**7. Conclusion**

The automotive industry conveniently built powerful and safer cars by embedding advanced materials and sensors. With the advent of wireless communication technologies, cars are being equipped with wireless communication devices, enabling them to communicate with others. Such communications are not plainly restricted to data transfers (such as GPS, video and audio, emails, etc.), but also create new opportunities for enhancing road safety. Some applications only require communication among vehicles, while many others require the coordination between vehicles and road-side infrastructure. Recent technological developments, notably in mobile computing, wireless communication, and 14 Will-be-set-by-IN-TECH

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

> 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 x 10−3

Fig. 5. Variance of the auto-correlation and cross-correlation function with different *a*

should be like this: smaller relative difference means that the transmitter and the receiver are in a relatively closer static state, so the randomness of channel tends to be smaller. As plotted in Fig. 5d, the variances of the cross-correlation of the proposed models are considerably

It should also be mentioned that our simulation results of variance of the correlation when *a* = 0 represent the case of I2V channels, which shows the comprehensiveness of our models

The automotive industry conveniently built powerful and safer cars by embedding advanced materials and sensors. With the advent of wireless communication technologies, cars are being equipped with wireless communication devices, enabling them to communicate with others. Such communications are not plainly restricted to data transfers (such as GPS, video and audio, emails, etc.), but also create new opportunities for enhancing road safety. Some applications only require communication among vehicles, while many others require the coordination between vehicles and road-side infrastructure. Recent technological developments, notably in mobile computing, wireless communication, and

Var[RZcZs(τ)]

Var[RZcZc(τ)]

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>0</sup>

Reference model Statistical model(1 simulation trial) Statistical model(10 simulation trials)

MEDS model

Normalize Time Delay τ

Reference model(a=0) Reference model(a=0.2) Reference model(a=1) Statistical model MEDS model

(b) a=0.2

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>0</sup>

Normalize Time Delay τ

(d) Variance of the cross-correlation function

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>0</sup>

Reference model Statistical model(1 simulation trial) Statistical model(10 simulation trials)

Reference model Statistical model(1 simulation trial) Statistical model(10 simulation trials)

MEDS model

lower than the reference model.

and performance analysis as well.

**7. Conclusion**

MEDS model

Normalize Time Delay τ

(a) a=0

<sup>0</sup> <sup>2</sup> <sup>4</sup> <sup>6</sup> <sup>8</sup> <sup>10</sup> <sup>0</sup>

Normalize Time Delay τ

(c) a=1

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05

Var[RZcZc(τ)]

Var[RZcZc(τ)]

remote sensing are pushing ITS towards a major leap forward. Vehicles become sophisticated computing systems, with several computers and sensors onboard, each dedicated to certain car operations. Interconnected vehicles do not only collect information about themselves and their environments, but they also exchange the information in real time with other nearby vehicles. As radio-communication-based solutions can operate beyond the line-of-sight constraints, they can enable cooperative approaches. Vehicles and infrastructure cooperate to perceive potentially dangerous situations in an extended space and time horizon. Appropriate vehicular communication architectures are necessary to create reliable and extended driving support systems for road safety and transportation efficiency.

A number of technical challenges need to be resolved in order to deploy vehicular networks and to provide related services for drivers and passengers in such networks. Scalability and interoperability are two important issues that should be addressed. The employed protocols and mechanisms should be scalable to numerous vehicles and interoperable with different wireless technologies, such as reliable link performance and MAC protocols, routing and dissemination, IP configuration and mobility management, security etc. As a key component of the ITS, vehicular wireless networks, has attracted research attention from both the academia and industry of US, EU, and Japan. Although many works have been done on communication and routing protocol, only few models have been developed to characterize the fading effect in vehicular wireless network.

In this chapter, we proposed a new statistical and deterministic SoS model for IVC fading channels with a LOS component in VANETs. The properties of the proposed models were derived and verified in terms of the auto-correlation and the cross-correlation by comparison between theoretical and simulation results. The statistics of them match those of the reference model for a large range of normalized time delays (0 ≤ *f*1*TS* ≤ 4). When the *K* factor increases, the proposed SoS models show an improved approximation of the desired auto-correlation and faster convergence. And then we described the curves of the statistics for the simulation models with different vehicle speed ratios, which indicates that the smaller relative speed difference of two vehicle speeds in VANETs contribute to the better performance of the proposed models. More importantly, Based on our models, we provided more comprehensive performance analysis and comparison compared to existing models.

It is observed that the the variances in the cross-correlation for the statistical model and the MEDS model are considerably lower than those of reference model. Meanwhile, For the same time delay *τ*, the statistical model shows better performance and faster convergence than the MEDS model. Hence, the statistical model may be more suitable for IVC fading channels with a LOS component in VANETs.
