veh to veh channel

INTRODUCTIONVehicle-to-vehicle (VTV) communications sys-tems have recently attracted much interest,because they potentially improve efficiency andsafety of surface traffic. The grand vision is thatall future vehicles gather sensor data and shareinformation on traffic dynamics with each other,and with the road infrastructure, via wirelesslinks. Each vehicle can thereby receive andaggregate information from other vehicles toimprove the capabilities of its braking system,enhance airbag functionality, and reduce fuel

consumption and travel time. To this aim, vehi-cles have to build up ad hoc communication net-works. Such networks, however, require reliablelow-latency VTV communication links that arecapable of meeting strict packet delay deadlines.An international standard, IEEE 802.11p, whichis part of the Wireless Access in Vehicular Envi-ronments (WAVE) initiative, was recently pub-lished. Based on the popular WiFi standard, it isintended for both VTV and vehicle-to-infra-structure traffic telematics applications [1]. Sys-tems based on IEEE 802.11p as well asalternative systems are also being developed inthe European Union and Japan.

The simulation and performance evaluationof such ad hoc vehicular communications sys-tems and their future enhancements require adeep understanding of VTV propagation com-munication channels. In the past, much researcheffort has been devoted to the cellular channelbetween a static base station and a mobile vehi-cle. It turns out, however, that the propagationcharacteristics of VTV communication channelsare significantly different from those of cellularchannels, especially in terms of the time and fre-quency selectivity and the associated fadingstatistics. Here, time selectivity describes the tem-poral fluctuations of the channel quality, where-as frequency selectivity relates to the occurrenceof “spectral holes” in the channel; both are veryimportant for system performance. Therefore,dedicated measurement campaigns are neededfor the accurate characterization of VTV propa-gation aspects, and dedicated VTV channelmodels are urgently needed to evaluate the reli-ability and latency of data packet transmissionsamong vehicles.

Research into VTV channels was fairly low-key until about 2006, when the WAVE initiativeand other potential commercial applicationsspurred interest in this important communica-tions medium. Since then, dozens of papers havebeen published in this field by a number of dif-ferent research groups all over the world. It isthus desirable to survey the recent progress andcurrent state of the art. In this article we sum-marize methodology and results from the rele-

IEEE Wireless Communications • December 200912 1536-1284/09/$25.00 © 2009 IEEE

This work was carried out with partial funding from anINGVAR grant of the Swedish Strategic Research Foun-dation (SSF), the SSF Center of Excellence for High-Speed Wireless Communications (HSWC), the FTWproject REALSAFE, and COST Action 2100.

VE H I C U L A R WIRELESS NETWORKS

ANDREAS F. MOLISCH, UNIVERSITY OF SOUTHERN CALIFORNIAFREDRIK TUFVESSON AND JOHAN KAREDAL, LUND UNIVERSITY

CHRISTOPH F. MECKLENBRÄUKER, TECHNISCHE UNIVERSITÄT WIEN

ABSTRACTTraffic telematics applications are currently

under intense research and development formaking transportation safer, more efficient, andmore environmentally friendly. Reliable traffictelematics applications and services require vehi-cle-to-vehicle wireless communications that canprovide robust connectivity, typically at datarates between 1 and 10 Mb/s. The developmentof such VTV communications systems and stan-dards require, in turn, accurate models for theVTV propagation channel. A key characteristicof VTV channels is their temporal variabilityand inherent non-stationarity, which has majorimpact on data packet transmission reliabilityand latency. This article provides an overview ofexisting VTV channel measurement campaignsin a variety of important environments, and thechannel characteristics (such as delay spreadsand Doppler spreads) therein. We also describethe most commonly used channel modelingapproaches for VTV channels: statistical as wellas geometry-based channel models have beendeveloped based on measurements and intuitiveinsights. Extensive references are provided.

A SURVEY ON VEHICLE-TO-VEHICLEPROPAGATION CHANNELS

The authors providean overview of existing VTV channelmeasurement campaigns in a variety of importantenvironments, andthe channel characteristics (suchas delay spreads andDoppler spreads)therein.

MOLISCH LAYOUT 12/3/09 12:15 PM 2

Authorized licensed use limited to: IEEE Xplore. Downloaded on January 11, 2010 at 06:55 from IEEE Xplore. Restrictions apply.

IEEE Wireless Communications • December 2009 13

vant measurement campaigns, as well as differ-ent types of channel models derived from thosemeasurements. The goal of this article is to helpcommunications system designers to gain anoverview of the pertinent channel characteristics,and propagation researchers to assess where themost pressing needs for further work lie.

KEY ISSUES IN VTV CHANNELS

The ultimate goal of VTV channel measure-ments and modeling is to enable performancecharacterization of VTV communications sys-tems. This section thus first reviews the mostimportant features and metrics that describe fad-ing channels and how they impact system perfor-mance.

PROPAGATION CHANNEL CHARACTERIZATIONIn a wireless system, the signals can propagatefrom the transmitter to the receiver via differentpaths, each of which can involve reflection,diffraction, waveguiding, and so on. The differ-ent paths give rise to multiple attenuated,delayed, and phase-shifted echoes of the trans-mitted signal arriving at the receiver. All theseradio propagation effects are subsumed into theimpulse response of the channel, which can beinterpreted as the superposition of the contribu-tions by all multipath components (MPCs). Notethat the impulse response is time-variant becausethe propagation channel changes as transmitter,receiver, and scatterers move around. A com-plete description of the channel is thereforegiven by the time-variant channel impulseresponse [2].1 However, a sequence of impulseresponses is too cumbersome to work withdirectly. For this reason, several statistical chan-nel metrics, which provide a more condensedcharacterization, have been derived and widelyadopted: pathloss, fading statistics, Dopplerspread, and delay spread.

Pathloss is the average attenuation (reductionin power) of a radio signal as it propagates.Pathloss includes the propagation losses causedby free space and effects due to absorption,diffraction, and others. It has been found inmany experiments that the pathloss increases afunction of distance d like dn, where n is calledthe pathloss exponent. The pathloss is the singlemost important quantity of any wireless channel.As the pathloss increases, the signal-to-noiseratio (SNR) decreases; this restricts the achiev-able range and data rate of the system.

Fading statistics are used to describe the fluc-tuations in the received power over a distance(i.e., deviations from the power predicted by thesimple pathloss law described above). Fast fluc-tuations, also called small-scale fading, can occurdue to the interference of MPCs, which —depending on the exact location — can be con-structive or destructive. Small-scale fading occursduring motion over short distances (approxi-mately one wavelength), and leads to possiblyreduced reliability, necessitating appropriatecountermeasures (increasing transmit power,using frequency diversity or antenna diversity,etc.) [3]. Since exact characterization is usuallytoo complicated, a description of the fadingamplitude statistics is commonly used. The small-

er the signal-power variations (i.e., the more“peaked” the probability density function of thefluctuations), the smaller the danger of systemoutage due to fading. Finding the fading statis-tics is a first step in identifying how serious thefading problem might become, and how mucheffort has to be put into the countermeasures.The most common model for small-scale fadingis Rayleigh fading. Large-scale fading, or shadow-ing, is due to objects obstructing propagationpaths. Such fluctuations are observed on locallyaveraged received powers.

The power delay profile (PDP) is the squaredmagnitude of the impulse response, averagedover the small-scale fading. It thus describes howmuch power is carried, on average, by MPCswith a certain delay. We can further obtain theroot mean square (rms) delay spread as the sec-ond central moment of the PDP, providing avery compact description of the delay dispersion(or frequency selectivity) of a channel (i.e., if wetransmit a short pulse, how “spread out” is thereceived waveform?). The delay dispersion deter-mines the available frequency diversity, and alsodictates the required length of cyclic prefix (foran orthogonal frequency-division multiplexing,OFDM, system) or equalizer (for a single-carriersystem).

The Doppler spectrum describes the widen-ing of the spectrum that occurs because differentMPCs experience different Doppler (frequency)shifts. The rms Doppler spread thus characterizesthe channel’s frequency dispersion or, equiva-lently, the time selectivity of the channel; to arough approximation, a channel can be consid-ered to be constant over a timescale that is theinverse of the Doppler spread. The Dopplerspread is a quantity that is of interest in itself forOFDM systems, because it leads to intercarrierinterference, as part of the signal emanatingfrom one subcarrier is not “in the spectral nulls”of the adjacent subcarriers anymore. It is fur-thermore an important characterization methodfor the time variability of the channel.

PDP and Doppler spectrum (and thereforedelay spread and Doppler spread) are obtainedfrom an ensemble of impulse responses, underthe assumption that the statistics of the channeldo not change with time. This assumption iswidely used in cellular communications channels,but violated in VTV channels: there, we have todefine a PDP or Doppler spectrum that is onlyvalid for a short time; a complete description ofthe channel then also has to include how theychange with time.

DIFFERENCE FROM CELLULAR PROPAGATIONThe characteristics of VTV channels, such astime selectivity or pathloss, differ from those ofmobile cellular communications channels. Thesedifferences originate from the following specificfeatures of VTV radio propagation:

•In VTV systems the transmitter (TX) andreceiver (RX) are at the same height, and insimilar environments (peer-to-peer communica-tions). In cellular communications, on the otherhand, communication is between a base stationthat is high above street level and a mobile sta-tion at street level. As a consequence, the domi-nant propagation mechanisms of the multipath

1 Note, however, that formulti-antenna systems, adouble-directionalimpulse response isrequired for a completedescription of the channel.

The Doppler spectrum describes

the widening of thespectrum that occurs

because differentMPCs experience different Doppler

shifts. The rmsDoppler spread thus

characterizes thechannel’s frequency

dispersion, or equivalently,

the time-selectivity ofthe channel.

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IEEE Wireless Communications • December 200914

components are different. For example, in cellu-lar communications propagation of waves overrooftops is important, while in VTV systemspropagation in the horizontal plane, with diffrac-tion and reflection, for example, at street cor-ners, is more important. Also, for a VTVchannel, scattering can occur around both theTX and the RX, while for cellular channels, thearea around the base station is usually free ofscatterers. Furthermore, the distance over whichcommunications can take place is much smallerin VTV channels (< 100 m) than in typical cel-lular scenarios (~ 1 km).

•In a VTV channel, both the TX and RX aswell as many of the important scatterers aremoving, whereas in cellular channels, only one ofthe TX or RX is moving, and moving scatterershave less relative importance. This implies thatthe channel fluctuations in VTV channels arefaster and that commonly used assumptions onstationarity usually are not valid.

•VTV systems operate mostly at 5.9 GHzcarrier frequency, while cellular communicationsoccurs mostly at 700–2100 MHz. Due to theirhigher carrier frequency, VTV channels havehigher signal attenuation, and specific propaga-tion processes like diffraction are less efficientthan in cellular radio.

ENVIRONMENTSChannel characteristics of VTV channels areinfluenced by the properties of the environmentaround the communicating cars, as well as thetypical traffic characteristics. Although classifica-tion of different traffic environments is some-what arbitrary and there are sometimes largevariations between different parts of the world,the following categories are often distinguishedin the literature:

•Highways have two to six lanes in each direc-tion, usually with middle dividers and no housessituated in their immediate vicinity. Speeds onhighways are usually limited to 25–30 m/s in theUnited States, Asia, and large parts of Europe,but can be higher than 40 m/s in Germany and afew other European countries.2 Traffic density is

usually high on urban highways (up to 10,000cars/h), but much lower on highways throughgenerally rural areas (e.g., large parts of theinterstate highway systems in the United States).

•Rural streets usually have two lanes, with fewor no buildings on the side, although hills [4] orforests [5] can give rise to additional multipathcomponents. Traffic density is usually very light,but velocities can be 20–30 m/s.

•Suburban streets are usually one or two laneswide. In the United States houses are often setback 8–10 m from the curb [4], whereas inEurope and Japan houses can be much closer toit. Traffic density is light, and velocities are usu-ally limited to 15 m/s or less. In contrast to theother environments, trucks are rarely found onsuburban streets.

•Urban streets (Fig. 1) are characterized bywider streets (two to four lanes), houses closerto the curb, and higher traffic density than sub-urban streets. It must be noted that urbanstreets in Europe can be very narrow and wind-ing, while in the United States they are usuallywider and straight. Even though the buildingenvironment is the same, it is often worthwhileto separately model urban streets with light traf-fic and with heavy traffic; the presence of manycars, which may block both the line of sight(LOS) between TX and RX as well as other sig-nificant paths, can change the channel parame-ters significantly.

The distance between vehicles is strongly cor-related with the speed, up to the official speedlimit [4]. This can be explained by the fact thatrational drivers keep a larger distance from thevehicle in front of them at higher speeds. As aconsequence, any results that depend on speedand distance between vehicles are correlated.

VEHICLE TYPES AND ANTENNASTwo other important “environmental factors”are the type of vehicle that wants to communi-cate and the location of the antennas on thevehicle. The combined effect of these factorsdetermines not only the influence from the sub-ject car, but also how much the channel is influ-enced by other, obstructing, cars, because itimpacts which multipath components can reachthe antenna. Most car-mounted antennas forcurrent applications are located either on theroof or near the rear window (e.g., for GSMcommunications or FM radio), although somecars have additional antennas in the bumper(mainly for radar applications) and possibly onthe dashboard. In light of this, it is noteworthythat practically no existing VTV measurementcampaigns have been performed with antennasthat are actually in use in commercial vehicles.Rather, most campaigns in the literature makeuse of antennas placed either at an elevatedposition within a van [6, 7] or on top of a van[8]. Antennas placed inside a car (e.g., on a seator near a dashboard) have been found to lead tohigher pathloss (5–10 dB [9]) and stronger multi-path propagation [8]. It should be noted, howev-er, that the packet error rate is quite sensitive tothe exact antenna position [10]. Trucks wouldallow placing the antennas even higher abovethe ground, so they presumably could communi-cate over the roofs of passenger cars, thus

Figure 1. Channel measurements in an urban environment (Lund, Sweden);the red pickup truck carries the receiver.

2 1 m/s corresponds to3.6 km/h or 2.25 mi/h.




enabling larger communications ranges; howev-er, this aspect seems to have drawn little atten-tion in the literature up to now.

MEASUREMENTS

Measurements are vital for the understanding ofpropagation channels, either giving directinsights, or by verifying (or disproving) theoreti-cal considerations. We thus first describe thetype of measurement equipment that is beingused for obtaining channel characteristics. Theoutput of those measurement devices is usually atime-variant transfer function or time-variantimpulse response [11], from which the channelparameters discussed earlier can be obtained.More detailed insights can be obtained fromintricate channel models that might be parame-terized from measurement campaigns; this willbe discussed in the “VTV Channel Modeling”section.

MEASUREMENT EQUIPMENTThe type of measurement equipment that can beemployed for VTV channels is mostly the sameas that for cellular channels. Just like in cellularchannels, the required equipment depends, natu-rally, on which channel characteristics we wantto measure — the more general the desiredmeasurement result, the more complex (andexpensive) the required equipment.

Narrowband measurement systems try to iden-tify the channel gain and Doppler shift experi-enced by a narrowband (sinusoidal) signal; bydefinition they cannot measure other parameterssuch as frequency selectivity. Narrowband mea-surement systems can be based on a sine wavegenerator combined with a vector signal analyzer[4, 12, 13] or spectrum analyzer [14].

Wideband sounders determine the impulseresponse (or transfer function) of the channeland the parameters derived from it, such asdelay spread, as well as the narrowband parame-ters. The most popular form is correlative chan-nel sounders, which transmit a pseudo noise(PN) sequence and correlate the received signalwith the same PN sequence at the RX. It can beshown that if the channel is time-invariant forthe duration of the PN sequence, the output ofthe RX correlator is the convolution of thechannel impulse response with the autocorrela-tion function of the PN sequence; if the PNsequence has suitable properties, this outputapproximates the channel impulse response [3].

A correlative sounder can be either a dedicat-ed device, as in the measurements of Ohio Uni-versity [8], or constructed from anarbitrary-waveform generator (working as TX),as in the measurements of Carnegie-Mellon Uni-versity (CMU) [15], Georgia Tech [16], Berkeley[17], and Heinrich-Hertz-Institut (HHI) [18]. A50 MHz chip rate (i.e., inverse duration of the+1 and –1 of the PN sequence) has often beenused for VTV measurements. A vector signalanalyzer or sampling scope can be used as RX.With a sampling scope, sampling is usually doneat twice the chip rate, but with strict synchro-nization and a repeated TX signal, undersam-pling can be used to virtually create a highsampling rate and allow for large bandwidth

[18]. The correlation of the received signal withthe PN sequence is most often performed offlineon a computer or workstation.

A different wideband sounding principle ismultitone sounding, which is based on soundingsignals similar to those of OFDM systems [6].Sounding with multitone signals requires morecomplicated equipment, but provides channelcharacterization that is of equal quality in allparts of the considered bandwidth, while PN-based sounding is less accurate near the bandedges. It must also be noted that channel sound-ing by means of vector network analyzers(VNAs), which is very popular for the measure-ment of impulse responses in indoor environ-ments, is not viable for VTV channels, as suchchannels change much faster than VNAs canmeasure.

While the WAVE standard foresees only asingle antenna element at the TX and RX, thetrend to higher reliabilities and data rates lets usanticipate the future use of multiple antennaelements at the transmitter and receiver. Forsuch multiple-input multiple-output (MIMO)systems we need to (quasi-)simultaneouslyrecord the impulse responses from each transmitto each receive antenna element. AppropriateMIMO sounders can be constructed from wide-band sounders by one of two principles:• Using multiple parallel RF chains at the trans-

mitter and receiver• Using the switched-array principleThe former case allows the reception of multiplesignals simultaneously at the receiver, while dif-ferent transmit signals (e.g., an m-sequence withdifferent offsets [19]) are put onto the TX anten-

Figure 2. Block diagram of a wideband multitone sounder based on theswitched array principle: a) the transmitter; b) the receiver.

DSP PC

Disp.

Disc

BP BP

(a)

(b)

Rubidiumfrequencyreference

Rubidium frequency reference

Arbitrarywaveformgenerator

Localoscillator

Localoscillator

A/D



nas through parallel TX chains. In a switched-array sounder, a single available radio frequency(RF) chain (at TX and RX, respectively) is con-nected sequentially to the elements of an anten-na array through an electronic switch [6] (Fig.2). While cheaper and easier to calibrate, suchsounders have the drawback of requiring alonger time period to record a complete MIMOsnapshot (from each TX to each RX element),due to the sequential nature of the sounding.This drawback is more important in VTV envi-ronments than in cellular channels due to thehigher Doppler frequency.

MEASUREMENT RESULTSAs previously mentioned, the fast temporal vari-ations of VTV channels impacts channel charac-terization, since channel parameters often varywith time. Much research effort has thus beenspent on establishing connections betweenparameters and time-variant environment effectssuch as car densities, speed, and TX-RX separa-tion. However, consistent conclusions are yet tobe established in a statistically reliable way.

•Pathloss: Pathloss exponents around n =1.8–1.9 were observed in light traffic highwayenvironments [7, 20–22] as well as rural environ-ments [20, 22], which is in reasonable agreementwith two-ray models [20, 22].3 Reference [21]also analyzed crowded highways, and found thepathloss to be more severe and experience largervariations. For an urban environment, [20] foundthat n = 1.6, whereas [21] used a breakpointmodel. A breakpoint model was also found suit-able for a suburban environment: pathloss coef-ficients of n = 2–2.1 up to a distance of 100 m,and around n = 4 beyond that distance [4]. Thepathloss coefficients for rural, highway, andurban environments are similar to values mea-sured for cellular channels under line-of-sight(LOS) conditions, while the suburban resultsmentioned above are comparable to non-line-of-sight (NLOS) cellular channels. Note that manyresults for VTV channels do not distinguishbetween LOS and NLOS conditions; rather, all

channel samples are lumped together in theanalysis.

•Fading statistics: The narrowband small-scalefading statistics in VTV communications havebeen subject to considerable debate in the litera-ture. Reference [4] argues that small-scale andlarge-scale fading cannot easily be distinguished,and proceed to suggest the Nakagami distribu-tion for the compound fading statistics. TheNakagami m-factor can be quite high (3–4) if thedistance between TX and RX is less than 5 m,which means that fading is not very pronounced.However, when the distance exceeds 70–100 m,the Nakagami m-factor was observed to be lessthan unity, which means that fading is “worsethan Rayleigh” (m = 1 corresponds to Rayleighfading). Other authors did distinguish betweensmall-scale and large-scale fading, eliminatingthe large-scale (shadowing) fading before analyz-ing the small-scale distributions. For the narrow-band fading statistics, [14] found good agreementof measured data with either the Nakagami orRice distributions. Note that narrowband fadingstatistics in cellular channels are commonlyassumed to be Rayleigh (for NLOS) or Rice (forLOS).

A somewhat different problem is the analysisof fading in wideband measurements, where it isuseful to consider a discretized channel impulseresponse,

where B is the system bandwidth, and ci(t) arethe (complex) amplitudes of the “resolvabledelay bins” or “taps”; that is, we “lump togeth-er” all multipath components that are arriving atthe RX within a time interval that is approxi-mately the inverse bandwidth of the consideredsystem. Most wideband receivers (e.g., a code-division multiple access, CDMA, receiver, or areceiver for a single-carrier system with appro-priate equalizer) can process the signals arrivingin different delay bins separately; it is thusmeaningful to also analyze the fading statistics ofeach bin. Reference [8] fitted the measurementresults to a Weibull distribution, whereas [23]fitted data to Weibull and Nakagami distribu-tions. References [24, 25] describe the fading ineach resolvable delay bin as Rician, and showfrom their experiments that the Rice factor inthe first delay bin can be very high (up to 20dB), while it is much lower for later delay bins.Reference [23] found that the fading can also beRician for later delay bins; a similar behavior isfound in [5] where fading of individual scatterersis studied. Summarizing, we can say that in LOSsituations, the first delay bin shows “better thanRayleigh” fading characteristics, whereas binswith longer delays show a large variety of “fad-ing depth,” with distributions ranging fromRician to Rayleigh fading, with “worse thanRayleigh” also occurring in a significant percent-age of cases; this qualitative behavior is similarto that of cellular channels.

•Doppler spreads: VTV channels tend to showhigher Doppler spreads than conventionalmobile-radio channels, because the relative

h(t,τ ) = ci (t)δ (τ − i / B),i=0

N

∑


Figure 3. Example of Doppler-resolved channel impulse response. TX and RXare driving in opposite directions.

Delay (ns)

Dopplerfrequency (Hz)

–20

Pow

er (

dB) –10

–30

-40

100 0

0

200 300

400 500 1500

1000

500

0

–500

–1000

–1500

3 Reference [22] uses abreak-point model, withn = 4 for d >~ 220 m.




velocities of TX and RX can be higher, andbecause many important scatterers actually move(which often causes even higher Doppler shifts)in VTV channels (Fig. 3). Mean rms Dopplerspreads are usually on the order of 100–300 Hz(Table 1), though results close to 1000 Hz havebeen reported in highway environments [17].The high mobility also leads to large variationsof the Doppler spread during a measurement; alog-normal distribution has been found a suit-able description for rural, urban and highwayenvironments [20]. The Doppler spread is alsoconnected to the velocity of TX and RX. A lin-ear dependence between the average rmsDoppler spread frms,D with the “effective speed”of the TX and RX,

has been observed [13], whereas the Dopplershift of the LOS component was found, not sur-prisingly, to be exactly explained by the relativespeed of TX and RX [7, 13].

•Delay dispersion: Statistics of the rms delayspreads and maximum excess delays, as well asthe coherence bandwidth, were measured in anumber of VTV campaigns. The distribution ofthe rms delay spread can often be fitted by a log-normal distribution [26]. In general, median rmsdelay spreads are on the order of 100–200 ns(Table 1), although [20] measured approximately50 ns. Among the various environments, subur-ban and rural environments show the lowestdelay spreads, while highways display slightlyhigher results [15, 17, 27] (Fig. 4). The delayspread in urban environments can be consider-ably higher; a median value of 370 ns wasobserved in [27]. These results are comparableto the range of delay spreads that have beenobserved for urban and rural cellular propaga-tion channels.

The maximum excess delay has also been

analyzed. While some measurements found only0.5 μs on highways [6], other campaigns showeda very high maximum excess delay (up to 5 μsoccurred in rare circumstances) in highway andurban environments [15, 17].4 The large varia-tions of the results can be explained by the factthat faraway objects (which can lead to very highexcess delays) exist only in certain locations.Measurements in a variety of morphologies arethus essential to get better insights into the sta-tistical importance of these effects.

•MIMO measurements: As previously stated,multiple antenna elements can be used toincrease robustness against fading, and possiblyachieve higher data rates. In either of thosecases, the correlation coefficient of the fading atthe antenna elements gives insights into theachievable gains. Usually, a low correlation coef-ficient results in a large performance improve-ment.

Evaluations on the correlation coefficient arescarce in the literature. Reference [5] evaluatedthe correlation coefficient between the elementsof a four-element patch array that are pointingin different directions, and found them to exhibitconsiderable variations with time. The same con-clusion was drawn regarding the multipath rich-ness (sum of the logarithms of the eigenvalues)by [9] — this quantity is especially useful forassessing the potential of using multiple datastreams over one link. The channel richness wasincreased when the antennas were placed insidethe cars. Reference [23] did not specify any val-ues of correlation coefficients, but evaluated thestationarity of VTV MIMO channels throughthe correlation matrix distance.

VTV CHANNEL MODELING

For system simulations and system testing,detailed channel models need to be establishedthat quantify the effect of the propagation chan-nel more precisely than the “single-number”

veff = vTX2 + vRX

2 ,

Table 1. Summary of reported parameters for VTV propagation channels.

Scenario Pathloss exponent Delay spread (mean) Delay spread (10%–90%) Doppler spread (mean)

Highwayn = 1.8 [7]n = 1.85 [20]n = 1.9/4 0a [22]

247 ns [7]41 ns [20]141–398 nsb [17]165 ns [27]53/127 nsd [8]

120–340 ns [7]50–190 ns [15]30–300 ns [27]0.3/0 5–90/260 nsd [8]

92 Hzf [20]120 Hz [14]761–978 Hzb [17]

Ruraln = 1.79 [20]n = 2.3/4 0a [22]

52 ns [20]22 ns [17] 20–150 ns [15] 108 Hzf [20]

782 Hz [17]

Suburbann = 2.5 [4]n = 2.1/3 9a [4]

104 ns [27] 40–110 ns [15]20–230 ns [27]

Urban n = 1.61 [20]

47 ns [20]158–321 nsc [17]373 ns [27]126/236 nse [8]

30–1100 ns [27]3/20–250/570 nse [8]

33 Hzf [20]86 Hz [14]263–341 Hzc [17]

a Breakpoint model; b TX-RX separations of 300–400 m; c TX-RX separations of 200–600 m; d low/high traffic density; e antenna out-side/inside car; f median value

4 Reference [8] did notevaluate the maximumexcess delay, but foundrms delay spreads up to1.7 μs, and 90 percentdelay windows [28] of upto 2.5 μs.




descriptions discussed earlier. Just like in chan-nel modeling for cellular systems, there are threefundamental approaches to channel modeling:deterministic (e.g., ray tracing), stochastic, andgeometry-based stochastic. The deterministicapproach computes the realization of the propa-gation channel in a specific location and envi-ronment. In the stochastic approach the statisticsof the propagation characteristics are modeledrather than a site-specific realization. Finally, thegeometry-based stochastic approach has a similaroutput as the stochastic approach, but uses (sim-plified) ray tracing together with random place-ment of scatterers to obtain it. For an extensivediscussion of their principles, advantages, anddrawbacks, the reader is referred to [3, 29]. Akey difference between cellular and VTV com-munications channels is that VTV channelsoften exhibit non-stationary channel statistics;that is, not only the impulse responses, but alsotheir statistical properties such as small-scalefading distribution, PDP, and Doppler spectrumchange. Any model should thus be able to han-dle the rapid fluctuations typical for VTV com-munications channels. Below, we discuss how thedifferent modeling approaches handle thisimportant issue. In the following we first describethe main modeling approaches separately (Fig.5). Subsequently, we compare them, and describethe pros and cons for different applications.

MODELING APPROACHES•Narrowband stochastic channel models: Nar-

rowband stochastic models do not characterizethe frequency selectivity of the propagationchannel, but rather focus on characterization ofthe fading statistics together with the Dopplerspectrum — the latter being the key quantitythat distinguishes a VTV channel from a cellularchannel.

In cellular channels, where Doppler shifts are

created mainly by movement of the mobile sta-tion, the most common model for the Dopplerspectrum is the Jakes spectrum, which is basedon the assumption of a uniform angular powerspectrum (i.e., multipath components arriving atthe mobile station from all directions with equalstrength) and an omnidirectional antenna pat-tern at the mobile station [30]. The Jakes spec-trum has a characteristic “bathtub” shape, withstrong contributions at ±fcv/c0, where fc is theconsidered carrier frequency, v the velocity ofmovement, and c0 the speed of light.

Generalizing this approach to VTV channels,[31] considered a situation where both TX andRX are moving, the angles of incidence areindependent at transmitter and receiver, and theangular power spectrum and antenna pattern atTX and RX are uniform. The resulting Dopplerspectrum does not show the “bathtub” shape,but is somewhat smoother. Further interestingresults and generalizations are given in [32, 33].

Channel simulators need to create channelrealizations whose Doppler spectra have the cor-rect shapes, or equivalently have the correctautocorrelation function [34]. Two fundamental-ly different approaches have been developed: astatistical simulation model that essentially per-forms simplified ray tracing (see also the geo-metrical models below); the autocorrelationfunction is satisfied in this model when an expec-tation over different scatterer locations is taken.An alternative is a deterministic channel model,which adds up a number of sinusoids with differ-ent parameters and creates the correct temporalcorrelation function of the resulting fading whentime-averaged correlation is considered. The rela-tive advantages and disadvantages of these twoapproaches are rather subtle, and we refer to[35] for a detailed discussion.

•Wideband stochastic channel models: Wide-band stochastic channel models provide thestatistics of the power received with a certaindelay, Doppler shift, and possibly even angle ofarrival. In particular, the tapped delay linemodel, which is based on the wide-sense-station-ary uncorrelated scattering (WSSUS) assump-tion [11], is in widespread use for cellular systemsimulations. This model describes the channelimpulse response by means of a finite impulseresponse filter with a number of discrete taps,each of which is fading according to a prescribedprobability density function (usually complexGaussian) and Doppler spectrum (Fig. 5d). Inparticular, the IEEE 802.11p channel modelsemploy the 6- and 12-tap models developed byIngram and coworkers [16, 25] in different typesof environments. Since each tap can contain sev-eral paths (where each path can have a differenttype of Doppler spectrum), this allows almostarbitrary Doppler spectra to be synthesized foreach tap even though the spectrum of each pathis selected from a small class of shapes. Tapped-delay-line models are popular due to their lowcomplexity, although they may suffer from lessaccurate representation of the non-stationaritiesin VTV channels.

•Ray tracing: Ray tracing for VTV systems,which was pioneered by Wiesbeck and cowork-ers [14, 37–38], solves (a short-wavelengthapproximation to) the wave equation for specific

Figure 4. Cumulative distribution functions (CDFs) of highway delay spreadsreported in various measurement campaigns.

RMS delay spread (ns)0.2 0

0.2

0

CD

F

0.4

0.6

0.8

1

0.4 0.6

Sen and Matolak, low density [8]Sen and Matolak, high density [8]Cheng et al. [15]Renaudin et al. [27]Paier et al. [7]



scatterer locations and associated boundary con-ditions. Such a deterministic approach providesa site-specific, very realistic simulation of thepropagation channel. By appropriately modelingthe environment (houses, traffic signs, parkedcars, etc.; Fig. 5c), agreement between measuredand simulated receive powers can be broughtwithin 3 dB standard deviation [36, 38]. Raytracing has also been used to investigate theeffects of different antenna positions on thevehicles [39]. The main drawback of ray tracinglies in its computational demands.

•Geometry-based stochastic channel models:VTV geometry-based stochastic channel models(GSCMs) are based on the “classical” GSCMapproach, simulating the channel by randomlyplacing (according to suitable statistical distribu-tions) scatterers around TX and RX, and thenperforming simplified ray tracing. The simplestgeometrical VTV channel model is the two-ringmodel, where one ring of scatterers is placedaround the TX and one around the RX (Fig.5a). Generalizations allow for the existence of anLOS component [40], single-scattering eithernear the TX or the RX, where the relativestrengths of those processes are model parame-ters [41], or inclusion of additional scatterers onan ellipse [42]. A geometrical model, in particu-lar the two-ring model, also allows a joint space-time correlation function to be derived, fromwhich the temporal evolution of the correlationcoefficient between the antenna elements in aMIMO system can be derived [43].

The conventional two-ring model assumesthat all scatterers are placed in the horizontalplane (i.e., have zero elevation). This might notbe a realistic assumption, especially in NLOS sit-uations in urban environments. A generalizationof the two-ring model to the three-dimensionalcase was analyzed in [44], which considered thedouble-scattering case, as well as a series ofpapers by Zajic and Stuber (e.g., [45, 46]).

The reference models described above do notaim to reproduce the physical reality, but ratherare intended for the comparison of differenttransmission schemes. A more realistic model isproposed in [5], where location as well as prop-erties of scatterers are closely adapted to mea-sured results (Fig. 5b). This model makes adistinction between discrete and diffuse scatter-ing (more precisely, interaction), where discretescatterers are typically cars, houses, road signs,and other significant (strong) scattering pointsalong the measurement route, whereas diffusescattering is found to mainly stem from smallerobjects at the sides of the measurement route(TX-RX path); the importance of the latter typeof objects was also pointed out in [47]. A similarapproach was suggested independently in [48].

NON-STATIONARITIESAs discussed previously, one of the most impor-tant points that distinguishes VTV channelsfrom conventional cellular channels is the non-stationarity of the channel, that is, the channelstatistics (not only the instantaneous channel


Figure 5. Overview of some common VTV modeling approaches: a) geometry-based stochastic with scatter-ers on regular shapes; b) geometry-based stochastic with scatterers in realistic positions; c) ray tracing; d)stochastic (tapped delay line).

Delay

Doppler frequency

Power

3D-environment

Vehicles

(a) (b)

(c) (d)

Point scatterers

Point scatterers (diffuse)

Point scatterers (discrete)

Transmitter array Receiver arrayThe conventional

two-ring modelassumes that all

scatterers are placedin the horizontalplane, i.e., have

zero elevation. This need not be a

realistic assumption,especially in NLOSsituations in urban

environments.




impulse response) can change within a rathershort period of time (Fig. 6); therefore, theWSSUS assumption, which is widely used in thedescription and modeling of cellular communica-tions channels, cannot be used. Rather, thechannel statistics are valid only for a short peri-od of time (“region of stationarity”). For exam-ple, in each region of stationarity, the Dopplerspectrum of the first delay tap can be different,because the Doppler shift of the LOS compo-nent can change. If we would just average theDoppler spectra over the different regions ofstationarity, a broadening of the spectrum of thefirst tap (which has no correspondence to physi-cal reality) would result. The non-stationaritiescan be modeled by:• A birth/death process to account for the appear-

ance and disappearance of taps [8]; it must benoted that while this approach provides a non-stationary description, it does not account forthe “drift” of scatterers into a different delaybin, and can also lead to a sudden appearanceand disappearance of strong multipath com-ponents (MPCs)

• Defining different tap models for regions of ameasurement route that have significantly dif-ferent delay spreads, or whose power delayprofiles (PDPs) lead to significantly differentbit error rates (BERs) [24]; however, such anapproach does not provide a continuous (withtime) characterization of the channel

• Using GSCMs or ray tracers, which take non-stationarities into account automatically [5]The non-stationarities of the channel have a

significant impact on system performance. It hasbeen found that the assumption of WSSUS leadsto (erroneous) optimistic BER simulation results

in single-carrier and multicarrier systems [49].Also, [24] showed that if the simulation model isbased on a single averaged PDP from which therealizations are drawn, the resulting BERs in asystem simulation deviate considerably from theBERs resulting from the raw measured channeldata.

SELECTING A SUITABLE MODELING METHODEach of the channel modeling methods men-tioned above has specific advantages and draw-backs. Analytical models for the narrowbandDoppler spectrum, as well as models based ontwo-ring geometries, are suitable for analyticalcomputations and provide reference channelsthat can be used for calibrating a simulator;however, they do not reflect realistic behavior ofVTV channels. Tapped delay line models aresomewhat more realistic and can be parameter-ized in a flexible way to describe channels in dif-ferent environments. A main problem is thatmany tapped delay line implementations, mostnotably the IEEE 802.11p channel models, donot describe non-stationarities of the channels.Furthermore, correlation between antenna ele-ments of MIMO systems is not provided in the802.11p models, and generally would requireadditional modeling effort in tapped delay linemodels. These problems do not occur in GSCMs,which implicitly model non-stationarities andantenna correlations. However, generation ofrealizations of channel impulse responses from aGSCM requires more computer time thantapped delay line models. Finally, the ray tracingapproach, which also implicitly includes non-sta-tionary channel statistics, constitutes the mostrealistic channel model; drawbacks are the con-siderable simulation time and the requirementof an accurate terrain database as well as modelsfor cars and other scatterers in the environment.

SUMMARY AND CONCLUSIONS

The VTV propagation channel has strong impacton the coverage, reliability, and real-time capa-bilities of VTV networks. Wrong assumptionsabout fading lead, for example, to erroneousconclusions on the dependability of intervehiclewarning systems at intersections [50]. Thus, it isvital to use well characterized measurement-based models of VTV communications channels.Much progress has been made since 2005: it isnow well understood that VTV channels areusually non-stationary, and that performancepredictions based on stationary (WSSUS) chan-nels are optimistic. Similarly, channel estimatorsand any other signal processing algorithms thatrely on channel statistics have to be modified inlight of this insight. Delay spreads have beeninvestigated in various environments, and whileunder many circumstances the channels provideappreciable delay diversity, there are a signifi-cant number of cases where such diversity is notavailable, motivating the use of multiple antennaelements for enhanced robustness.

Despite all this progress, many open topicsremain. The small amount of available VTVchannel measurements do not allow the formula-tion of statistically significant statements aboutreal-world VTV channels. Moreover, the propa-

Figure 6. Example of the time-varying power delay profile (average squaredmagnitude of impulse response), where the Doppler spectrum in Fig. 3 is cal-culated around 5.2 s. The delay of the first taps varies as the TX and RXapproach each other, meet, and move away from each other. It can further beobserved that while the LOS tap experiences fading, the interposition betweenstrong components (clusters) and the LOS tap changes with time, and there is split-ting of clusters over time as well.

Delay [ns] 100 0

1

0

Tim

e [s

]

2

3

4

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6

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200 300 400 500 600 700 800 900 1000




gation aspects of vehicular environment cate-gories are expected to vary regionally due to dif-ferences in speed limits, road and buildingconstruction, vehicle population, and trafficstatistics. It will therefore be important to per-form extensive measurement campaigns in dif-ferent parts of the world. A comparison of whatan “urban” channel looks like in, say, Tokyo,Rome, and Los Angeles, would be of great inter-est.

Also little explored is the impact of vehiclesbetween the TX and RX in VTV links (which leadto shadowing of the desired paths), the effects ofthe placement of antennas on vehicles, and thegains from multiple antennas. Measurement cam-paigns for this purpose, possibly augmented by fullelectromagnetic simulations for analyzing theinteractions between antennas and cars, are desir-able. For channel modeling, it would be importantto derive a model that combines the flexibility ofGSCM with the fast simulation times of tappeddelay line models. This abundance of open issues,combined with the increasing importance of VTVcommunications, will make sure that VTV propa-gation channels will remain a vibrant researcharea in the next years.

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This abundance ofopen issues,

combined with theincreasing

importance of VTVcommunications,

will ensure that VTVpropagation channelswill remain a vibrantresearch area in the

next years.




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BIOGRAPHIESANDREAS F. MOLISCH [F] is a professor of electrical engineer-ing and head of the Wireless Devices and Systems (WiDeS)group at the University of Southern California, Los Angeles.His research interests are wireless propagation channels,MIMO, UWB, and cooperative communications. He hasauthored four books, more than 300 papers, 70 patents,and 60 standards contribution. He is a Fellow of the IETand recipient of several awards.

FREDRIK TUFVESSON received his Ph.D. in 2000 from Lund Uni-versity, Sweden, and is now an associate professor in theDepartment of Electrical and Information Technology. Hismain research interests are channel measurements andmodeling for wireless communication, including channelsfor both MIMO and UWB systems.

JOHAN KAREDAL received his M.S. degree in engineeringphysics in 2002 and his Ph.D. in radio communications in2009, both from Lund University, Sweden. He is currently apostdoctoral fellow at the Department of Electrical andInformation Technology, Lund University, where his mainresearch interests concern measurements and modeling ofwireless propagation channels.

CHRISTOPH F. MECKLENBRÄUKER [S’88, M’97, SM’08] receivedhis Dipl.-Ing. degree in electrical engineering from ViennaUniversity of Technology (VUT), Austria, in 1992 and hisDr.-Ing. degree from Ruhr-University Bochum, Germany, in1998. He was with Siemens AG Austria during 1997–2000where he worked on 3G mobile communications. During2000–2006, he was with the Forschungszentrum Telekom-munikation Wien (FTW) in Vienna, Austria. In 2006 hejoined the Institute of Communications and Radio Frequen-cy Engineering at VUT as a full professor. His currentresearch interests include ultra wideband radio and vehicu-lar wireless MIMO systems.



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