An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks

<p/> <p>Today's advanced simulators facilitate thorough studies on VANETs but are hampered by the computational effort required to consider all of the important influencing factors. In particular, large-scale simulations involving thousands of communicating vehicles cannot be served...

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Main Authors: Killat Moritz, Hartenstein Hannes
Format: Article
Language:English
Published: SpringerOpen 2009-01-01
Series:EURASIP Journal on Wireless Communications and Networking
Online Access:http://jwcn.eurasipjournals.com/content/2009/721301
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spelling doaj-d085873e6ec74512a146cb3829515c2c2020-11-24T21:36:27ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14721687-14992009-01-0120091721301An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc NetworksKillat MoritzHartenstein Hannes<p/> <p>Today's advanced simulators facilitate thorough studies on VANETs but are hampered by the computational effort required to consider all of the important influencing factors. In particular, large-scale simulations involving thousands of communicating vehicles cannot be served in reasonable simulation times with typical network simulation frameworks. A solution to this challenge might be found in hybrid simulations that encapsulate parts of a discrete-event simulation in an analytical model while maintaining the simulation's credibility. In this paper, we introduce a hybrid simulation model that analytically represents the probability of packet reception in an IEEE 802.11p network based on four inputs: the distance between sender and receiver, transmission power, transmission rate, and vehicular traffic density. We also describe the process of building our model which utilizes a large set of simulation traces and is based on general linear least squares approximation techniques. The model is then validated via the comparison of simulation results with the model output. In addition, we present a transmission power control problem in order to show the model's suitability for solving parameter optimization problems, which are of fundamental importance to VANETs.</p>http://jwcn.eurasipjournals.com/content/2009/721301
collection DOAJ
language English
format Article
sources DOAJ
author Killat Moritz
Hartenstein Hannes
spellingShingle Killat Moritz
Hartenstein Hannes
An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
EURASIP Journal on Wireless Communications and Networking
author_facet Killat Moritz
Hartenstein Hannes
author_sort Killat Moritz
title An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
title_short An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
title_full An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
title_fullStr An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
title_full_unstemmed An Empirical Model for Probability of Packet Reception in Vehicular Ad Hoc Networks
title_sort empirical model for probability of packet reception in vehicular ad hoc networks
publisher SpringerOpen
series EURASIP Journal on Wireless Communications and Networking
issn 1687-1472
1687-1499
publishDate 2009-01-01
description <p/> <p>Today's advanced simulators facilitate thorough studies on VANETs but are hampered by the computational effort required to consider all of the important influencing factors. In particular, large-scale simulations involving thousands of communicating vehicles cannot be served in reasonable simulation times with typical network simulation frameworks. A solution to this challenge might be found in hybrid simulations that encapsulate parts of a discrete-event simulation in an analytical model while maintaining the simulation's credibility. In this paper, we introduce a hybrid simulation model that analytically represents the probability of packet reception in an IEEE 802.11p network based on four inputs: the distance between sender and receiver, transmission power, transmission rate, and vehicular traffic density. We also describe the process of building our model which utilizes a large set of simulation traces and is based on general linear least squares approximation techniques. The model is then validated via the comparison of simulation results with the model output. In addition, we present a transmission power control problem in order to show the model's suitability for solving parameter optimization problems, which are of fundamental importance to VANETs.</p>
url http://jwcn.eurasipjournals.com/content/2009/721301
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