A Measurement Based Shadow Fading Model for Vehicle-to-Vehicle Network Simulations

International Journal of Antennas and Propagation (Impact Factor: 0.66). 03/2012; DOI: 10.1155/2015/190607
Source: arXiv


The vehicle-to-vehicle (V2V) propagation channel has significant implications
on the design and performance of novel communication protocols for vehicular ad
hoc networks (VANETs). Extensive research efforts have been made to develop V2V
channel models to be implemented in advanced VANET system simulators for
performance evaluation. The impact of shadowing caused by other vehicles has,
however, largely been neglected in most of the models, as well as in the system
simulations. In this paper we present a simple shadow fading model targeting
system simulations based on real measurements performed in urban and highway
scenarios. The measurement data is separated into three categories,
line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non
line-of-sight (NLOS) by buildings, with the help of video information, recorded
during the measurements. It is observed that vehicles obstructing the LOS
induce an additional attenuation of about 10 dB in the received signal power.
We use a Markov chain based state transition diagram to model the transitions
between the LOS, OLOS and NLOS states. Further, sample state transition
intensities based on the measurements and simulated traffic are presented. An
approach to incorporate the LOS/OLOS model into existing VANET simulators is
also provided. Finally, system level VANET simulation results are presented,
which show the difference between the LOS/OLOS model and a channel model based
on Nakagami-m fading.

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Available from: Taimoor Abbas, Dec 13, 2013
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    • "where P tx and P rx (d) are the transmitted and received signal power at distance d in between, d 0 is the close-in reference distance, and β is called the path loss exponent, which depends on the environment. In vehicular communication scenarios, the path loss exponents ranging from 1.4 to 3.5 and from 2.8 to 5.9 have been reported in lineof-sight (LOS) and non-line-of-sight (NLOS) situations, respectively[24,25]. The wireless transmission range r 0 of each node can then be calculated as the equivalent transmission power using a receiving threshold, P rx,th , also referred to as the reception sensitivity. "
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    ABSTRACT: A vehicular ad hoc network (VANET) is a specific type of mobile ad hoc networks (MANETs); it can provide direct or multi-hop vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), vehicle-to-pedestrian (V2P), and vehicle-to-internet (V2I) communications based on the pre-existing road layouts. The emerging and promising VANET technologies have drawn tremendous attention from the government, academics, and industry over the past few years and have been increasingly available for a large number of cutting edge applications that can be classified into road safety, traffic efficiency, and infotainment categories. Due to the unique characteristics of VANETs, such as high mobility with an organized but constrained pattern, and diverse radio propagation conditions, the conventional researches dedicated for general MANETs cannot be directly applied to VANETs. This paper presents an analytical framework to investigate the minimum node degree of k-connected VANETs, with a homogeneous range assignment in highway scenarios. We simulate the mobility patterns with realistic vehicular traces, model the network topology as a two-path fading geometric random graph, and conduct extensive experiments on the derived analytical results. Through a combination of mathematical modeling and simulations, we derive a probabilistic bound for the minimum node degree of a homogeneous vehicular ad hoc network in highway scenarios. The analytical framework is useful in the study of connectivity and estimation of performance in one-dimensional vehicular ad hoc networks.
    Preview · Article · Dec 2016 · EURASIP Journal on Wireless Communications and Networking
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    • "The impact of obstructing vehicles has been studied in detail by Abbas et al. in [15]. Our simulations employ such a channel model which is based on the work reported in [15] "
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    ABSTRACT: Accurately estimating node density in Vehicular Ad hoc Networks, VANETs, is a challenging and crucial task. Various approaches exist, yet none takes advantage of physical layer parameters in a distributed fashion. This paper describes a framework that allows individual nodes to estimate the node density of their surrounding network independent of beacon messages and other infrastructurebased information. The proposal relies on three factors: 1) a discrete event simulator to estimate the average number of nodes transmitting simultaneously; 2) a realistic channel model for VANETs environment; and 3) a node density estimation technique. This work provides every vehicle on the road with two equations indicating the relation between 1) received signal strength versus simultaneously transmitting nodes, and 2) simultaneously transmitting nodes versus node density. Access to these equations enables individual nodes to estimate their real-time surrounding node density. The system is designed to work for the most complicated scenarios where nodes have no information about the topology of the network and, accordingly, the results indicate that the system is reasonably reliable and accurate. The outcome of this work has various applications and can be used for any protocol that is affected by node density.
    Full-text · Article · Jun 2015 · Radioengineering
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    • "The ML method on the other hand, is able to correctly estimate both parameters in this example. Fig. 2 shows the same thing as Fig. 3, but is for measured data from a vehicle-to-vehicle (V2V) measurement campaign for NLOS scenarios at 5.6 GHz [12]. In this case, the parameter estimates obtained using OLS show significantly smaller values compared to the parameter estimates for the ML method. "
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    ABSTRACT: The pathloss exponent and the variance of the large-scale fading are two parameters that are of great importance when modeling and characterizing wireless propagation channels. The pathloss is typically modeled using a log-distance power law with a large-scale fading term that is log-normal. In practice, the received signal is affected by the dynamic range and noise floor of the measurement system used to sound the channel. Estimating the pathloss exponent and large scale fading without considering the effects of the noise floor can lead to erroneous results. In this paper, we show that the path loss and large scale fading estimates can be improved if the effects of censored samples, i.e., samples below the noise floor, are taken into account in the estimation step. It is also shown that if the information about the censored samples are not included in the estimation method, it can result in biased estimation of both the pathloss exponent and the large scale fading.
    Full-text · Article · Apr 2015 · IEEE Wireless Communication Letters
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