MIMO Vehicular Networks: Research Challenges ... - Semantic Scholar

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JOURNAL OF COMMUNICATIONS, VOL. 7, NO. 7, JULY 2012

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 benefits 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 reliability 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 intervehicular 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 spacefrequency 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. I NTRODUCTION 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 communications 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 multiuser 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 research 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.

© 2012 ACADEMY PUBLISHER doi:10.4304/jcm.7.7.500-513

but also to showing the synergy and ample opportunity to leverage the unique benefits offered by MIMO in vehicular scenarios. These benefits could range from resourceefficient and reliable support of safety applications with stringent quality of service (QoS) requirements to supporting bandwidth hungry multimedia streaming applications 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 modelling, 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 background 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


wireless infrastructure and mobile extensions. Key to realizing this vision is not only boosting the point-topoint 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 received considerable attention in the point-to-point literature due to its substantial spectral efficiency and reliability advantages for the same power and bandwidth resources. Space-time signal processing has undergone major development, 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) communications where capturing and exploiting independent multipath 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 “orthogonal” 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 MT transmit and MR 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 MR antennas, this gain makes the average SNR at the output of the combiner MR 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 reduction 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 independent 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, © 2012 ACADEMY PUBLISHER

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i.e., asymptotically achievable, is MT MR 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 multiplexing gain, that is asymptotically achievable, is given by min(MT , MR ) 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 MT transmit antennas, MR receive antennas, and i.i.d. frequency-flat Rayleigh fading between each antenna pair is given by C(SNR) = min{MT , MR } 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 beamforming/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 interferencelimited dense multi-user settings. III. W HY MIMO

FOR

VANET S ?

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 infotainment applications. For instance, spatial multiplexing would best suite high data rate applications, e.g., media streaming. On the other hand, diversity schemes are best

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for safety applications mandating reliable communications for short warning messages. In addition, transmit beamforming techniques can be used to focus the transmitted 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 multipath 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, spacetime signal processing and decoding schemes, e.g., VBLAST and space-time coding. Broadband: MIMO VANETs constitute a natural extension 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 valueadded, media-centric, infotainment applications and services. 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 wellknown 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 moving 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]. © 2012 ACADEMY PUBLISHER


IV. MIMO R ESEARCH C HALLENGES IN VANET S 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 vehicular 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 motion 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 presence 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 handling 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


spreads whose reciprocals are used to find the coherence bandwidth and coherence time of the channel, respectively. Knowledge of these parameters is vital for the optimal design of the physical layer. An overview of statistical channel models for V2V cooperative communication systems can be found in [23]. An alternate method for characterizing the Doppler spread and coherence bandwidth 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 isolation 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 assumptions 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 intervehicular 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 © 2012 ACADEMY PUBLISHER

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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 MT transmit antennas and MR receive antennas, a total number of MT MR 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 different 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 composed 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 concentriccylinders 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 experimentally investigating the impact of the antenna mounting position on the performance of vehicular wireless communication systems.

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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. Channel estimation algorithms can be classified into three categories; training-based, blind, and semi-blind algorithms. 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 complexity 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 communication 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 estimated 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, direct application of the two-dimensional filtering algorithms 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 complicates 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© 2012 ACADEMY PUBLISHER


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 compensation is obtained by maximizing the average signalto-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 transmitter 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-tonoise 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 infrastructure. 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


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 cooperative 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 crosslayer optimization problem in order to fully reap the benefits 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 expands 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 networking 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 bottomup 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 entails 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 © 2012 ACADEMY PUBLISHER

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the MAC perspective, the adopted interference/collision avoidance mechanisms would highly depend on whether the adopted MIMO mode emits directional (beam forming) or omni-directional (spatial multiplexing and diversity) 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 extending the concept of interference-aware routing (IAR) [59] to MIMO V2V networks, and scattering opportunities can be leveraged. In addition, MIMO could play a fundamental 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 protocols 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 recipients 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: beyond 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 network connectedness. V. P RELIMINARY R ESEARCH R ESULTS 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 channels 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

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MIMO-V2V channels that is derived using the geometrical 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 MT (MR ). The angle of the direction of the relative velocity between the receiver and transmitter is denoted by αv . 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 am and bm , 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 (m) slots (TDP) (delay bin). The mth ellipse contains Nc where each slot contains a scatterer. The scattering from the mth ellipse contributes to the mth channel coefficient whose delay is τm = mTs where Ts 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 vehicular environment, e.g., terrain type, density of vehicles on (m) (m) the road, etc. We use ϕT and ϕR to respectively denote the angles of departure and arrival of a ray travelling from the transmitter to the receiver via a scatterer located on (m) (m) the mth ellipse. The angle ϕT (ϕR ) 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, zn,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 zn,m [q] = 1 if a scatterer is present in the nth slot of the mth ellipse at the qth time instant and zn,m [q] = 0 otherwise. We model zn,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 © 2012 ACADEMY PUBLISHER


Figure 1: Geometric elliptical scattering model for an MT × MR 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 ) ( (n,m) (n,m) λ01 1 − λ01 (n,m) (1) Λ = (n,m) (n,m) 1 − λ10 λ10 where the i, jth entry of the matrix Λ(n,m) denotes the probability that the Markov chain will make a transition (n,m) to state j − 1 given that it is in state i − 1. Let πi denote the steady state probability that the Markov chain (n,m) (n,m) (n,m) = 1, and π1 +π1 will be in state i. Hence, π0 can be written as (n,m)

(n,m)

π1 (n,m)

=

λ01 (n,m)

λ10

(n,m)

(2)

+ λ01

(n,m)

The ratio λ01 /λ10 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 (n,m) (n,m) λ01 /λ10 will be relatively high. Let hkl [p, q] denote the discrete-time basebandequivalent 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., hkl [p, q] =

M ∑

(m)

hkl [q]δ(p − m)

(3)

m=0

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


507

are modelled as random variables uniformly distributed between 0 and 2π. Using the above assumptions and after some mathematical manipulations, we can write the temporal correlation of the mth channel coefficient as Nc(m)

r(m)[p, q] =

∑

n=0

where

Figure 2: Temporal correlation function of the channel versus time and delay

be written as ∫

Nc(m) (m) hkl [q]

=

∑

n=0 (n,m)

(n,m)

zn,m [q]

gkl

(m)

(m)

(ϕR , q)dϕR

(4)

(n,m)

ΦR (m)

where gkl (ϕR , 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 (m) mth ellipse and received at an angle ϕR at the receive array. Note that the scattering slot extends over the receive (n,m) (n,m) (m) angular interval ΦR . We can write gkl (ϕR , q) as ( ( (m) )) (n,m) (m) (m) gkl (ϕR , q) = En,m (ϕR ) exp jθn,m ϕR ( ( (m) ) ) (n,m) ( (m) ) exp −jK0 Dkl ϕR + j2πFD ϕR qTs (5) (m) En,m (ϕR )

where is the amplitude density function of the scattered wave from the nth scattering slot( in the) mth (m) (m) ellipse with respect to the angle ϕR , θn,m ϕR is a random phase shift due to the scattering process, K0 = 2π/λ where λ (is the) wavelength of the RF propagating (m) signal, and FD ϕR is defined as ( (m) ) ( (m) ) FD ϕR = fD cos ϕR − αv (6) where fD is the maximum Doppler frequency. In (5), (n,m) (m) Dkl (ϕR ) 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 (m) and received at an angle ϕR . 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 (m) corresponds to a distinct scatterer. 3) En,m ϕR = (m) En,m is independent of ϕR , i.e., the scatterers have a uniform radar section. 4) The scattering phase ( (m)cross ) angles θn,m ϕR are independent for different n, m, (m) and ϕR and independent of the process zn,m [q]. They © 2012 ACADEMY PUBLISHER

(n,m)p Λ(i,j)

(n,m)p

(n,m)

|En,m|2 Λ(2,2) π1

(n,m)

[q]I(ΦR

, p).

(7) is the i, jth entry of the p-step state p

(n,m)

transition matrix Λ(n,m) and the integration I(ΦR is given by ∫ (m) (n,m) (m) I(ΦR , p) = ej2πFD pTs dϕR .

, p)

(8)

(n,m)

ΦR

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 zn,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 ∫

(m)

∑

Nc (m) rkl,k′ l′ (T

, R) =

n=0

(n,m)

|En,m|2 π1

(n,m)

Cll′

(n,m)

(m)

(T ) Kkk′ (R) dϕR

(n,m)

ΦR

(9)

where the above integration can be evaluated numerically (m) (m) using the relationship between ϕT and ϕR and (n,m) Cll′ (T

) = e

(n,m) Kkk′ (R)

= e

( ) (m) (m) jK0 dT cos(ϕT −αT )−dT ′ cos(ϕT −αT ′ ) l l l l

( ) (m) (m) jK0 dR cos(ϕR −αR )−dR′ cos(ϕR −αR′ ) k k k k

where dTl (dRk ) 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 αTl (αRk ) 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 fc = 5.85 GHz with bandwidth 10 MHz, and hence, the sampling frequency of the CIR is Ts = 100 nsec. The relative speed between the two vehicles is 100 km/hr and the inclination angle of the velocity vector is αv = 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 Nc = 2 scattering slots that extend over the angular intervals (1) (2) ΦR = [98◦ , 108◦ ] and ΦR = [110◦ , 235◦ ]. The transition probabilities of the Markov chain associated with the two scattering slots are identical and its parameters

508


−220

350

−240

Receive Azimuth angle (degrees)

300

250

−260

200 −280 150 −300 100 −320 50

0

Figure 3: Spatial correlation between channel coefficients h11 and h22

−340 0

50

100 150 200 250 300 Transmit Azimuth angle (degrees)

350

Figure 4: Angular power spectra of the simulated MIMO channel. L Subcarriers

d0

are given by λ01 = 0.005 and λ10 = 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 = pTs and (n) the time t = qTs . The initial probabilities π1 [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 characteristics of the channel model. We consider a uniform linear transmit and receive array equipped with MT = MR = 8 elements each and their tilt angles are given by αT = αR = 0. Fig. 3 shows the spatial correlation function between two channel coefficients h11 (t, τ ) and h22 (t, τ ) versus the normalized transmit antenna elements spacing δT /λ and the normalized receive antenna elements spacing δR /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-ofdeparture/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 (1) first slot extends over the DODs ΦT = [14.5◦ , 17.5◦ ] (1) and DOAs ΦR = [98◦ , 108◦ ] and the second slot © 2012 ACADEMY PUBLISHER

d2

d4

TX1

··· ···

−d∗1

−d∗3

−d∗5

··· ···

··· ···

··· ···

d∗0

d∗2

d∗4

··· ···

··· ···

−d∗N−3 −d∗N−1

Codeword

d1

d3

d5

d∗N−4

d∗N−2

TX2 OFDM subcarriers

Figure 5: Alamouti SFBC with separation across sub-carriers. (2)

extends over the DODs ΦT (2) ΦR = [110◦ , 235◦ ].

= [18◦ , 336◦ ] and DOAs

B. Space-frequency Block Coding Using multiple antennas at the transmitter, spatial diversity 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 = [d0 , d1 , . . . , dN −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 baseband 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 x1 and


509

Figure 6: Banded structure of the equivalent channel matrices

˜1 (a) Structure of G

G(1) and G(2) after applying the window.

˜ 2 , where each (b) Structure of G shaded block is of size 1 × 2(t − 1)

˜ 1 and G ˜ 2. Figure 7: Structure of the two matrices G

x2 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 intersymbol interference. The received symbol after removing the cyclic prefix is given by y = H (1) x1 + H (2) x2 + u

(10)

where H (i) is the ith time-domain channel matrix between 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 matrix, 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 considered 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 components, 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 −1 −1 maximum eigen value of the matrix RT 2 Rs RT 2 where the N ×N diagonal matrix RT contains the main diagonal ∑2 H of the matrix i=1 H (i) H (i) on its main diagonal, and © 2012 ACADEMY PUBLISHER

the N × N matrix Rs is given by RTs =

2 ∑ ∑

diag{f j }(F H D (i) )(F H D (i) )H diag{f ∗j }

i=1 j∈St

where f k is the kth column of F and diag{x} is a diagonal matrix with the vector x on its main diagonal, (i) H defined and D (i) is a rearrangement of G(i) c = FH F 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 applying 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 ˜ 1d + G ˜ 2 d∗ + U ˜ Y˜ = G

(12)

˜ is the noise vector and each of where the N × 1 vector U ˜ ˜ 2 in (12) is a linear function the two matrices G1∗ and G ∗ (1) (2) (1) of G , G , G , and G(2) . By virtue of the design ˜ 1 and G ˜ 2 have the of the window, the two matrices G structure shown in Fig. 7a and Fig. 7b, respectively. We ˜ 1 is a blockcan see from Fig. 7a that the matrix G diagonal matrix, i.e., it consists of N/(2L) non-zero subblocks on its main diagonal each of size 2L × 2L. In ˜ 2 has a contrast, we can see from Fig. 7b that the matrix G ˜ 2 can few number of significant elements. In particular, G 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. ˜ 2 did not contain any significant elIf the matrix G ˜1 ements, the block-diagonal structure of the matrix G would allow the detection of each sub-symbol from the

510


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 ˜ 2 . According to this structure, the data of the matrix G symbol is divided into N/(2L) sub-symbols. Each subsymbol 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 ˜ 2 arises from the first and by the significant entries of G last 2(t − 1) elements of each sub-symbol only. Hence, using the channel state information and the pilots, the selfinterference 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 subsymbol and due to the structure of each diagonal block in ˜ 1 , a low-complexity DFE can be exploited to the matrix G 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 bandwidth 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 separation 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. © 2012 ACADEMY PUBLISHER

Figure 9: BER curve for the proposed SFBC-OFDM

VI. C ONCLUSION The use of multiple antennas in vehicular communications 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 multiuser interference. In addition, the transmit and/or receive arrays can be configured depending on the traffic density (dense vs. sparse network scenarios), surrounding propagation 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. R EFERENCES [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 communicationsbased 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. Valenzuela, “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.


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Ahmed Attia received the B.Sc. degree with honors in Electrical Engineering from Alexandria University, Egypt in 2010. Since then, he has been a research assistant at the Wireless Intelligent Networks Center (WINC), Nile University, Giza, Egypt, where he is pursuing the M.Sc. degree in Wireless Communications from the communications and information technology school. His research interests are in the fields of information theory, channel coding, estimation and detection theory, with focus on OFDM and MIMO systems. Ahmad A. ElMoslimany received the B.S. degree in electrical engineering 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 Electrical 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 interests 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 Electrical 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


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 Electrical 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 Scientist leading the Communications and Networking R&D group. In Oct. 2009, he joined the School of Communication 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. ElBatt 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 international 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 Publications Co-Chair of IEEE Globecom 2012. Dr. ElBatt currently serves on the Editorial Board of IEEE Transactions 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 20102011.


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Fan Bai is a Senior Researcher in the Electrical & Control Integration Lab., Research & Development and Planning, General Motors Corporation, since Sep., 2005. Before joining General Motors, he received the B.S. degree in automation engineering from Tsinghua University, Beijing, 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-tovehicle 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 bachelor’s degree from Bogazici University, 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 Analysis Department at Bell Laboratories, 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 responsibilities; 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 management 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.