Performance Evaluation of Safety Applications over DSRC
Vehicular Ad Hoc Networks
Jijun Yin Tamer ElBatt
Gavin Yeung Bo Ryu
Information Sciences Laboratory
HRL Laboratories, LLC
Malibu, CA 90265, USA
jijun@hrl.com,telbatt@hrl.com
Stephen Habermas
Hariharan Krishnan
Timothy Talty
General Motors Corporation
Warren, MI 48090, USA
ABSTRACT
In this paper we conduct a feasibility study of delay-critical safety
applications over vehicular ad hoc networks based on the emerging
dedicated short range communications (DSRC) standard. In partic-
ular, we quantify the bit error rate, throughput and latency associ-
ated with vehicle collision avoidance applications running on top of
mobile ad hoc networks employing the physical and MAC layers of
DSRC. Towards this objective, the study goes through two phases.
First, we conduct a detailed simulation study of the DSRC physi-
cal layer in order to judge the link bit error rate performance un-
der a wide variety of vehicles speeds and multi-path delay spreads.
We observe that the physical layer is highly immune to large de-
lay spreads that might arise in the highway environment whereas
performance degrades considerably at high speeds in a multi-path
environment. Second, we develop a simulation testbed for a DSRC
vehicular ad hoc network executing vehicle collision avoidance ap-
plications in an attempt to gauge the level of support the DSRC
standard provides for this type of applications. Initial results re-
veal that DSRC achieves promising latency performance, yet, the
throughput performance needs further improvement.
Categories and Subject Descriptors
C.2.5 [Computer-Communication Networks]: Local and Wide-
Area Networks; C.2.1 [Computer-Communication Networks]:
Network Architecture and Design—Wireless communication
General Terms
Performance,Theory
Keywords
Intervehicle communications, MANETs, DSRC, bit error rate, safety
applications, simulation, OFDM, throughput, latency.
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VANET’04, October 1, 2004, Philadelphia, Pennsylvania, USA.
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1. INTRODUCTION
The rapid evolution of wireless data communications technolo-
gies witnessed recently creates ample opportunity to utilize these
technologies in support of vehicle safety applications. At one hand,
Cellular-based systems (e.g. OnStar) [1] have contributed the abil-
ity to report accidents in a timely and reliable manner. On the
other hand, new wireless technologies have the potential to en-
able intervehicle communications for the purpose of crash avoid-
ance. Mobile ad hoc networking (MANET) is a potential technol-
ogy for supporting these applications in a secure, resource-efficient,
and reliable manner. In addition, the DSRC standard at 5.9 GHz
band is projected to support low-latency wireless data communi-
cations between vehicles and from vehicles to roadside units. The
DSRC specification [2] is meant to be an extension of the IEEE
802.11 [3] technology into the outdoor high-speed vehicle environ-
ment. In fact, the physical layer (PHY) of DSRC is adapted from
IEEE 802.11a PHY based on orthogonal frequency division mul-
tiplex (OFDM) technology. Moreover, the multiple access control
(MAC) layer of DSRC is very similar to the IEEE 802.11 MAC
based on the carrier sense multiple access with collision avoidance
(CSMA/CA) protocol with some minor modifications.
Inter-vehicle communications open the door for a plethora of
applications and services ranging from automated highway sys-
tems [4] to distributed passengers teleconference. These applica-
tions may be classified to safety and non-safety applications. Un-
der safety applications, Vehicle Collision Avoidance (VCA) has at-
tracted considerable attention since it is directly related to minimiz-
ing number of accidents on the road. On the other hand, non-safety
applications may include real-time road traffic estimation for trip
planning, high-speed tolling, collaborative expedition, information
retrieval, and entertainment applications. In addition, we envision
that vehicular ad hoc networks (VANET) may play an important
role in improving the capacity and coverage of future wireless net-
works via: i) Complementing the existing cellular infrastructure
in hot spot areas where the system gets overloaded and it may be
favorable for vehicles to assist one another in reaching the base sta-
tion (BS) (via multi-hopping) rather than continously competing to
access the uplink and ii) Extending the coverage of the cellular in-
frastructure via enabling an out-of-range vehicle to forward its data
through multiple hops until a BS is reachable.
In [5], the authors outline a number of challenges introduced by
the DSRC operating environment to IEEE 802.11 MAC, namely
multi-hop operation, high mobility and QoS support. The study in
[6] introduces a location-based broadcast communication protocol
for supporting safety applications in the 5.9 GHz band. However,
the wireless propagation model does not account for the multi-path
1
delay spread and Doppler effect which may have significant impact
on performance. In addition, the vehicle mobility model underlying
this study is not provided in the paper. A communication protocol
for low-latency delivery of emergency warnings under various road
situations has been investigated in [7]. The study in [8] analyzes
the packet error rate performance of the IEEE 802.11a/RA PHY
(variant of the IEEE wireless LAN standard developed for Road-
side Applications) in the outdoor high-speed vehicular communi-
cation environment. The results reveal high packet error rate which
confirms the significant impact the physical layer performance will
have on the end-to-end VANET performance in such harsh wire-
less environment. In [9], the authors introduce a distributed wire-
less token ring MAC protocol in an attempt to guarantee bounded
delay and achieve fairness in networks of vehicular platoons. Sim-
ilarly, [10] presents a collision-free medium access control mech-
anism based on vehicles’ location information in order to support
the delay-bounded requirement dictated by safety applications. The
simulation study in [11] shows the benefits of multi-hopping over
alternate paths when direct communication between vehicles can
not be achieved. In [12], the authors introduced an approach for
disseminating safety messages among highly mobile hosts. Finally,
[13] discusses the rationale behind choosing position-based rout-
ing [14] and UTRA-TDD [15] MAC protocols, among other alter-
natives, as candidate protocols for the Fleetnet project [16]. The
common limitation among [7, 9, 10, 11, 12, 16] is that the physical
layer model was highly abstract and did not account for multi-path
delay spread and Doppler effect. This, in turn, motivated us to con-
duct a VANET simulation study that incorporates a detailed PHY
model of DSRC.
Our contribution in this paper is two-fold: i) Quantify the phys-
ical layer performance of the current DSRC standard, measured
in bit error rate (BER), under large Doppler and multi-path delay
spread encountered in the outdoor high-speed vehicle environment
and ii) Characterize the throughput and latency performance of ve-
hicle collision avoidance applications over a DSRC VANET. First,
we develop a detailed physical layer simulation for the DSRC stan-
dard which accurately models the vehicle-to-vehicle wireless link
propagation at 5.9 GHz. Next, we incorporate the BER results ob-
tained earlier into a VANET simulation testbed that analyzes the
performance of vehicle collision avoidance applications in light of
the quality of service (QoS) requirements provided by the Vehicle
Safety Communications Consortium (VSCC) [19].
The paper is organized as follows: In section II, we introduce
a brief overview of the DSRC standard. Afterwards, the DSRC
PHY layer performance evaluation study is conducted in section
III. This is followed by analyzing the performance of vehicle col-
lision avoidance applications over DSRC VANETs in section IV.
Finally, conclusions are drawn in section V.
2. DSRC OVERVIEW
In October 1999, the Federal Communications Commission (FCC)
allocated the 5.9 GHz band for DSRC-based intelligent transporta-
tion systems (ITS) applications and adopted basic technical rules
for DSRC operation. On July 10, 2003, the standards writing group
(an ASTM working group) approved the ASTM-DSRC Standard
for DSRC operations. This standard is based on IEEE 802.11a
physical layer and IEEE 802.11 MAC layer and was published as
ASTM E2213-03 [2] in Sep. 2003. FCC report and order, issued
in Feb. 2004, has established service and licensing rules to govern
the use of the DSRC band. In addition, it adopted [2] to ensure
the inter-operability and robust safety/public safety communica-
tions among these DSRC devices nationwide. Currently, the ASTM
E2213-03 standard is being migrated to the IEEE 802.11 standard
where it is undergoing revision by the newly formed WAVE study
group within the IEEE 802.11 community.
The 5.9Ghz band consists of seven ten-megahertz channels which
includes one control channel and six service channels. DSRC,
which involves vehicle-to-vehicle and vehicle-to-infrastructure com-
munications, is expected to support both safety/public safety and
non-safety applications. However, priority is given to safety ap-
plications since the non-public safety use of the 5.9 GHz band
would be inappropriate if it leads to degrading the performance of
safety/public safety applications [17]. This is attributed to the fact
that safety applications are meant to save lives via warning drivers
of an impending dangerous condition or event in a timely manner
in order to take corrective actions. Therefore, response time and re-
liability are basic requirements of safety applications as discussed
later in the paper.
DSRC PHY uses OFDM modulation scheme to multiplex data.
Along with the successful deployment of IEEE 802.11a WLAN
services and devices in recent years, OFDM has gained increased
popularity in the wireless communication community due to its
high spectral efficiency, inherent capability to combat multi-path
fading and simple transceiver design. In a nut shell, the input
data stream is divided into a set of parallel bit streams and each
bit stream is then mapped onto a set of overlapping orthogonal
subcarriers for data modulation and demodulation. All of the or-
thogonal subcarriers are transmitted simultaneously. By dividing a
wider spectrum into many narrow band subcarriers, a frequency se-
lective fading channel is converted into many flat fading channels
over each subcarrier if the subcarrier spacing is small compared
to the channel coherence bandwidth. Thus, a simple equalization
technique could be used in the receiver to combat the inter-symbol
interference. DSRC uses 64 subcarriers where 52 subcarriers are
actually used for signal transmission. Out of these 52 subcarriers,
48 are data subcarriers and 4 subcarriers are pilot symbols used for
phase tracking. Fig. 1 shows the training sequence structure both
in time and frequency. Two long training symbols are across all the
subcarriers and 4 pilot subcarriers are only embedded in subcarriers
-21,-7,7,21.
Figure 1: Two long preambles and pilot subcarriers in time and
frequency
Fig. 2 shows the physical layer data frame structure. A1-A10
are ten identical short training symbols, each is 16 samples long.
A subset of these symbols are used for packet detection, automatic
gain control(AGC), and various diversity combining schemes. The
remaining short training symbols are used for coarse frequency off-
set estimation and coarse symbol timing estimation. These short
training symbols are followed by two long identical training sym-
2
Figure 2: DSRC PHY Frame Format
bols, C1-C2, which are used for channel estimation, fine frequency
and symbol timing estimation. C1 and C2 are 64 samples long and
the 32-sample long CP1 is the cyclic prefix which protects against
intersymbol interference (ISI) from the short training symbols. Af-
ter short and long training symbols, comes the actual modulated
payload OFDM symbols. The first OFDM data symbol is the phys-
ical layer header which is BPSK modulated and specifies the mod-
ulation scheme used in the payload OFDM symbols that follows.
Each OFDM symbol consists of 64 samples and a 16-sample long
CP which is pre-appended for each OFDM symbol to combat ISI.
Some of the key physical layer parameters used in DSRC are listed
in Table I.
Finally, it is worth noting that IEEE 802.11a is primarily de-
signed for indoor WLAN applications. Thus, all PHY parame-
ters are optimized for the indoor low-mobility propagation envi-
ronment. Aside from the fact that the DSRC signal bandwidth
is 10 MHz (half of the IEEE 802.11a signal bandwidth) in addi-
tion to some differences in the transmit power limit, the DSRC
PHY follows exactly the same frame structure, modulation scheme
and training sequences specified by IEEE 802.11a PHY. However,
DSRC applications require reliable communication between On-
Board Units (OBUs) and from OBU to Roadside Unit (RSU) when
vehicles are moving up to 120 miles/hour and having communi-
cation ranges up to 1000 meters. This environment is drastically
different from the indoor low-mobility environment and its impli-
cations on the DSRC PHY performance turn out to be non-trivial
as demonstrated in the next section.
Table 1: Key Parameters in the DSRC Physical Layer Standard
Data Rate 3, 4.5, 6, 9, 12, 18, 24, 27 Mbps
Modulation BPSK, QPSK, 16-QAM,
64-QAM
Coding Rates 1/2, 2/3, 3/4
# Subcarriers 52
# Pilot Tones 4
OFDM Symbol 8 µsec
Duration
Guard Interval 1.6 µsec
Subcarrier Spacing 156.25 KHz
Signal Bandwidth 10 MHz
3. DSRCPHYSICALLAYERPERFORMANCE
EVALUATION
In this section, an implementation of the current DSRC physi-
cal layer standard is described and the BER performance is evalu-
ated, under a wide variety of vehicles speeds, using Matlab simula-
tions.
1
The motivation to conduct a detailed physical layer study of
DSRC is multi-fold: first, to evaluate the parameters in the cur-
rent PHY specification and find out whether we could improve
the system performance for outdoor high-speed vehicle environ-
ment by modifying some of these parameters; second, to figure
out the best packet size and date rate for different applications and
third, to generate BER curves necessary for building an integrated
PHY-network simulation testbed for VANETs as illustrated in sec-
tion IV. To meet these objectives, we first discuss the transmitter
and receiver baseband processing algorithms used in the simula-
tion. Numerous approaches has been proposed in the literature for
designing various aspects of the transmitter and receiver process-
ing blocks and we pick the most robust and frequently used ones.
Next, we review the channel models and measurements reported
in the literature and utilize them to characterize the DSRC channel
parameters at 5.9 GHz. Finally, we present the simulation results
which confirm the robustness of DSRC against large delay spreads,
yet, its sensitivity to high vehicle speeds.
3.1 DSRC Wireless Link Model
3.1.1 DSRC Transmitter
Fig. 3 illustrates the transmitter processing blocks. Input bit
streams are first scrambled using pre-defined random bit sequence,
then scrambled data bits are encoded using a 64 state 1/2 rate con-
volutional code. Higher coding rate is achieved by puncturing from
the same 1/2 rate convolutional encoded data bits. Using a single
convolutional code at both transmitter and receiver simplifies the
encoder and decoder design.
Figure 3: DSRC Transmitter Processing Blocks
After data bits are encoded, they proceed through a block in-
terleaver. The interleaver redistributes the transmitted bits in both
time and frequency such that continuous bursty bit errors caused by
channel fading would have little impact on the performance of the
convolutional decoder. A desirable interleaver pattern depends on
the channel characteristics. For instance, using an interleaver un-
der additive white Gaussian noise (AWGN) channel does not im-
1
Although the detailed frame structure and training symbols are
specified in the standard, different chip vendors have their own pro-
prietary baseband processing algorithms.
3
prove the system performance. For a typical indoor operating envi-
ronment, the channel could be characterized as a slow frequency-
selective fading channel [20]. Due to low mobility, the channel
coherence time is generally much larger than the packet transmit
duration, thus the channel is assumed constant for the duration of
a transmitted packet. IEEE 802.11a sets the interleaving depth to
one OFDM symbol which, in terms of bit length, depends on the
modulation scheme used. Basically, there is no additional benefits
by interleaving across multiple OFDM symbols for the indoor envi-
ronment since the channel is assumed static for contiguous OFDM
symbols. On the other hand, for the high mobility outdoor envi-
ronment, channel coherence time will be much less than that of the
indoor environment and turns out to be on the order of the packet
length. In this case, the system performance might be improved
using a longer interleaver depth, e.g. interleaving across both fre-
quency and different OFDM symbols.
After interleaving, the data bits are mapped into symbols accord-
ing to different modulation schemes. Inverse Fast Fourier Trans-
form (IFFT) is performed on these modulated symbols, thus data
symbols are carried on a set of orthogonal subcarriers. A cyclic
prefix (also called Guard Interval) is inserted at the front of each
OFDM symbol to combat the ISI introduced by the channel and
short and long training preambles are inserted at the beginning of
the packet. At this stage, the packet would be ready for transmis-
sion.
3.1.2 Channel Model
DSRC devices are projected to operate around 5.9 GHz in a high
speed mobile outdoor environment with communication ranges up
to 1000 meters. However, there are hardly any results available
in the open literature on wide-band channel modeling for this op-
erating environment. Extensive studies for narrow-band channel
measurements has been conducted in the cellular mobile environ-
ment [20]. However, due to the communication range, carrier fre-
quency and signal bandwidth differences, these results can not be
directly utilized in characterizing the DSRC channel. Some wide-
band channel measurements has been gathered for IEEE 802.11
type devices in the indoor low-mobility environment around 5.3
GHz [21][22]. The channel parameters derived from these mea-
surements are significantly different from the DSRC channel in
terms of delay spread and Doppler spectrum characteristics.
Channel modeling involves two important aspects: large scale
path loss and small scale fading. The former is used to deter-
mine the mean received signal power at a particular distance from
the transmitter (DSRC applications could also use the path loss
model to derive the relative distance between the transmitter and
the receiver). On the other hand, small scale fading generally in-
volves the modeling of multi-path fading, power delay profile, and
Doppler spectrum. It is worth noting that both large and small scale
signal variations have great impact on the packet error rate per-
formance. A generic impulse response of a time-varying wireless
channel h(t, τ ) could be expressed as
h(t, τ)=
N
n=0
β
n
(t)δ(t − τ
n
) (1)
where N is the number of multiple propagation paths, τ
n
is the
excess delay of the nth path, and β
n
(t) is the complex channel
gain associated with nth path. Typically τ
n
is specified in terms
of multiples of the OFDM sample period T , and T is the inverse
of the signal bandwidth. In DSRC, T = 100ns. Let P
n
denote
the power of the nth path, so P
n
= E{|β
n
|
2
}. The channel delay
spread, fading statistics, and Doppler spectrum are typically used
to fully describe the mobile small scale channel characteristics and
channel path loss is taken into account by β
n
in Eq.(1).
Channel measurements are reported in [23] for vehicle-to- vehi-
cle (no mobility) channel at 900 MHz and up to 40 feet separation
distance between transmitter and receiver. The root mean square
(RMS) delay spread turns out to be around 20 ns and the Rician
K factor is also computed, yet, it was argued that the fading statis-
tics do not strictly follow the Rician model. Instead, a strong line
of sight (LOS) component combined with a strong reflected wave
from the road way might be more appropriate to model the short
range vehicle-to-vehicle communication channel [23]. Path loss
and delay spread measurements for vehicle-to-vehicle communi-
cation are also reported in [24]. In this experiment, two vehicles
having a fixed 10 to 30 meters separation are moving together in
and out of a tunnel, and the RMS delay spread was found to vary
from 10 ns to 40 ns.
In [25], a wide-band outdoor channel measurement campaign
at 5GHz has been conducted in urban, suburban, and rural areas.
The transmit antenna is held at fixed locations at heights 4, 12, and
45 meters respectively and the receiver antenna is mounted on the
rooftop of a slowly moving vehicle. The measurement distances
are within 30-300 meters and path loss exponents within 1.4-3.5
in LOS and 2.8-5.9 for non-LOS are reported. The mean excess
delay and mean RMS delay spread are typically within 29-102 ns
and 22-88 ns respectively. The scenario where the transmit antenna
was held at 4 meters could be useful to characterize the communi-
cation channel between the OBU and RSU in DSRC. Delay spread
measurements results at 1.8 GHz in urban environments are also re-
ported in [28]. Transmit antenna heights of 5, 7 and 10 meters are
used in the testing and the range is up to 600 meters. Mean RMS
delay spread was found to range from 20 ns to 70 ns depending on
the location of the street and maximum RMS delay spread reaches
levels up to 512 ns. Larger RMS delay spread usually correlates
to the intersections. Path loss measurement in residential areas at
5.8GHz is also conducted in [32] and the results could also be used
in vehicle to RSU communications.
A narrow band measurement of signal fading statistics and Doppler
spectrum had been conducted in [27] for a vehicle-to-vehicle com-
munication ranging from 30-300 meters. A Rician distribution was
found to fit best to the measured received signal power in a least
square sense and this conclusion is mainly due to fact that a strong
LOS is mostly present during the testing. Doppler spectrum is tri-
angular shaped and there is a strong peak at DC frequency.
Various theoretical analysis of Doppler spectrum for mobile to
mobile communication could be found in [30][29] [31]. Under
the assumption of 3-D multi-path scattering power density func-
tions, generic antenna radiation pattern, and absence of a deter-
ministic component, the Doppler spectrum for a mobile-to-mobile
radio channel is derived in [30] as a function of V 1/V 2, where
V 1 and V 2 are the vehicle speeds of the transmitter and receiver.
Also traditional 2-D Jake’s spectrum [36] is a special case of this
3-D model. In [29], a statistical mobile-to-mobile channel model
is also derived based on the assumption of Gaussian process of the
channel and level crossing rate and duration of the fade are com-
puted based on the proposed model.
Practical Doppler spectrum measurements and theoretical inves-
tigation had been conducted in [31] for different operating envi-
ronments: urban, suburban, and rural. There are three types of
Doppler spectrum that occur often on each tap of the channel im-
pulse response: the ”horned” spectrum, the narrow spectrum, and
the flat spectrum. The horned spectrum arises when the received
signal in the azimuth plane has a wide range of angle of arrivals.
The narrow spectrum happens when the scattering is over a narrow
4
range of angles in the azimuth plane. The flat spectrum occurs
when both the scattering and the receiver’s antenna patterns are
isotropic in three dimensions. In typical vehicle-to-vehicle com-
munications the horned and the narrow Doppler spectrum will be
present with high probability since the wave propagates mainly in
the azimuth plane between transmitter and receiver. In particular,
the narrow spectrum usually happens in the first or second tap of
the channel impulse response when there is strong LOS between
transmitter and receiver.
In summary, various studies and measurements results in the lit-
erature reveal that the DSRC channel will exhibit different char-
acteristics under different operating environments. For vehicle-to-
vehicle communications in the LOS scenario, RMS delay spread
would be less than 50 ns which translates to at most two delay taps
in Eq.(1) since DSRC sample period T = 100ns. For a relatively
short range communication in LOS scenario, the direct path and
the reflected path from the ground will not be resolvable from the
receiver hardware point of view, and these two paths will be com-
bined into one single complex channel gain. For a longer communi-
cation range in a non-LOS scenario, worst case RMS delay spread
reported could be up to 400ns and in this scenario substantial multi-
path and high vehicle speeds will have a dramatic impact on the bit
error rate of the system. The DSRC channel model adopted in our
simulations is described in section IV.B.
3.1.3 DSRC Receiver
Fig.4 shows the DSRC receiver baseband processing blocks. Af-
ter the carrier sensing circuit declares sufficient RF energy present
in the received signal, the firststep in the baseband processing is the
packet detection. Packet detection is carried out using the delay and
correlate method in [33]. In essence, this method takes advantage
of periodicity of the ten short training sequences. A normalized
cross correlation between received signal and its delayed version
is computed, and the delay is 16 samples long which is the period
of the ten short training sequences. This cross correlation value is
continuously computed using sliding window until enough number
of cross correlation values exceed the threshold for reliable packet
detection.
Figure 4: DSRC Receiver Baseband Processing Blocks
After the packet is detected, fine timing and frequency synchro-
nization is performed using the long training sequence. The objec-
tive of fine timing estimation is to find the starting point of FFT op-
eration on the following received OFDM symbols. The fine timing
estimation method is based on the search for the maximum cross
correlation value between received long training sequence and the
actual long training sequence. Due to the multi-path, noise and
interference, the fine timing estimator itself is a random variable
around its true mean value. It’s better to shift this mean value to-
wards inside the range of cyclic prefix rather than to delay it into
OFDM symbol since if the timing estimation falls inside the OFDM
symbol, it will give wrong FFT window for further processing.
Due to the symbol clock difference between transmitter and re-
ceiver and the Doppler frequency present in the DSRC system,
frequency offset needs to be estimated from the long training se-
quence. OFDM system is sensitive to the carrier frequency offset
since nonzero carrier frequency offset will cause inter-carrier inter-
ference. The effect of frequency offset on the SNR is studied in
[34]. In DSRC devices, the required symbol clock accuracy should
be less than 20 ppm, and this translates to about 117 KHz maximum
frequency offset introduced in the received signal. The maximum
Doppler frequency that will be present in the DSRC system is about
2KHz assuming the maximum relative speed of 240 miles per hour.
Compared to the carrier frequency and the maximum symbol clock
drifts, it could be ignored. Utilizing the two identical long training
sequences, the frequency offset estimation could be based on the
averaging of the phase difference between a received signal sample
and its delayed version over many received signal samples [35].
The more samples are used in the averaging the better the quality
of the frequency offset estimation.
After the received signal is corrected by the estimated frequency
offset, it is transformed to frequency domain using FFT. Channel
estimation is done by averaging of the computed complex channel
gain of each subcarrier using the two long training sequences. Ba-
sically, a simple division of the received long training signal after
FFT and the original long training sequence yields the estimated
channel gain. Residual frequency offset and carrier phase offset
estimation is achieved with the aid of the pilot subcarrier inserted
between data subcarriers. In DSRC, vehicles could move at very
high speeds, and the channel might change within a packet dura-
tion. Thus, phase tracking errors might cause the processing of
higher modulation schemes to perform poorly. In order to remedy
this potential problem, a new pilot design is proposed and evaluated
in [8], where pilot subcarriers both in time and frequency are used
to perform better phase tracking. However, the drawback of using
the new scheme is the processing delay and increased complexity
of the algorithms.
Afterwards, the received signal is mapped into soft bits and de-
interleaved. Finally, soft bits information are passed into Viterbi
decoder, CRC check is performed on the decoded bits to determine
whether there is bit error in the packet or not.
3.2 DSRC PHY Simulation Results
Current DSRC physical layer has been simulated in details us-
ing Matlab and we follow the specifications for training sequences,
scrambling sequences, convolutional encoder, and interleaver in the
DSRC ASTM E2213-03 Standard [2]. Packet error rate measure-
ments using Atheros baseband chip sets have also been conducted
to verify our baseband simulation results in the AWGN scenario.
In Fig. 5, the DSRC channel model used in the simulation is
depicted, where β
n
and τ
n
are defined in Eq. (1). We assume 400
ns RMS delay spread for the multi-path environment along with
an exponentially decaying power delay-profile in the simulation.
Jakes’ Doppler spectrum [36] is used to generate fading taps under
various vehicle speeds.
Fig. 6 shows the bit error rate versus SNR for a DSRC receiver
at 12 Mbps which is the direct outcome of using 16-QAM modu-
lation scheme along with 1/2 rate convolutional coding. The date
rate in DSRC ranges from 3Mbps to 27Mbps, and hence, this set of
5
