impact of the radio channel modelling on the performance

Telecommun SystDOI 10.1007/s11235-010-9396-x

Impact of the radio channel modelling on the performanceof VANET communication protocols

Javier Gozalvez · Miguel Sepulcre · Ramon Bauza

© Springer Science+Business Media, LLC 2010

Abstract The expected traffic safety and efficiency benefitsthat can be achieved through the development and deploy-ment of vehicular ad-hoc networks has attracted a signifi-cant interest from the networking research community thatis currently working on novel vehicular communication pro-tocols. The time-critical nature of vehicular applications andtheir reliability constraints require a careful protocol designand dimensioning. To this aim, adequate and accurate mod-els should be employed in any research study. One of thecritical aspects of any wireless communications system isthe radio channel propagation. This is particularly the casein vehicular networks due to their low antenna heights, thefast topology changes and the reliability and latency con-straints of traffic safety applications. Despite the research ef-forts to model the vehicle-to-vehicle communications chan-nel, many networking studies are currently simplifying andeven neglecting the radio channel effects on the performanceand operation of their protocols. As this work demonstrates,it is critical that realistic and accurate channel models areemployed to adequately understand, design and optimizenovel vehicular communications and networking protocols.

Keywords Wireless vehicular communications · Radiochannel modeling · Dimensioning and optimization ·System simulations

J. Gozalvez · M. Sepulcre (�) · R. BauzaUniversity Muguel Hernandez of Elche, Avda. Universidad s/n,03202 Elche, Spaine-mail: [email protected]

J. Gozalveze-mail: [email protected]

R. Bauzae-mail: [email protected]

1 Introduction

Vehicular Ad-Hoc Networks (VANETs) have been identifiedas a promising Intelligent Transportation System (ITS) tech-nology to improve traffic safety and efficiency while pro-viding Internet access on the move. To reach these ambi-tious goals, V2V (Vehicle-to-Vehicle) and V2I (Vehicle-to-Infrastructure) systems allow the wireless transmission ofinformation among vehicles and with road infrastructure,which effectively enables vehicles that are not within di-rect sight of each other to exchange information that willhelp preventing any potential road traffic danger or improv-ing road mobility. The potential of these new technologiesis such that the IEEE is currently developing an amendmentto the IEEE 802.11 standard (IEEE 802.11p) [1] and the Eu-ropean Telecommunications Standards Institute (ETSI) hasestablished a technical committee to develop the new vehic-ular communication standards [2].

The potential impact of vehicular communications hasbeen fueling lately important research work from the com-munications and networking engineering communities. Al-though hardware prototypes are being developed, the com-plexity of VANETs is such that most of the research is be-ing conducted through simulations. Nevertheless, these sim-ulation investigations are critical to adequately dimensionand configure the operation of VANET systems, especiallyconsidering the strict reliability and latency requirementsof traffic safety applications and the fast network topologychanges due to the vehicles’ mobility. The strict communi-cation requirements of vehicular applications result in theneed to carefully conduct VANET research studies usingadequate and realistic models that ensure achieving accu-rate results. For example, it has been shown that vehicularmobility models can considerably impact the performanceand operation of VANET simulations, and realistic mobility

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J. Gozalvez et al.

models are needed for a precise analysis of VANET proto-cols and applications [3].

Based on simulation, interesting studies have been con-ducted to analyse and improve the performance of the IEEE802.11p protocol, such as in [4] and [5]. In particular, thework in [4] proposes the dynamic adaptation of the IEEE802.11p backoff window sizes as a function of the numberof vehicles in the transmission range to improve the through-put. The work in [5] evaluates the Enhanced DistributedChannel Access (EDCA) protocol of IEEE 802.11p, whichconsiders service differentiation, taking into considerationthe specific conditions of the control channel, and presentsdetailed results in terms of throughput, packet losses, bufferoccupancy and delays. Based on IEEE 802.11p, other stud-ies analyse the performance of multihop routing and datadissemination protocols in VANETs. For example, the workin [6] proposes and analyses the performance of small-scaleand large-scale routing protocols in urban environments,considering realistic mobility models. The study presentedin [7] proposes an adaptive reactive routing protocol forVANETs based on the traffic density estimation of the pathsto be used, which has been shown to improve the perfor-mance of the protocol. In fact, for this type of protocols thetraffic density (or the number of neighboring vehicles) canbe an important parameter and has been analysed in otherstudies such as in [8], considering multipath and mobilityeffects in highway scenarios.

A key, but yet underexplored for vehicular environments,aspect of any wireless system is radio propagation. Radiopropagation modelling has been shown to have a significantimpact on the performance of communications techniquesin traditional mobile and wireless communication systems[9] and ad-hoc networking systems [10]. In [10] the authorsconduct an interesting investigation on the importance ofpropagation modelling to adequately study routing protocolsin low mobility MANETs (Mobile Ad-hoc Networks). Al-though some interesting work has already being carried outto characterize the radio propagation for vehicular commu-nication systems (see e.g. [11] and [12]), more research isneeded to adequately understand the vehicular channel anddevelop channel models suitable for system level investi-gations. The creation of such radio channel models wouldrepresent a valuable resource for designers and developers,not only to properly evaluate the performance of advancedcommunication schemes, but also to fairly compare differentnovel proposals.

Despite the demonstrated importance of the radio chan-nel modelling on the performance of traditional cellular sys-tems, many VANET research studies significantly simplify,or even neglect, the impact of radio propagation on the op-eration and benefits that can be achieved through the use ofVANET communications and networking protocols. This isparticularly dangerous for vehicular networks due to unfa-vorable propagation conditions that can be experienced due

to low transmission antenna heights in V2V communica-tions and highly mobile ad-hoc communication networks.

In this context, this work is not aimed at developing newradio channel models for the vehicular environment but atinvestigating the impact of the radio channel modelling onthe performance, dimensioning and operation of VANETcommunications and networking systems. In particular, thestudy focuses on the reliability and time-critical V2V safetyapplications and the challenging operation of VANET ad-hoc routing protocols. The final aim of this research is toprovide information to the research community about thelevel at which radio propagation needs to be modeled to con-duct valid investigations intended to design V2V and V2Isystems. Interesting initial investigations on this topic havebeen presented. In [13], the authors conducted a simulationstudy to investigate the effects of realistic channel character-istics on packet forwarding strategies for vehicular ad-hocnetworks. In particular, the authors analyzed these strate-gies using deterministic (Two-Ray-Ground) and probabilis-tic (Nakagami) radio propagation models and showed thatthe radio propagation model utilized can have a consider-able, not always negative, impact on protocol performance.The authors have also analyzed this issue with regard to thecapabilities of IEEE 802.11 to coordinate packet transmis-sions and avoid collisions [14]. In this context, this workcomplements these useful initial investigations consideringa different analysis approach. In this case, this work grad-ually increases the radio channel modelling accuracy withthe aim to identify and quantify the varying contributionsof each radio propagation effect (pathloss, shadowing andmultipath fading) on the dimensioning and configuration ofVANETs. In addition, this investigation is not limited to thesystem level impact but also considers the radio channelmodelling impact on the capacity to instantaneously guaran-tee the strict reliable communication requirements that char-acterize VANET systems.

To demonstrate the impact of the radio channel mod-elling on the performance, operation and understanding ofVANETs, two key communication scenarios are considered.The first one represents an intersection V2V collision avoid-ance application. In this case, guaranteeing the correct re-ception, and hence the adequate configuration of the com-munication parameters, of a broadcast beaconing messagefrom the potentially colliding vehicle with sufficient time forthe driver to react is key to avoid the collision at the intersec-tion. The second scenario implements a set of geographical-based VANET routing protocols to disseminate safety ortraffic efficiency information among vehicles. The capacityto route information without the participation of road sideinfrastructure is a key feature for the initial and gradual de-ployment of VANET systems. It is interesting to note thatthe selected evaluation scenarios allow analyzing the impactof the radio channel modelling on both one-hop and multi-hop VANET communications.

Impact of the radio channel modelling on the performance of VANET communication protocols

This paper is organized as follows. Section 2 describesthe main features of the IEEE 802.11p communicationstechnology, which is being especially designed to operatein the vehicular environment, and is the basis of this study.Section 3 details the radio channel effects and models con-sidered in this work. Section 4 analyses the impact of theradio channel modelling accuracy on the performance evalu-ation of 1-hop traffic safety VANET applications, includingscenarios with and without channel congestion. Section 5studies the effect of the accuracy of the radio channel mod-elling on the performance and operation of different multi-hop ad-hoc routing protocols in urban environments. Finally,Sect. 6 summarizes the main results obtained and concludesthe paper.

2 Wireless access for vehicular environments

This work is based on the Wireless Access for Vehicular En-vironments (WAVE) technology which is being developedby the IEEE to adapt the IEEE 802.11 operation to the ve-hicular environment and is being adapted to the Europeancontext by ETSI TC ITS in the ITS-G5 standard. In the US,WAVE is based on seven ten-megahertz channels consist-ing of one control channel and six service channels in the5.9 GHz band. Similarly, in Europe, 30 MHz have been re-served in the same frequency band for cooperative vehicularsystems and are currently divided in one control channel andtwo service channels [15]. The service channels are used forpublic safety and private services, while the control chan-nel is used as the reference channel to initially detect sur-rounding vehicles and establish all communication links. Asa consequence, the control channel is mainly used to peri-odically broadcast positioning, movement and status infor-mation to surrounding vehicles by means of CAMs (Coop-erative Awareness Messages) in Europe or Heartbeat WSMs(WAVE Short Messages) in the US [16].

The IEEE 802.11p [1] standard defines the WAVE phys-ical and MAC (Medium Access Control) layers of the pro-tocol stack. IEEE 802.11p is based on the DCF (DistributedCoordination Function) of IEEE 802.11 and consequentlymakes use of the CSMA/CA medium access mechanism togrant the vehicles access to the communications channel.IEEE 802.11p employs the EDCA mechanism from IEEE802.11e to support service priority and QoS differentiation.Four different queues corresponding to four different serviceclasses are provided. The ad-hoc mode is the only opera-tional mode allowed in the control channel. At the physicallayer, IEEE 802.11p uses Orthogonal Frequency DivisionMultiplexing (OFDM) with a maximum data transmissionrate of 27 Mbps in 10 MHz channels. The default data rateused in the control channel is 6 Mbps, which corresponds tothe QPSK transmission mode with a coding rate of 1/2.

3 Radio channel modelling

Accurate radio propagation models for system level inves-tigations must properly reflect the effects of pathloss (PL),shadowing (SH) and multipath fading (MP) [17]. While thepathloss represents the local average received signal powerrelative to the transmit power as a function of the distancebetween transmitter and receiver, the shadowing models theeffect of surrounding obstacles on the mean signal attenua-tion at a given distance. The multipath fading effect resultsfrom the reception of multiple replicas of the transmittedsignal at the receiver. Previous research has demonstratedthe importance of an accurate radio channel modelling to ad-equately evaluate communication techniques in traditionalmobile and wireless communication systems [18, 19]. How-ever, the relative youth of the research in the vehicular net-working domain has resulted in that many vehicular commu-nications and networking studies have been based on rathersimple radio channel models that are not capable to accu-rately reflect the challenging vehicular channel conditions[20, 21].

The received signal power (Pr) can be calculated in dBusing the following equation, that considers the transmissionpower (Pt) and the different propagation phenomena, anddoes not consider antenna gains and circuit losses:

Pr = Pt − PL − SH − MP. (1)

To analyse the impact of the radio propagation mod-elling on the understanding and evaluation of vehicular com-munication protocols and communication techniques, thiswork implements different radio propagation models vary-ing from deterministic propagation models to a more real-istic channel model accounting for the variability present inthe radio channel.

3.1 Pathloss

Many different pathloss models can be found in the litera-ture and three of them have been considered in this work.One of the implemented pathloss models is the Two RayGround model, normally employed for Line-of-Sight (LOS)propagation conditions, which approximates the pathloss as:

PL(d) =⎧⎨

⎩

10 log10(d2(4π)2

λ2 ) if d < dc,

10 log10(d4

h2Ah2

B

) if d ≥ dc

(2)

where

dc = 4πhAhB

λ, (3)

d is the distance between transmitter and receiver, hA andhB are their respective antenna heights, and λ is the car-rier wavelength, all of them in m. For d < dc, this model

J. Gozalvez et al.

is equivalent to the Free Space propagation model. TheTwo Ray Ground model has been considered as a referencemodel since it is widely used by the vehicular networkingresearch community, such as in [22], and it is incorporatedin ns2 [23], which is the simulation platform employed inthis work.

To consider the higher losses experienced in urban envi-ronments, a log-distance pathloss model has also been im-plemented, based on the following expression:

PL(d) = PL(d0) + 10nLog10

(d

d0

)

(4)

with n being the pathloss exponent, usually determined byfield measurements, and PL(d0) the pathloss experiencedat reference distance d0, calculated using the Free Spacepropagation model. Following the indications in [24], thepathloss exponent ranges from n = 2.7 to n = 5 for urbanscenarios. For n = 2, this model is equivalent to the FreeSpace propagation model. This model is also implementedin ns2 and has been considerably used by the vehicular net-working research community, such as in [25] or [26], espe-cially for urban environments.

The previous two models are not capable to differenti-ate between LOS and NLOS propagation conditions, whichhas been proven to significantly influence the received sig-nal. To this aim, a third pathloss model, named LOS/NLOS,that differentiates visibility conditions between transmitterand receiver due to the presence of buildings, has also beenimplemented. The LOS/NLOS pathloss is expressed underLOS conditions as [27]:

PLLOS(d) =

⎧⎪⎪⎨

⎪⎪⎩

22.7 log10(d) + 41 + 20 log10(f/5)

if d < Rbp,

40 log10(d) + 41 − 17.3 log10(Rbp)

+ 20 log10(f/5) if d ≥ Rbp

(5)

where

Rbp = 4(hA − 1)(hB − 1)

λ(6)

and f is the carrier frequency in GHz. For NLOS conditions,the pathloss is expressed as:

PLNLOS(dA, dB) = PLLOS(dA) + 20 − 12.5nj

+ 10nj log10(dB) (7)

where

nj = max(2.8 − 0.0024dA,1.84) (8)

and dA and dB are the transmitter and receiver distances tothe closest intersection.

Figure 1 shows the effect on the received signal level forthe different pathloss models implemented in this work. It is

Fig. 1 Received power level for the various implemented pathlossmodels (Pt = 1 W = 30 dBm)

important to highlight the high difference between the LOSand NLOS pathloss models (between 30 and 40 dB in av-erage) because it considerably impacts the communicationsbetween vehicles depending on whether they are obstructedby a building or not.

3.2 Shadowing

The shadowing is normally modeled following a log-normaldistribution with a zero mean and a standard deviation σ

that depends on the operating conditions, normally betweenσ = 4 dB and σ = 12 dB for outdoor propagation condi-tions [24]. Gudmunson also demonstrated that the shadow-ing is a spatially correlated process [28], which results inthat the shadowing experienced by a mobile at a given po-sition is correlated to that experienced at a nearby position.Given the impact of such spatial correlation on the perfor-mance of mobile and wireless radio systems, including ve-hicular communication systems [29], the Gudmunson modelconsidering an exponential autocorrelation function has alsobeen implemented for this work. This model describes thecorrelation of the shadowing process at a distance d as:

Ryy(d) = σ 2s · exp

(

−|d|dS

)

(9)

where σS is the shadowing standard deviation and dS equalsD/ ln(2), with D being the distance at which the normal-ized correlation is 0.5. To illustrate the effect of the shad-owing spatial correlation on the received signal level, Fig. 2compares the received power for a moving vehicle with andwithout considering the shadowing correlation.1

1This figure has been plotted considering the NLOS contribution of theLOS/NLOS pathloss model.


Fig. 2 Effect of shadowing correlation on the received signal levelwith Pt = 1 W = 30 dBm. (a) Uncorrelated shadowing. (b) Correlatedshadowing

3.3 Multipath fading

The multipath fading effect resulting from the reception ofmultiple replicas of the transmitted signal at the receiver hasalso been shown to have a significant impact on the perfor-mance of mobile and wireless communication systems. Asa result, a multipath fading implementation following theobservations reported in [27] has also been considered. Inparticular, the multipath fading is modeled by means of aRicean random distribution under LOS propagation condi-tions. The probability density function for the Ricean enve-lope r is given by:

PDF(r) = r

σ 2exp

(

− r2 + A2

2σ 2

)

I0

(Ar

σ 2

)

(10)

where I0 is the zero-order modified Bessel function of thefirst kind. The parameters A and σ are related with the K

parameter, which is normally used to describe a normalizedRicean distribution:

K = A2

2σ 2. (11)

Following the indications in [27], K depends on the distancebetween transmitter and receiver as follows:

K = 3 + 0.0142d (12)

where d is expressed in meters and K in dB. Under NLOSpropagation conditions, the signal variability is considerablyhigher than in LOS conditions and a Rayleigh random dis-tribution has been considered in this case [27].

A realistic system propagation modelling has to includemodels for the pathloss, shadowing and multipath fading ef-fects. The more complete model implemented in this work

Fig. 3 Realistic propagation model based on WINNER (Pt = 1 W)

and referred to as realistic model, considers the LOS/NLOSpathloss model, correlated log-normal shadowing and themultipath fading implementation reported in this section.This complete model has been obtained from a detailed ur-ban micro-cell propagation model developed in the WIN-NER project [27], which was extracted from field measure-ments in urban environments. This realistic model capturesall radio propagation effects and therefore provides a closerepresentation of real signal measurements. In this work,it will constitute the benchmark over which other channelmodels will be compared, in order to conclude whether theywould provide different conclusions to those obtained inreal systems. Despite not being developed for V2V com-munications, the operating conditions of the WINNER ur-ban micro-cell model are to the authors’ knowledge thosethat currently best fit the V2V communications scenario fora system level propagation modelling.2 Moreover, despiteconsiderable progress in V2V channel modelling [11, 12], tothe authors’ knowledge there is currently no complete sys-tem level channel model for wireless vehicular communi-cations systems. To illustrate the radio propagation effects,Fig. 3 shows the combined effect of pathloss, shadowing andmultipath fading on the received signal power for a movingvehicle receiving packets under LOS and NLOS propagationconditions considering the detailed WINNER urban micro-cell propagation model implemented.

3.4 Physical layer implementation

To reduce the complexity of system level simulations, theeffects of the physical layer (e.g. modulation and coding)

2The model considers an operating frequency in the 5 GHz band andfor antenna height as low as 5 meters.

J. Gozalvez et al.

resulting from the probabilistic nature of the radio environ-ment are generally modeled by means of simplified Look-Up Tables (LUTs). These LUTs, extracted from link levelsimulations, map the Packet Error Rate (PER) to the experi-enced channel quality conditions. In this work, the PER per-formance has been included at the system level, followingthe results from [30], with the PER as a function of the effec-tive Signal to Interference and Noise Ratio (SINR), Eav/N0,which represents the SINR reduced by a factor α to modelthe effect introduced by the cyclical prefix attached to eachOFDM symbol.

4 Impact of the radio channel modelling on theperformance and dimensioning of time-criticalVANET safety applications

One of the most important VANET applications is traf-fic safety, and in particular collision avoidance. In fact,VANETs are considered an active safety solution given theircapacity to prevent road accidents through the message ex-change between neighboring nodes. Traffic safety applica-tions are characterized by their time-critical nature and theneed for reliable instantaneous communications. In addi-tion, such reliability cannot only be ensured at a statisticallevel but needs to be permanently ensured to avoid roadaccidents. As a result, it is crucial that vehicular commu-nications are carefully configured and optimized to ensuretheir reliable and timely operation needed for traffic safety.Given that such reliable and timely operation heavily de-pends on the received signal levels, and thereby on the chan-nel propagation conditions, the design of vehicular com-munication techniques for traffic safety applications needsto adequately model the radio propagation conditions. Todemonstrate the impact of such modelling on the perfor-mance of traffic safety VANET applications, this study con-siders V2V communications in an urban intersection sce-nario.

4.1 Evaluation scenario

To investigate the impact of the accuracy of the radio chan-nel modelling on the performance and dimensioning ofVANET V2V communication protocols, a traffic safety ap-plication at a critical urban intersection scenario without vis-ibility has been considered; US studies show that more than25% of vehicles collisions occur at intersections [31]. In thiscase, two vehicles, A and B, are moving towards an intersec-tion with a risk of collision (Fig. 4). To detect each other’spresence, the vehicles periodically broadcast ten basic bea-cons per second on the control channel using the 6 Mbps1/2 QPSK transmission mode. The packet size has been setto 100 Bytes, which is considered enough to support most

Fig. 4 Urban intersection scenario

V2V communications [32] and includes information suchas the timestamp, vehicle’s latitude, longitude, acceleration,speed module and heading, transmission power or messagepriority.

The correct dimensioning of VANET protocols shouldguarantee the exchange of at least one of these messagesbetween A and B with sufficient time for the drivers to reactand avoid the accident at the intersection. In this context, wedefine the critical distance CD as the minimum distance tothe intersection at which vehicle A needs to receive a broad-cast message from vehicle B to avoid their potential colli-sion at the intersection. Considering a uniform decelerationmodel, the critical distance can be computed as follows:

CD = v · RT + 1

2

v2

amax(13)

where v represents the vehicle’s speed, RT the driver’s reac-tion time and amax the vehicle’s emergency deceleration. Inparticular, these parameters have been fixed to v = 70 km/h,RT = 0.75 s and RT = 1.5 s, and amax = 8 m/s2. In this sce-nario, this parameter definition will allow obtaining valuabletraffic safety performance results.

In terms of system load, two scenarios have been analys-ed. The first one, modelling only the two vehicles approach-ing the intersection with a risk of collision, represents a sce-nario where radio transmission errors are just due to prop-agation effects and not to channel congestion. The secondscenario, represented in Fig. 5, also considers other sur-rounding vehicles (100 vehicles/km) in order to emulatehigh density conditions where numerous vehicles broadcasttheir messages through the same control channel, therebyresulting in a higher channel congestion, and consequently,in an increased probability of packet errors due to packet


Fig. 5 High dense urban intersection scenario

collisions (specially due to the well-known hidden-terminalproblem).

To analyse the impact of the different radio propagationeffects on the performance and dimensioning of V2V traf-fic safety applications, the log-distance pathloss model withuncorrelated log-normal shadowing has been used as a ref-erence, since it is one of the basic urban propagation modelsincluded in the ns2 simulator, widely used by the VANETcommunity. Starting from this model, this study sequentiallyanalyses the effect of considering more realistic pathloss,shadowing and multipath fading models. This approach willenable identifying the most relevant propagation effectswith regard of accurately dimensioning VANET systems de-signed for improving road safety. As it will be demonstratedin this section, changing the propagation model modifies thecharacteristics of the signal propagation and therefore theproperties of the received signal level, following the im-plications of the different radio propagation effects high-lighted in Sect. 3. As a result, the propagation model em-ployed can considerably impact the performance and opera-tion of the protocols or applications under study. To conductthis study, the following four radio propagation models havebeen analysed (with model 4 corresponding to the realisticmodel described in Sect. 3 and based on the WINNER re-sults):

• Model 1: log-distance pathloss (n = 3.5) and uncorre-lated log-normal shadowing (σ = 4 dB) radio model.

• Model 2: LOS/NLOS pathloss and log-normal shadowing(σ = 3 dB and σ = 4 dB for LOS and NLOS conditions,respectively).

• Model 3: same as model 2 but including the shadowingcorrelation as proposed by Gudmunson.

• Model 4: same as the model 3 but including the multi-path fading following the WINNER indications (Realisticmodel).

Models 1 and 2 have been selected to analyse the impactof obstructing elements on the capacity of V2V communica-tion technologies to provide reliable and time-critical safetyapplications. Such applications are based on the correct re-ception of at least one broadcast message from the poten-tially colliding vehicle at least with sufficient time for thedriver to react. Since vehicles constantly broadcast messageson the control channel, the probability of receiving such alertdepends on the probability to correctly receive at least oneof the various broadcast messages. In this case, the potentialchannel correlation can have a significant impact on the in-stantaneous and reliable performance of V2V traffic safetycommunication techniques, and as a result, the third modelhas also been included. Finally, model 4 has been consid-ered to analyse the performance and dimensioning of V2Vcommunication techniques under realistic propagation con-ditions, since it includes all propagation effects and there-fore realistically represents the signal propagation.

4.2 Dimensioning V2V systems under no traffic load

The results included in this section correspond to the sce-nario where only vehicles A and B are broadcasting mes-sages on the control channel (packets errors are only pro-duced by radio propagation effects). Figure 6 shows, for atransmission power level of Pt = 0.75 W and for the dif-ferent radio propagation models, the cumulative distributionfunction (CDF) of the distance to the intersection at whicha vehicle correctly receives the first broadcast message fromthe potentially colliding vehicle. The figure also shows thecritical distances (CD), i.e. the distances from the intersec-tion at which a vehicle needs to have correctly received abroadcast message to avoid an accident, considering the twodriver’s reaction time values analysed. A direct comparisonof the first and second channel propagation models shows,

J. Gozalvez et al.

Fig. 6 CDF of the distance at which the first message is received(Pt = 0.75 W)

that in the considered scenario and operating conditions,there is a significant difference in the traffic safety applica-tion results obtained with the current communications con-figuration considering the log-distance pathloss model andthe LOS/NLOS model proposed in [27] (note that A andB are under NLOS propagation conditions). In fact, the re-alistic pathloss model produces higher power level loses atlong distances between transmitter and receiver, while bothpresent a similar behavior at close distances, as it could beobserved in Fig. 1 of Sect. 3, where the pathloss effect wasanalysed. This results in a reduction of the distance to the in-tersection at which the first packet is correctly received andtherefore in a decrease of the application performance whenconsidering the realistic pathloss model The results obtainedclearly show that while this transmission power level wouldseem sufficient to receive alerts with enough time for thedriver to react considering the simplistic pathloss model, theresults using the realistic model show that this is not thecase, in particular for RT = 1.5 s.

The shadowing correlation demonstrated by Gudmunsonand included in propagation model 3 can significantly affectthe traffic safety performance results, as depicted in Fig. 6.In particular, the shadowing correlation results in that theshadowing experienced by a vehicle at a given position iscorrelated to that experienced at a nearby position, result-ing in a reduction of the signal variability, as detailed inSect. 3. As a result of the reduction of the signal variability,the number of broadcast messages correctly received beforereaching the critical distance is decreased (see Fig. 7). Thisincreases the risk of collision due to the reduction of the dis-tance to the intersection at which the first broadcast messageis received (Fig. 6). In fact, while the first broadcast messagewas always received before reaching the critical distance forshort reaction time values (RT = 0.75 s) if shadowing cor-relation was not modeled, considering the shadowing corre-

Fig. 7 Percentage of vehicles that receive a particular number ofbroadcast messages before CD (RT = 0.75 s and Pt = 0.75 W)

lation present in radio environments induces that in around8% of emulated iterations (vehicles approaching a danger-ous intersection), the first broadcast message was not re-ceived with sufficient time for the driver to react. These re-sults clearly highlight the importance of modelling the shad-owing correlation effect, since not doing so can significantlyoverestimate the performance of wireless V2V communica-tions in terms of its ability to prevent traffic collisions.

The impact of multipath fading modelling on the perfor-mance and dimensioning of wireless vehicular communica-tions can be extracted from Fig. 6. The multipath fading ef-fect results in an increased variability of the received signallevel, as previously illustrated in Fig. 3 of Sect. 3. Althoughsuch increased variability can result in important instanta-neous signal level drops, it can also provoke important in-creases in the received signal levels. As a result, the increaseof the signal variability can be a positive effect to guaran-tee the correct reception of at least one broadcast messagewith sufficient time for a driver to react in front of a roaddanger, as it can be observed in the results shown in Fig. 6.For example, the probability of not receiving a message be-fore reaching the critical distance was reduced from 0.81 to0.6 when the multipath fading effect was modeled, as it canbe observed in Fig. 6 when propagation models 3 and 4 arecompared for RT = 1.5 s. The results illustrated in Fig. 6show such positive effect despite the fact that the channelvariability is also at the origin of a reduced probability ofsuccessful reception of a broadcast message for short dis-tances to the intersection, as illustrated in Fig. 8. It is impor-tant to note that, in this case, the obtained results show thatnot including the multipath fading results in a pessimisticdimensioning of wireless V2V communications for safetyapplications.

The results shown in Figs. 6 and 7 correspond to a trans-mitting power of 0.75 W. The use of higher transmission


Fig. 8 Probability of successful reception between vehicles A and B

Fig. 9 Probability of not receiving a message before CD for varioustransmission power levels

power levels increases the transmission range and the dis-tance at which messages are received (see Fig. 8). As a re-sult, the application performance is increased, i.e. the prob-ability of not receiving any broadcast message before CDis reduced, as Fig. 9 demonstrates. This performance im-provement with higher transmission power levels is inde-pendent of the propagation model considered. Figure 9 alsohighlights the need of considerably high transmission pow-ers to obtain adequate application performance levels underrealistic propagation environments (model 4), especially forhigh driver reaction times. Despite the general performanceimprovement obtained with higher transmission powers, thesame conclusions regarding the effect of the radio prop-agation modelling on the performance and dimensioningof wireless V2V communications can be reached for high

Fig. 10 CDF of the distance at which the first message is receivedunder high channel load (Pt = 0.75 W)

transmission powers, according to the results previouslyshown and the signal variability characteristics identified inSect. 3 where the different radio propagation effects wereanalysed.

4.3 Dimensioning V2V systems under high traffic load

While the previous results showed the system performancefor scenarios where no surrounding vehicles were modeled,this section is aimed at demonstrating the impact of the ra-dio channel modelling under high traffic densities. When allvehicles are periodically transmitting broadcast messages onthe control channel, the obtained results are not only affectedby the radio channel effects, but also by channel conges-tion and packet collisions. Despite the performance degrada-tion generally observed with higher system loads, the con-clusions regarding the channel modelling effect on the di-mensioning of wireless vehicular communication systemsare maintained, as shown in Fig. 10. However, the impactof surrounding vehicles transmitting on the control channelcan considerably vary depending on the channel model em-ployed. As shown in Fig. 11, there is an important differencebetween the propagation models studied when the reductionof the probability of reception due to packet collisions isanalysed. As it can be seen in Fig. 11, model 1 presents aconsiderable difference when compared with the rest of themodels due to the fact that models 2, 3 and 4 differentiatebetween LOS and NLOS propagation conditions, providingwith a higher transmission range along the streets and hencewith more potential interfering vehicles. Such difference be-tween model 1 and models 2, 3 and 4 can also be observed inFig. 12 in terms of the total rate of packets detected per sec-ond and per vehicle. Since this rate of detected packets quan-tifies the amount of time a vehicle’s communications inter-face is occupied receiving packets from other vehicles (with

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Fig. 11 Reduction of the probability of reception (Pt = 0.75 W)

Fig. 12 Total rate of packets detected per second under high channelload (Pt = 0.75 W)

or without error), the use of a simplified propagation modelthat does not differentiate between LOS and NLOS prop-agation conditions not only influences traffic safety perfor-mance results, but can also provide inadequate indicationsabout the channel load observed by each vehicle.

As it was expected from the results of Fig. 11, the re-sults obtained using model 1 present the lowest increase ofthe probability of not receiving a packet with enough timefor the driver to stop before the intersection when a highchannel load is emulated, as illustrated in Fig. 13. Despitethe fact that models 2, 3 and 4 present practically equal re-duction of the average probability of reception due to packetcollisions, the system performance degradation observed fortraffic safety applications is considerably different. This isproduced because the percentage of vehicles that receivea small quantity of broadcast messages before the critical

Fig. 13 Probability of not receiving a message before CD (RT = 1.5 s)

distance considerably vary between one model and anotherwhen no surrounding vehicles are emulated, as previouslydepicted in Fig. 7. Such vehicles are the most sensitive to ex-tra packet losses due to packet collisions. As a consequence,model 2 presents the worst performance degradation. As itcan be observed in Fig. 13, these effects are independentfrom the transmission power used. As in the case withoutsurrounding vehicles, the increase of the transmission powerimproves the application performance since it decreases theprobability of not receiving a packet before the CD distance.However, the performance degradation due to packet colli-sions is higher as the transmission power increases, as it canalso be observed in Fig. 13. This is due to the fact that in-creasing the transmission range increments the number ofpotential interfering vehicles, and hence the probability ofpacket losses due to radio collisions.

The results depicted in this section have clearly shownthat the radio channel modelling has a significant impact onthe performance and dimensioning of V2V communicationsand traffic safety applications. Considering the strict appli-cation and communication requirements of VANET trafficsafety applications, the VANET community should carefullymodel the radio channel to adequately configure VANETcommunication protocols.

5 Impact of radio channel modelling on theperformance and operation of VANET routingprotocols

Ad-hoc routing protocols are a key feature of VANETs dueto their capability to rapidly disseminate road safety or traf-fic conditions information in a given geographical area with-


out the need for any road side infrastructure. The perfor-mance and efficiency of these routing protocols is heavilyinfluenced by the selection process of the neighboring nodesthat are candidates to relay the information from source todestination, the density of neighboring nodes, the radio linkreliability and the number of relaying nodes needed to sendthe data packet from the source to the destination node.Given that these operational parameters are heavily influ-enced by the received signal level in VANETs, it is crucial,as this section will demonstrate, to adequately and accu-rately model the radio propagation conditions to understand,design, evaluate and optimize VANET routing protocols.

5.1 Evaluation scenario

To analyse the effect of the radio channel modelling on theperformance and operation of vehicular ad-hoc routing pro-tocols, a Manhattan-like urban scenario consisting of a uni-form grid of 6 × 6 blocks has been employed (Fig. 14). Inthis case, all streets have two lanes except the horizontalstreet which consists of four lanes and has traffic lights atthe intersections. In addition to the periodic broadcast trans-missions,3 data packets are generated every Td = 3 s at thesource node. The source node seeks then to forward thesedata packets to the destination node (see Fig. 14) using anad-hoc routing protocol.

Given that the performance and operation of ad-hoc rout-ing protocols can be strongly influenced by the networktopology, this study implements realistic vehicular mobil-ity patterns extracted from the open source microscopicroad traffic simulator SUMO (Simulation of Urban Mobil-ity) [33]. SUMO is based on a collision free vehicle move-ment model, and supports different vehicle types, multi-lanestreets and junction-based right-of-way rules, which signifi-cantly influence the vehicle’s mobility. In this case, an aver-age vehicular traffic density of 12 vehicles/km/lane is simu-lated, corresponding to a typical medium density urban sce-nario.

In the evaluation of VANET ad-hoc routing protocols un-der the influence of the diverse radio propagation effects, thefollowing radio propagation models have been considered:

• Model A: Two Ray Ground pathloss model without shad-owing or multipath fading.

• Model B: LOS/NLOS pathloss model without shadowingor multipath fading.

• Model C: Realistic model including LOS/NLOS pathloss,correlated shadowing and multipath fading (same asmodel 4 in Sect. 4).

3Beacons are transmitted every Ts = 0.1 s at a transmission power of0.5 W in the considered scenario. Given the unrealistic high transmis-sion range that de Two Ray Ground model reproduces when using atransmission power of 0.5 W, the power level for this particular modelhas been set to obtain a 400 m transmission range.

Fig. 14 Urban Manhattan-like scenario

Depending on the radio channel model employed, thecharacteristics of the signal propagation are modified fol-lowing the different effects described in Sect. 3. This resultsin different protocol operation and performance when the ra-dio channel model is changed. As for the V2V traffic safetycommunications dimensioning study presented in Sect. 4, itis crucial to analyse the impact of visibility conditions onthe performance and operation of routing protocols. To thisaim, the pathloss models A and B have been considered inthis work to illustrate the importance of differentiating theradio visibility conditions. The simplistic pathloss model Ais different from model 1 chosen in Sect. 4 since modelsA and 1 are the most commonly used simplistic propaga-tion models by the VANET community and it was the in-tention of the authors to check their varying influence. As aresult, each model has been used for a different evaluationscenario. Differently from the models A and B, which sim-plistically reproduce the radio propagation effects, model Cis employed to accurately measure the impact of realisticpropagation conditions on the study of VANET routing pro-tocols. In this section, model C is the same as model 4 inSect. 4 and captures all propagation effects. It therefore pro-vides the closer representation of real signal propagation andthe performance and operation of VANET routing protocolsobtained with this model will be compared with those ob-tained with the rest of the models.

In this case, the shadowing correlation effect has not beenstudied separately since its impact on routing protocols isnot expected as crucial as for traffic safety applications thatrequire reliable and instantaneous V2V communications. Inthe case of routing protocols, several timely separated re-transmissions are allowed.

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5.2 Vehicular ad-hoc routing protocols in urban scenarios

Several authors have demonstrated the potential benefits ofposition-based routing protocols over traditional topology-based ad-hoc routing protocols in VANETS. This is de-rived from the highly dynamic nature of vehicular networkswhich prevents topology-based routing protocols to effec-tively operate in such environments [34]. Consequently, toanalyse the impact of the radio channel modelling on theperformance and operation of multihop routing protocols,three position-based wireless ad-hoc routing protocols havebeen employed and implemented in this study. One of themost referenced position-based routing protocols is GPSR(Greedy Perimeter Stateless Routing) [35]. In GPSR, pack-ets generated at the source node are routed to the final desti-nation node using positioning information. In GPSR, eachintermediate node selects the following forwarding nodebased on the position of the destination node and the positionof its neighboring forwarder-candidate nodes; the neigh-bors’ positions can be obtained with a periodic beaconingalgorithm such as the one employed in WAVE. By default,all nodes employ the greedy forwarding strategy and for-ward the data packet to the neighbor geographically closestto the destination. If a node cannot find any neighbor closerto the destination than itself, it follows the perimeter for-warding strategy in order to overcome the area with absenceof neighboring nodes [35].

In spite of the fact that road topology can influence themobility of the selected relaying node towards the desti-nation, routing protocols such as GPSR do not normallyconsider the street layout in the forwarding node selectionprocess. In order to include road topology information onthe routing process, [36] proposes the SAR (Spatially AwareRouting) protocol. In SAR, the source node forces datapackets to be routed through specific intermediate intersec-tions in the path towards the destination. Intermediate in-tersections are normally chosen following the shortest pathbetween the source node and the destination node.

Both GPSR and SAR are position-based unicast routingprotocols that base their forwarding decisions on the posi-tions of all the neighbors in the transmission range of theforwarding node. However, due to the high vehicle’s mobil-ity and the consequent varying topology dynamics, this in-formation can be frequently outdated, decreasing the packetdelivery ratio. To solve this problem, the CBF (ContentionBased Forwarding) protocol was proposed in [37]. In CBF,a forwarding node transmits the data packet as a single-hopbroadcast message. All vehicles that correctly receive thebroadcast packet start a timer with a duration proportionalto their distance to the destination. As a result, the timerof the closest neighbor to the destination will expire in firstplace and this node will broadcast/forward the message tobe transmitted. All the other nodes receiving such broadcast

message cancel their timers and thereby do not forward thepacket again.

5.3 Routing protocols performance

The impact of the different propagation models analysed onthe performance of the three implemented vehicular ad-hocrouting protocols can be observed in Fig. 15. The figure dif-ferentiates between packets correctly routed to the destina-tion, and packets that could not reach the destination. For theunicast protocols (i.e. GPSR and SAR), the packets that can-not reach the destination node can be dropped by an interme-diate node because the intermediate node does not have anyneighbor node to forward the packet (Dropped RTR) or be-cause the maximum number of retransmissions at the MAClevel is reached (Dropped MAC). For the CBF protocol, adata packet is not able to reach the destination when a broad-casted message could not find any node to further rely thepacket to the destination. The figure clearly shows that theconsidered radio propagation models strongly influence thevehicular ad-hoc routing performance and thereby the proto-col’s operation. In fact, the figure highlights that the numberof packets lost due to the lack of neighboring vehicles in thecase of unicast protocols significantly increases under theLOS/NLOS (model B) and Realistic (model C) radio prop-agation models, with respect to the Two Ray Ground model(model A). Accurately modelling buildings as obstacles todetermine the visibility conditions between the transmitterand the receiver reduces the number of neighbors that eachnode detects, as depicted in Fig. 16 for the analysed uni-cast protocols. This effect is due to the fact that propaga-tion models B and C adequately differentiate between LOSand NLOS propagation conditions, which results in highersignal losses under NLOS conditions, and consequently in

Fig. 15 Routing protocols packet delivery ratio for the various radiopropagation models analysed


Fig. 16 Cumulative Distribution Function (CDF) of the number ofneighbors detected by any wireless vehicular node; i.e. nodes that fallwithin its radio coverage

a reduced capability for vehicles to communicate throughbuildings and detect neighboring nodes. It is important tohighlight that while GPSR and SAR achieved similar perfor-mance under the simplistic Two Ray Ground model, SAR’sperformance is considerably degraded under realistic propa-gation due to a significant degradation in the number of po-tential relaying neighbors resulting from an adequate mod-elling of NLOS conditions and the predetermined SAR routeselection.

When evaluating the performance of any wireless ad-hoc routing protocol, it is important to analyse the differ-ent packet dropping factors in order to provide indicationson how to improve and optimize it. In this case, dependingon the signal variability and propagation modelling, packetdropping for unicast protocols is due to different factors. Forexample, in GPSR the percentage of packets dropped at theRTR level is lower considering propagation model C thanconsidering propagation model B due to the increased sig-nal variability of the realistic propagation model that occa-sionally makes possible the communications between dis-tant vehicles and thereby increases the number of neigh-boring nodes available to route the packet to the destination(Fig. 16). However, the signal variability reduces the link’sreliability, and therefore increases the number of packetsdropped at the MAC level. In the case of SAR protocol, thesignal variability introduced by the realistic radio propaga-tion model does not result in an increased number of neigh-bors due to the restrictive SAR path selection route. As aresult, SAR performance degradation for propagation mod-els B and C is mainly due to the RTR packet dropping. Theseresults clearly show how unicast protocols’ performance canconsiderably vary depending on the propagation model usedin the analysis.

Fig. 17 GPSR protocol packet delivery ratio for different traffic den-sities

Contrary to unicast protocols, the CBF protocol perfor-mance presents a much more limited variation across the dif-ferent propagation models due to its relaying node selectionprocess based on actual correct reception of broadcast mes-sages by relaying candidates. In fact, unicast protocols canonly reach similar performance levels under simplistic prop-agation models that ignore important radio propagation ef-fects. On the other hand, Fig. 15 shows that such effects canactually benefit the performance of broadcast protocols thatachieve their higher performance under the realistic propa-gation model C. The results shown in this section highlightthat underestimating the propagation effects can yield inade-quate routing protocols performance estimations, while alsoproviding incorrect indications about the actual inefficien-cies of unicast vehicular routing protocols.

The impact of traffic density on the performance of rout-ing protocols can also considerably depend on the chan-nel model considered, as shown in Fig. 17 for GPSR pro-tocol. For deterministic propagation models such as theLOS/NLOS model, the higher the traffic density, the largerthe percentage of packets correctly received at the desti-nation mainly due to the reduction of packets dropped atthe routing layer. However, when considering the Realisticpropagation model, the signal variability notably increasespacket losses at the MAC level and the maximum percent-age of packets successfully received is produced at mediumtraffic densities. These results clearly show the considerableimpact of the radio channel model also when analysing theeffect of traffic density on the performance of VANET rout-ing protocols.

It is also interesting to analyse the impact of the radiochannel modelling effects on the performance of VANETrouting protocols when changing the transmission power,

J. Gozalvez et al.

and hence the transmission range. When the transmissionpower is increased, the performance of the routing proto-

Fig. 18 GPSR protocol packet delivery ratio for different transmissionpower levels and medium traffic density

cols is generally improved, as it can be observed in Fig. 18for the GPSR protocol and considering medium traffic den-sity. However, the figure also shows that the transmissionpower increase does not cause the same impact on the rout-ing protocols performance depending on the channel modelconsidered. Increasing the transmission power consideringstatic models can significantly increase the percentage ofpackets correctly received at the destination. Conversely, amuch more limited increment is obtained considering re-alistic models, mainly due to the higher signal variabilityand packet losses at the MAC level. As Fig. 18 shows, thepropagation model considerably impacts the packet drop-ping reason irrespectively of the transmission power con-sidered, which could be a critical factor for the protocol im-provement and optimization.

5.4 Routing protocols operation

After analysing the impact of radio propagation modellingon the performance estimation of broadcast and unicast ve-hicular routing protocols, this section studies their operationto better understand the radio propagation effects. Such un-derstanding will help in subsequent research to design ro-

Fig. 19 Routing path from thesource node to the destinationnode in the emulated urbanscenario


Fig. 20 Geographicdistribution of packetretransmission attempts

bust and efficient vehicular routing protocols that overcomethe current proposals limitations highlighted in this paper.Figure 19 clearly shows that differentiating between LOSand NLOS propagation conditions considerably affects thepath that the data packets use to reach the destination. Con-sidering the simple propagation model A, the data pack-ets tend to follow a straight line between the source andthe destination node, which includes V2V communicationsacross buildings. On the other hand, a realistic propagationmodelling prevents such communications and thereby con-fines V2V communications to routes following the under-lying streets. This also results into an interesting observa-tion of the CBF operation and performance under variouspropagation models. While with unicast routing protocolsonly one data packet replica per generated data packet isreceived at the destination node, more than one can be re-ceived in the case of broadcast routing protocols. Moreover,a different number of data replicas can be obtained depend-ing on the propagation model considered, as shown in Ta-ble 1. As this table shows, the number of replicas signifi-cantly increases when considering propagation models thatdifferentiate LOS and NLOS conditions. This is due to thefact that radio signals are confined to the underlying streets,which increases the probability of splitting the routing pathat the intersections. In this case, the same data packet canbe routed through different paths, which explains the highernumber of replicas received at the destination. The results

Table 1 Average data replicas received at destination node for differ-ent protocols and medium traffic density

Routing protocol Radio propagation model

Model A Model B Model C

GPSR 1 1 1

SAR 1 1 1

CBF 1.64 2.17 3.54

shown in Table 2 demonstrate that the number of replicasreceived at the destination node and thereby the number ofunnecessary data packet transmissions can considerably in-crease with traffic density due to the higher probability ofhaving vehicle neighbors in perpendicular streets.

According to the different routing path selections ob-tained with the various propagation models depicted inFig. 19, the geographic distributions of the channel load varydepending on the propagation model considered. Figure 20illustrates the geographic distribution of packet retransmis-sion attempts or GPSR and SAR routing protocols. As itcan be observed, a very different geographic packet distri-bution can be obtained with simplistic and realistic propa-gation models, especially for the GPSR protocol, that doesnot predefine any routing path to reach the destination. Thisemphasizes the importance of adequately consider physicallayer effects to correctly estimate the performance of vehic-

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Table 2 Data replicas received at destination node for CBF protocol and different traffic densities considering realistic propagation model C

Parameter Traffic density

Very low Low Medium High Very high

Average 2.95 3.35 3.54 3.72 3.84

Standard deviation 1.31 1.33 1.30 1.28 1.27

Table 3 Routing metrics for GPSR

Parameter Radio propagation model

Model A Model B Model C

Average travelled distance 1665 m 2281 m 2273 m

from source to destination

Average distance between 368.1 m 399.0 m 506.7 m

forwarding nodes

Average number of 4.55 5.63 4.54

forwarding nodes

Fig. 21 Packet distribution at MAC level for unicast protocols

ular ad-hoc routing protocols and, in particular, to predictmost likely channel congestion areas.

Considering the GPSR example, Table 3 further illus-trates the need of accurate propagation models when study-ing the operation of vehicular routing protocols. In partic-ular, Table 3 shows that propagation models differentiatingbetween LOS and NLOS conditions significantly increasethe travelled distance between source and destination giventhat the packet forwarding route is forced to follow the roadtopology. Also, the received signal level variability intro-duced in model C increases the average distance betweenforwarding nodes and therefore reduces the number of for-warding nodes needed to route the information from sourceto destination.

Fig. 22 Probability of packet reception for different propagation mod-els

When analysing the operation of any routing protocol, itis interesting to identify the causes of packet reception errorsat MAC level that provokes an increase in the number of re-transmissions. Figure 21 classifies for the unicast4 routingprotocols the packets transmitted at MAC level dependingon whether they were correctly received (RCV) or not. Inthe latter case, they could be dropped because of radio chan-nel error (ERR), packet collision (COL), radio channel errorand packet collision (ECO) or because they could not be de-tected due to low received signal level (NDET). Figure 21shows that radio channel errors represent the most impor-tant packet dropping reason at MAC level. Radio channelerrors are produced with unicast routing protocols becauseneighboring vehicles with low link reliability are selected toforward data packets. Depending on the channel model con-sidered, the probability of having neighbors with low linkreliability considerably differs. This effect can be observedin Fig. 22, which highlights with arrows the areas where un-reliable links can be created, i.e. the areas with probabil-ity of packet reception lower than one. Moreover, based onthe channel model employed, the geographic distribution of

4It is important to note that MAC analysis for broadcast protocols suchas CBF would be significantly different since many different nodescontribute to routing the message from source to destination. As a re-sult, broadcast protocols can result in very high destination packet de-livery ratios experiencing low average MAC system performance.


Fig. 23 Geographicdistribution of packet lossescaused by radio channel errors

packet losses due to radio channel errors can considerablyvary. As shown in Fig. 23, when modelling buildings as ob-stacles, packet losses due to radio channel errors are mainlylocated near the intersections. However, with simpler chan-nel models such as propagation model A, packet errors aredistributed over a larger area.

Apart from the study of radio channel errors, it is also im-portant to analyse the variance of packet collision probabil-ity across the different models shown in Fig. 21. The highercollision probability observed for the simpler propagationmodel is due to the higher detection of neighboring nodes(Fig. 16) observed with the model under the emulated envi-ronment, which results in a higher interference probabilityand packet collisions.

The results shown in this section have demonstratedthe strong impact of the radio channel modelling not onlyon the performance of geographic-based VANET routingprotocols, but most importantly on their operation. Ade-quately understanding and reflecting such operation in re-search studies is a crucial factor to design VANET routingprotocols viable for future implementations in real VANETnetworks.

6 Conclusions

The radio channel propagation highly influences the perfor-mance and operation of wireless communication systems.The influence can be even more remarkable in vehicularcommunication networks given the low antenna heights, thehighly dynamic network topology and the strict performancerequirements established by traffic safety applications. De-spite the expected impact of radio channel on the perfor-mance and operation of VANET systems, many VANET-related studies significantly simplify the radio channel mod-elling. In this context, this study has analysed, quantifiedand demonstrated the strong impact of the radio channelmodelling on the performance, and most significantly, theoperation of VANET networking and communication tech-niques. The conducted study has extensively proved that in-

accurate, and under certain conditions even wrong, conclu-sions about the performance and operation of vehicular com-munication protocols can be obtained when not adequatelymodelling the radio propagation conditions. This is partic-ularly the case when considering road safety applicationswith strong reliability and low latency instantaneous com-munication requirements. As a result, the conclusions of thispaper encourage for further research in the VANET channelunderstanding and modelling.

Acknowledgements This work was supported in part by the SpanishMinisterio de Fomento under the project T39/2006 and by the Gener-alitat Valenciana under research grant BFPI06/126.

References

1. IEEE P802.11p/D4.0. (2008). Draft part 11: WLAN MAC andPHY specifications: wireless access in vehicular environments(WAVE). IEEE Standards Association, March 2008.

2. European Telecommunications Standards Institute TC ITS.(2009). Intelligent transport systems (ITS); communications; ar-chitecture. Draft ETSI TS 102 665 V0.0.9, March 2009.

3. Sommer, C., & Dressler, F. (2008). Progressing toward realisticmobility models in VANET simulations. IEEE CommunicationsMagazine, 46(11), 132–137.

4. Wang, Y., Ahmed, A., Krishnamachari, B., & Psounis, K. (2008).IEEE 802.11p performance evaluation and protocol enhancement.In Proceedings of IEEE international conference on vehicularelectronics and safety ICVES (pp. 317–322), Ohio, USA, Septem-ber 2008.

5. Gallardo, J. R., Makrakis, D., & Mouftah, H. T. (2005). Perfor-mance analysis of the EDCA medium access mechanism over thecontrol channel of an IEEE 802.11p WAVE vehicular network. InProceedings of IEEE international conference on communicationsICC (pp. 1–6), Dresden, Germany, 14–18 June 2009.

6. Wang, W., Xie, F., & Chatterjee, M. (2009). Small-scale and large-scale routing in vehicular ad hoc networks. IEEE Transactions onVehicular Technology, 58(9), 5200–5213.

7. Tee, C. A. T. H., & Lee, A. (2009). Adaptive reactive routing forVANET in city environments. In Proceedings international sym-posium on pervasive systems, algorithms, and networks ISPAN(pp. 610–614), Kaoshiung, Taiwan, 14–16 December 2009.

8. Stibor, L., Zang, Y., & Reumerman, H.-J. (2007). Neighborhoodevaluation of vehicular ad-hoc network using IEEE 802.11p. InProceedings of European wireless conference EW, Paris, France,April 2007.

J. Gozalvez et al.

9. Monserrat, J., Gozalvez, J., Fraile, R., & Cardona, N. (2005). Ef-fect of shadowing correlation modeling on the system level per-formance of adaptive radio resource management techniques. InProceedings of IEEE international symposium on wireless com-munication systems ISWCS (pp. 460–464), Siena, Italy, September2005.

10. Gruber, I., & Hui, L. (2004). Behavior of ad hoc routing protocolsin metropolitan environments. In Proceedings of IEEE vehiculartechnology conference VTC-Fall (pp. 3175–3180), Los Angeles,USA, September 2004.

11. Cheng, L., Henty, B. E., Bai, F., & Stancil, D. D. (2008). Highwayand rural propagation channel modelling for vehicle-to-vehiclecommunications at 5.9 GHz. In Proceedings of IEEE interna-tional symposium of antennas and propagation AP-S (pp. 1–4),San Diego, USA, July 2008.

12. Sen, I., & Matolak, D. W. (2008). Vehicle–vehicle channel modelsfor the 5-GHz band. IEEE Transactions on Intelligent Transporta-tion Systems 9(2), 235–245.

13. Torrent-Moreno, M., Schmidt-Eisenlohr, F., Fussler, H., & Harten-stein, H. (2006). Effects of a realistic channel model on packetforwarding in vehicular ad hoc networks. In Proceedings of IEEEwireless communications and networking conference WCNC(pp. 385–391), Las Vegas, USA, April 2006.

14. Torrent-Moreno, M., Corroy, S., Schmidt-Eisenlohr, F., & Harten-stein, H. (2006). IEEE 802.11-based one-hop broadcast commu-nications: understanding transmission success and failure underdifferent radio propagation environments. In Proceedings of ACMinternational symposium on modeling, analysis and simulation ofwireless and mobile systems MSWIM (pp. 68–77), Malaga, Spain,October 2006.

15. COMeSafety consortium. (2009). D31: European ITS communi-cation architecture: overall framework proof of concept imple-mentation. COMeSafety European Specific Support Action PublicDeliverable, March 2009.

16. Vehicle infrastructure integration consortium. (2009). Final re-port: vehicle infrastructure integration; proof of concept; tech-nical description: vehicle. US Department of TransportationDTFH61-05-H-00003, May 2009.

17. Tranter, W. H., Shanmugan, K. S., Rappaport, T. S., & Kosbar,K. L. (2003). Principles of communication systems simulationwith wireless applications. New York: Prentice Hall.

18. Gozalvez, J., & Dunlop, J. (2004). Link level modelling tech-niques for analysing the configuration of link adaptation algo-rithms in mobile radio networks. In Proceedings of Europeanwireless EW (pp. 325–330), Barcelona, Spain, February 2004.

19. Takai, M., Martin, J., & Bagrodia, R. (2001). Effects of wirelessphysical layer modelling in mobile ad hoc networks. In Proceed-ings of ACM international symposium on mobile ad hoc network-ing and computing MobiHoc (pp 87–94), California, USA, Octo-ber 2001.

20. Lazo, C., & Fernandez, M. (2007). QoS in vehicular and intelli-gent transport networks using multipath routing. In Proceedingsof IEEE international symposium on industrial electronics ISIE(pp. 2556–2561), Vigo, Spain, June 2007.

21. Saito, M., Tsukamoto, J., Umedu, T., & Higashino, T. (2007).Design and evaluation of intervehicle dissemination protocol forpropagation of preceding traffic information. IEEE Transactionson Intelligent Transportation Systems, 379–390.

22. Alshaer, H., & Horlait, E. (2005). An optimized adaptive broad-cast scheme for inter-vehicle communication. In Proceedings ofIEEE vehicular technology conference VTC spring (pp. 2840–2844), Stockholm, Sweden, June 2005.

23. Ns2—The Network Simulator 2. Online: http://www.isi.edu/nsnam/ns/.

24. Rappaport, T. (2001). Wireless communications: principles andpractices. New York: Prentice Hall.

25. Naumov, V., & Gross, T. R. (2007). Connectivity-aware routing(CAR) in vehicular ad-hoc networks. In Proceedings of IEEE in-ternational conference on computer communications INFOCOM(pp. 1919–1927), Anchorage, AK, USA, May 2007.

26. Baumann, R., Heimlicher, S., & May, M. (2007). Towards realisticmobility models for vehicular ad-hoc networks. In Proceedings ofmobile networking for vehicular environments workshop MOVE(pp. 73–78), Anchorage, USA, May 2007.

27. WINNER consortium. (2007). D1.1.2. WINNER II channel mod-els. WINNER European Research project Public Deliverable, Sep-tember 2007.

28. Gudmundson, M. (1991). Correlation model for shadow fading inmobile radio systems. Electronic Letters, 27(23), 2145–2146.

29. Sepulcre, M., & Gozalvez, J. (2009). Adaptive wireless vehicu-lar communication techniques under correlated radio channels. InProceedings of IEEE vehicular technology conference VTC spring(pp. 761–765), Barcelona, Spain, April 2009.

30. Zang, Y., Stibor, L., Orfanos, G., Guo, S., & Reumerman, H. J.(2005). An error model for inter-vehicle communications in high-way scenarios at 5.9 GHz. In Proceedings of international work-shop on performance evaluation of wireless ad hoc, sensor, andubiquitous networks PE-WASUN (pp. 49–56), Montreal, Canada,October 2005.

31. Subramanian, R., & Lombardo, L. (2007). Analysis of fatal motorvehicle traffic crashes and fatalities at intersections, 1997 to 2004(Technical Report). NHTSA’s National Center for Statistics andAnalysis.

32. Vehicle Safety Communications consortium. (2005). Vehiclesafety communications project task 3—Final report: identify in-telligent vehicle safety applications enabled by DSRC. DOT HS809 859, March 2005.

33. SUMO—Simulation of Urban Mobility. Online: http://sumo.sourceforge.net/.

34. Füßler, H., Mauve, M., Hartenstein, H., Käsemann, M., &Vollmer, D. (2002). Location-based routing for vehicular ad-hocnetworks. In Proceedings of ACM/IEEE international conferenceon mobile computing and networking mobi-com (pp. 47–49), At-lanta, USA, September 2002.

35. Karp, B., & Kung, H. (2000). Greedy perimeter stateless rout-ing for wireless networks. In Proceedings of ACM/IEEE interna-tional conference on mobile computing and networking mobi-com(pp. 243–254), Boston, USA, August 2000.

36. Tian, J., Han, L., Rothermel, K., & Cseh, C. (2003). Spatiallyaware packet routing for mobile ad hoc inter-vehicle radio net-works. In Proceedings of IEEE international conference on intel-ligent transportantion systems (pp. 1546–1551), Shangai, China,October 2003.

37. Füßler, H., Widmer, J., Käsemann, M., Mauve, M., & Hartenstein,H. (2003). Contention-based forwarding for mobile ad-hoc net-works. Elsevier’s Ad-Hoc Networks, 1(4), 351–369.

Javier Gozalvez received an elec-tronics engineering degree from theFrench Engineering School EN-SEIRB and a Ph.D. in mobile com-munications from the University ofStrathclyde, Glasgow, U.K. SinceOctober 2002, he has been withthe University Miguel Hernándezof Elche, Spain, where he is cur-rently an Associate Professor andDirector of the Uwicore ResearchLaboratory. At Uwicore, he is lead-ing research activities in the areasof wireless vehicular communica-

http://www.isi.edu/nsnam/ns/

http://www.isi.edu/nsnam/ns/

http://sumo.sourceforge.net/

http://sumo.sourceforge.net/


tions, radio resource management, heterogeneous wireless systems,and wireless system design and optimization. He currently serves asMobile Radio Senior Editor of IEEE Vehicular Technology Maga-zine, and previously served as AE of IEEE Communication Letters.He was TPC Co-Chair of the 2009 IEEE VTC Spring, and GeneralCo-Chair of the 3rd Int. Symposium on Wireless CommunicationsSystems (ISWCS’2006). He is also the founder and General Co-Chairof the IEEE Int. Symposium on Wireless Vehicular Communications(WiVeC) in its 2007, 2008 and 2010 editions. He has recently beenelected to the Board of Governors of the IEEE Vehicular TechnologySociety (2011–2014).

Miguel Sepulcre received a Tele-communications Engineering de-gree in 2004 and a Ph.D. in Com-munications Technologies in 2010,both from the University MiguelHernández of Elche (UMH), Spain.In 2004, he spent six months at theEuropean Space Agency (ESA) inNoordwijk (The Netherlands) work-ing on the communications physicallayer of earth exploration satellites.He then joined in 2005 the Univer-sity Miguel Hernández of Elche as anetworks manager and teaching as-

sistant. In March 2006, he obtained a Ph.D. fellowship from the Valen-cian regional government and joined the Uwicore research laboratory.In 2009, he spent three months at the Karlsruhe Institute of Technol-ogy (KIT) in Karlsruhe (Germany) working on cooperative vehicular

communications. He is currently a research fellow at the Uwicore Re-search Laboratory of UMH, working on cooperative vehicular commu-nications, radio resource management, and modeling and simulation ofwireless communications systems.

Ramon Bauza received a Telecom-munications Engineering degreefrom the University Miguel Hernán-dez of Elche (Spain) in 2008; dur-ing his final degree project he de-veloped and studied novel multi-hoprouting protocols and disseminationdata techniques for vehicular net-works. He then joined the Uwicoreresearch laboratory, and worked ini-tially on developing an extension ofthe WiGIPLAN planning softwarefor wireless coverage in space mis-sions to Mars and the Moon under

a research contract with INSA for a European Space Agency (ESA)project. He is now working in the European FP7 iTETRIS researchproject that aims at creating an open and integrated traffic and wirelesscommunications platform for analysing the large-scale impact of co-operative vehicular communications on traffic management efficiency.His duties include the modelling of WAVE, WiMAX, 3G and DVBtechnologies for vehicle to vehicle and vehicle to infrastructure com-munications, and the development of novel cooperative networkingprotocols.

impact of the radio channel modelling on the performance

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