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Spatial Modeling and Analysis of Vehicular Networks.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatial Modeling and Analysis of Vehicular Networks./
作者:	Jeyaraj, Jeya Pradha.
面頁冊數:	1 online resource (169 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
Contained By:	Dissertations Abstracts International83-08B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28866202click for full text (PQDT)
ISBN:	9798762102681

Spatial Modeling and Analysis of Vehicular Networks.
Jeyaraj, Jeya Pradha.

Spatial Modeling and Analysis of Vehicular Networks. - 1 online resource (169 pages)

Source: Dissertations Abstracts International, Volume: 83-08, Section: B.

Thesis (Ph.D.)--University of Notre Dame, 2021.

Includes bibliographical references

Through vehicle-to-vehicle communication (V2V), vehicles can exchange information required for safety alerting other vehicles in their vicinity. Due to the mission-critical nature of safety applications, high reliability is a key requirement of V2V systems. Reliability is the probability that a V2V link can sustain a certain target data rate. It depends on the locations of transmitting and receiving vehicles and interferers, street geometry, and wireless medium. The reliability results obtained by conducting a large number of trials in the real world fail to provide crisp insights into the effects of network design parameters on reliability despite their high costs. We aim to complement/reduce these large-scale experiments by applying mathematical tools from stochastic geometry to (i) model vehicular networks, and (ii) analyze the reliability of V2V communication. In particular, we introduce the notion of model equivalence, which shows that the number of models can be drastically reduced to only a few classes. Also, we prove that many existing models can be substantially simplified to the proposed transdimensional models with virtually no loss in accuracy. Furthermore, we investigate the meta distribution (MD) of the signal-to-interference ratio (SIR), which is a much sharper performance metric than the SIR distribution that is usually studied. The SIR MD answers questions such as 'what fraction of the V2V links are 99% reliable if the target data rate is 10 Mbps?' This metric is the key towards designing V2V networks with guaranteed reliability. With respect to the broadcast communication, we formulate the binned meta distribution of the SIR, which answers questions like 'what fraction of the V2V links that are in the distance range of 100-200 m are 99% reliable if the target data rate is 10 Mbps?' The binned variant of the MD provides insights into the effective range for reliable broadcast communication. Having the binned SIR MD as the performance metric, we compare different stochastic geometry models to system-level simulations. The Poisson point process-based vehicular networks rank the highest in terms of analytical tractability with loose approximations to system-level simulations; the Matern hard-core process-based models provide tight approximations but are highly intractable; the determinantal point process-based models rank the highest in terms of accuracy with good tractability.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798762102681Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Determinantal Point ProcessesIndex Terms--Genre/Form:

542853
Electronic books.

Spatial Modeling and Analysis of Vehicular Networks.
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500 $a Source: Dissertations Abstracts International, Volume: 83-08, Section: B.
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502 $a Thesis (Ph.D.)--University of Notre Dame, 2021.
504 $a Includes bibliographical references
520 $a Through vehicle-to-vehicle communication (V2V), vehicles can exchange information required for safety alerting other vehicles in their vicinity. Due to the mission-critical nature of safety applications, high reliability is a key requirement of V2V systems. Reliability is the probability that a V2V link can sustain a certain target data rate. It depends on the locations of transmitting and receiving vehicles and interferers, street geometry, and wireless medium. The reliability results obtained by conducting a large number of trials in the real world fail to provide crisp insights into the effects of network design parameters on reliability despite their high costs. We aim to complement/reduce these large-scale experiments by applying mathematical tools from stochastic geometry to (i) model vehicular networks, and (ii) analyze the reliability of V2V communication. In particular, we introduce the notion of model equivalence, which shows that the number of models can be drastically reduced to only a few classes. Also, we prove that many existing models can be substantially simplified to the proposed transdimensional models with virtually no loss in accuracy. Furthermore, we investigate the meta distribution (MD) of the signal-to-interference ratio (SIR), which is a much sharper performance metric than the SIR distribution that is usually studied. The SIR MD answers questions such as 'what fraction of the V2V links are 99% reliable if the target data rate is 10 Mbps?' This metric is the key towards designing V2V networks with guaranteed reliability. With respect to the broadcast communication, we formulate the binned meta distribution of the SIR, which answers questions like 'what fraction of the V2V links that are in the distance range of 100-200 m are 99% reliable if the target data rate is 10 Mbps?' The binned variant of the MD provides insights into the effective range for reliable broadcast communication. Having the binned SIR MD as the performance metric, we compare different stochastic geometry models to system-level simulations. The Poisson point process-based vehicular networks rank the highest in terms of analytical tractability with loose approximations to system-level simulations; the Matern hard-core process-based models provide tight approximations but are highly intractable; the determinantal point process-based models rank the highest in terms of accuracy with good tractability.
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538 $a Mode of access: World Wide Web
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650 4 $a Statistical physics. $3 536281
650 4 $a Applied mathematics. $3 2122814
650 4 $a Automotive engineering. $3 2181195
653 $a Determinantal Point Processes
653 $a Poisson Line Process
653 $a Poisson Point Processes
653 $a Stochastic Geometry
653 $a V2X Communication
653 $a Vehicle-to-vehicle communication
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690 $a 0217
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773 0 $t Dissertations Abstracts International $g 83-08B.
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