Properties and Applications of the Suburban

Vehicle-to-Vehicle Propagation Channel at 5.9 GHz

Lin Cheng, Benjamin E. Henty, Daniel D. Stancil∗, Fan Bai and Priyantha Mudalige†

Abstract — We describe a system capable of makingchannel measurements as a function of location whilethe vehicles are in motion to study the properties ofthe vehicle-to-vehicle wireless channel. We presentresults from on-road experimental tests in suburbanareas of Pittsburgh. The dependence of Dopplerspread on both velocity and vehicle separation isdiscussed. We introduce the Speed-Separation (S-S)diagram as a new tool for understanding and esti-mating Doppler spread in the vehicle-to-vehicle en-vironment.

1 INTRODUCTION

Wireless information exchange between moving ve-hicles has received a lot of attention recently. In1999, the Federal Communications Commission(FCC) allocated 75 MHz of licensed spectrum, from5.85 to 5.925 GHz, for Dedicated Short Range Com-munications (DSRC) in the United States. Asone emerging category of Intelligent transporta-tion Systems (ITS), Vehicular Ad hoc NETworks(VANET) enable a wide range of new protocols andapplications including safety, security and servicesrunning on various modes like vehicle-to-vehicle,vehicle-to-roadside, electronic-toll, etc. Amongthem, the vehicle-to-vehicle scenario is probably themost challenging one to implement, owing to thehighly dynamic nature of the vehicle-to-vehicle en-vironment. Consequently, a thorough understand-ing of the propagation channel is required to de-sign VANET systems with the ability to exchangesafety-critical messages and other information reli-ably.

Relevant reported work in empirical studies in-clude narrow-band measurements at 5.2 GHz [1]and joint Doppler-delay power profile measurementat 2.4 GHz [2]. Measurements and modeling of thevehicle-to-vehicle channel at 5.9 GHz have also beenreported [3], but without real-time speed and dis-tance information. Performance evaluations weredescribed in [4] using the IEEE 802.11a protocol at5.2 GHz between a moving vehicle and a fixed basestation.

∗Department of Electrical and Computer Engineering,

Carnegie Mellon University, Pittsburgh, PA, 15213, USA,

e-mail: lincheng, henty, stancil@andrew.cmu.edu†Electrical and Controls Integration Laboratory, General

Motors Research Center, Warren, MI, 48090, USA, e-mail:

fan.bai, priyantha.mudalige@gm.com

(a)

(b)

Figure 1: (a) the setup on the transmitting vehicle,and (b) the setup on the receiving vehicle.

We believe additional experimental VANET im-plementation and field studies at the 5.9 GHzDSRC band are needed. In this paper we presenta study of the propagation characteristics and ap-plications of the 5.9 GHz DSRC band for vehicle-to-vehicle communications, based on our field im-plementation platform. We present results fromanalyzing data sets taken in suburban Pittsburgh,including the dependence of Doppler spread onboth velocity and vehicle separation. We confirmthe theoretically predicted dependence from exper-imental data. We introduce the Speed-Separation(S-S) diagram as a new tool for understanding andestimating Doppler spread in the vehicle-to-vehicleenvironment.

2 MEASUREMENT PLATFORM

We have developed an experimental platform to ac-curately analyze the properties of the vehicle-to-

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vehicle DSRC channel. It integrates precision lab-oratory RF components as well as GPS receivers.

For the RF part, the transmitter architecture isdepicted in Fig. 1(a). An Agilent E4433A DigitalSignal Generator (DSG) is used for generating var-ious forms of signals. For example, to obtain a CWsignal at 5.9 GHz, a 2.95 GHz signal from the gen-erator is frequency doubled using a mixer and thesignal from the coherent carrier output. We use aband-pass filter to ensure undesired mixer productsare removed before transmission. Amplification isused to boost the signal levels up to the expectedDSRC transmit power of 20 dBm.

Fig. 1(b) describes the receiver architecture. Weuse a broadband, low noise amplifier to maximizethe sensitivity of the receiver. We use a high-passfilter to prevent a loss of gain due to out of bandsignals. The filter provides a minimum of 40 dB ofattenuation to all signals below 3.8 GHz and 1 dBof attenuation across the 5.9 GHz frequency band.The received RF signal is down-converted into the0 to 40 MHz range via a mixer, and subsequentlyanalyzed by the Agilent 89600 Vector Signal Ana-lyzer (VSA). We set the VSA to measure a smallrange of frequencies around the CW tone to furtherimprove the dynamic range. We have developedautomation software to record a 200 ms data cap-ture at 1-second intervals. We denote each of these200 ms as a VSA measurement sweep. An Agilent8251A precision RF signal generator provides a LOsignal to the mixer for the down-conversion.

In the vehicle-to-vehicle environment, channelimpairments such as path loss and fading arecoupled together with mobility parameters suchas speed, separation, and driver behavior. Con-sequently we also incorporate differential GPS(DGPS) receivers into the platforms, allowing dy-namic measurements of the mobility parameterstogether with the channel impairments, while thevehicles are being driven in actual roadway condi-tions. The accuracy of the DGPS position is on theorder of one meter. The DGPS receivers also helpus overcome the difficulties in synchronizing mea-surements performed while the two vehicles are inmotion during field tests.

We have conducted narrow-band ContinuousWave (CW) experiments using the above systemin suburban driving environments near CarnegieMellon University in Pittsburgh, PA. Two vehicles,a transmitter and receiver, are equipped with theaforementioned measurement system. Both vehi-cles were driven at each driver’s prerogative, to pre-serve normal driving conditions. For driver behav-ior and path-loss we refer the readers to [5,6]. Thechannel study in this paper is conducted utilizing

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spr

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[Hz]

(vR2 +v

T2)0.5 [m/s]

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Figure 2: Doppler spread versus effective speed.(a) contour plot of the Doppler spread versusvT and vR. (b) Doppler spread versus veff =√

v2R(d) + v2

T (d). The dashed line corresponds toEq. 4, while the solid lines represent the linear re-gression.

about 6000 VSA measurement sweeps. The con-fidence intervals estimated for our measurements(shown in subsequent data plots) implies that thesedata sweeps are adequate for statistically meaning-ful analysis. These routes consist primarily of 2-lane suburban streets, and the experimental vehi-cles did not pass each other during the data runs.

3 DOPPLER SPREAD ANALYSIS

Perhaps one of the most important parameters usedto describe the channel impairments is the Dopplerspread. In a typical wireless environment, the re-ceiver antenna senses incoming signals from mul-tiple paths. In a mobile environment, the ob-

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served frequency of the signal from each path willbe Doppler shifted depending on the component ofthe vehicle velocity along the angle of arrival of thepath. The superposition of the signal componentsfrom multiple paths results in a broadened spec-trum compared to the transmitted signal. Thisphenomenon is known as Doppler spread.

Spectral estimation is used to characterize theDoppler spread quantitatively. The nth order cen-tral moment of a spectrum, Mn, is described by

Mn =∑

(fi − f0)nS(fi)∑S(fi)

(1)

where fi is the ith frequency in the spectrum sam-ple, f0 is the first order moment of the spectrum

f0 ≡∑

fiS(fi)∑S(fi)

, (2)

and S(fi) is the magnitude of the Doppler spec-trum at frequency fi. In the above equations, thedenominator acts to normalize the spectrum to unitarea.

The second order central moment, M2, is usedto obtain a spectral width estimator for Dopplerspread, which is given by

BD,E =√

M2. (3)

To correlate channel measurements with mobilityparameters, the GPS position and velocity of eachvehicle is logged for each VSA measurement sweep.The separation between the vehicles is readily ob-tained from these GPS logs.

3.1 Doppler spread vs. effective speed

According to models with isotropic scatterers andno line-of-sight, the Doppler spread in the mobile-to-mobile environment is given by [7, 8]

fD =(

1λ

)√v2

R + v2T

2=

(1

λ√

2

)veff , (4)

where vR and vT are the speeds (magnitudes of thevelocities) of the receiving and transmitting vehi-cles, respectively. We denote veff =

√v2

R + v2T as

effective speed.The measured Doppler spread values versus ef-

fective speed are shown in Fig. 2 for both datasets. The contour plot in Fig. 2(a) shows the gen-eral trend of circular contours as expected from adependence on speed of the form

√v2

R(d) + v2T (d).

Fig. 2(b) shows the dependence of Doppler spreadon the effective velocity. Also shown is the theo-retical curve given by Eq. 4. A linear regression tothe data is also plotted as a solid line. The data

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(vR2

+v T2

)0.5 [m

/s]

Figure 3: Scatter plots of√

(v2R + v2

T ) versus ve-hicle separation for each measurement. The solidlines show the bounds given by the phenomenolog-ical model adjusted to fit the observed driving be-havior.

confirms the predicted linear dependence, thoughthe slope is smaller, and an offset is apparent atzero speed. We believe the smaller slope is the re-sult of fewer, non-isotropically oriented scatterersthan assumed in the models, resulting in smallerDoppler spreads on average than those theoreticalmodel predictions. The offset is likely due to thefact that there is motion in the environment evenwhen both vehicles are stationary.

3.2 The Speed-Separation Diagram as aPrediction Tool

Prior to discussing the correlation between Dopplerspread and separation, we need to examine any un-derlying correlation between separation and speed.

To this end, we introduce a tool denoted asSpeed-Separation (S-S) diagram, which is a scat-ter plot of veff versus separation. An example isshown in Fig. 3. As we can see, beyond a mini-mum separation of about 5 m, the speed increaseswith separation up to a maximum determined bythe speed limit. The correlation of speed with dis-tance reflects the natural tendency of drivers to al-low greater separation as the speed increases.

Since we have established that the Dopplerspread increases with effective speed, the correla-tion between effective speed and separation sug-gests that an increase of Doppler spread with sep-aration should be observed as well.

3.3 Doppler spread vs. separation

Fig. 4 describes the measured Doppler spread ver-sus separation. The green squares with error barsshow the computed value of Doppler spread at aparticular separation bin using Eq. 3. For these

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aver

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dopp

ler

spre

ad [H

z]

Figure 4: Doppler spread versus Distance. Curvesmarked with “*” result from the S-S model.

calculations, we first compute the Doppler spreadfor each sweep, then we average the spreads in eachdistance bin. The error bars in the plot representthe 95% confidence intervals.

Having discussed the measured Doppler versusseparation, we now use this as an example todemonstrate how we use the speed-separation cor-relation shown in the S-S diagram tool to make pre-dictions.

Section. 3.1 has established from experimentaldata that the Doppler spread is proportional to theeffective speed veff (d) =

√v2

R(d) + v2T (d). We use

this observation to estimate the Doppler spread fora given distance bin employing the S-S diagram toolin Fig. 3. Since the Doppler spread is proportionalto the effective speed, each point in a given distancebin contributes to a particular value of spread. Wecan therefore estimate the Doppler spread for aparticular distance bin by averaging all of the re-sulting Doppler spreads in that bin. The predictedDoppler spread versus distance curves using the S-S diagram tool is shown as the curve marked with“*” in Fig. 4. The good agreement with the valuesof Doppler spread calculated from the experimentalspectra demonstrates the validity and usefulness ofthe prediction tool.

4 CONCLUSION

Using a field implementation of a VANET system,we have presented an experimental study of thevehicle-to-vehicle DSRC channel at 5.9 GHz. Inaddition, we have developed insights into the im-pact of speed and separation distance on channelproperties such as the Doppler spread. We haveintroduced the Speed-Separation (S-S) diagram asa new tool for understanding and predicting theproperties of vehicle-to-vehicle channels.

Acknowledgments

The authors would like to thank our colleagues, Dr.Hariharan Krishnan and Dr. Varsha Sadekar ofGeneral Motors Research Center, for their insight-ful discussion in the brainstorming phase of thisresearch.

References

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[2] Guillermo Acosta, Kathleen Tokuda, andMary Ann Ingram, “Measured joint doppler-delay power profiles for vehicle-to-vehicle com-munications at 2.4 GHz,” GLOBECOM ’04.IEEE, vol. 6, pp. 3813 – 3817, 2004.

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[5] Lin Cheng, Benjamin Henty, Daniel Stancil,Fan Bai, and Priyantha Mudalige, “Phe-nomenological driving behavior model of thesuburban vehicle-to-vehicle propagation chan-nel at 5.9 GHz,” IEEE INFOCOM MObile Net-works for Vehicular Environments Workshop,2007.

[6] Lin Cheng, Benjamin Henty, Daniel Stancil,Fan Bai, and Priyantha Mudalige, “Fully mo-bile, GPS enabled, vehicle-to-vehicle measure-ment platform for characterization of the 5.9GHz DSRC channel,” IEEE International Sym-posium on Antennas and Propagation, 2007.

[7] F. Haber A. S. Akki, “A statistical modelof mobile-to-mobile land communication chan-nel,” IEEE Trans. on Vehicle Technology, vol.VT-35, 1986.

[8] Chirag Patel, Gordon Stuber, and Thomas G.Pratt, “Simulation of rayleigh-faded mobile-to-mobile communication channels,” IEEE Trans-actions on Communications, vol. 53, pp. 1876–1884, 2005.

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