 contains our concluding remarks.
I. INTRODUCTION
In recent years, the White Sands Missile Range Test Support Network (WSMR-TSN) has been leading range digitization and technology upgrades for the Army test and training ranges. While the technology upgrades have been largely "hard-wire" based, there is also an interest in bringing in wireless for range communications. One effort is being undertaken to develop a broadband wireless network for integration with the existing "hard-wire" optical range communication network, based on primarily a modeling and simulation approach. As one of the leading network simulators, OPNET [1] provides powerful simulation capability for the study of network architectures and protocols. It is widely used and extensively studied all over the world. A comparison of several computer network simulators is presented in [2] with OPNET being highlighted. It also contains details of implementation of the network models in OPNET and along with some simulation examples. In [3], OPNET Modeler is utilized to optimize the available wireless bandwidth in a wireless network. In particular, a simulation model of NetMoVie is proposed for the management of adaptive multimedia streaming, based on the Real-time Transport Protocol (RTP) standard. In [4], a distributed and web-based 3-tier simulation architecture is created, and the approach is
1-4244-0445-2/07/$20.00 ©2007 IEEE
II. IMPLEMENTATION OF FADING IN OPNET
In a typical wireless communication environment, multiple propagation paths often exist from a transmitter to a receiver due to scattering by different objects. Signal copies following different paths can undergo different attenuation, distortions, delays and phase shifts. At the
407
side,
receiver
constructively
these
multipath
or
significant
fluctuation in
rate
error
Fading
signal-to-noise
(BER) increase,
more
realistic
For
copies
add
may
This leads to the so-called
fading.
small-scale
failure.
link
signal
different
destructively.
or
manifests
(SNR),
ratio
as
bit-
frequency packet
loss and
simulation
wireless
of
communications, the effect of fading should be taken into account.
OPNET, the SNR is computed
In the Wireless Suite of
signal
from the power of the received and
the
SNR is
corresponding
used
for
By
BER.
and the noise power
look-up
a
default,
table
obtain
to
OPNET
the
assumes
Fig.
The "Channel Mode" attribuite
2.
a
in
the enhanced wireless
model and simulation scenario.
Gaussian channel model is used and does not consider any
fading.
The
Gaussian
channel
is
practical
any SNR of
higher
channel is much
Fig. 1).
As
interest
result, OPNET may lead to
a
environment. To address the
Rayleigh fading
curves
fading
the BER in
a
occur
problem,
in
we
node
fading
fading
a
in OPNET. The BER
on
so
as
a
Gaussian
fading
the
we
have modified the wireless
that the channel model of
experiment
or
a
wireless
Rayleigh fading
channel.
effect,
have
we
Fig.2.
shown in
as
wireless nodes in the scenario
wireless
considered
There
(a sender and
two
The distances from the sender to the receivers
implemented
Receiver the
uses
take into account the
curves
impact
an
be set
simulation
channel model and modified the BER
effect and have
can
examine
To
(see
overly optimistic
have
fading effect,
model in OPNET
channel. For
Gaussian channel
a
To model the
benign
more
fading
a
(>0 dB),
than that in
results that do not represent what
the
much
a
environment for communication than
a
three
receivers). 200m.
are
the Gaussian channel model and receiver
uses
Rayleigh fading Mbps.
of the sender is
the OPNET simulation
are
channel model. The
traffic
source
The simulation time is 3600s.
results at the network level. 1. 2
00
1
:E0.
1oU4
Fr,
LU m
10
I
00. .
G( 0)
0.2
H.-
-G(0.
00 1)
R(O. 001)
lo-,
O)
R( 0
0
600
Gauss'ian
RayliIgh Fadin
in-12L
20
-15
5
-5
16
i
iuO
20
15
Fig.
SNP dB
Fig.
BER of
1.
binary
Differential
in Gaussian and
As
one
example, and
shown in
Fig. 1,
does not include curve
fading
fading
are
channels
two BER
fading
less than
10-12 at
for the
DPSK in
This shows that
The red
effect and is for curves are
OPNET
has
an enormous
in turn, the
are
line in
Rayleigh
same
impact
on
Fig. 1)
OPNET line in
SNR, the
"R
10-2
that
uses
Fig. 1)
(0)" and
0.00 1,
in
uses
the to
"R
408
3600~
Rayleigh fading chamnels.
shown in
Fig.3.
error are
figure,
In the
correction threshold
automatically
the Gaussian BER
Rayleigh fading
Rayleigh fading
(the
corrected in
with
the
From
Fig.3,
error
a
curve
(e.g.,
given
SNR. On the
the red
channel model is used,
BER
curve
(e.g.,
the blue
get the BER value. In Fig. 3, "G (0.001)",
(0.001)" denote the results obtained
Rayleigh fading
threshold set to
wireless communications.
and
to determine the BER for
Gaussian channel with the
the BER
networking performance
errors
other hand, if the
drastically
3000
2400 t irre(s)
simulation) is 0. When the Gaussian channel model
is used, OPNET
curve
for the Gaussian channel the BER is
SNR=15 dB, whereas at the
fading
percentage of bit
(see Fig. 1). As
curves.
Rayleigh fading channel is higher than
performance and,
Giaussian
in
channel model is used and the
binary
and is for the Gaussian channel. The
includes the
particular,
1800
(0)" denotes the results obtained when the Gaussian
"G
channels.
consider the BER of
channel. It is clear that the two
different. In
BER
there
Throughputs
3.
The simulation results
Phase-Shift-Keying (DPSK)
Rayleigh fading
Rayleigh fading
Gaussian
blue
we
1200
Si rrul at ion
error
channel
in the
correction threshold set to with the
error
correction
U.UU1, and Rayleigh fading channel model correction threshold
we can see
set
toO, respectively.
that under the Gaussian channel, the
throughput is sensitive to the value of the error correction threshold and remains around 1Mbps (see the blue and pink curves in the figure). However, under the Rayleigh fading channel model, the throughput decreases to 0.55Mbps (see the yellow line) When the error correction threshold is 0.001, or 0 (see the green curve) when the threshold is 0. Due to fading, there is a significant increase of the BER. In turn, the throughput decreases dramatically.
Begin Get "Pathloss Exponent" value from node attribute
Pass "Pathloss Exponent" value to ""wlan_rx" model
III. IMPROVING PATHLOSS IN OPNET
Pathloss describes the loss in power as the radio signal propagates in space. In any real channel, signals attenuate as they propagate. For a radio wave transmitted by a point source in free space, the loss in power, known as pathloss, is given by 04,d 2
Read the value in "wlan_power" wireless pipeline stage
Determine if the value is between 2 and 5 and use it
where A is the wavelength of the signal, and d is the distance between the source and the receiver. The power of the signal decays as the square of the distance. In land mobile wireless communication environments, similar situations are observed. The mean power of a signal decays as the n -th power of the distance: L = cdv, where c is a constant and the exponent n typically ranges from 2 to 5. The exact values of c and n depend on the particular environment. The loss in power is a factor that limits the coverage of a transmitter.
End Fig. 5.
The flowchart of implementing an adjustable pathloss exponent.
The main codes of our implementation are listed as follows: //define variables Objid rx_objid; double
exponent = 0.0, tmp = 0.0;
I/get "Pathloss Exponent" value
rx_objid = op_td_get_int (pkptr, OPC_TDA_RA_RX_OBJID); (rx objid, "Pathloss op_ima obj_attr_get Exponent", &exponent); I/judge and use "Pathloss Exponent" value if (prop-distance > 0.0)
if((exponent > 2.0) && (exponent
