Hybrid Environment-based Channel Model forVehicular Communications

Jose Angel Leon Calvo, Florian Schroder and Rudolf Mathar

Institute for Theoretical Information TechnologyRWTH Aachen University, 52074 Aachen, GermanyEmail: {leon, schroeder, mathar}@ti.rwth-aachen.de

Abstract—A novel hybrid environment-based approach formodeling the vehicular communication channel is developed inthis study. It is a combination of a deterministic ray-launchingalgorithm (PIROPA) to model the Large-Scale parameters, anda stochastic model, to obtain the Small-Scale parameters. Thishybrid channel model is site-dependent, i.e., the propagationparameters of the channel are obtained based on the particularscenario, giving a higher accuracy in modeling the channel thanusing an abstract representation of the scenario. The use of astochastic approach also provides the model of a flexibility re-quired for different scenario situations, specially in a dynamic en-vironment like vehicular communications. The simulation resultsshow the significance of the scenario and how the peculiaritiesof the vehicular communications, i.e., low antenna height andhigh mobility of the receivers and transmitters, impact on thecommunication channel. The proposed channel model can beused for different applications, such as anti-collision systems,autonomous driving or video applications.

Keywords—vehicular communications, channel modeling, ray-

launching, context awareness, urban outdoor propagation

I. INTRODUCTION

Vehicular communications are characterized by the highmobility of the network elements and the impact of the envi-ronment geometry. These networks are composed of a largenumber of participants with a dynamic nature. Moreover, thelow antenna height, compared to traditional mobile commu-nications, makes the vehicular channel notably challenging tomodel. These peculiarities impede the possibility of applyingthe traditional well-known cellular channel models to obtainan accurate vehicular radio channel model.

In recent years, different approaches to model the vehicularchannel have been developed. Some models using a pure ray-tracing algorithm, such as Maurer et al. [1]. This model usesa high detailed description of the scenario, and calculatesthe channel statistics analyzing the strongest paths obtainedusing the ray-tracing algorithm. Karedal et al. [2] proposed adifferent perspective; where the model parameters are extractedfrom several measurement campaigns done in highway andurban environments. Other models, as Mangel et al. [3], addedinformation about the street architecture and intersections tothe model. However, every approach has its own drawbacks,such as non site-dependent properties, because the parametersare obtained from different measurement campaigns, or therequirement of a highly detailed scenario information, whichsometimes cannot be easily obtained. Moreover, using anabstract realization of the scenario, it is not possible to modelthe particularities of the environment, obtaining a less accurate

channel model.In order to overcome these difficulties, a novel hybrid

radio channel model is proposed in this work. The mostrelevant characteristic of this channel model is the combinationof a deterministic ray-launching algorithm (PIROPA) [4] [5]for Large-Scale parameters, along with a stochastic approachfor Small-Scale propagation parameters. PIROPA is especiallydesigned for urban scenarios, where it simulates the multipathcomponents of a radio link. Since PIROPA uses geographicaldata as input, the resulting Large-Scale parameters are site-dependent, i.e., the obtained parameters are specific for achosen scenario. This dependency of the scenario has a note-worthy drawback, which is the requirement of geographicalinformation of the studied scenario. However, data acquiredfrom open sources like OpenStreetMaps [6] have a sufficientlevel of detail.

Small-Scale parameters cannot be efficiently calculated us-ing PIROPA, since it only uses the physics under the radiowavepropagation and it does not use statistical distributions. There-fore, a stochastic approach to model the randomness of theSmall-Scale parameters is used in this research. In urban sce-narios, the number of scatters and surrounding object situatedclose to the network elements is large. Combined with thehigh mobility of the scenario elements, it makes modeling theSmall-Scale parameters non trivial. First of all, the extensivelyexploited models for cellular systems are insufficient, due tothe peculiarities of the vehicular communication channel. Inaddition, the higher frequency used for vehicular networks (5.9GHz) is envisioned for short distances (10-1000 m), whereasin cellular systems the range of frequencies goes from 700-2100 MHz and the distances are larger, reaching a maximumof about 10 km. Hence, a novel stochastic approach is requiredin order to accurately model the Small-Scale parameters of thecommunication channel. Due to the fluctuating nature of theSmall-Scale parameters, these values are yielded by statisticaldistributions. In this proposed model, these statistical distri-butions were calculated based on the surrounding scenario,making them site-dependent.

Since the implementation of vehicular communications isimminent, the accuracy of the radio channel model should beas high as possible, to be used in future applications. Becausethe channel model proposed is site-specific, a higher accuracythan in abstract random scenarios is expected. In addition, thescenario-based model is more flexible against the peculiaritiesof each scenario, due to the possibility of recreating them,giving a more realistic channel model.

2015 14th International Conference on ITS Telecommunications (ITST)

978-1-4673-9382-9/15/$31.00 ©2015 IEEE 6

II. CHANNEL MODEL

A novel concept of radio channel for vehicular communi-cations is described in this section. The proposed radio channelmodel is specially designed for urban scenarios, and requiresgeographical input data. The radio channel is created with thepurpose of considering specific scenarios, obtaining a high ac-curacy as well as modeling the particularities of each scenario.In this proposed vehicular channel, the considered networkelements are cars. Different canonical situations present thecharacteristics of the channel model in these scenarios.

The channel model implemented in this paper is basedon the combination of two different approaches which areextensively developed in the following subsections.

A. Environment Scenarios

The characteristics of the vehicular radio propagation pa-rameters are strongly related to the surrounding scenario.Moreover, since the proposed channel model encloses a de-terministic ray-launching algorithm, the environment geometryplays a crucial role. In this paper, two relevant urban vehicularscenarios are studied.

1) Overtaking: This scenario displays a typical situationin urban road traffic, where safety can be conditioned by thecontext awareness of the driver and some efforts can be doneusing a video assisting method [7]. Two cars driving in thesame direction, but with different speeds and in different lanesof the same road, as shown in Fig.1. The relevance in thisscenario is the high mobility and different velocity of thenetwork elements influencing the propagation parameters. Insuch a situation, the Doppler shift is of significance, sincethe spread in frequency affects the communication link and itappears when the elements of the network have a big differencein their speed. It is noteworthy, that the Doppler impact is apeculiarity of vehicular networks, since the Doppler shift is nothigh enough to affect the communication in classical cellularsystems when only pedestrians are involved.

Fig. 1. Overtaking scenario

2) Cross: This scenario simulates a cross involving twocars, as displayed in Fig.2. The relevance of this scenariois to show the importance of the geographical information,regarding the propagation parameters. In a cross, the networkelements suffer from different link situations, i.e., Line-of-Sight (LoS) or Non-Line-of-Sight (NLoS). Therefore, theknowledge of the surrounding scenario elements is essential.Particularly in this type of scenarios, it is expected to obtaina greater accuracy using the proposed model than applyingabstract realizations of the scenario, due to the geographicalinput data. Moreover, this scenario is especially interesting insafety applications, since in such scenarios occur high numbersof collisions.

Fig. 2. Crossing scenario

B. Ray-launching algorithm

PIROPA (Parallel Implemented Ray Optical PropagationAlgorithm) is a deterministic ray-launching algorithm. Dueto its determinism, it could perfectly recreate the results,when given the same input information. It is specializedon urban scenarios operating on a geometric environmentdescription. And to yield the desired performance for criticalapplications, as simulating vehicular communication channels,it is implemented in C++ and optimized towards runtimeperformance. The biggest drawback of this algorithm approachstorage way is the requirement of detailed scenario informationto generate the radio propagation parameters. However, sourcesare available, as for example, using OpensStreetMaps, thisdisadvantage is recessed. Therefore, having the complete inputdata, a scenario-based channel model can be obtained.

PIROPA yields a variety of simulation results such as,pathloss, delay, Angle of Arrival (AoA), Angle of Departure(AoD) and number of rays per cluster. These Large-Scaleparameters are mostly influenced by the geometry and theyhave no stochastic behavior. The Large-Scale parameters arebased on electromagnetic wave propagation prediction andphysical properties.

The impulse response obtained by applying PIROPA isbased on the Double-Directional radio channel [8], where

h(t, ⌧, ✓,') =

NX

n=1

An�(⌧ � ⌧n)�(✓ � ✓n)�('� 'n) (1)

2015 14th International Conference on ITS Telecommunications (ITST)

7

being An the complex amplitude of the received ray, ⌧ thedelay of each single path, ✓ the Angle of Arrival and ' theAngle of Departure for each cluster. Using Eq.1 the receivedpower is calculated as

P (t, ⌧, ✓,') =

Z 1

0|h(t, ⌧, ✓,')|2 dt. (2)

As shown in Eq.2, all the parameters involved in the calcula-tion of the received power are generated by the ray-launchingalgorithm, i.e., they are deterministic. This assumption is validbecause ✓, ' and An of each cluster do not change rapidly.However, the phase of the inter-cluster rays fluctuates fast andneed to be determined stochastically. Moreover, the fast-fadingphenomenon and the Doppler shift will be calculated usingstatistical distributions.

C. Stochastic Approach

The Small-Scale stochastic parameters are used to finelyadjust the channel model to its random nature. Parameters toofine grained for modeling in the deterministic step, e.g., pedes-trians or traffic signs, are considered by adding a stochasticcomponent including such elements in the overall performanceof the radio channel model.

1) Doppler Shift: The Doppler spectrum gives the statisti-cal power of the channel at a given frequency. This spectrumis given by the motion of the surrounding objects and by themovement of the transmitter and receiver. Therefore, the biggerthe relative speed between both ends of the communicationchannel and the higher the mobility of the environment ele-ments, the higher the Doppler spread will be. The Dopplerspectrum is given by the following expression:

DoppS =

Fc

c

((v1 � v2) cos(✓) + (v2 � v1) cos(')) (3)

=

Fc

c

(vrel cos(✓)� vrel cos(')). (4)

being Fc the central frequency used in the radio channel, c

the speed of light, ✓ the Angle of Arrival, ' the Angle ofDeparture and v1,v2 the speeds of the transmitter and receivergiven in m · s�1, respectively.

2) Inter-cluster Phases: Using the deterministic ray-launching algorithm, the angle of arrival of each cluster iscalculated. However, due to the deterministic nature of thealgorithm, the motion of the surrounding network elementsis not considered. As it is known, the movement of theseelements produces a change in the incident phase of the rays.Therefore, a stochastic model is required, since the movementof the network elements cannot be completely predicted. Theshift in the phase is created as a function of the surroundingelements motion, as follows:

InterClustPh

0= K · InterClustPh+X (5)

being X uniformly distributed values in the range [0, 2⇡], Ka value which fluctuates between [-1,1], due to constructive ordestructive interference, in function of the number of reflec-tions the rays suffer and InterClustPh the phase obtainedusing PIROPA.

3) Fast-fading: The last stochastic parameter calculated isthe fast-fading characteristic of the radio wave propagationchannel. It is defined as the fluctuation suffered by the trans-mitted signal due to the multipath components. As a result ofthe different replicas reaching at the receiver side, the resultingaddition at the end could be constructive or destructive, beinginfluenced by the phase of the receiving rays. In the proposedstochastic approach, the fast-fading is calculated in function ofthe surrounding objects, using as reference [3]. In this study,it has been proved how the geometry of the surrounding roadsaffects the profile of the propagation loss, giving differentpathloss models depending on the geometry. Moreover, thetype of scenario is studied, e.g., rural, urban or quasi-urban andthe NLOS/LOS situation is determined using the deterministicalgorithm. The fast-fading is modeled as Rician fading and thescaling factor of the distribution is biased by the environment.Therefore, in a LoS situation, the fast-fading will have asmaller impact on the communication channel than in a NLoSsituation where the multipath propagation will have a biggerinfluence.

III. RESULTS

In the simulation of both scenarios, the transmitter andreceiver antennas are situated 1.5 m above ground level,emulating the height of a car. Since the height of the antennasis much smaller than the surrounding buildings, the roofrefraction is neglected, and only the diffractions and reflectionson the walls off the buildings are considered. The transmittedpower used is 1W, to obtain a clear value of the pathloss.

A. Overtaking Scenario

1) Power Delay Profile: In Fig. 3, the PDP of the overtak-ing scenario shows a typical profile dependent of the distancebetween transmitter and receiver, since the network elementsare in a LoS situation. The received power is inversely pro-portional to the square of the distance in the directly receivedrays and only the multipath components suffer a higher delayand lower power.

Received Power [dB]-110 -100 -90 -80 -70 -60 -50 -40 -30 -20 -10

Del

ay [s

]

×10-6

0

0.2

0.4

0.6

0.8

1

1.2Power Delay Profile

Dis

tanc

e to

the

rece

iver

[m]

4

6

8

10

12

14

16

18

20

Fig. 3. Power Delay Profile in an overtaking scenario

2) Doppler Shift: The Doppler Shift in an overtakingscenario behaves as can be expected, as shown in Fig. 4. Inthe first 20 measurement points, the receiver and transmitterhave the same speed, giving an almost null Doppler shift.

2015 14th International Conference on ITS Telecommunications (ITST)

8

Nevertheless, the Doppler shift rises once the relative speedbetween both increases.

Measurement Points0 10 20 30 40 50 60

Dopple

r S

hift

[H

z]

-250

-200

-150

-100

-50

0

50

100

150

200

250Evolution of Doppler Shift

Dela

y [s

]

×10-10

1

2

3

4

5

6

7

8

9

10

11

Fig. 4. Doppler Shift in an overtaking scenario

B. Cross Scenario

1) Power Delay Profile: In Fig. 5, the PDP of a crossingscenario shows different behavior when compared with theovertaking scenario. While having a main part of the graphwhich follows the same negative exponential behavior as theovertaking scene, and since this part of the channel is in aLoS situation, the two other paths suffer from a differentphenomenon. These two paths are created due to the NLoSof the channel and, both have a lower received power, due tothe higher number of bouncing-off suffered until they reachthe other part of the communication link.

Received Power [dB]-120 -110 -100 -90 -80 -70 -60 -50 -40 -30

Dela

y [s

]

×10-7

0

0.2

0.4

0.6

0.8

1

1.2Power Delay Profile

Dis

tan

ce t

o t

he

re

ceiv

er

[m]

300

310

320

330

340

350

360

370

380

390

400

Fig. 5. Power Delay Profile in a crossing scenario

2) Doppler Shift: Fig. 6 displays the results for the Dopplershift. Due to the NLoS situation in most of the simulatedscenario, the Doppler shift is higher than in the overtakingscenario. This is caused by the higher number of reflections inthe surrounding scenario scatters. In this case, the maximumvalue obtained by the Doppler shift is between [-600, 600 Hz],having a low impact on the communication link, but it cannotbe neglected. In the first 40 measurement points of Fig. 6,the Doppler shift is increasing almost constantly, due to the

constant acceleration of the cars, increasing the relative speedbetween the cars. Nevertheless, once the speed is stable, theDoppler shift is constant around ± 550 Hz.

Measurement Points0 10 20 30 40 50 60 70 80

Dopple

r S

hift

[H

z]

-600

-400

-200

0

200

400

600Evolution of Doppler Shift

Dela

y [s

]

×10-8

1

2

3

4

5

6

7

8

9

10

Fig. 6. Doppler Shift in a crossing scenario

IV. APPLICABILITY

In this section different examples of the possible applica-tions for the vehicular channel model are introduced. Theseapplications are focused to exploit the context awareness ofthe channel model, and are directed towards creating a feasiblevehicular network. The cumulative distribution function for thereceived power is illustrated in Fig. 7. Using this parameter, itis possible to observe the connectivity level between two cars,i.e., we select a power threshold where an application willwork, and using this parameter, the percentage of availabilitycan be detected. Moreover, using this attribute, it is possibleto modify the transmitted power in different scenarios, conse-quently conserving the energy.

Received Power [dB]-120 -100 -80 -60 -40 -20 0

CD

F

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1Cumulative Distribution Function Comparison

OvertakingCross

Fig. 7. Received Power CDF Comparison

In Fig. 7, we can see the differences in the performance of thereceived power depending on the scenario. In the overtakingscenario, since the network elements are in a LoS situation,the received power is approximately 20 dB higher than in thecrossing scenario.

The context aware channel model is specially suitable for

2015 14th International Conference on ITS Telecommunications (ITST)

9

the planning and deployment of RSU (Road Side Unit), sincethe parameters of the channel are site-dependent, and thechangeable characteristics of the environment are taken intoaccount. For the purpose of deployment a vehicular network,the same concepts as in the classical mobile communicationscan be used, because the RSU is elevated over the buildingsand static.

Another important aspect to determine the applicabilityof the channel model is to obtain the maximum delay andangle spread value. Both parameters are second order statisticsobtained using the ray-tracing algorithm. Delay spread is animportant parameter for the receiving end of the communi-cation link, since a really high delay will negatively affectthe performance of the decoder. Therefore, knowing the upperbound of the delay spread will help to design the receivingsystem. In addition, the angle spread has been also analyzed.This parameter is important in the receiver side whether adirectional beam is used. The angle spread has been calculatedfor both studied scenarios, using the following expression:

�A =

sZ(✓ � ✓0)

2 · Power(✓) d✓ (6)

where ✓ is the Angle of Arrival, ✓0 is the mean value ofthe Angle of Arrival and Power(✓) is the received power infunction of the angle for each ray.

Likewise, the Doppler spread has been calculated using thefollowing equation:

�D =

sZ(⌧ � ⌧0)

2 · Power(⌧) d⌧ (7)

where ⌧ is the delay, ⌧0 is the mean delay value and Power(⌧)

is the received power in function of the delay for each ray.Using these expressions, the obtained values are displayed inTable I:

TABLE I. DELAY AND ANGULAR SPREAD

Scenario Delay Spread [s] Angular Spread [degrees]Overtaking 2.9355 ⇥ 10�10 100.8281

Crossing 2.3844 ⇥ 10�8 106.5911

Observing the Table I, we can see the delay spread is higher,as expected, in the crossing scenario where the multipath isa predominant phenomenon. However, the Angle spread iscomparable in both situations. For this reason, the Angle ofArrival was also analyzed using the angular distribution forboth scenarios.

The Angle of Arrival distribution is displayed in Fig. 8.The AoA for a cross scenario is concentrated in the rangeof [90, 270 degrees], with peaks around 270 degrees, whenthe overtaking maneuver is being done and 45 degrees dueto the reflections created by the buildings surrounding thevehicles. Moreover, there is also a great concentration ofpaths around 0 degrees, which are created when However, inthe cross scenario the AoA is distributed uniformly, havingonly a higher concentration of received rays around 0 degreeswhen the overtaking maneuver is completed. This result isconsistent with the higher impact of the multipath in a crossingscenario. Using this result, the directional beam of the receiverantenna can be designed to take advantage of this knowledge.Having the receiver antennas pointing to the most likely arrival

20

40

60

30

210

60

240

90

270

120

300

150

330

180 0

Angle of Arrival DistributionCrossOvertaking

Fig. 8. Angle of Arrival Distribution

directions will give a better performance of the vehicular radiochannel, since the gain will be higher.

V. CONCLUSION

In this paper, a novel vehicular radio channel model isintroduced and analyzed. The channel model was created usinga combination of a deterministic ray-tracing algorithm andstochastic methods to model the randomness of a variablescenario. The radio channel model was used in the simulationof two canonical scenarios for vehicular communications, i.e.,an overtaking and a crossing scenario involving two vehicles.The results obtained show the characteristics of the vehicularradio channel and the challenges faced to model it perfectly.Moreover, using the characteristics of the channel model,different applications were introduced and analyzed.

The main goal of this proposed radio channel model isto develop a context aware model to exploit the knowledgeof the scenario to model the vehicular channel with increasedaccuracy. In the near future, vehicular communications willbe a reality and therefore, the precise knowledge of thesurrounding scenario will help to create different applicationsfor safety and/or entertainment.

REFERENCES

[1] J. Maurer, T. Fugen, T. Schafer, and W. Wiesbeck, “A new inter-vehicle communications (ivc) channel model,” in Vehicular TechnologyConference, 2004. VTC2004-Fall. 2004 IEEE 60th, vol. 1, Sept 2004,pp. 9–13 Vol. 1.

[2] J. Karedal, F. Tufvesson, N. Czink, A. Paier, C. Dumard, T. Zemen,C. Mecklenbrauker, and A. Molisch, “A geometry-based stochastic mimomodel for vehicle-to-vehicle communications,” Wireless Communica-tions, IEEE Transactions on, vol. 8, no. 7, pp. 3646–3657, July 2009.

[3] T. Mangel, O. Klemp, and H. Hartenstein, “A validated 5.9 ghz non-line-of-sight path-loss and fading model for inter-vehicle communication,” inITS Telecommunications (ITST), 2011 11th International Conference on,Aug 2011, pp. 75–80.

[4] F. Schroder, M. Reyer, and R. Mathar, “Fast radio wave propagationprediction by heterogeneous parallel architectures with incoherentmemory,” in Proceedings of Wave Propagation and Scattering inCommunication, Microwave Systems and Navigation, TU Chemnitz,Dec. 2010, pp. 89–93. [Online]. Available: http://www.ti.rwth-aachen.de/publications/output.php?id=783\&table=proceeding\&type=pdf

2015 14th International Conference on ITS Telecommunications (ITST)

10

[5] ——, “Efficient implementation and evaluation of parallel radiowave propagation,” in EuCAP 2011 - 5th European Conference onAntennas and Propagation, EUR Congressi in Rome, Italy, Apr.2011, pp. 2466–2470. [Online]. Available: http://www.ti.rwth-aachen.de/publications/output.php?id=791\&table=proceeding\&type=pdf

[6] Openstreetmap. [Online]. Available: http://www.openstreetmap.org[7] A. Vinel, E. Belyaev, K. Egiazarian, and Y. Koucheryavy, “An overtaking

assistance system based on joint beaconing and real-time video transmis-sion,” Vehicular Technology, IEEE Transactions on, vol. 61, no. 5, pp.2319–2329, Jun 2012.

[8] M. Steinbauer, A. F. Moslich, and E. Bonek, “The double-directionalradio channel,” IEEE Antennas and Propagation Magazine, 2001.

2015 14th International Conference on ITS Telecommunications (ITST)

11