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Today's advanced simulators facilitate thorough studies on Vehicular Ad hoc NETworks (VANETs). However the choice of the physical layer model in such simulators is a crucial issue that impacts the results. A solution to this challenge might be found with a hybrid model. In this paper, we propose a semi-deterministic channel propagation model for VANETs called UM-CRT. It is based on CRT (Communication Ray Tracer) and SCME—UM (Spatial Channel Model Extended—Urban Micro) which are, respectively, a deterministic channel simulator and a statistical channel model. It uses a process which adjusts the statistical model using relevant parameters obtained from the deterministic simulator. To evaluate realistic VANET transmissions, we have integrated our hybrid model in fully compliant 802.11 p and 802.11 n physical layers. This framework is then used with the NS-2 network simulator. Our simulation results show that UM-CRT is adapted for VANETs simulations in urban areas as it gives a good approximation of realistic channel propagation mechanisms while improving significantly simulation time.

Vehicular Ad hoc NETworks (VANETs) are a very promising research area interesting the scientific community, car manufacturers, and mobile telephony operators. Vehicular applications should be thoroughly tested before they are deployed in the real world. Because the setup of experimental VANETs would imply huge investments, computer simulations are generally preferred.

One of the major issues when using simulators for VANETs concerns the vehicular environment and therefore the realistic modeling of the wireless propagation channel. Indeed, there are still several problems linked to the impact of the mobility and the traffic density on channel statistics yet to solve, for example, packets loss, rate of flow, frequency correlation, and amplitude distribution.

Many research and development works relating to routing [

From this, one can understand that the radio propagation model used by the network simulation tool is a key factor in MANETs (Mobile Ad Hoc NETworks) and particularly in the VANETs subclass. Developing a radio channel model, which would describe the realistic radio channel conditions as accurately as possible, has been a continuous challenge. This is precisely what this work addresses.

There already exist reliable channel models which are customizable according to the environment [

As far as VANETs are concerned, deterministic channel models are not suitable because of the high mobility, the diversity of the environment encountered, and the high number of communicating nodes. The study of the higher layers of the OSI model (in particular the Network and Application layers) requires a low simulation time (i.e., a couple of minutes) in order to allow statistical analyses on large simulation series. To answer the challenge of channel modeling in VANETs, several works propose various methods which can be classified in two categories according to the research domain of their authors.

In the network community, Dhoutaut et al. [

In the physical channel modeling community, one can find different statistical channel models which have been derived from intensive measurement campaigns. The stochastic parameters of these models are extracted from the measurement data. It has been shown that the measured amplitude samples follow Rice, Rayleigh, or Weibull distributions [

Another fundamental topic in VANETs simulation concerns the mobility model used for simulations. Many works, like the working by Marfia et al. [

The rest of this paper is organized as follows. In Section

Statistical and deterministic channel models are the two common ways to describe the radio channel behavior in VANETs simulations. In this section, we describe an example of each of these approaches: for the statistical one, the Spatial Channel Model Extended in its

The Spatial Channel Model Extended (SCME) statistical channel model is an evolution of the 3GPP Spatial Channel Model (SCM) [

SCM and SCME are so-called geometric models for which scatterers are placed stochastically in the simulation scene. SCME considers clusters of scatterers. Each cluster corresponds to a resolvable path. Each path is made up of several nonresolvable subpaths. Figure

Geometric parameters of the SCME model.

SCME is a natively Multiple Input Multiple Output (MIMO) model. It allows for the simulation of three types of environments: Urban Macrocell, Suburban Microcell (distance between MS and BS 3 km maximum), and Urban Microcell (distance between MS and BS of 1 km maximum). In the context of urban VANETs, because of intervehicular distances less than one kilometer we have chosen the Urban Microcell (UM) environment. The authors of [

Let us now describe the Communication Ray Tracer (CRT) software. It is a deterministic propagation simulator developed by the Xlim-SIC laboratory from the University of Poitiers (France) [

Figure

A CRT simulation of multipath propagation in a realistic 3D urban environment.

In order to reduce the computation time in a context of mobility, an optimization based on the stationarity property of the channel [

The presentation of the two previous models shows several key elements.

The SCME-UM statistical model is very efficient to produce CIRs in environments modeled by several clusters placed randomly in the scene. However, it does not take into account the geometrical specificities of real environments. On the contrary, CRT is able to provide CIRs directly connected with the environment by a complete modeling of the interactions between radio waves and building. However, it necessitates an important computation time for each CIR calculation. Therefore, for VANETs applications, which introduce the mobility for each node and a significant number of possible radio links between the nodes in the network, a deterministic solution leads to a huge computation time.

To address this problem, we propose in this paper a hybridisation of the two models presented previously named UM-CRT. Indeed, we will combine at the same time the major wave propagation phenomena (existence or not of the direct path) with its deterministic component and the low computation time allowed by its statistical one.

According to the principle presented in the previous section, UM-CRT is created from the association of two models. Figure

Principle of the UM-CRT model.

Classically, for all radio links existing between nodes, on the one hand, CRT computes CIRs according to the simulation of all the received multi-paths according to an environment modeled in 3D. On the other hand, SCME-UM provides statistically generated CIRs.

Here, we propose to limit the search path by ray-tracing only to the direct path because it is well known that this path has the main impact on the received signal. This limitation has two advantages.

It takes into account the geometrical characteristic of the considered environment: we can determine the Line of Sight (LOS) and Nonline of Sight (NLOS) radio links.

It considerably reduces the computation time of the deterministic simulation.

On this basis, it is possible to generate representative CIRs with SCME-UM.

Notice that complete multi-path simulations with CRT are possible in order to exploit other information included in the deterministic CIRs such as delay spread. But it will be very time consuming.

Moreover, in order to reduce again the computation time, we propose to consider the stationarity property of the channel introduced previously. We assume that a CIR of a radio link remains constant when the move of its extremities is less than 8 meters in relation to a reference position. So we do not compute the CIRs at each time but only for a finite number of distances between the transmitter and the receiver. In practice, as the SCME-UM is limited to 600 meters between the transmitter and the receiver, we consider a set of 90 distances to calculate the CIRs. These CIRs can be precalculated in order to accelerate the computation time for statistical studies of VANETS performance.

To summarize, for a VANETs scenario based on a set of vehicles, UM-CRT allows computing a statistical CIR of each radio link between two mobile nodes, at each time, according to a LOS/NLOS deterministic analysis.

In order to evaluate the performance of a VANETs scenario in a realistic environment, it is necessary to introduce real transmission conditions in a network simulator. We consider here the NS2 platform. These transmission conditions are based on the channel model and on the specific digital communication chain considered. This constitutes a realistic physical layer.

Firstly, we explain the considered physical layer. Secondly, we introduce the UM-CRT framework in an NS2 context. With this framework, it is possible to compute the performance analysis according to some QoS metrics (Packet Delivery Ratio, Delay, etc.) in several configurations, as it will be shown in Section

Concerning the physical layer in VANETs context, there exists the 802.11p standard adopted at the the end of 2010 [

The 802.11p standard does not account for the Multiple-Input Multiple-Output technology although it is known to improve significantly the reliability and the throughput of data transmission [

In [

From the impulse response calculated by SCME-UM, we use the 802.11 physical layer described previously to calculate a BER. Each BER value gives an accurate information about the quality of the communication between two nodes.

In order to evaluate the accuracy of our realistic BER computation approach, we consider the 802.11p standard in the environment in which we will run our simulations, that is to say a VANETs scenario with 40 vehicles moving in the Munich city center. Figure

BER evolutions according to SNR obtained by UM-CRT and CRT.

We can observe that the results are very close and consequently the approach proposed in our framework is valid in terms of BER.

Finally, this realistic physical layer based on UM-CRT and called UM-CRT framework is introduced in the NS2 platform. It communicates with the upper layers. All these steps are summarized in Figure

The UM-CRT framework.

Figure

Instantaneous representation of the radio link quality in terms of BER between vehicles in the Munich city center.

To conclude, the NS2 platform modified with the UM-CRT framework constitutes a VANETs simulator which allows to evaluate QoS performance with realistic transmission conditions in a specific environment.

To evaluate the UM-CRT propagation model, we compare it with the CRT simulator using NS-2 simulations in a typical urban environment: that is, the center of Munich City (cf. Figure

Please note that in this evaluation, the results of the statistical SCME-UM model used alone are not presented because they do not take into account a real propagation environment. LOS and NLOS results will always either be nearly perfect (~100% of packets reach their destination) or bad (~0% of packets reach their destination).

The simulations were run in SISO and MIMO modes in the 5 GHz band. In order to have enough different cases for comparing UM-CRT to CRT, we ran simulations with different VANET realistic mobility scenarios generated by VanetMobiSim. Each of them is defined with different starting points, traffic lights configurations, and mobilities. The mobility is random and the nodes’ speed is variable with time and limited by 3 maximum allowed speeds (0, 8, and 15 m/s). The routing protocol we used is AODV in its basic setup. The traffic generated for inter-node communications is UDP based. The simulations were performed on a Linux Mandriva system with an updated NS-2.29 simulator version.

We first compare UM-CRT to CRT in the SISO mode (802.11p). The comparison is done in terms of packet delivery ratio (PDR) between a transmitter and a receiver. This is a common criterion used in VANETs performance evaluation. We also compare them in terms of number of hops and end-to-end delay. We finally evaluate how MIMO impacts the PDR (802.11n case). Note that in these evaluations we present averaged results for every simulation over 5 different 40-second simulation time.

Figure

UM-CRT PDR evaluation.

In Figure

UM-CRT versus CRT PDR in four mobility situations.

Figures

Delay evaluation.

Average number of hops evaluation.

Results have the same trend in the case of a static situation in the simulations. This is a current limitation of our model which is not suitable for null speed. In this case, the LOS-NLOS criterion is not suitable alone to produce results that match the deterministic model.

To summarize, when we are not in a static situation, the UM-CRT model gives results quite similar to CRT in all situations. This can be explained by the number of statistical outcomes that increase because of the mobility. So we can conclude that the UM-CRT model gives results quite close to the deterministic model.

Furthermore, as the speed gets higher, one can see that the channel deteriorates (the received packets rate decreases) and that it is more difficult to achieve a reliable communication (delay and average number of hops increase). We can see that, for a maximum speed of 15 m/s, the received packet rate does not exceed 50%. This is a second expression of the determinism that our statistically based model produces.

We will now see the impact of the use of UM-CRT in a MIMO mode instead of a SISO mode.

The impact of the MIMO mode on the packet delivery ratio is presented in Figure

MIMO Impact.

Indeed, results between CRT and UM-CRT are close. As expected, we can also notice that a MIMO channel is more robust than a SISO one. The received packets rates are better in MIMO cases, no matter the nodes speed. Approximately 70% of the packets are received with a 15 m/s speed in this scenario for the MIMO mode, whereas it is less than 50% for the SISO mode.

From these results, one can conclude that the MIMO mode improves the transmission’s robustness in VANETs. So MIMO technology allows reducing transmission power with a packet delivery ratio equal to the SISO mode in order to limit perturbations, or it can help to improve the packet delivery ratio. In both cases, MIMO technology seems to be very interesting to answer VANETs' challenges.

As shown in Section

In Table

Simulation time comparison.

CRT | UM-CRT | SCME-UM | ||||||||||

Scenario | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 | 1 | 2 | 3 | 4 |

Preprocessing Full CRT (h) | 18 | 18 | 17 | 18 | ||||||||

Preprocessing LOS/NLOS (h) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||

Simulation (h) | 57 | 65 | 55 | 63 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 | 0.5 |

TOTAL Time (h) | 75 | 83 | 72 | 81 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 | 1.5 |

In this paper we have presented UM-CRT, a semi-deterministic channel propagation model for VANETs. UM-CRT, which was integrated into the NS-2 network simulator, is based on the stochastic SCME-UM model and the deterministic CRT channel simulator.

The implementation of this new model allows to run network simulations in a very fast way. Indeed the computation time is reduced from more than 70 hours to less than 2 hours. This makes UM-CRT quite suitable for VANETs simulations having a large number of high-mobility nodes. Moreover, our results showed UM-CRT to be appropriate for mobility scenarios and realistic vehicular networks simulations, typically urban scenarios.

Furthermore, results show that UM-CRT is also adapted for the MIMO technology. As expected, simulations involving this configuration have clearly showed the robustness of the MIMO scheme compared to SISO one.

We currently focus our work on the selection of new relevant criteria extracted from the CIR such as the RMS delay spread or the link capacity.