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Measurement Based Shadow Fading Model for
VehicletoVehicle Network Simulations
Taimoor Abbas, Student Member, IEEE, Fredrik Tufvesson, Senior Member, IEEE,
Katrin Sj¨ oberg, Student Member, IEEE, and Johan Karedal
Abstract
The VehicletoVehicle (V2V) propagation channel has significant implications on the design and performance of novel
communication protocols for Vehicular Ad Hoc Networks (VANETs). Extensive research efforts have been made to develop V2V
channel models to be implemented in advanced VANET system simulators for performance evaluation. The impact of shadowing
caused by other vehicles has, however, largely been neglected in most of the models, as well as in the system simulations. In this
paper we present a simple shadow fading model targeting system simulations based on real world measurements performed in
urban and highway scenarios. The measurement data is separated for the situations lineofsight (LOS), obstructed lineofsight
(OLOS) by vehicles, and non lineofsight (NLOS) by buildings with the help of video information available during measurements.
It is observed that vehicles obstructing the LOS induce an additional attenuation of about 10dB in the received signal power. We
use a Markov chain based state transition diagram to model transitions from LOS to obstructed LOS and present an example of
state transition intensities for a real traffic mobility model. We also provide a simple recipe, to incorporate our shadow fading
model in VANET network simulators and provide simulation results which show performance degradation due to OLOS.
I. INTRODUCTION
VehicletoVehicle (V2V) communication allows vehicles to communicate directly with minimal latency. The primary
objective with the message exchange is to improve active onroad safety and situation awareness, i.e., collision avoidance,
traffic rerouting, navigation, etc. The propagation channel in V2V networks is significantly different from that in cellular
networks because V2V employs an ad hoc network topology, both transmitter (TX) and receiver (RX) are highly mobile, and
TX/RX antennas are situated on approximately the same height and close to the ground level. Thus, to develop an efficient
and reliable system a deep understanding of V2V channel characteristics is required also stated in [1].
A number of V2V measurement campaigns have been performed to study the statistical properties of V2V propagation
channels [2]–[6]. Signal propagation over the wireless channel is often divided by three statistically independent phenomena
named deterministic path loss, small scale fading, and largescale or shadow fading [7]. Path loss is the expected (mean) loss
at a certain distance compared to the received power at a reference distance. The signal from the TX can reach the RX via
several propagation paths and these multipath components (MPC) can have different amplitudes and phase. A change in the
signal amplitude due to constructive or destructive interference of the different MPCs is called smallscale fading. Finally,
obstacles in the propagation paths of one or more MPCs cause great attenuation and the effect is called shadowing. Shadowing
give rise to largescale fading and it occurs not only for the lineofsight (LOS) component but also for any other major MPC.
Understanding of all these phenomena is equally important to characterize the V2V propagation channel.
In real scenarios there can be light to heavy road traffic, involving vehicles with variable speeds and heights, and there are
sometimes buildings around the roadside. Hence, it might be the case that the LOS is obstructed by another vehicle or a house.
The received power depends very much on the propagation environment, and the availability of LOS. Moreover, in [8] it is
reported that, in the absence of LOS, most of the power is received by single bounce reflections from physical objects. Therefore
for a realistic simulation and performance evaluation it is important that the channel parameters are separately characterized
for LOS and NLOS conditions.
A number of different V2V measurements campaigns with their extracted channel parameters are comprehended in [9]. For
most of the investigations mentioned above it is assumed that the LOS is available for the majority of recorded snapshots. Thus
the samples from both the LOS and NLOS cases are lumped together for the modeling purpose, which is somewhat unrealistic,
especially for larger distances. The LOS path being blocked by buildings (NLOS) greatly impacts the reception quality in
situations when vehicles are approaching the street intersection or road crossings. The buildings at the corners influence the
received signal not only by blocking the LOS but also act as scattering point which helps to capture more power in the absence
of LOS. A few measurement results for a NLOS environment are available [10]–[14] in which the path loss model is presented
for different type of street crossings.
In addition to the NLOS situation, the impact of neighboring vehicles can not be ignored. In [5] it is reported that the
received signal strength gets worse on the same patch of an open road in heavy traffic hours as compared to no traffic hours.
This work was partially funded by the ELLIIT Excellence Center at Link¨ opingLund In Information Technology and partially funded by Higher Education
Commission (HEC) of Pakistan.
T. Abbas, F. Tufvesson, and J. Karedal are with the Department of Electrical and Information Technology, Lund University, Lund, Sweden (email:
taimoor.abbas@eit.lth.se; Fredrik.Tufvesson@eit.lth.se; Johan.Karedal@eit.lth.se).
K. Sj¨ oberg is with Centre for Research on Embedded Systems, Halmstad University, Halmstad, Sweden (email: katrin.sjoberg@hh.se)
arXiv:1203.3370v2 [cs.NI] 3 Oct 2012
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These observed differences can only be related to other vehicles obstructing LOS since the system parameters remained same
during the measurement campaign. Similarly, Zhang et. al. in [15] presents an abstract error model in which the LOS and
NLOS cases are separated using the thresholding distance. It is stated that the signals will experience more serious fading
in crowded traffic scenario when the distance is larger than the thresholding distance. In [16] and [17] it is shown that the
vehicles as obstacle have a significant impact on LOS obstruction in both dense and sparse vehicular networks, implying that
shadowing caused by other vehicles cannot be ignored in V2V channel models. To date, in majority of the findings for V2V
communications except [16] and [17], the shadowing impact of vehicles has largely been neglected when modeling the path
loss. It is important to model vehicles as obstacles, ignoring this can lead to an unrealistic assumptions about the performance
of the physical layer, which in turn can effect the behavior of higher layers of V2V systems. The channel properties for all
three cases; LOS, the shadowing caused by other vehicles (OLOS), and, the LOS path being blocked by buildings (NLOS),
are distinct and their individual analysis is required. No path loss model is available today dealing all three cases together in
a comprehensive way.
The main contribution of this paper is a shadow fading channel model distinguishing between LOS and OLOS based on
real world measurements in highway and urban scenarios. The model targets Vehicular Ad Hoc Network (VANET) system
simulations therefore we also provide simple recipe to distinguish LOS, OLOS and NLOS conditions in the simulator. State
transitions from LOS to OLOS and NLOS are modeled by a Markov chain based state transition diagram and sample state
transition intensities for a real traffic mobility model are provided based on our measurements. We model temporal correlation
of shadow fading as an autoregressive process. Finally, simulation results are presented where we compare the results obtained
from our shadow fading model against the commonly used Nakagamim pathloss model.
The remainder of the paper is organized as follows. Section II outlines the outdoor measurement campaign conducted and
explains the methods for separating LOS, OLOS and NLOS data samples which serves as first step to model the effects of
shadow fading. Section III covers methods for data analysis, and it includes derivation of path loss and modeling of LOS and
OLOS data as lognormal distribution. The channel model is provided in section IV. First the extension in traffic mobility
models is suggested to include the effect of largescale fading and then path loss model is presented and parameterized based
on the measurements. VANET simulation results are discussed in Section V. Finally, section VI concludes the paper.
II. METHODOLOGY
A. Measurement Setup
Channel measurement data was collected using the RUSKLUND channel sounder, which performs multipleinput multiple
output (MIMO) measurements based on the switched array principle. The measurement bandwidth was 200MHz centered
around a carrier frequency of 5.6GHz and a total Nf = 641 frequency points. For the analysis the complex timevarying
channel transfer function H(f,t) was measured for two different time durations: short term (ST), 25s, and long term (LT),
460s. The shortterm and longterm channel transfer functions were composed of total Nt = 49152 and Nt = 4915 time
samples, sampled with a time spacing of ∆t = 0.51ms and ∆t = 94.6ms, respectively. The test signal length was set to
3.2µs.
Two standard 1.47m high station wagons, Volvo V70 cars, were used during the measurement campaign. An omnidirectional
antenna was placed on the roof of TX/RX vehicles, taped on a Styrofoam block that, in turn, was taped to the shark fin on
the center of the roof, side wise, and 360mm from the back edge of the roof. Videos were taken through the windscreen of
each TX/RX car and GPS data was also logged during each measurement. Video recordings and GPS data together with the
measurement data were used in the post processing to identify LOS/OLOS/NLOS conditions, important scatterers and to keep
track of the distance between the two cars. The videos and the measurements were synchronized during measurements.
B. Measurement routes
Eight routes in two different propagation environments were chosen with differences in their traffic densities, roadside
environments, number of scatterers, pedestrians, and houses along the road side. All measurements were conducted in and in
between the cities of Lund and Malm¨ o, in the south of Sweden.
Highway; Measurements were performed, when both the TX and RX cars were moving in a convoy at a speed of 22−25m/s
(80 − 90km/h), on a 4 lane highway, 2 lanes in each direction. There were few to many vehicles moving in the opposite
direction and also in the same direction as the TX and RX. Along the road side there were trees, vegetation, road signs, street
lights and few buildings situated at random distances. The direction of travel was separated by a (≈ 0.5m tall) concrete wall
whereas the outer boundary of the road was guarded by a metallic rail.
Urban; Measurements were performed, when both the TX and RX cars were moving in a convoy as well as in opposite
directions, in densely populated areas in Lund and Malm¨ o. TX and RX cars were moving with different speeds 0 − 14m/s
(0 − 50km/h), depending on the traffic situation. The 12 − 20m wide streets were either single or double lane including side
walks on both sides, lined with 2 − 4storied buildings or trees on either side. Moreover, there were road signs, street lights,
some trees, bicycles and many parked cars, mostly, on both sides and sometimes, only on a single side of the street. The streets
were occupied with a number of moving vehicles as well as few pedestrians walking on the sidewalks.
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0400800
LOS/OLOS Intervals (m)
12001600 20002400
0
0.2
0.4
0.6
0.8
1
CDF
Highway−LOS
Highway−OLOS
(a)
0 100200 300 400500600700
0
0.2
0.4
0.6
0.8
1
LOS/OLOS Intervals (m)
CDF
Urban−LOS
Urban−OLOS
(b)
Fig. 1.Cumulative Distribution Function (CDF) of LOS and OLOS time intervals for all measurements; (a) highway scenario, (b) urban scenarios.
In total 3 short term (ST) and 2 long term (LT) measurements for highway, and, 7 short term (ST) and 4 long term (LT)
measurements for urbanconvoy were performed. During each measurement, the LOS was often obstructed by other cars, taller
vans, trucks, buses, or, houses at the street corner.
C. LOS, OLOS and NLOS separation
To distinguish LOS from OLOS and NLOS, the geometric information available form the video recording from the
measurements was used. LOS condition is defined as when it is possible for one of the cameras to see the middle of the
roof of the other vehicle. Otherwise we say that the LOS is blocked. The blocked LOS situation is further categorized in two
groups; when one or more vehicles obstruct the LOS path, OLOS, and, when the buildings block the LOS path, NLOS.
However, from an electromagnetic wave propagation point of view, impact of an obstacle can be assessed qualitatively, by
the concept of the Fresnel ellipsoids. It is required to have Fresnel zone free of obstacles in order to have LOS and only the
visual sight does not promise the availability of LOS [7]. If the obstacle does not obstruct the visual sight but the Fresnel
ellipsoid, partially or completely, it may have some impact on the strength of the received signal. The availability of LOS
based on Fresnel ellipsoids depends very much on the information about the height of the obstacle, its distance from TX and
RX, the direct distance between TX and RX as well as the wavelength λ. Since, the videos and the measurement data do not
include detailed information about obstacles, such as, their height and their relative distance from TX and RX at each instant,
it is very hard to take Fresnel zone into account while separating the LOS samples from OLOS and NLOS. With this limitation
the visual sight seem to be the best solution for a straightforward separation process.
III. ANALYSIS
In Table I the driven distances for both the scenarios; urban and highway, are tabulated together with the distances where
TX/RX were in LOS, OLOS and NLOS, respectively. Each time the TXRX pair is in one of the LOS, OLOS or NLOS state,
it remains in that state for some time interval. The Cumulative Distribution Function (CDF) of these LOS/OLOS intervals are
shown in Fig. 1(a), and 1(b). During the whole measurement run the TXRX link transited from LOS to OLOS/NLOS and
back, a number of times, i.e., LOSOLOS; 61 times in urban and 23 times in highway scenario, similarly, LOSNLOS; 4 times
in urban and 0 times in highway scenario. No transition took place from OLOS to NLOS. The NLOS do not usually occur on
highways, moreover, the data samples for NLOS data in our urban measurements are not enough therefore they are not shown.
A. Pathloss Derivation
The time varying powerdelayprofile (PDP) is derived for each time sample in order to determine the path loss. The effect of
small scale fading is eliminated by averaging the time varying PDP over Navgnumber of time samples and let the averagedPDP
(APDP) be given by [18] as,
1
Navg
n=0
Ph(tk,τ) =
Navg−1
?
h(tk+ n∆t,τ)2,
(1)
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TABLE I
AVERAGE DISTANCE TRAVELED IN LOS AND OLOS CONDITIONS.
ScenarioTotal MinMaxMean Median
LOS (m)
Highway
Urban
6622
5477
24.4
0.95
2157
519
299
84.6
125
35.3
OLOS (m)
Highway
Urban
10752
2429
18.6
2.4
2298
656
467
39.8
150
20.5
NLOS (m)
Urban 415
−120−110−100−90−80 −70−60−50
0
0.02
0.04
0.06
0.08
0.1
Channel Gain [dB]
Relative occurance
10
Distance between TX and RX [m]
1
10
2
−100
−80
−60
−40
Channel Gain [dB]
Fig. 2.
of channel gains taken from log spaced distance bin, marked by vertical lines in the inset plot, 20.4 − 29.1m. The LOS and OLOS data is taken together in
this figure.
Inset plot shows overall channel gain for urban measurement data as a function of direct distance between TX and RX. Large plot is the histogram
for tk= 0,Navg∆t,...,?Nt/Navg− 1?Navg∆t, where h(tk+n∆t,τ) is the complex time varying channel impulse response
derived by an inverse Fourier Transform of a Channel Transfer function H(f,t) for singleinput singleoutput (SISO) antenna
configuration. The Navgis calculated by Navg=
movement of TX and RX by 15 wavelengths and v is the velocity of TX and RX given in each scenario description.
The zeroth order moment of the noise thresholded, smallscale averaged APDPs gives the averaged channel gain for each
link as,
Gh(tk) =
Nτ
s
v∆t, where ∆t is the time spacing between snap shots, s corresponds to the
1
?
τ
Ph(tk,τ),
(2)
where τ is the propagation delay. The cable attenuation and the effect of the lownoiseamplifier (LNA) were removed from
the measured gains. Hence, the channel gains presented in the paper are the gains experienced from the TX antenna connector
to the RX antenna connector. Moreover, noise thresholding of each APDP is performed by allowing all signals with power
below the noise floor, i.e., noise power plus a 3dB additional margin, to zero. The noise power is determined from the part of
PDP, at larger delays, where no contribution from the transmitted signal is present.
The path loss PL(d) is equal to the negative of the distance dependent channel gain Gh(d), which is obtained by matching
the time dependent channel gain Gh(tk) to its corresponding distance d between TX and RX at time instant tk. GPS data,
recorded during the measurements, was used to find the distance between TX and RX which corresponds to the propagation
distance of first arriving path for each time sample in the presence of lineofsight. The time resolution of the GPS data was
limited to one GPS position/second. Thus, to make GPS data sampling rate equal to the time snapshots, interpolation of the
GPS data was performed with the cubic spline method. The distance obtained from the GPS data was validated, later, by
tracking the first arrived MPC, in the presence of LOS, with a high resolution tracking algorithm [19].
B. Largescale fading
As explained earlier the effect of small scale fading is removed by averaging the received signal power over a distance of
a few wavelengths. The averaged envelope is a random variable due to the largescale variations caused by the shadowing
from large objects such as building, and vehicles. The most widely accepted approach is to model the largescale variations
with a lognormal distribution function [7], [20]. For the analysis the distance dependent channel gain Gh(d) is divided into
logspaced distance bin and the distribution of the data associated to each bin is studied independently. Before the separation of
LOS, OLOS, and NLOS, the data in each distance bin was modeled and it was observed that a lognormal distribution did not
provide a good match, see Fig. 2. Moreover, an additional attenuation was observed which made the spread in the channel gain
large and the spread was different for different distance bins. As a first guess, the attenuation could possibly be associated to
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−100 −90 −80−70−60−50−40−30
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Channel Gain [dB]
Relative occurance
Below Noise Threshold
Meas. Data OLOS
Meas. Data LOS
Normal PDF OLOS
Normal PDF LOS
Fig. 3.
from the urban measurement data; pdf fitting the Gaussian distribution.
Histogram of the same channel gains as in Fig. 2, when separated as LOS and NLOS. The data is taken from log spaced distance bin 20.4−29.1m
−95−90−85−80 −75−70−65−60−55
0
0.2
0.4
0.6
0.8
1
Channel Gain [dB]
Cumulative distribution function (CDF)
Meas. DataOLOS
Meas. DataLOS
Log−normal CDFOLOS
Log−normal CDFLOS
Fig. 4.
distribution with 95% confidence interval.
The separated LOS and OLOS data taken from log spaced distance bin 20.4 − 29.1m from the Urban measurement data; cdf fitting the lognormal
the obstruction of LOS. Therefore, the LOS, the OLOS, and the NLOS data were separated before analysis. Then it is observed
that the largescale variations for both the LOS and the OLOS data sets can be modeled to be lognormally distributed (see,
e.g., Fig. 3, 4) with an offset of almost 10dB in their mean. This observation go well in line with the independent observations
presented in [17].
The channel gain in the OLOS condition, at instants, falls below the noise floor of the channel sounder and power levels
of samples below the noise threshold can not be detected correctly. It is observed that the OLOS data in each bin for shorter
distances, with no missing samples, fits well to a lognormal distribution and we assume that the data continues to follow
a lognormal distribution for the larger distance bins where the observed data is incomplete. Moreover, the exact count of
missing samples is also available, which can be used to estimate the overall data distribution. To get the higher order statistics
of Gaussian distributed LOS and OLOS data we compute the maximum likelihood estimates (MLE) of scale and position
parameters from incomplete data by [21] where Dempster et. al. presents a broadly applicable algorithm which iteratively
computes MLE from incomplete data via expectation maximization (EM).
IV. CHANNEL MODEL
In this section a very simple largescale fading model for VANET simulations is provided. This model is targeting network
simulations, where there is a need for a realistic but simple model taking shadowing effects into account. Further, a Markov
model is used as a basis for the mobility model. State transition intensities for vehicles are extracted from measurements,
which can be used for modeling the time duration in LOS, OLOS, and NLOS state, respectively. The Markov model approach
has the advantage that it introduces correlated path loss levels (through the states), a property that we think is important for
more realistic network simulations. However, for this type of analysis the measurement data set is constrained therefore, these
intensities can only be treated as an example. The existing traffic mobility models already provide instantaneous position of
vehicles and by simple geometric manipulation it is easy to identify whether the TX and RX vehicles are in LOS, OLOS or
NLOS state, which makes this model easy to implement in many VANET simulators.
A. Extension in the Traffic Mobility Models
Today’s traffic mobility models implemented in VANET simulators are very advanced, e.g., SUMO (Simulation of Urban
Motility) [22] is one example of such an open source mobility model. These advanced models are capable to take into account the
vehicle position, exact speed, inter vehicle spacing, acceleration, overtaking attitude, lanechange behaviors, etc. However, the
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LOS
OLOS
NLOS
P
p
R
r
Q
q
1(P+Q) 1(p+R)
1(q+r)
Fig. 5.State transition diagram for the Traffic Mobility Model (TMM).
possibility of treating the vehicles as obstacles and modeling the intensities by which they obstruct the LOS for other vehicles
are still missing in the simulators. Therefore, a simple extension for including shadowing effects in network simulators is
provided herein. Since the vehicular mobility models implemented in the simulators give instantaneous information about each
vehicle, shadow fading can be implemented by simple geometric manipulation as follows.
• Model each vehicle or building as a rectangle in the simulator.
• Draw a straight line starting from the antenna position of each TX vehicle to the antenna position of each RX vehicle.
• If the line does not touch any other rectangle, TX/RX has LOS.
• If the line passes through another rectangle, LOS is obstructed by a vehicle or by a building, the two cases can easily be
distinguished by using the geographical information available in the simulator.
• Once the propagation condition is identified, the simulator can simply use the relevant model to calculate the power loss.
TABLE II
STATE TRANSITION INTENSITIES IN OUR MEASUREMENTS.
Trans. Intensities(m−1)PpQqRr
Highway
Urban
0.0035
0.011
0.0020
0.024
0
0.00073
0
0.0095
0

0

To further simplify the implementation, the vehicles can also be modeled as circles with antenna position as a center and
the width of a standard car as a diameter of the circle. If a mobility model is not available, state transition intensities given
in Table II can be used directly. The state transition intensities can be calculated easily as the measurement data contains
geometric information about when the LOS, OLOS, and NLOS conditions occur and their respective durations. However, these
intensities are specific to the environment, the driver attitude and the traffic density during the measurement campaign.
For the largescale fading model we use a Markov chain with three states and its working principle is the same as the
GilbertElliot model [23]. The state transition diagram for the traffic mobility model is shown in Fig. 5 and the state transition
intensities are provided in Table II. A similar approach was used in [24] where a lightweight model to evaluate the impact of
vehicle’s mobility and their incorporation was modeled as a Markov chain process.
It is assumed that the transitions between the different states take no time (see Fig. 5). The state transition intensities,
P,p,Q,q,R,r are calculated from the measurements, simply, by dividing the number of transitions from one state to another
with the total distance traveled in initial state. For example, the intensity P = NLOS→OLOS/SLOS = 0.0111m−1, when
SLOS = 5412m is the distance traveled in LOS state and NLOS→OLOS = 61 is the number of transitions from LOS to
OLOS. Other intensities are calculated in a similar way. Again, if geometrical data is available from a mobility model, the
states can be derived directly from there, and no transition intensities are needed for the simulations.
B. Pathloss Model
The measurement data is split into three data sets; LOS, OLOS and NLOS. The parameters of the path loss model are
extracted only for the LOS and the OLOS data sets, whereas, not enough data is available to model the path loss for the third,
NLOS, data set.
The measured channel gain for LOS and OLOS data for the highway and the urban scenario is shown as a function of
distance in Fig. 6 and 7, respectively. A simple logdistance power law [7] is often used to model the path loss to predict the
reliable communication range between the transmitter and the receiver. The generic form of this logdistance power law path
loss model is given by,
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10
1
10
2
−110
−100
−90
−80
−70
−60
−50
Distance between TX and RX [m]
Channel Gain [dB]
Meas. Data LOS
Meas. Data OLOS
Dual−slope LOS
Dual−slope OLOS
Fig. 6.Measured channel gain for the highway environment and the least square best fit to the deterministic part of (3).
PL(d) = PL0+ 10nlog10
?d
d0
?
+ Xσ,
(3)
where d is the direct distance between TX and RX, n is the path loss exponent estimated by linear regression and Xσ is
zeromean Gaussian distributed random variable with standard deviation σ and possibly with some time correlation. PL0is
the freespace path loss plus the accumulative antenna gain (PL0= PLf+Ga) at a reference distance d0in dB. The antenna
gain was not measured when mounted on the roof of the car, therefore a subtraction of the antenna gain from the measured
data is not possible.
In practice it is observed that a dualslope model, as stated in [25], can represent measurement data more accurately. We
thus characterize a dualslope model as a piecewiselinear model with the assumption that the power decays with path loss
exponent n1and standard deviation σ until the breakpoint distance (db) and from there it decays with path loss exponent n2
and standard deviation σ. The dualslope model is given by,
PL(d) =
PL0+ 10n1log10
?
d
d0
db
d0
?
+ Xσ,
if d0≤ d ≤ db
if d > db
PL0+ 10n1log10
??
+
10n2log10
?
d
db
?
+ Xσ.
(4)
TABLE III
PARAMETERS FOR THE DUALSLOPE PATH LOSS MODEL
Scenario
n1
n2
PL0
σdc
LOS
Highway
Urban
1.66
1.81
2.88
2.85
58.7
56.5
3.95
4.15
23.25
4.25
OLOS
Highway
Urban
3.18
2.74
68.7
66.5
6.12
6.67
32.5
4.51.93
The distance between TX and RX is extracted from the GPS data which can be unreliable when TXRX are very close
to each other. Moreover, there are only a few samples available for d < 10m, thus the validity range of the model is set to
d > 10m and let d0= 10m. The typical flat earth model consider dbas the distance at which the first Fresnel zone touches the
ground or the first ground reflection has traveled db+ λ/4 to reach RX. For the measurement setup the height of the TX/RX
antennas was hTX= hRX= 1.47m, so, dbcan be calculated as, db=4hTXhRX−λ2/4
carrier frequency. A dbof 104m was selected to match the values with the path loss model presented in [25], it also implies
a somewhat better fit to the measurement data.
The path loss exponents before and after dbin (4) are adjusted to fit the median values of the LOS and OLOS data sets in least
square sense and are shown in Figs. 6 and 7. The extracted parameters are listed in Table III. For the highway measurements
OLOS occurred only when the TX/RX vehicles were widely separated, i.e., when d > 80m, which means that there are too
few samples to model the path loss exponent in OLOS for shorter distances. Whereas in practice, this is not always the case,
the OLOS can occur at shorter distances if there is traffic congestion on a highway with multiple lanes. Thus, the path loss
exponent for OLOS for shorter distances is assumed to be same as in LOS.
λ
= 161m for λ = 0.0536m at 5.6GHz
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10
1
10
2
−110
−100
−90
−80
−70
−60
−50
Distance between TX and RX [m]
Channel Gain [dB]
Meas. Data LOS
Meas. Data OLOS
Dual−slope LOS
Dual−slope OLOS
Fig. 7. Measured channel gain for the urban environment and the least square best fit to the deterministic part of (3).
It is interesting to notice that the slopes are not that different but there is an offset in the channel gain for the LOS and
OLOS data sets which is of the order of 9 − 10dB and is very similar to the results previously been reported. In [16] an
additional attenuation of 9.6dB is attributed to the impact of vehicle as an obstacle. Meireles et. al. in [17] state the OLOS
can cause 10 − 20dB of attenuation depending upon traffic conditions.
It is highly important to model the path loss in the NLOS situation because power level drops quickly when the LOS is
blocked by buildings. As mentioned above, the available measured data in NLOS is not sufficient to model the path loss
therefore it is derived from available models specifically targeting similar scenarios, such as, [11], [12], [26] and COST 231
WelfishIkegami model (Appendix 7.B in [7]). Among these, Mangel et. al. in [12] presents a realistic and a well validated
NLOS path loss model which is of low complexity, thus, enabling largescale packet level simulations in intersection scenarios.
The basis for the path loss equation in [12] is a cellular model proposed in [26], which is slightly modified to correspond well
to V2V measurements. For completeness the Mangel’s model [12] is used for the NLOS situation and it is given as follows,
PL(dr,dt,wr,xt,is) = 3.75 + is2.94
??
10log10
(xtwr)0.81
+
10log10
d0.957
t
(xtwr)0.814πdr
d0.957
t
λ
?nNLOS?
,
if dr≤ db
if dr> db
??
4πd2
λdb
r
?nNLOS?
,
(5)
where dr/dtare distance of TX/RX to intersection center, wris width of RX street, xtis distance of TX to the wall, and is
specifies suburban and urban with is= 1 and is= 0, respectively. In the network simulator the road topology and TX/RX
positions are known, so, these parameters can be obtained easily. The path loss exponent in NLOS is provided in the model
as nNLOS= 2.69 and Gaussian distributed fading with σ = 4.1dB. However, for very simple network simulations this NLOS
model can be simplified by assuming that the distance of the TX and RX to the intersection center is equal at each instant,
i.e., dt= dr> wrand xt=1
For larger distance (dr> db) the model introduces increased loss due to diffraction, around the street corners, being dominant.
The NLOS model is developed for TX/RX in adjacent streets. If the TX/RX are not in adjacent streets but in parallel streets
with buildings blocking the LOS then this NLOS model is not sufficient. The direct communication in such setting might not
be possible or not required but these cars can introduce interference for each other due to diffraction over roof tops. This
propagation over the roof top can be well approximated by diffraction by multiple screens as it is done in the COST 231
model. However, in [27] simulation results are shown which state that the path loss in nonadjacent street is always very high,
> 120dB. The value is similar to the one obtained with theoretical calculations for diffraction by multiple screens. As the
losses for the vehicles in parallel streets are high, interference from such vehicles can simply be ignored.
3wr.
C. Spatial Correlation of Shadow Fading
Once a link goes into a shadow region, it remains there for some time. If the vehicle is in a shadow region its existence may
not be noticed for some time. Hence, it is important to study the spatial correlation of shadow fading as part of the analysis.
The largescale variation of shadow fading can be well described as a Gaussian random variable (discussed in section III).
By subtracting the distance dependent mean from the overall channel gain the shadow fading can be assumed a stationary
process. Then the spatial autocorrelation of the shadow fading can be written as,
rx(∆d) = E{XσXσ(d + ∆d)}.
(6)
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9
010 2030 405060 7080 90100
−0.2
0
0.2
0.4
0.6
0.8
∆d [m]
rx(∆d)/rx(0)
Measurement Data LOS
Measurement Data OLOS
Gudmundson Model LOS
Gudmundson Model OLOS
(a)
05 10 1520 25
−0.2
0
0.2
0.4
0.6
0.8
∆d [m]
rx(∆d)/rx(0)
Measurement Data LOS
Measurement Data OLOS
Gudmundson Model LOS
Gudmundson Model OLOS
(b)
Fig. 8.Measured autocorrelation function and model according to (7) for LOS and OLOS data; (a) highway scenario, (b) urban scenarios.
The autocorrelation of the Gaussian process can then be modeled by a wellknown analytical model proposed by Gud
mundson [28], which is a simple negative exponential function,
rx(∆d) = e−∆d/dc,
(7)
where ∆d is an equally spaced distance vector and dc is a decorrelation distance which is scenariodependent real valued
constant. In Gudmundson model, dcis defined as the value of ∆d at which the value of the autocorrelation function rx(∆d)
is equal to 1/e. The value of the decorrelation distance dcis determined from both the LOS and OLOS measured autocorrelation
functions and are given in Table III, for both the highway and urban scenarios, respectively. The estimated correlation distance
is thus used to model the measured autocorrelation functions using (7), and is shown in Fig. 8(a) and 8(b).
Looking at decorrelation distances dc, the implementation of shadow fading in the simulator can be simplified by treating
it as a block shadow fading, where dccan be assumed as a block length in which the signal power will remain, more or less,
constant.
V. NETWORK SIMULATIONS
Finally, we include V2V network simulations to support our claim that the LOS obstruction by vehicles degrades the
performance of vehicular networks. The simulation scenario is a 10km long highway with 12lanes (six lanes in each direction).
The vehicles appear with a Poisson distribution with an interarrival rate of 3s. Every vehicle broadcasts 400byte long position
messages 10times/sec (10Hz) using a transfer rate of 6Mbps and an output power of 20dBm (100mW). The channel access
procedure is selforganizing time division multiple access (STDMA) [29] that has been proposed as medium access control
(MAC) method for VANETs [30] [31]. The vehicle speeds are independently Gaussian distributed with a standard deviation
of 1m/s, with different mean values (23m/s, 30m/s, 37m/s) depending on lane. The vehicles maintain the same speed as long
as they are on the highway. The shadowing based channel model presented herein has been compared against a traditional
Nakagamim model [25] in the network simulations, where the latter is not capable of distinguishing between LOS and OLOS.
The Nakagamim model is also based on an outdoor channel sounding measurement campaign, performed at 5.9 GHz. The
smallscale fading and the shadowing are both represented by the Nakagamim model [25]. The fading intensities, represented
by the m parameter of the Nakagami distribution, are different depending on the distance between TX and RX, and the m
values are taken from data set 1 in [25]. The averaged received power for the Nakagami model is computed using the following
formula:
PRX(d) = PTX− PL(d) − PIL+ Ga,
(8)
where PL(d) is calculated as eq. 4 with theoretical path loss exponents n1= 2.1 and n2= 3.8. In the model the antenna gain
is included in the channel gain, therefore the total difference in power PL0at d0is 9.1dB between our LOS model and the
Nakagami model [25]. The difference is assumed as antenna gain, with 4.5 dB antenna gain for each TX/RX. Compensating
for this antenna gain, the reference levels are the same in LOS for the proposed model and for the Nakagami model. In addition
to that the compensation for implementation losses must be done, e.g., cable losses. If a 2m long cable is used on each side
(TX and RX), assuming a cable loss of 1.7dB/m, then a total loss of 6.8dB is received. If this implementation loss PILis
removed then the loss at reference distance d0will be close to 65.5dB, which resembles the free space path loss.
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0 200400 60080010001200
0
0.5
1
Distance between TX and RX [m]
Probability
LOS
OLOS
Fig. 9.The probabilities of being in LOS and OLOS, respectively, depending on distance between TX and RX.
10
1
10
2
10
3
−120
−100
−80
−60
−40
Distance between TX and RX [m]
Received Power [dBm]
Lund LOS
Lund OLOS
Nakagami
Lund model as eq.(9)
Fig. 10.The averaged received power for the LOS/OLOS model and the Nakagami model, respectively.
The averaged received power for the LOS part, the OLOS part and the Nakagami m model, is depicted in Fig. 10. At shorter
distances there is little chance that another vehicle is between any two communicating vehicles but as the distance increases
the chances of being under OLOS either by vehicle, object, or due to the curvature of the earth, increases. The probabilities of
being in LOS and being in OLOS have been calculated from the network simulator for the highway scenario and are depicted
in Fig. 9. To receive the averaged power similar to the Nakagami model these probabilities can be multiplied with the averaged
received power for LOS and OLOS at different distances using the following equation:
PRX(d) = Prob(LOSd)PRX−LOS(d)
+Prob(OLOSd)PRX−OLOS(d)
(9)
By using Eq. 9 the averaged received power for the LOS and OLOS conditions coincides with the Nakagami averaged
received power, see Fig. 10, which is very interesting to notice.
In Fig. 11 the packet reception probability averaged over all RXs within a certain distance from a TX is depicted for the two
different channel models. The two upper bound curves show the packet reception probability for a system with no interference,
i.e., no other transmission is ongoing at any place in the network. This is the best performance that can be achieved with the
two different channel models and therefore, this is called an upper bound. The vehicle density is 9 − 10 vehicles/km/lane.
Between 300400 meters the upper bound curves for both channel models more or less coincide. The upper bound curve for
the LOS/OLOS model is slightly better for longer distances due to the averaged received power for this model is higher for
longer distances, see Figure 13(b).
Both channel models experience similar performance degradation when increasing the vehicle density but there is a per
formance degradation of up to 8% when considering LOS/OLOS receptions compared to the Nakagami model, which is a
considerable loss.
Further, simulations will be conducted using the MAC method carrier sense multiple access (CSMA), which has been
selected as the channel access procedure for the first generation of vehicletovehicle communications systems. In STDMA,
a node is always granted access to the medium regardless of the number of nodes within radio range and when the system
is overloaded nodes transmit at the same time as someone else in the system situated furthest away from itself. Therefore,
STDMA needs position information which is present in the position messages transmitted. The scheduling of transmission in
space implies that STDMA can maintain a high packet reception probability for the nodes situated closest to the transmitter.
VI. SUMMARY AND CONCLUSIONS
In this paper a simple shadow fading model based on measurements performed in urban and highway scenarios is presented,
where a separation between LOS, obstructed LOS by vehicle (OLOS) and obstructed LOS by building (NLOS), is performed. In
the past, despite extensive research efforts to develop more realistic channel models for vehicletovehicle (V2V) communication,
the impact of vehicles obstructing LOS has largely been ignored. We have observed that the LOS obstruction by vehicles
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0 200
Distance between TX and RX [m]
400600 800 1000
0
0.2
0.4
0.6
0.8
1
Packet reception probability
Nakagami upper bound
LOS/OLOS upper bound
Nakagami − 3 sec
LOS/OLOS − 3 sec
Fig. 11. Packet reception probability for the LOS/OLOS model and the Nakagami model compared against the upper bound for each channel model.
(OLOS) induce an additional loss, of about 10 dB, in the received power. Network simulations have been conducted showing the
difference between a traditional Nakagami based channel model (often used in VANET simulations) and the LOS/OLOS model
presented herein. There is considerable performance degradation for the LOS/OLOS model compared to the Nakagami model.
We thus conclude that the obstruction of LOS cannot be ignored when evaluating the performance of V2V communications and
there is a need for a LOS/OLOS model in VANET simulators. Further, if there is no mobility model in the VANET simulator
the state transition intensities principle presented herein can be used for modeling the LOS and OLOS states. Probabilities
for state transition intensities have been provided based on the measurements. The LOS/OLOS model is easy to implement
in VANET simulators due to the usage of a dualpiece wise path loss model and the shadowing effect is modeled using a
zeromean lognormal distribution.
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