Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach

A major component of future communication systems is vehicle-to-vehicle (V2V) communications, in which vehicles along roadways transfer information directly among themselves and with roadside infrastructure. Despite its numerous potential advantages, V2V communication suffers from one inherent short...

Full description

Bibliographic Details
Main Author: Guha, Sayantan
Other Authors: Electrical and Computer Engineering
Format: Others
Published: Virginia Tech 2017
Subjects:
V2V
Online Access:http://hdl.handle.net/10919/78467
id ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-78467
record_format oai_dc
spelling ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-784672021-04-28T05:32:17Z Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach Guha, Sayantan Electrical and Computer Engineering Dietrich, Carl B. Dhillon, Harpreet Singh Ruohoniemi, J. Michael Wireless Communications Stochastic Geometry Vehicular Communications V2V Cellular Networks A major component of future communication systems is vehicle-to-vehicle (V2V) communications, in which vehicles along roadways transfer information directly among themselves and with roadside infrastructure. Despite its numerous potential advantages, V2V communication suffers from one inherent shortcoming: the stochastic and time-varying nature of the node distributions in a vehicular ad hoc network (VANET) often leads to loss of connectivity and lower coverage. One possible way to improve this coverage is to allow the vehicular nodes to connect to the more reliable cellular network, especially in cases of loss of connectivity in the vehicular network. In this thesis, we analyze this possibility of boosting performance of VANETs, especially their node coverage, by taking assistance from the cellular network. The spatial locations of the vehicular nodes in a VANET exhibit a unique characteristic: they always lie on roadways, which are predominantly linear but are irregularly placed on a two dimensional plane. While there has been a signifcant work on modeling wireless networks using random spatial models, most of it uses homogeneous planar Poisson Point Process (PPP) to maintain tractability, which is clearly not applicable to VANETs. Therefore, to accurately capture the spatial distribution of vehicles in a VANET, we model the roads using the so called Poisson Line Process and then place vehicles randomly on each road according to a one-dimensional homogeneous PPP. As is usually the case, the locations of the cellular base stations are modeled by a planar two-dimensional PPP. Therefore, in this thesis, we propose a new two-tier model for cellular-assisted VANETs, where the cellular base stations form a planar PPP and the vehicular nodes form a one-dimensional PPP on roads modeled as undirected lines according to a Poisson Line Process. The key contribution of this thesis is the stochastic geometric analysis of a maximum power-based cellular-assisted VANET scheme, in which a vehicle receives information from either the nearest vehicle or the nearest cellular base station, based on the received power. We have characterized the network interference and obtained expressions for coverage probability in this cellular-assisted VANET, and successfully demonstrated that using this switching technique can provide a significant improvement in coverage and thus provide better vehicular network performance in the future. In addition, this thesis also analyzes two threshold-distance based schemes which trade off network coverage for a reduction in additional cellular network load; notably, these schemes also outperform traditional vehicular networks that do not use any cellular assistance. Thus, this thesis mathematically validates the possibility of improving VANET performance using cellular networks. Master of Science 2017-07-29T06:00:14Z 2017-07-29T06:00:14Z 2016-02-04 Thesis vt_gsexam:6744 http://hdl.handle.net/10919/78467 In Copyright http://rightsstatements.org/vocab/InC/1.0/ ETD application/pdf Virginia Tech
collection NDLTD
format Others
sources NDLTD
topic Wireless Communications
Stochastic Geometry
Vehicular Communications
V2V
Cellular Networks
spellingShingle Wireless Communications
Stochastic Geometry
Vehicular Communications
V2V
Cellular Networks
Guha, Sayantan
Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
description A major component of future communication systems is vehicle-to-vehicle (V2V) communications, in which vehicles along roadways transfer information directly among themselves and with roadside infrastructure. Despite its numerous potential advantages, V2V communication suffers from one inherent shortcoming: the stochastic and time-varying nature of the node distributions in a vehicular ad hoc network (VANET) often leads to loss of connectivity and lower coverage. One possible way to improve this coverage is to allow the vehicular nodes to connect to the more reliable cellular network, especially in cases of loss of connectivity in the vehicular network. In this thesis, we analyze this possibility of boosting performance of VANETs, especially their node coverage, by taking assistance from the cellular network. The spatial locations of the vehicular nodes in a VANET exhibit a unique characteristic: they always lie on roadways, which are predominantly linear but are irregularly placed on a two dimensional plane. While there has been a signifcant work on modeling wireless networks using random spatial models, most of it uses homogeneous planar Poisson Point Process (PPP) to maintain tractability, which is clearly not applicable to VANETs. Therefore, to accurately capture the spatial distribution of vehicles in a VANET, we model the roads using the so called Poisson Line Process and then place vehicles randomly on each road according to a one-dimensional homogeneous PPP. As is usually the case, the locations of the cellular base stations are modeled by a planar two-dimensional PPP. Therefore, in this thesis, we propose a new two-tier model for cellular-assisted VANETs, where the cellular base stations form a planar PPP and the vehicular nodes form a one-dimensional PPP on roads modeled as undirected lines according to a Poisson Line Process. The key contribution of this thesis is the stochastic geometric analysis of a maximum power-based cellular-assisted VANET scheme, in which a vehicle receives information from either the nearest vehicle or the nearest cellular base station, based on the received power. We have characterized the network interference and obtained expressions for coverage probability in this cellular-assisted VANET, and successfully demonstrated that using this switching technique can provide a significant improvement in coverage and thus provide better vehicular network performance in the future. In addition, this thesis also analyzes two threshold-distance based schemes which trade off network coverage for a reduction in additional cellular network load; notably, these schemes also outperform traditional vehicular networks that do not use any cellular assistance. Thus, this thesis mathematically validates the possibility of improving VANET performance using cellular networks. === Master of Science
author2 Electrical and Computer Engineering
author_facet Electrical and Computer Engineering
Guha, Sayantan
author Guha, Sayantan
author_sort Guha, Sayantan
title Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
title_short Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
title_full Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
title_fullStr Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
title_full_unstemmed Cellular-Assisted Vehicular Communications: A Stochastic Geometric Approach
title_sort cellular-assisted vehicular communications: a stochastic geometric approach
publisher Virginia Tech
publishDate 2017
url http://hdl.handle.net/10919/78467
work_keys_str_mv AT guhasayantan cellularassistedvehicularcommunicationsastochasticgeometricapproach
_version_ 1719399489468366848