Content uploaded by Tamer Elbatt
Author content
All content in this area was uploaded by Tamer Elbatt on Oct 13, 2015
Content may be subject to copyright.
MIMO Vehicular Networks: Research
Challenges and Opportunities
Ahmed Attia
†
, Ahmad A. ElMoslimany
†
, Amr El-Keyi
†
, Tamer ElBatt
†
, Fan Bai
§
, and Cem Saraydar
§
† Wireless Intelligent Networks Center, Nile University, Egypt
§
Electrical & Control Integration Laboratory, General Motors Corporation, U.S.A.
Email:{ahmed.atyia, ahmad.amr}@nileu.edu.eg, {aelkeyi, telbatt}@nileuniversity.edu.eg, {fan.bai,
cem.saraydar}@gm.com
Abstract— In this paper, we provide a review of the ben-
efits of employing multiple-input multiple-output (MIMO)
signal processing techniques in vehicular ad hoc networks
(VANETs). These benefits include increasing the range of
communication via beamforming, improving the reliabil-
ity of communication via spatial diversity, increasing the
throughput of the network via spatial multiplexing, and
managing multiuser interference due to the presence of
multiple transmitting terminals. We also present a number
of key research challenges facing MIMO VANETs. The
first one is deriving statistical MIMO vehicular channel
models that take into account the spatial correlation between
the transmit and receive antennas and validating them
via extensive channel measurement campaigns. Deriving
channel estimation and tracking algorithms for MIMO inter-
vehicular channels is another challenging problem due to
their non-stationary behavior and high Doppler spread.
Further research is also needed to fully reap the benefits
of multiple antennas in VANETs via space-time and space-
frequency processing. In addition, cross layer optimization
spanning the medium access control (MAC) and networking
layers besides the physical layer is essential to satisfy the
emerging applications of VANETS ranging from safety,
convenience to infotainment.
I. INTRODUCTION
Multiple-input multiple-output (MIMO) and vehicular
ad hoc networks (VANET) are two disparate technologies
that have been introduced and studied by independent,
and largely different, research communities. At one hand,
MIMO research has been pioneered by the wireless com-
munications and information theory communities where
the focus on the point-to-point link constitutes the lion’s
share of the research. More recently, the problem of multi-
user and mobile MIMO has started to receive attention
[1]–[3], yet, with no particular focus on VANETs or
their unique challenges and use cases. On the other hand,
VANET research has been led by a joint effort from
multiple communities, namely wireless communications
and networking, mobile computing and automotive re-
search communities. This is attributed to its inherent
multi-disciplinary nature that brings emerging wireless
networking and mobile computing technologies closer to
the requirements of emerging automotive applications [4].
This position paper constitutes an attempt towards not
only bridging the gap between these two communities
This work was supported by a grant from GM, U.S.A.
but also to showing the synergy and ample opportunity to
leverage the unique benefits offered by MIMO in vehic-
ular scenarios. These benefits could range from resource-
efficient and reliable support of safety applications with
stringent quality of service (QoS) requirements to sup-
porting bandwidth hungry multimedia streaming applica-
tions on the move for a variety of purposes, e.g., law
enforcement and mobile healthcare. On the other hand,
leveraging MIMO in vehicular scenarios brings about
a number of key research challenges that needs further
attention from the community at large, e.g., channel mod-
elling, channel estimation, space-time signal processing
for highly dynamic vehicle-to-vehicle (V2V) channels,
and cross-layer optimization and dynamic V2V topology.
The purpose of this paper is to shed some light on these
unique opportunities and key challenges as well as discuss
sample of our recent research on MIMO V2V, particularly
on channel modelling as a core part of understanding
the channel dynamics in highly dynamic V2V scenarios,
and space-frequency block coding for doubly-selective
channels. MIMO-VANET research is still in its infancy
as it has recently attracted very limited attention in the
literature. In [5], an algorithm to update the channel
estimation for flat fading channels, as part of the MIMO
V-BLAST architecture [6], is introduced.
This paper is organized as follows. In section II, a back-
ground on MIMO is presented. Section III is dedicated to
making the case for the importance and utility of MIMO
in V2V scenarios. Afterwards, we discuss a number of key
research challenges pertaining to MIMO when applied to
vehicular scenarios in Section IV. These challenges range
from channel modeling, PHY layer design all the way
to cross-layer optimization and MIMO VANETworking.
In Section V, we present some preliminary results for
channel modelling and space-frequency block coding for
MIMO-V2V channels. Finally, the conclusion is provided
in Section VI.
II. BACKGROUND
Future communication and networking paradigms are
driven by the ever increasing demand for high rates,
attributed to the emergence of bandwidth hungry media
streaming applications, as well as the ubiquity of the
500
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
doi:10.4304/jcm.7.7.500-513
wireless infrastructure and mobile extensions. Key to
realizing this vision is not only boosting the point-to-
point link capacity (and bit error rate (BER) performance)
but also mitigating multi-user interference in order to
maximize the overall network capacity. Multiple-input
multiple-output (MIMO) communication [7] is a major
breakthrough in wireless communications that has re-
ceived considerable attention in the point-to-point litera-
ture due to its substantial spectral efficiency and reliability
advantages for the same power and bandwidth resources.
Space-time signal processing has undergone major de-
velopment, over the past decade, since its inception in
the 1998 landmark papers by Alamouti [8] and Tarokh
et al. [9]. A considerable body of MIMO research has
been dedicated to point-to-point (single-user) communi-
cations where capturing and exploiting independent multi-
path fading has been the overarching goal. For instance,
sending dependent signals through different spatial paths,
multiple independently fading replicas of the data symbol
can be obtained at the receiver end. This, in turn, yields
reliable reception attributed to the so-called diversity gain
[9]. Another paradigm, namely spatial multiplexing, has
demonstrated that the spatial dimension can be exploited
to create multiple parallel channels [10]. Accordingly,
the data rate (link capacity) can be increased, through
the notion of spatial multiplexing gain, especially in the
high signal-to-noise ratio (SNR) regime, by transmitting
independent data streams in parallel through the “orthogo-
nal” spatial channels. Interestingly enough, a fundamental
trade-off between diversity and multiplexing has been
characterized in [11] for point-to-point links and later
extended in [12] to multiple access channels.
In the following, we briefly review the distinct role of
different gains of a MIMO link with M
T
transmit and
M
R
receive antennas [7].
Array Gain: This gain can be made available at the
transmitter and/or receiver and results in an increase in
the average SNR due to coherently combining signals
from different antennas, even in the absence of multi-path
fading. Since it requires channel state information (CSI),
this gain can be easily attained at receivers where CSI
is typically available, unlike transmitters. For a receiver
with M
R
antennas, this gain makes the average SNR at
the output of the combiner M
R
times greater than the
average SNR at any single antenna element.
Diversity Gain: Diversity, at the transmitter or receiver,
is a powerful technique to exploit fading in wireless
channels. Diversity techniques rely on transmitting the
signal over multiple independently fading paths, in time,
frequency or space. The diversity gain refers to the reduc-
tion in the SNR variance at the output of the combiner,
relative to the variance of SNR prior to combining. At the
transmitter side, the diversity gain can be attained through
transmitting correlated data, carefully constructed on in-
dependent signal paths created between the transmitter
and the receiver. This can be achieved via either beam
forming (if the CSI is available) or space-time coding (if
the CSI is not available). The maximum diversity gain,
i.e., asymptotically achievable, is M
T
M
R
if the MIMO
channel is full rank and the transmitted signal is suitably
constructed.
Spatial Multiplexing Gain: Spatial multiplexing exploits
the spatial dimension to increase the link capacity for no
additional power or bandwidth expenditure. The spatial
multiplexing gain is attained via transmitting independent
data signals simultaneously on parallel spatial data pipes
on the same frequency. The maximum spatial multiplex-
ing gain, that is asymptotically achievable, is given by
min(M
T
, M
R
) if the MIMO channel is full rank and
a spatial multiplexing scheme (e.g., V-BLAST [6]) is
employed. Notice the linear increase of the multiplexing
gain with the number of antennas that is in contrast to a
logarithmic increase in capacity if the multiple antennas
capture only the array and diversity gains. It is shown
in [10] that in the high SNR regime, the open-loop
capacity of a channel with M
T
transmit antennas, M
R
receive antennas, and i.i.d. frequency-flat Rayleigh fading
between each antenna pair is given by
C(SNR) = min{M
T
, M
R
} log(SNR) + O(1)
Interference Reduction: When multiple antennas are used,
the spatial signatures of the desired user and interferers
can be exploited to reduce interference. However, this
requires knowledge of the desired user’s CSI, and possibly
the CSI of the interferer depending on the interference
reduction scheme. If CSI is available, transmitter beam
forming achieves interference reduction via minimizing
the interference energy sent to neighbors other than the
intended receiver. On the other hand, receiver beam-
forming/nulling minimizes signals from neighbors other
than the intended transmitter. Interference reduction is of
particular interest to vehicular scenarios due to its key
role in complementing spatial multiplexing and diversity,
to optimize the performance of MIMO in interference-
limited dense multi-user settings.
III. WHY MIMO FOR VANETS?
In this section, we discuss the potential benefits that the
MIMO technology could bring to not only meet major
challenges but also exploit opportunities in the, rather
complex, V2V scenarios and applications. MIMO brings
about the following key benefits to VANETs:
MIMO versatility best matches diverse applications
and scenarios: The versatility of the MIMO technology
renders it a key enabler for V2V communications. This
versatility is manifested in the ability to configure the
multiple antenna array in multiple modes, depending
on the interference intense (dense vs. sparse network
scenarios), surrounding propagation environment (e.g.,
scattering-richness) and most importantly the vehicular
application of interest, in order to meet stringent safety
requirements and acceptable user experience for info-
tainment applications. For instance, spatial multiplexing
would best suite high data rate applications, e.g., media
streaming. On the other hand, diversity schemes are best
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
501
© 2012 ACADEMY PUBLISHER
for safety applications mandating reliable communica-
tions for short warning messages. In addition, transmit
beamforming techniques can be used to focus the trans-
mitted signal spatially, hence, extending the range of
communication significantly for the same transmit power.
This can be useful especially in highway and rural areas
where the density of the vehicles may be relatively low.
MIMO best exploits the highly dynamic V2V channel:
the V2V channel is highly dynamic due to the multi-
path fading experienced in scattering-rich environment,
e.g., urban and metropolitan areas. In the MIMO context,
intense multi-path fading is translated to channel matrices
with rank greater than one. This, in turn, creates the
opportunity for MIMO to reap, or at least, approach the
theoretical diversity and multiplexing gains characterized
in the literature, with the aid of novel pre-coding, space-
time signal processing and decoding schemes, e.g., V-
BLAST and space-time coding.
Broadband: MIMO VANETs constitute a natural ex-
tension and key part of the Mobile Broadband vision.
The broadband support of MIMO brings about an ample
opportunity to introduce bandwidth hungry applications,
e.g., multimedia streaming, to the VANET arena. It is
projected that by 2015, 68.5% of the Internet traffic will
be generated by mobile video. This class of applications
may not only be for safety use (e.g., law enforcement and
first responders) but would also open a unique opportunity
for the vibrant automotive community to introduce value-
added, media-centric, infotainment applications and ser-
vices. It is evident that single input single output radios,
e.g., radios based on the IEEE 802.11p DSRC standard
[13], will not be able to support high definition video
(HDV) with 20 Mbps requirement per stream or HD
IPTV with 12-15 Mbps per stream, due to the theoretical,
interference-free data rate limit of 27 Mbps specified by
the IEEE 802.11p standard. The high data rate ( 100
Mbps) supported by MIMO could also be leveraged
for minimizing the transmission delays of short, urgent
warning messages to levels acceptable to the requirements
of safety applications (≤ 100 msec).
Reliable Communications: reliable communications is
a strict mandate for safety applications in order to save
lives and avoid crashes on the road. MIMO technology
seamlessly lends itself to reliable communications due to
its inherent ”diversity” benefits manifested through well-
known signal processing and pre-coding techniques at
the transmitter side, namely beamforming and space-time
coding (STC).
Finally, vehicular communications opens up a unique
opportunity to introduce the MIMO technology to mov-
ing platforms primarily due to the relatively relaxed
constraints with respect to the antenna form factor and
energy consumption as opposed to resource-constrained
platforms, e.g., mobile and smart phones. This is further
supported by the recent witnessed advances in conformal
antenna arrays [14].
IV. MIMO RESEARCH CHALLENGES IN VANETS
In this section, we present a number of key research
challenges facing MIMO VANETs. These challenges are
largely technical and partly business related, e.g., cost
impact. This is driven by the technology maturity in the
areas of antennas, RF front-end (amplifiers and filters)
and baseband processing which is the least in terms of
cost.
The IEEE 802.11n standard constitutes a first attempt in
the IEEE 802.11 standards community to bring the MIMO
technology, particularly 2×2 MIMO, to the WiFi market.
However, the IEEE 802.11n is not intended for highly
dynamic wireless channels encountered in V2V scenarios.
It is essentially, like other standards in the IEEE 802.11x
family, targeted towards relatively static in-door/out-door
environment with portability as opposed to mobility.
In the following, we will review some of the recent
advances in the application of MIMO techniques to ve-
hicular scenarios.
A. Channel Modelling
Vehicular channels experience high relative velocities
between the transmitter and the receiver in addition to
a dynamic ambient environment. This results in a rich
multipath fading environment in which the rapid mo-
tion of scatterers leads to continuous variation in the
Power Delay Profile (PDP) of these multipaths. Classical
statistical channel models typically use the Wide Sense
Stationary Uncorrelated Scattering (WSSUS) assumption
[15]. However, for V2V channels, this assumption is
not valid for prolonged time intervals. In fact, V2V
channels are statistically nonstationary because of the
physical environment dynamics. The reasons behind that
are mainly due to the motion of the transmitter, receiver,
and significant reflectors/scatterers. For example, the pres-
ence of a large truck on the side of the transmitter or
receiver can contribute to a multipath component for
a generally short duration (until the vehicle passes the
truck). In addition, the antennas for the transmitter and
receiver are at relatively low elevations, and hence, over
moderate spatial scales, reflectors/scatterers will “appear
and disappear” [16]. There are two approaches for han-
dling the nonstationary nature of practical V2V channels.
The first approach is based on the concept of a local
scattering function, developed in [17] to estimate the time
interval over which the WSSUS assumption is valid. The
second approach is based on modelling the channel as a
tapped delay line with the tap amplitudes following some
probabilistic distribution and modulated by a birth/death
(on/off) process [18]. The on/off process for each tap
was modelled by a first-order Markov chain with a
prespecified state transition matrix in [19].
Due to these unique features, V2V channels do not lend
themselves easily to standard channel models, e.g., [20],
[21]. Instead, statistical models are needed to represent
the time varying nature of the Channel Impulse Response
(CIR) that can be used to find the channel parameters
[22]. The most basic parameters are the delay and Doppler
502
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
spreads whose reciprocals are used to find the coherence
bandwidth and coherence time of the channel, respec-
tively. Knowledge of these parameters is vital for the
optimal design of the physical layer. An overview of
statistical channel models for V2V cooperative communi-
cation systems can be found in [23]. An alternate method
for characterizing the Doppler spread and coherence band-
width of V2V channels was proposed by Lin Cheng from
Carnegie Mellon University and Fan Bai from GM and
others in [24]. In this work, measurements of the received
signal strength were performed and the collected data was
used to characterize the path loss and the fading properties
of V2V channels. The authors also introduced the speed
separation diagram; a novel tool for understanding and
predicting the properties of V2V channels.
Statistical channel models cannot be developed in iso-
lation of measurements. Measurements of V2V channels
have been the focus of recent research efforts [24]–[28].
The measured parameters include the PDP which is used
to characterize the multipath nature of the channel, the
Doppler shift and the Doppler spread of the channel in
relation to the relative velocity between the transmitter
and the receiver as in [29], as well as the path loss
factor which determines the degradation of the signal
level as a function of distance. For MIMO channels,
the spatial correlation between different antennas at the
transmitter and receiver can be used to verify the as-
sumptions made concerning the statistical properties of
the angle-of-departure and/or angle-of-arrival of different
time-differentiable paths at the transmitter and/or receiver.
Several measurement campaigns for SISO inter-
vehicular channels have been reported in the literature.
An overview of some existing V2V channel measurement
campaigns in a variety of important settings, and the
channel characteristics such as delay spreads and Doppler
spreads can be found in [30]. In [24], the authors utilize
a channel characterization platform (detailed in [25])
which comprises an accurate synchronization and position
location system to study the large scale path loss models
at 5.9 GHz. It is found that the fading statistics change
from near-Rician to Rayleigh as the vehicle separation
increases. Furthermore, [24] provides analysis of Doppler
spread and coherence time and their dependence on
both velocity and vehicle separation. The same authors
use an extension of this measurement platform in [26]
to relate measured wideband channel parameters to the
parameters of the time scaled IEEE 802.11a waveforms
being proposed for the IEEE 802.11p WAVE standard
[31]. It is shown that the original waveform of 20 MHz
bandwidth would not be suitable due to inadequacy of
the guard interval. On the other hand, if the same packet
length is preserved, a 5 MHz packet would take longer
transmission time than the coherence bandwidth. They
conclude that a 10 MHz scaled version is the most
suitable for WAVE applications. In [28], the authors report
on measurements taken in various LOS and non-LOS
conditions with a test signal of bandwidth approximately
11 MHz and centered at 5.860 GHz. Several environments
were considered; a controlled uncluttered environment
with few multipath sources resembling a rural area, an
urban environment with several high rise buildings, and
a highway environment with various traffic conditions.
Average values of the delay and Doppler spreads were
measured and compared with the proposed physical layer
parameters of the IEEE 802.11p. It is found that channel
invariance cannot be assumed for large packets (in excess
of 367 bytes).
For a MIMO system with M
T
transmit antennas and
M
R
receive antennas, a total number of M
T
M
R
channels
have to be measured [32]. There are two multiplexing
techniques for measuring these channels. The first is based
on time-division multiplexing (TDM) where, at any time
instant, only one antenna is used at the transmitter and one
antenna is used at the receiver. Switching between dif-
ferent antennas is performed through electronic switches
[33]. An example of a commercial channel sounding
system that uses TDM is the RUSK channel sounder
[34]. The second technique is based on frequency division
multiplexing (FDM). The system of Takada et al. is an
example of using such technique to distinguish between
simultaneously transmitting antenna elements [35]. In
both techniques, the multiplexing parameters (channel
switching rate in TDM and frequency separation of tones
in FDM) have to be carefully designed to account for
the high Doppler shifts encountered in inter-vehicular
channels.
Several Measurement campaigns for MIMO vehicular
channels were also recently reported in the literature. In
[36], an overview of a V2V radio channel measurement
campaign at 5.6 GHz was presented using the RUSK
channel sounder. The transmitter and receiver were com-
posed of a 4-element uniform linear array with half
wavelength spacing. The measurement campaign focused
on some scenarios that are important for safety-related
ITS applications, e.g., road crossings and merge lanes, and
the power-delay profile and Doppler spectral density were
presented. In [37], a channel-sounding campaign was
conducted for V2V channels between vehicles travelling
along surface streets and expressways in a metropolitan
area. 4-element uniform linear arrays were also employed
at the transmitter and receiver and were mounted on the
rooftops of the vehicles. The measurement campaigns
were used to validate the 3-D geometrical concentric-
cylinders model proposed in [38].
More measurement campaigns for MIMO vehicular
channels are needed to obtain larger number of sample
sets for diverse propagation environment and settings
in order to increase the statistical significance of the
developed channel models. These measurements will also
be very helpful in characterizing the impact of trucks or
other shadowing objects on V2V channels, analyzing the
directional characteristics of these channels, and experi-
mentally investigating the impact of the antenna mounting
position on the performance of vehicular wireless com-
munication systems.
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
503
© 2012 ACADEMY PUBLISHER
B. Channel Estimation
Accurate acquisition of channel state information (CSI)
is essential for reaping the advantages of the presence of
multiple antennas in the communication system. Chan-
nel estimation algorithms can be classified into three
categories; training-based, blind, and semi-blind algo-
rithms. For time-varying channels, training-based schemes
require the frequent transmission of training sequences
which can result in wasting the system resources [39]. On
the other hand, blind channel estimation techniques rely
on the statistical properties of the information sequences
to estimate the channel coefficients. However, they are in
general computationally expensive and suffer from low
convergence speed [40]. Semi-blind channel estimation
techniques strike a balance between computational com-
plexity and consuming the system resources.
The IEEE 802.11p frame contains two types of pilots;
block pilot symbols occupying all the 52 subcarriers
of the first 2 OFDM symbols and comb pilot symbols
transmitted on 4 subcarriers in the remaining OFDM
symbols of the frame [41]. Due to the high Doppler shift
and nonstationarity experienced in several V2V commu-
nication scenarios, the amount of intercarrier interference
within each OFDM symbols is significantly higher than
that occurring in wireless networks with limited mobility,
e.g., WLAN. As a result, simple low-complexity channel
estimation algorithms such as least squares do not yield
acceptable performance [41]. Furthermore, in situations
of poor line-of-sight contribution, an acceptable frame
error rate is not achievable even at high signal-to-noise
ratios. Therefore, more complex channel estimation and
equalization techniques based on the current standard pilot
pattern have to be developed to cope with the properties
of the vehicular radio channel.
The channel estimation problem is more pronounced
for MIMO channels where the channels from every
transmit to every receive antenna have to be esti-
mated simultaneously. With OFDM as the underlying
physical layer transmission strategy, the MIMO-OFDM
channel estimation is converted into a two-dimensional
(space/frequency) estimation problem [42]. However, di-
rect application of the two-dimensional filtering algo-
rithms to MIMO channel estimation is challenging due
to the complexity considerations. Furthermore, due to
the nonstationary nature of vehicular channels, recursive
channel estimation techniques are required that can track
the impulse response of the MIMO channel.
In addition to the above challenges, the OFDM-based
physical layer is inherently sensitive to errors in Carrier
Frequency Offset (CFO) estimation. This further compli-
cates the channel estimation problem as the CFO has to
be jointly estimated with the channel coefficients. The
estimation problem is further complicated in a MIMO
setting. Furthermore, when a virtual MIMO system is
formed from a cooperative scheme, which is expected
in a V2V environment, CFO estimation becomes further
complicated due to the noncoherent phase of different
carriers. For example, in [43], a similar problem is consid-
ered, where the MIMO system is formed by collaborating
base stations (as opposed to collaborating vehicles). A
training sequence based estimator is proposed as well as
suboptimal estimators which approach the Cram
´
er-Rao
lower bound at high SNRs. An OFDM specific scenario
was considered in [44] in which the optimal CFO com-
pensation is obtained by maximizing the average signal-
to-interference-and-noise ratio. In the asynchronous case;
when a time lag between the OFDM transmitters exists, a
receiver with joint equalization and synchronization was
proposed in [45].
C. Space-Time Signal Processing for V2V Channels
Successful implementation of safety of life applications
relies on meeting two types of constraints. First, those
mandated by the time critical nature of safety applications
at the application layer. This nature poses constraints on
the latency and reliability of packet delivery as well as the
rate of repetition of incident warnings. Such constraints
were studied in [46] as functions of various parameters
of V2V environments, e.g., mean vehicular velocity and
road grip coefficient. On the other hand, the focus in
this section is on a second type of constraints posed
by the physical layer. These constraints result from the
unique nature of MIMO-V2V mobile channels, explained
in previous sections. This unique nature, and the desire
to exploit MIMO channel benefits motivate the use of
space-time and space-frequency processing to improve the
reliability of the physical layer transmission strategy.
MIMO vehicular systems have salient characteristics.
First, an LOS component may exist between the transmit-
ter and the receiver, specially in highway low scattering
environments, in which case the MIMO channels cannot
be considered independent and may experience significant
loss of capacity. A similar scenario was recently studied
in [47] in the context of fixed MIMO channels and it
was found that the use of a “repeater” may help restore
lost capacity. This can be extended for a MIMO-V2V
broadcast system, where the repeater may be replaced by a
cooperative vehicle. Second, the antenna spacing, and the
angle of arrivals of the multiple element antenna system
at the mobile unit may result in correlated channels.
Several techniques exist for combating the effects of such
correlation. Proper design of space-time (ST) codes for
correlated channels was introduced in [48]. More recent
contributions to this technique, include finite signal-to-
noise ratio (SNR) designs over correlated Rician channels
[49]. Moreover, it is possible to use multiple antennas for
interference cancellation using adaptive array processing
and selection combining as in [50].
An ST code used in a MIMO-V2V system must be
capable of 1) achieving superior performance at relatively
low SNR, 2) having relatively reduced complexity, and
3) suiting cooperative scenarios to allow vehicles to relay
safety of life messages without requiring an infrastruc-
ture. There has been growing interest in lattice codes as
candidates for such codes. In [51], the authors present a
receiver design for a class of lattice codes, which uses an
504
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
MMSE-DFE preprocessing stage and then performs joint
detection and decoding (only linear Gaussian channels are
considered). In [52], such codes were used in a cooper-
ative scenario employing dynamic decode and forward.
More recently in [53], ST codes based on lattice coset
coding were constructed for the short block-length case.
In [54], a low complexity linear receiver was proposed
for fading channels. It would be of interest to find
low complexity practical implementations of the above
information theoretic receivers which can increase the
reliability of the MIMO-V2V system, and still facilitate
possible cooperation between users.
D. Cross-layer Optimization
MIMO networking, in general, is inherently a cross-
layer optimization problem in order to fully reap the ben-
efits of such powerful physical layer technology at higher
layers of the stack, namely MAC, network and above.
Thus, MIMO networking is predominantly a bottom-up
paradigm whereby the MIMO technology is exploited at
the higher layers in order to best exploit the channel
dynamics (e.g., scattering richness) and/or minimizing
interference. Proposals have been recently introduced in
the literature on how to design MAC [55]–[57] and mobile
ad hoc networks (MANET) routing protocols [58] that
best leverage MIMO in network scenarios to amplify
its gains beyond merely the PHY layer gains. MIMO
vehicular networking is no exception, yet, it further ex-
pands the cross-layer scope to span higher layers, namely
emerging automotive applications. Furthermore, VANETs
are highly driven by the emerging applications ranging
from safety, convenience to infotainment [4]. This, in
turn, brings a top-down paradigm to the vehicular net-
working problem where the application of interest adapts
and optimizes the underlying networking stack, including
the MIMO PHY, to satisfy its QoS requirements and
communication needs.
The interaction of these two paradigms with, possibly
conflicting adaptation decisions, at intermediate layers of
the networking stack gives rise to interesting research
problems that have not been explored before in the MIMO
networking literature. We touch upon few representative
research challenges in the next few bullets:
Networking stack optimized for the highly dynamic
MIMO V2V channel: this is directly related to the bottom-
up paradigm where the VANETworking stack attempts
to exploit the opportunities and mitigate the challenges
caused by the dynamic wireless channel. The objective
is to develop link/MAC protocols that decide the optimal
MIMO mode based on diverse information fed by the
PHY (e.g., scattering richness, i.e. rank of the MIMO
channel and interference intense) as well as vehicle
sensors (e.g., speed, acceleration) reflecting the density
of vehicles on the road. Accordingly, this decision en-
tails related cross-layer decisions at the link layer, e.g.,
the desired strength of Forward Error Correction (FEC)
schemes which would be highest in case of the least robust
MIMO scheme, namely Spatial Multiplexing (SM). From
the MAC perspective, the adopted interference/collision
avoidance mechanisms would highly depend on whether
the adopted MIMO mode emits directional (beam form-
ing) or omni-directional (spatial multiplexing and diver-
sity) transmissions. At the network layer, the objective
would be to develop novel MIMO-aware routing metrics
so that interference hot spots can be avoided, via extend-
ing the concept of interference-aware routing (IAR) [59]
to MIMO V2V networks, and scattering opportunities can
be leveraged. In addition, MIMO could play a funda-
mental role in controlling the topology of the vehicular
network to avoid network disconnect as will be discussed
later.
MIMO serving automotive application needs: this is
directly related to the aforementioned top-down paradigm
whereby the V2V application adapts the MIMO signal
processing mode (i.e. diversity, multiplexing or beam
forming) and the associated link and network layer pro-
tocols to best fit the application QoS needs. For instance,
in case of a safety application with a warning message
targeted towards only rear vehicles in the same lane (e.g.,
Forward Collision Warning (FCW)), then there is no need
for broadcasting this message omni-directionally, wasting
RF energy and causing unnecessary interference, with
the possibility of directing it towards the intended recipi-
ents only using beam forming techniques. Furthermore,
diversity schemes (e.g., space-time coding) find ample
room to achieve reliable communications essential in
safety applications. In essence, the application of interest
mandates the ”optimal” MIMO mode to serve its needs
which, in turn, adapts the link and network layer protocols
accordingly.
Stabilizing the highly dynamic VANET topology: be-
yond the multiplexing and diversity gains, MIMO could
play a key role in enhancing the stability of the VANET
topology via exploiting the range extension capabilities
of beam forming for the same power and bandwidth
resources. This is of particular importance in sparse
network scenarios where the risk of a network disconnect
is imminent. The one-dimensional nature of vehicular
network topologies, especially on straight segments of
highways, renders beam forming much easier and of
practical relevance due to the negligible amount of beam
steering and receiver tracking required to preserve net-
work connectedness.
V. PRELIMINARY RESEARCH RESULT S
In this section, we present some of the preliminary
results we have obtained in various areas related to
MIMO-V2V communication.
A. Channel Modelling
Due to the nonstationary nature of MIMO-V2V chan-
nels and the low elevation angle of the transmitter and
receiver, more accurate models are required that capture
the spatial and temporal characteristics of these channels.
In this section, we present a novel model for wideband
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
505
© 2012 ACADEMY PUBLISHER
MIMO-V2V channels that is derived using the geometri-
cal elliptical scattering approach [60]. However in [60],
scattering is assumed to occur uniformly on an ellipse.
This assumption does not suit the V2V scenario where
the low elevation of antennas precludes a such a rich
scattering assumption. Here, we associate a persistence
process with each physical scatterer in the environment.
The persistence process models the existence and absence
of the physical scatterers, and hence, we can capture
the dynamics of the scatterers in V2V channels. Note
that, in general, each time-differentiable path (TDP) is
due to the contribution of multiple scatterers and not
a single one. Hence, the proposed model is a more
accurate representation of the vehicular environment than
the approach in [19] which modulates the tap coefficient
representing a TDP by a birth/death process [19].
Let us consider a transmitter and a receiver equipped
with multiple antennas each where the number of transmit
(receive) antennas is given by M
T
(M
R
). The angle of
the direction of the relative velocity between the receiver
and transmitter is denoted by α
v
. The proposed wideband
MIMO-V2V channel model is derived from the geometric
multi-elliptical scattering model shown in Fig. 1. In this
model, we assume that the centers of the transmit and
receive arrays are located on the two foci of M ellipses.
The distance between the two focal points is 2f. The
major axis half-length and minor axis half-length of the
mth ellipse are denoted by a
m
and b
m
, respectively. The
scatterers (vehicles, trees, buildings, etc.) that contribute
to the same TDP lie on the same ellipse, where each
ellipse corresponds to a different time-differentiable path
(TDP) (delay bin). The mth ellipse contains N
(m)
c
slots
where each slot contains a scatterer. The scattering from
the mth ellipse contributes to the mth channel coefficient
whose delay is τ
m
= mT
s
where T
s
is the sampling
interval of the baseband-equivalent transmitted signal. The
scattering slots are distributed along the ellipses and their
number and distribution depend on the physical and vehic-
ular environment, e.g., terrain type, density of vehicles on
the road, etc. We use φ
(m)
T
and φ
(m)
R
to respectively denote
the angles of departure and arrival of a ray travelling from
the transmitter to the receiver via a scatterer located on
the mth ellipse. The angle φ
(m)
T
(φ
(m)
R
) is measured from
the center of the transmit (receive) array. Note that the
model can be simply extended to include a line-of-sight
component that contributes to the first TDP.
We use a multiplicative birth/death process, z
n,m
[q], to
account for the appearance and disappearance of scatterers
in each ellipse. However, we do not account for the drift of
scatterers into a different delay bin. The process models
the persistence of the scatterer where z
n,m
[q] = 1 if a
scatterer is present in the nth slot of the mth ellipse at the
qth time instant and z
n,m
[q] = 0 otherwise. We model
z
n,m
[q] using a first-order Markov model that assumes
that the presence of the scatterer in the slot depends
only on the current state and not on how long the slot
was occupied. The state transition probabilities of the
Markov chain reflect the degree of nonstationarity of the
Figure 1: Geometric elliptical scattering model for an
M
T
× M
R
MIMO channel with local scattering clusters
lying on the ellipse.
environment. For example, as the velocity of the vehicles
increases, the probability that the Markov chain will make
a transition from 0 to 1 or from 1 to 0 will increase since
the scatterers will appear and disappear more frequently.
We can write the probability transition matrix of this
Markov chain as
Λ
(n,m)
=
1 − λ
(n,m)
01
λ
(n,m)
01
λ
(n,m)
10
1 − λ
(n,m)
10
!
(1)
where the i, jth entry of the matrix Λ
(n,m)
denotes the
probability that the Markov chain will make a transition
to state j − 1 given that it is in state i − 1. Let π
(n,m)
i
denote the steady state probability that the Markov chain
will be in state i. Hence, π
(n,m)
0
+π
(n,m)
1
= 1, and π
(n,m)
1
can be written as
π
(n,m)
1
=
λ
(n,m)
01
λ
(n,m)
10
+ λ
(n,m)
01
(2)
The ratio λ
(n,m)
01
/λ
(n,m)
10
determines the ratio between the
long-run proportion of time that the scatterers will be
present in the scattering slots to that in which they will
be absent. For example, in a dense urban environment
where vehicles move regularly and rarely change their
relative position with respect to each other, the ratio
λ
(n,m)
01
/λ
(n,m)
10
will be relatively high.
Let h
kl
[p, q] denote the discrete-time baseband-
equivalent impulse response of the channel between the
lth transmit antenna and the kth receive antenna where
the index p is the delay index and q is the time index,
i.e.,
h
kl
[p, q] =
M
X
m=0
h
(m)
kl
[q]δ(p − m) (3)
where δ(p − m) is the Dirac delta function. According
to the geometrical elliptical scattering model, the mth
TDP is due to the contribution of the scatterers on the
mth ellipse. Therefore, the mth channel coefficient can
506
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
Figure 2: Temporal correlation function of the channel
versus time and delay
be written as
h
(m)
kl
[q] =
N
(m)
c
X
n=0
z
n,m
[q]
Z
Φ
(n,m)
R
g
(n,m)
kl
(φ
(m)
R
, q)dφ
(m)
R
(4)
where g
(n,m)
kl
(φ
(m)
R
, q) is the contribution of the ray
transmitted from the lth transmit antenna to kth receive
element and scattered via the nth scatterer slot in the
mth ellipse and received at an angle φ
(m)
R
at the receive
array. Note that the scattering slot extends over the receive
angular interval Φ
(n,m)
R
. We can write g
(n,m)
kl
(φ
(m)
R
, q) as
g
(n,m)
kl
(φ
(m)
R
, q) = E
n,m
(φ
(m)
R
) exp
jθ
n,m
#
φ
(m)
R
exp
−jK
0
D
(n,m)
kl
#
φ
(m)
R
+ j2πF
D
#
φ
(m)
R
qT
s
(5)
where E
n,m
(φ
(m)
R
) is the amplitude density function of
the scattered wave from the nth scattering slot in the mth
ellipse with respect to the angle φ
(m)
R
, θ
n,m
#
φ
(m)
R
is a
random phase shift due to the scattering process, K
0
=
2π/λ where λ is the wavelength of the RF propagating
signal, and F
D
#
φ
(m)
R
is defined as
F
D
#
φ
(m)
R
= f
D
cos
#
φ
(m)
R
− α
v
(6)
where f
D
is the maximum Doppler frequency. In (5),
D
(n,m)
kl
(φ
(m)
R
) is the total distance travelled by a ray
emitted from lth transmit element to the kth receive
element via a scatterer in the nth slot in the mth ellipse
and received at an angle φ
(m)
R
.
In order to simplify the derivation of the correlation
functions of the channel coefficients, we assume that 1)
The channel coefficients that account for different TDPs
are uncorrelated as they result from interactions from
scatterers on different ellipses. 2) Scattering from different
slots in the same ellipse is uncorrelated as each slot
corresponds to a distinct scatterer. 3) E
n,m
#
φ
(m)
R
=
E
n,m
is independent of φ
(m)
R
, i.e., the scatterers have
a uniform radar cross section. 4) The scattering phase
angles θ
n,m
#
φ
(m)
R
are independent for different n, m,
and φ
(m)
R
and independent of the process z
n,m
[q]. They
are modelled as random variables uniformly distributed
between 0 and 2π.
Using the above assumptions and after some mathemat-
ical manipulations, we can write the temporal correlation
of the mth channel coefficient as
r
(m)
[p, q]=
N
(m)
c
X
n=0
|E
n,m
|
2
Λ
(n,m)
p
(2,2)
π
(n,m)
1
[q]I(Φ
(n,m)
R
, p).
(7)
where Λ
(n,m)
p
(i,j)
is the i, jth entry of the p-step state
transition matrix Λ
(n,m)
p
and the integration I(Φ
(n,m)
R
, p)
is given by
I(Φ
(n,m)
R
, p) =
Z
Φ
(n,m)
R
e
j2πF
(m)
D
pT
s
dφ
(m)
R
. (8)
The resulting expression for the temporal correlation in
(7) does not depend on the index of the transmit or receive
antenna and indicates that the channel coefficients are
non-stationary as it depends on the index q through the
process z
n,m
[q].
Next, we evaluate the spatial correlation function as
a function of the geometry of the transmit array T
and receive array R. Let us consider the mth channel
coefficient of two channels at the time instant q; the first
from transmit antenna k to receive antenna l, and the
second from transmit antenna k
′
to receive antenna l
′
. It
can be shown that the spatial correlation function of the
mth coefficient of these two channels is given by
r
(m)
kl,k
′
l
′
(T , R) =
N
(m)
c
X
n=0
|E
n,m
|
2
π
(n,m)
1
Z
Φ
(n,m)
R
C
(n,m)
ll
′
(T ) K
(n,m)
kk
′
(R) dφ
(m)
R
(9)
where the above integration can be evaluated numerically
using the relationship between φ
(m)
T
and φ
(m)
R
and
C
(n,m)
ll
′
(T ) = e
jK
0
d
T
l
cos(φ
(m)
T
−α
T
l
)−d
T
′
l
cos(φ
(m)
T
−α
T
′
l
)
K
(n,m)
kk
′
(R) = e
jK
0
d
R
k
cos(φ
(m)
R
−α
R
k
)−d
R
′
k
cos(φ
(m)
R
−α
R
′
k
)
where d
T
l
(d
R
k
) is the distance between the lth element of
the transmit array (kth element of the receive array) and
the center of the transmit (receive) array, and α
T
l
(α
R
k
)
is the inclination angle of the lth transmit element (kth
receive element).
We will present a numerical example to illustrate the
temporal and spatial correlation characteristics of the
proposed model. We consider a vehicular channel at
f
c
= 5.85 GHz with bandwidth 10 MHz, and hence, the
sampling frequency of the CIR is T
s
= 100 nsec. The
relative speed between the two vehicles is 100 km/hr and
the inclination angle of the velocity vector is α
v
= 0.
We consider only one TDP corresponding to one ellipse.
We set the lengths of the major and minor axes as 20
and 12, respectively. We assume that there are N
c
= 2
scattering slots that extend over the angular intervals
Φ
(1)
R
= [98
◦
, 108
◦
] and Φ
(2)
R
= [110
◦
, 235
◦
]. The tran-
sition probabilities of the Markov chain associated with
the two scattering slots are identical and its parameters
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
507
© 2012 ACADEMY PUBLISHER
Figure 3: Spatial correlation between channel coefficients
h
11
and h
22
are given by λ
01
= 0.005 and λ
10
= 0.001. Note that the
selected transition probabilities indicate that the scatterers
exist in each slot for 83.33% of the time. The parameters
of the simulation correspond to a highway environment
with high mobility. Fig. 2 shows the temporal correlation
of the channel coefficient versus the delay τ = pT
s
and
the time t = qT
s
. The initial probabilities π
(n)
1
[q] were
selected as 0.5 for n = 1, 2. From the figure we can
see that the channel coefficient is not stationary as the
temporal correlation is a function of time.
Next, we investigate the spatial correlation characteris-
tics of the channel model. We consider a uniform linear
transmit and receive array equipped with M
T
= M
R
= 8
elements each and their tilt angles are given by α
T
=
α
R
= 0. Fig. 3 shows the spatial correlation function
between two channel coefficients h
11
(t, τ ) and h
22
(t, τ )
versus the normalized transmit antenna elements spacing
δ
T
/λ and the normalized receive antenna elements spac-
ing δ
R
/λ. We can see from this figure that the channel
coefficients become almost uncorrelated when the spacing
between the antenna elements of the transmit and receive
arrays is in the order of 3λ. Hence, spatial diversity gains
can be harvested with an antenna array with inter-element
spacing in the order of 15 cm that can be easily mounted
on vehicles and roadside units.
Next, we evaluate the joint direction-of-
departure/direction-of-arrival (DoD/DoA) angular
power spectrum (APS) of the channel coefficients
generated according to the model. We consider the same
parameters used in the last example except that we
have a horizontal circular arrays at the transmitter and
receiver sides whose elements are separated by 0. 75λ.
The APS is calculated using the Capon beamformer
from the sample spatial covariance matrix of the
generated channel coefficients [61]. Fig. 4 shows the
APS of the MIMO channel. We can see from this
figure that the highest values of the APS are in the
angular directions of the two scattering slots where the
first slot extends over the DODs Φ
(1)
T
= [14.5
◦
, 17.5
◦
]
and DOAs Φ
(1)
R
= [98
◦
, 108
◦
] and the second slot
Receive Azimuth angle (degrees)
Transmit Azimuth angle (degrees)
0 50 100 150 200 250 300 350
0
50
100
150
200
250
300
350
−340
−320
−300
−280
−260
−240
−220
Figure 4: Angular power spectra of the simulated MIMO
channel.
d
0
d
1
d
3
d
2
d
4
d
5
· · · · · ·
−d
∗
3
· · · · · ·
· · · · · ·
· · · · · ·
· · · · · ·
· · · · · ·
−d
∗
5
d
∗
4
d
∗
2
d
∗
0
−d
∗
1
−d
∗
N−1
−d
∗
N−3
d
∗
N−4
d
∗
N−2
Codeword
L Subcarriers
OFDM subcarriers
T
X2
T
X1
Figure 5: Alamouti SFBC with separation across sub-carriers.
extends over the DODs Φ
(2)
T
= [18
◦
, 336
◦
] and DOAs
Φ
(2)
R
= [110
◦
, 235
◦
].
B. Space-frequency Block Coding
Using multiple antennas at the transmitter, spatial di-
versity techniques such as space-frequency block coding
(SFBC) can be exploited to improve the communication
reliability. However, conventional space-frequency coding
techniques are not directly applicable to MIMO-V2V
channels due to the doubly selective nature of these
channels. In this section, we present a novel SFBC-OFDM
design technique for doubly-selective channels.
We consider an SFBC-OFDM system employing the
Alamouti coding scheme with two transmit antennas and
a single receive antenna. Let the N × 1 vector d =
[d
0
, d
1
, . . . , d
N−1
]
T
denote the baseband modulated data
symbols where N is the number of sub-carriers per
OFDM symbol, and (·)
T
and (·)
H
denote the transpose
and Hermitian transpose operators, respectively. The base-
band modulated symbols d are Alamouti-coded across
the two antennas using the OFDM sub-carriers instead of
time slots. The two components of the Alamouti codeword
are separated by L sub-carriers as shown in Fig. 5. The
separation is selected to be smaller than the coherence
bandwidth of the channel to guarantee that the channel is
almost constant across the codeword components [62].
The output of the SFBC encoder is the two N × 1
vectors X
1
and X
2
that represent the frequency-domain
OFDM symbols transmitted from each antenna. Note
that the two OFDM symbols X
1
and X
2
contain N/2
Alamouti codewords as shown in Fig. 5. The two symbols
are then converted to the time-domain symbols x
1
and
508
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
Figure 6: Banded structure of the equivalent channel matrices
G
(1)
and G
(2)
after applying the window.
x
2
using an N-point IFFT operation. The cyclic prefix
is then added with length equal to or larger than the
length of the channel impulse response to prevent inter-
symbol interference. The received symbol after removing
the cyclic prefix is given by
y = H
(1)
x
1
+ H
(2)
x
2
+ u (10)
where H
(i)
is the ith time-domain channel matrix be-
tween the ith transmit antenna and the receiver and u is
the time-domain noise vector. The noise is white circular
Gaussian with zero mean and covariance σ
2
I
N
.
At the receiver, we propose the use of windowing by
multiplying the time-domain received signal vector y by
the N × N diagonal matrix W . As a result, the received
frequency-domain vector becomes
Y = G
(1)
X
1
+ G
(2)
X
2
+ U (11)
where the equivalent frequency-domain channel matrix
G
(i)
is given by G
(i)
= F W H
(i)
F
H
, the N × N
matrix F is the unitary discrete Fourier transform ma-
trix, and the N × 1 vector U represents the equivalent
frequency-domain noise vector, i.e., U = F W u. The
applied window is designed to modify the conventional
frequency-domain channel matrices to have a structure as
shown in Fig. 6 where the entries in the unshaded region
have insignificant values and t is a design parameter that
controls the size of the shaded region. The elements of
G
(i)
that lie within the unshaded region in Fig. 6 are con-
sidered non-desired “interference” components whereas
the shaded entries can be considered as the “desired
signal” components.
The window is designed to maximize the signal to
interference ratio (SIR) which is defined as the ratio
between the energy contained in the desired signal compo-
nents, and that contained in the interference components.
After some mathematical manipulations, we can obtain
the N × 1 vector w that contains the diagonal elements
of the matrix W as the eigen vector associated with the
maximum eigen value of the matrix R
−
1
2
T
R
s
R
−
1
2
T
where
the N ×N diagonal matrix R
T
contains the main diagonal
of the matrix
P
2
i=1
H
(i)
H
(i)
H
on its main diagonal, and
(a) Structure of
˜
G
1
(b) Structure of
˜
G
2
, where each
shaded block is of size 1 × 2(t − 1)
Figure 7: Structure of the two matrices
˜
G
1
and
˜
G
2
.
the N × N matrix R
s
is given by
R
T
s
=
2
X
i=1
X
j∈S
t
diag{f
j
}(F
H
D
(i)
)(F
H
D
(i)
)
H
diag{f
∗
j
}
where f
k
is the kth column of F and diag{x} is a
diagonal matrix with the vector x on its main diagonal,
and D
(i)
is a rearrangement of G
(i)
c
= F H
(i)
F
H
defined
by
D
(i)
(m, n) = G
(i)
c
(< m + n − 2 >
N
, n),
where X(m, n) is the element in the mth row and nth
column of the matrix X, < · >
N
denotes the modulo-N
operator.
Decoding of the Alamouti code is performed by apply-
ing the complex conjugate operator, (·)
∗
, on the received
signal corresponding to the second component of the
codeword followed by maximal ratio combining. Hence,
we first re-arrange the frequency domain received vector
Y to make the two components of the same codeword
adjacent (reversing the separation that was done at the
transmitter) and apply the conjugate operation on the even
entries of the rearranged vector. The resulting N ×1 vector
˜
Y can be written as
˜
Y =
˜
G
1
d +
˜
G
2
d
∗
+
˜
U (12)
where the N × 1 vector
˜
U is the noise vector and each of
the two matrices
˜
G
1
and
˜
G
2
in (12) is a linear function
of G
(1)
, G
(2)
, G
(1)
∗
, and G
(2)
∗
. By virtue of the design
of the window, the two matrices
˜
G
1
and
˜
G
2
have the
structure shown in Fig. 7a and Fig. 7b, respectively. We
can see from Fig. 7a that the matrix
˜
G
1
is a block-
diagonal matrix, i.e., it consists of N/(2L) non-zero sub-
blocks on its main diagonal each of size 2L × 2L. In
contrast, we can see from Fig. 7b that the matrix
˜
G
2
has a
few number of significant elements. In particular,
˜
G
2
can
be considered as a horizontal concatenation of N/(2L)
sub-matrices each of dimensions N ×2L. Each sub-matrix
contains only four blocks of significant elements that exist
in the first and last 2(t − 1) columns only.
If the matrix
˜
G
2
did not contain any significant el-
ements, the block-diagonal structure of the matrix
˜
G
1
would allow the detection of each sub-symbol from the
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
509
© 2012 ACADEMY PUBLISHER
Figure 8: Structure of the transmitted data symbol d where
shaded symbols are pilots.
corresponding entries of the vector
˜
Y only. We propose
the structure in Fig. 8 for the OFDM data symbol d to
overcome the problems caused by the significant entries
of the matrix
˜
G
2
. According to this structure, the data
symbol is divided into N/(2L) sub-symbols. Each sub-
symbol contains 2L elements where the first and last
2(t − 1) elements are pilots. Therefore, the total number
of pilots within each OFDM symbol is given by 4(t −
1)N/ (2L). Note that these pilots can be used for example
for channel estimation. Recall that the interference caused
by the significant entries of
˜
G
2
arises from the first and
last 2(t − 1) elements of each sub-symbol only. Hence,
using the channel state information and the pilots, the self-
interference caused by the d
∗
in (12) can be subtracted
from the received symbol
˜
Y . Hence, using the inserted
guard/pilot tones at the beginning and end of each sub-
symbol and due to the structure of each diagonal block in
the matrix
˜
G
1
, a low-complexity DFE can be exploited to
decode the Alamouti codewords within each sub-symbol.
Finally, We evaluate the performance of the proposed
SFBC via numerical simulations. We consider an OFDM
system with N = 1024 subcarriers and 10 MHz band-
width employing an Alamouti SFBC. The data bits are
modulated using QPSK modulation. We consider an urban
channel with TU-06 delay profile defined by the COST
207 project where each discrete channel tap is generated
by an independent complex Gaussian random variable
with time correlation based on Jakes model. The sepa-
ration between the codeword components is selected as
L = 16 and the width of the desired signal region is
selected as t = 2. We compare the performance of the
proposed algorithm with that of the conventional SFBC
and the SFBC-OFDM with separation and 3-taps MMSE
equalizer proposed in [62]. The channel is time-varying
with normalized Doppler shift equal to 0.25 and perfect
channel knowledge is assumed for all algorithms. As a
lower bound on the BER performance, we also consider
a conventional SFBC-OFDM system operating in a static
channel (without Doppler). Fig. 9 shows the BER versus
the received signal to noise ratio (SNR). We can see from
this figure that the conventional SFBC fails completely
even at high SNR due to the relatively high Doppler shift.
We can also see that separating the codeword components
is not sufficient to overcome the ICI for this high value
of the Doppler shift. In contrast, the proposed scheme
preserves the diversity gain which is evident from the
similarity between the high SNR BER slopes for the
proposed scheme and the conventional receiver with no
Doppler.
Figure 9: BER curve for the proposed SFBC-OFDM
VI. CONCLUSION
The use of multiple antennas in vehicular communi-
cations brings several benefits that not only meet major
challenges but also exploit opportunities in the, rather
complex, inter-vehicular communication scenarios and
applications. These benefits include extending the range
of communication, increasing the data rate, providing
secure and reliable communication, and managing mul-
tiuser interference. In addition, the transmit and/or receive
arrays can be configured depending on the traffic density
(dense vs. sparse network scenarios), surrounding propa-
gation environment (e.g., rural vs. scattering-rich urban)
and most importantly the vehicular application of interest,
in order to meet stringent safety requirements and deliver
acceptable user experience for infotainment applications.
REFERENCES
[1] B. Chen and M. Gans, “MIMO communications in ad
hoc networks,” IEEE Transactions on Signal Processing,
vol. 54, pp. 2773–2783, July 2006.
[2] D. Gesbert, M. Kountouris, R. Heath, C.-B. Chae, and
T. Salzer, “Shifting the MIMO paradigm,” IEEE Signal
Processing Magazine, vol. 24, pp. 36–46, September 2007.
[3] J. Zhang, R. Chen, J. Andrews, A. Ghosh, and R. Heath,
“Networked MIMO with clustered linear precoding,” IEEE
Transactions on Wireless Communications, vol. 8, pp.
1910–1921, April 2009.
[4] F. Bai, T. ElBatt, G. Holland, H. Krishnan, and V. Sadekar,
“Towards characterizing and classifying communications-
based automotive applications from a wireless networking
perspective,” in Proceedings of 1st IEEE Workshop on
Automotive Networking and Applications, San Francisco,
USA, December 2006.
[5] G. Abdalla, M. Abu-Rgheff, and S.-M. Senouci, “A
channel update algorithm for VBLAST architecture in
VANET,” IEEE Vehicular Technology Magazine, vol. 4,
pp. 71–77, March 2009.
[6] P. W. Wolniansky, G. Foschini, G. Golden, and R. Valen-
zuela, “V-BLAST: An architecture for realizing very high
data rates over the rich-scattering wireless channel,” in
Proc. URSI International Symposium on Signals, Systems,
and Electronics, Sept. 1998.
510
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
[7] A. J. Paulraj, D. Gore, R. Nabar, , and H. Bolcskei,
“An overview of MIMO communications-A key to gigabit
wireless,” Proceedings of the IEEE, vol. 92, Feb. 2004.
[8] S. M. Alamouti, “A simple transmit diverstiy technique
for wireless communications,” IEEE Journal on Selected
Areas in Communications, vol. 16, Oct. 1998.
[9] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-
time codes for high data rate wireless communication:
Performance criterion and code construction,” IEEE Trans.
Inf. Theory, vol. 44, Mar. 1998.
[10] G. J. Foschini, “Layered space-time architecture for wire-
less communications in a fading environment when using
multiple antennas,” Bell Labs Technical Journal, vol. 1,
no. 2, 1996.
[11] L. Zheng and D. N. C. Tse, “Diversity and multiplexing: A
fundamental trade-off in multiple-antenna channels,” IEEE
Trans. Inf. Theory, vol. 43, May 2003.
[12] M. Grossglauser and D. Tse, “Mobility increases the
capacity of ad hoc wireless networks,” IEEE/ACM Trans-
actions on Networking, vol. 10, pp. 477–486, August 2002.
[13] Standard Specification for Telecommunications and Infor-
mation Exchange Between Roadside and Vehicle Systems
- 5 GHz Band Dedicated Short Range Communications
(DSRC) Medium Access Control (MAC) and Physical
Layer (PHY) Specifications, ASTM E2213-03, Sept. 2010.
[14] L. Josefsson and P. Persson, Conformal Array Antenna
Theory and Design. Wiley-IEEE Press, Feb. 2006.
[15] P. Bello, “Characterization of randomly time-variant linear
channels,” IEEE Trans. Commun. Syst., vol. 11, pp. 360 –
393, Dec. 1963.
[16] M. Schwartz, W. R. Bennett, and S. Stein, Communication
Systems and Techniques. New York: McGraw-Hill, 1996.
[17] G. Matz, “On non-WSSUS wireless fading channels,”
IEEE Trans. Wireless Commun., vol. 4, pp. 2465–2478,
2005.
[18] I. Sen and D. Matolak, “Vehicle channel models for the
5-GHz band,” IEEE Trans. Intell. Transp. Syst., vol. 9, pp.
235 –245, June 2008.
[19] D. W. Matolak, “Channel Modeling for Vehicle-To-Vehicle
Communications,” IEEE Commun. Mag., vol. 46, pp. 76–
83, May 2008.
[20] J. D. Parsons, The Mobile Radio Propagation Channel.
Wiley, 2000.
[21] J. Cavers, Mobile Channel Characteristics. Kluwer, 2000.
[22] P. Bello, “Characterization of randomly time-variant linear
channels,” IEEE Transactions on Communications Sys-
tems, vol. 11, pp. 360–393, December 1963.
[23] B. Talha and M. Patzold, “Channel models for mobile-to-
mobile cooperative communication systems: A state of the
art review,” IEEE Vehicular Technology Magazine, vol. 6,
pp. 33–43, June 2011.
[24] L. Cheng, B. Henty, D. D. Stancil, F. Bai, and P. Mu-
dalige, “Mobile Vehicle-to-Vehicle narrow-band chan-
nel measurement and characterization of the 5.9 GHz
dedicated short range communication (DSRC) frequency
band,” IEEE Journal on Selected Areas in Communica-
tions, vol. 25, pp. 1501–1516, October 2007.
[25] ——, “A fully mobile, GPS enabled, vehicle-to-vehicle
measurement platform for characterization of the 5.9 GHz
DSRC channel,” Proceedings of IEEE Antennas and Prop-
agation Society International Symposium, pp. 2005–2008,
June 2007.
[26] L. Cheng, B. Henty, R. Cooper, D. Standi, and F. Bai, “A
measurement study of time-scaled 802.11a waveforms over
the mobile-to-mobile vehicular channel at 5.9 GHz,” IEEE
Communications Magazine, vol. 46, no. 5, pp. 84–91, May
2008.
[27] I. Sen and D. Matolak, “Vehicle-Vehicle channel models
for the 5-GHz band,” IEEE Transactions on Intelligent
Transportation Systems, vol. 9, pp. 235–245, June 2008.
[28] I. Tan, W. Tang, K. Laberteaux, and A. Bahai, “Mea-
surement and analysis of wireless channel impairments in
DSRC vehicular communications,” Proceedings of IEEE
International Conference on Communications, pp. 4882–
4888, May 2008.
[29] L. Cheng, B. Henty, D. Stancil, and F. Bai, “Doppler com-
ponent analysis of the suburban vehicle-to-vehicle DSRC
propagation channel at 5.9 GHz,” Proceedings of IEEE
Radio and Wireless Symposium, pp. 343–346, January
2008.
[30] A. Molisch, F. Tufvesson, J. Karedal, and C. C. Meck-
lenbrauker, “A survey on vehicle-to-vehicle propagation
channels,” IEEE Wireless Communications, vol. 16, pp.
12–22, Dec. 2009.
[31] D. Jiang and L. Delgrossi, “IEEE 802.11p: Towards an
international standard for wireless access in vehicular
environments,” Proceedings of IEEE Vehicular Technology
Conference, pp. 2036–2040, May 2008.
[32] D. Laurenson and P. Grant, “A review of channel sounding
techniques,” in Proceedings of European Signal Processing
Conference, Florence, Italy, September 2006.
[33] B. T. Maharaj, L. P. Linde, J. W. Wallace, and
M. A. Jensen, “A cost-effective wideband MIMO channel
sounder and initial co-located 2.4 GHz and 5.2 GHz mea-
surements,” in Proceedings of IEEE International confer-
ence on Acoustics, Speech, and Signal Processing, vol. 3,
Philadelphia, USA, March 2005, pp. 981–984.
[34] U. Trautwein, C. Schneider, G. Sommerkorn,
D. Hampicke, R. Thom
¨
a, and W. Wirnitzer, “Measurement
data for propagation modeling and wireless system
evaluation,” in COST 273 meeting, Barcelona, Spain,
January 2003.
[35] J. Takada, K. Sakaguchi, and K. Araki, “Development of
high resolution MIMO channel sounder for the advanced
modeling of wireless channels,” in Proceedings of Asia-
Pacific Microwave Conference, Taipei, Taiwan, December
2001, pp. 563–568.
[36] A. Paier, L. Bernado, J. Karedal, O. Klemp, and
A. Kwoczek, “Overview of vehicle-to-vehicle radio chan-
nel measurements for collision avoidance applications,”
in Proceedings IEEE Vehicular Technology Conference,
Taipei, Taiwan, May 2010.
[37] A. G. Zajic, G. L. Stuber, T. G. Pratt, and S. Nguyen,
“Wideband MIMO mobile-to-mobile channels: Geometry-
based statistical modeling with experimental verification,”
IEEE Transactions on Vehicular Technology, vol. 58, pp.
517–534, Feb. 2009.
[38] A. G. Zajic and G. L. Stuber, “A three-dimensional
parametric model for wideband MIMO mobile-to-mobile
channels,” in Proc. IEEE GLOBECOM, Washington, DC,
Nov. 2007, pp. 3760–3764.
[39] Y. J. Li, L. X. Yang, and Z. Y. He, “MIMO channel
estimation using implicit training,” China Institue Journal
of Communications, vol. 27, pp. 1–5, May 2006.
[40] J. K. Tugnait, “Blind estimation and equalization of digital
communication FIR channels using cumulant matching,”
IEEE Transactions on Communications, vol. 43, pp. 1240–
1245, February 1995.
[41] L. Bernado, T. Zemen, N. Czink, and P. Belanovic,
“Physical layer simulation results for IEEE 802.11p using
vehicular non-stationary channel model,” in Proc. IEEE
Int. Conf. Commun, Cape Town, South Africa, May 2010.
[42] Z. Guo and W. Zhu, “2-D channel estimation for
OFDM/SDMA,” in Proc. IEEE World Wireless Congress,
San Francisco, CA, May 2002.
[43] B. Zarikoff and J. Cavers, “Multiple frequency offset
estimation for the downlink of coordinated MIMO sys-
tems,” IEEE Journal on Selected Areas in Communica-
tions, vol. 26, pp. 901–912, August 2008.
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
511
© 2012 ACADEMY PUBLISHER
[44] K. Deng, Y. Tang, S. Shao, and K. Sun, “Correction of
carrier frequency offsets in OFDM-based spatial multi-
plexing MIMO with distributed transmit antennas,” IEEE
Transactions on Vehicular Technology, vol. 58, no. 4, pp.
2072–2077, May 2009.
[45] T. Tang and R. Heath, “A SpaceTime receiver with joint
synchronization and interference cancellation in asyn-
chronous MIMO-OFDM systems,” IEEE Transactions on
Vehicular Technology, vol. 57, no. 5, pp. 2991–3005,
September 2008.
[46] S. Biswas, R. Tatchikou, and F. Dion, “Vehicle-to-vehicle
wireless communication protocols for enhancing highway
traffic safety,” IEEE Communications Magazine, vol. 44,
pp. 74–82, January 2006.
[47] B. Walkenhorst and M. Ingram, “Repeater-assisted capac-
ity enhancement (RACE) for MIMO links in a line-of-
sight environment,” in IEEE International Conference on
Communications, June 2009, pp. 1–6.
[48] S. Siwamogsatham and M. Fitz, “Robust space-time codes
for correlated Rayleigh fading channels,” IEEE Trans-
actions on Signal Processing, vol. 50, pp. 2408–2416,
October 2002.
[49] B. Clerckx and C. Oestges, “Finite-SNR performance
analysis of Space-Time coding in correlated Ricean MIMO
channels,” IEEE Transactions on Information Theory,
vol. 53, pp. 4761–4777, December 2007.
[50] T. Pratt, B. Walkenhorst, and H. Kang, “Analysis of
multiantenna architectures for non-los mobile-to-mobile
communications in cochannel interference,” IEEE Trans-
actions on Vehicular Technology, vol. 56, pp. 2168–2179,
July 2007.
[51] A. Murugan, H. El Gamal, M. Damen, and G. Caire, “A
unified framework for tree search decoding: Rediscovering
the sequential decoder,” IEEE Transactions on Information
Theory, vol. 52, pp. 933–953, March 2006.
[52] A. Murugan, K. Azarian, and H. El Gamal, “Cooperative
lattice coding and decoding in half-duplex channels,” IEEE
Journal on Selected Areas in Communications, vol. 25,
no. 2, pp. 268–279, February 2007.
[53] K. Kumar and G. Caire, “Space-Time codes from struc-
tured lattices,” IEEE Transactions on Information Theory,
vol. 55, pp. 547–556, February 2009.
[54] K. Kumar, G. Caire, and A. Moustakas, “Asymptotic
performance of linear receivers in MIMO fading channels,”
IEEE Transactions on Information Theory, vol. 55, pp.
4398–4418, October 2009.
[55] K. Sundaresan, R. Sivakumar, M. Ingram, and T.-Y. Chang,
“Medium access control in ad hoc networks with MIMO
links: Optimization considerations and algorithms,” IEEE
Transactions on Mobile Computing, vol. 3, pp. 350–365,
October 2004.
[56] M. Zorzi, J. Zeidler, A. Anderson, B. Rao, J. Proakis,
A. Swindlehurst, M. Jensen, and S. Krishnamurthy,
“Cross-layer issues in mac protocol design for MIMO ad
hoc networks,” IEEE Wireless Communications Magazine,
vol. 13, pp. 62–76, August 2006.
[57] T. ElBatt, “On the scheduling, multiplexing and diversity
trade-off in MIMO ad hoc networks: A unified frame-
work,,” ElSevier Ad hoc Networks, accepted.
[58] K. Sundaresan and R. Sivakumar, “Routing in ad-hoc net-
works with MIMO links,” in Proceedings of the 13th IEEE
International Conference on Network Protocols (ICNP),
Boston, USA, November 2005.
[59] T. ElBatt and T. Andersen, “A cross-layer framework for
multiple access and routing design in wireless multi-hop
networks,” Wiley Wireless Communications and Mobile
Computing Journal, vol. 11, pp. 1155–1167, August 2011.
[60] A. Chelli and M. Patzold, “A MIMO Mobile-to-Mobile
Channel Model Derived from a Geometric Street Scatter-
ing Model,” Proc. Int. Symp. Wireless Commun. Syst., pp.
792–797, Oct. 2007.
[61] M. Ozcelik, N. Czink, and E. Bonek, “What makes a good
MIMO channel model,” in Proc. Vehicul. Techn. Conf.,
June 2005, pp. 156–160.
[62] S. Lu, B. Narasimhan, and N. Al-Dhahir, “A novel SFBC-
OFDM scheme for doubly selective channels,” IEEE
Transactions on Vehicular Technology, vol. 58, no. 5, pp.
2573–2578, June 2009.
Ahmed Attia received the B.Sc.
degree with honors in Electrical En-
gineering from Alexandria Univer-
sity, Egypt in 2010. Since then, he
has been a research assistant at the
Wireless Intelligent Networks Cen-
ter (WINC), Nile University, Giza,
Egypt, where he is pursuing the
M.Sc. degree in Wireless Commu-
nications from the communications and information tech-
nology school. His research interests are in the fields of
information theory, channel coding, estimation and detec-
tion theory, with focus on OFDM and MIMO systems.
Ahmad A. ElMoslimany received
the B.S. degree in electrical engi-
neering from AinShams University,
Cairo, Egypt, in 2004 and 2009,
respectively, and the M.Sc. degree
in Electrical Engineering from Nile
University in 2011. He is currently
pursuing the Ph.D. degree in Elec-
trical Engineering at Arizona State
University, Arizona, United States. He was a Research
Assistant with the Wireless Intelligent Networks Center
(WINC) at Nile University from Fall 2009 to Summer
2011. Since 2011, he has been a Graduate Research
Associate at Arizona State University. His research in-
terests include signal processing, channel modelling and
estimation, compressed sensing and underwater acoustic
systems.
Amr El-Keyi received the B.Sc.
(with highest honors) and M.Sc.
degrees in Electrical Engineering
from Alexandria University in 1999
and 2002, respectively, and the
Ph.D. degree in 2006 in Elec-
trical Engineering from McMaster
University, Hamilton, ON, Canada.
From November 2006 till April
2008, he was a postdoctoral research fellow with the
Department of Electrical and Computer Engineering at
McGill University. From May 2008 till February 2009,
he was an Assistant Professor at Alexandria University
where he participated in teaching several undergraduate
courses. In April 2009, he joined Nile University as an
Assistant Professor at the School of Communication and
Information Technology. His research interests include
512
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
© 2012 ACADEMY PUBLISHER
array processing, cognitive radio, channel estimation, and
interference management and cooperative relaying for
wireless communication systems.
Tamer ElBatt received the B.S.
and M.S. degrees in Electronics
and Communications Engineering
from Cairo University, Giza, Egypt,
in 1993 and 1996, respectively,
and the Ph.D. degree in Electri-
cal and Computer Engineering from
the University of Maryland, College
Park, MD, in 2000. From 2000 to
2006, he was with HRL Laboratories, LLC, Malibu, CA
as a Research Scientist. From 2006 to 2008, he was with
San Diego Research Center as a Senior Research Staff
Member. From 2008 to 2009, he was with Lockheed
Martin ATC, Palo Alto, CA as a Senior Research Sci-
entist leading the Communications and Networking R&D
group. In Oct. 2009, he joined the School of Commu-
nication and Information Technology and the Wireless
Intelligent Networks Center (WINC) at Nile University,
Cairo, Egypt as an Assistant Professor. He also holds
an appointment with the Electronics & Communications
Dept., Faculty of Engineering, Cairo University. Dr. El-
Batt research has been supported by DARPA, General
Motors and Boeing and is currently being supported
by Qatar QNRF, the Egyptian NTRA, ITIDA, EU FP7,
General Motors, Microsoft and Google. He has published
more than 45 papers in prestigious journals and inter-
national conferences. Dr. ElBatt holds seven issued U.S.
patents and four more pending applications.
Dr. ElBatt is a Senior Member of the IEEE and has
served on the technical program committees of numerous
IEEE and ACM conferences in the areas of wireless and
sensor networks and mobile computing. He is the Pub-
lications Co-Chair of IEEE Globecom 2012. Dr. ElBatt
currently serves on the Editorial Board of IEEE Trans-
actions on Mobile Computing and Wiley International
Journal of Satellite Communications and Networking. Dr.
ElBatt has also served on NSF and Fulbright review
panels. He has been invited to participate in Google’s
EMEA Faculty Summit in Zurich, Feb. 2010. In Aug.
2010, Dr. ElBatt was a Visiting Professor at the Dept.
of Electronics, Politecnico di Torino, Italy. His research
has thus far collected more than 1600 citations on the
Google Scholar Index and has been cited by media, such
as EE Times and Information Week. Dr. ElBatt is the
recipient of the 2002, 2004 HRL Achievement Award. His
research interests lie in the broad areas of performance
analysis and design of wireless and mobile networks with
emphasis on cognitive radios and networks, cooperative
networking, cross-layer optimization, MAC, sensor and
vehicular networks and emerging mobile applications. Dr.
ElBatt is listed in Marquis Who’s Who in the World 2010-
2011.
Fan Bai is a Senior Researcher in
the Electrical & Control Integration
Lab., Research & Development and
Planning, General Motors Corpora-
tion, since Sep., 2005. Before join-
ing General Motors, he received the
B.S. degree in automation engineer-
ing from Tsinghua University, Bei-
jing, China, in 1999, and the M.S.E.E. and Ph.D. degrees
in electrical engineering, from University of Southern
California, Los Angeles, in 2005.
His current research is focused on the analysis and
design of protocols/systems for next-generation Vehicular
Ad hoc Networks (VANET), for safety, telematics and
infotainment applications. Dr. Bai has published about 40
book chapters, conference and journal papers. In 2006,
he received Charles L. McCuen Special Achievement
Award from General Motors Corporation in recognition
of extraordinary accomplishment in area of vehicle-to-
vehicle communications for drive assistance & safety.
He serves as Technical Program Co-Chairs for IEEE
WiVec 2007 and IEEE MoVeNet 2008. He is an associate
editor of IEEE Transaction on Vehicular Technology and
serves as guest editors for IEEE Wireless Communication
Magazine, IEEE Vehicular Technology Magazine and
Elsevier Ad Hoc Networks Journal.
Cem Saraydar received his bache-
lor’s degree from Bogazici Univer-
sity, Istanbul, Turkey, and master’s
and Ph.D. degrees from WINLAB,
Rutgers University, all in Electrical
Engineering. Following Ph.D., he
worked for the Performance Anal-
ysis Department at Bell Laborato-
ries, Holmdel, NJ, as a member of
technical staff, and, subsequently, at
the ECE Department at NJIT, Newark, NJ as a Research
Associate where in addition to his research responsi-
bilities; he supervised graduate student thesis work and
taught several classes in the ECE and Math departments,
both at the graduate and undergraduate levels. He is
currently a business planning manager in General Motors
Global R&D in Warren, Michigan. His current research
interests include wireless ad hoc networks and wireless
sensor networks. His earlier work covers topics such as
optimal pricing in wireless data networks, applications
of game theory in wireless networks, graph theoretic
models in communications systems, mobility manage-
ment in cellular systems, and traffic modeling and rate
control for wireless data networks. Dr. Saraydar is the
author of over 30 publications and the co-inventor of over
a dozen patents/patent applications. He has served the
technical community in various roles such as technical
program committee member on numerous conferences,
workshop chair, guest editor, NSF panelist and doctoral
thesis committee member.
JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012
513
© 2012 ACADEMY PUBLISHER
