Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification

The mmwave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing...

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Detalles Bibliográficos
Autor: Ganugapanta, Bharath Reddy
Tipo de recurso: tesis de maestría
Fecha de publicación:2021
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/368151
Acceso en línea:https://hdl.handle.net/2117/368151
Access Level:acceso abierto
Palabra clave:Antennas (Electronics)
Mobile communication systems
NR-V2X
mmWave
ML
Smart Antenna
Smart Beam Management
Antenes (Electrònica)
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenes
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oai_identifier_str oai:upcommons.upc.edu:2117/368151
network_acronym_str ES
network_name_str España
repository_id_str
spelling Smart Pattern V2I Handover Based on Machine Learning Vehicle ClassificationGanugapanta, Bharath ReddyAntennas (Electronics)Mobile communication systemsNR-V2XmmWaveMLSmart AntennaSmart Beam ManagementAntenes (Electrònica)Comunicacions mòbils, Sistemes deÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenesThe mmwave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmwave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this thesis, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using machine learning algorithms, can improve both network performance and communication stability in high mobility vehicular communications.Universitat Politècnica de CatalunyaJofre Roca, Lluís20212021-07-2720222022-06-08master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/368151reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3681512026-05-27T15:37:01Z
dc.title.none.fl_str_mv Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
title Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
spellingShingle Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
Ganugapanta, Bharath Reddy
Antennas (Electronics)
Mobile communication systems
NR-V2X
mmWave
ML
Smart Antenna
Smart Beam Management
Antenes (Electrònica)
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenes
title_short Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
title_full Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
title_fullStr Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
title_full_unstemmed Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
title_sort Smart Pattern V2I Handover Based on Machine Learning Vehicle Classification
dc.creator.none.fl_str_mv Ganugapanta, Bharath Reddy
author Ganugapanta, Bharath Reddy
author_facet Ganugapanta, Bharath Reddy
author_role author
dc.contributor.none.fl_str_mv Jofre Roca, Lluís
dc.subject.none.fl_str_mv Antennas (Electronics)
Mobile communication systems
NR-V2X
mmWave
ML
Smart Antenna
Smart Beam Management
Antenes (Electrònica)
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenes
topic Antennas (Electronics)
Mobile communication systems
NR-V2X
mmWave
ML
Smart Antenna
Smart Beam Management
Antenes (Electrònica)
Comunicacions mòbils, Sistemes de
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Antenes i agrupacions d'antenes
description The mmwave frequencies will be widely used in future vehicular communications. At these frequencies, the radio channel becomes much more vulnerable to slight changes in the environment like motions of the device, reflections or blockage. In high mobility vehicular communications the rapidly changing vehicle environments and the large overheads due to frequent beam training are the critical disadvantages in developing these systems at mmwave frequencies. Hence, smart beam management procedures are desired to establish and maintain the radio channels. In this thesis, we propose that using the positions and respective velocities of the vehicles in the dynamic selection of the beam pair, and then adapting to the changing environments using machine learning algorithms, can improve both network performance and communication stability in high mobility vehicular communications.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-07-27
2022
2022-06-08
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/368151
url https://hdl.handle.net/2117/368151
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424156136701952
score 15,300719

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