Title
Performance comparison of IEEE 802.11p and LTE-V2X through field-tests and simulationsxmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
Eurecat Centre Tecnològic de Catalunyahttps://ror.org/033vfbz75
Version
http://purl.org/coar/version/c_ab4af688f83e57aa
Rights
© 2024 IEEEAccess
http://purl.org/coar/access_right/c_f1cfPublisher’s version
https://doi.org/10.1109/VNC61989.2024.10576013Published at
IEEE Vehicular Networking Conference (VNC) Pp. 81-88. Kobe, 29-31 mayo, 2024Publisher
IEEEKeywords
Communication Technologiespower measurement
Abstract
Vehicular communication is a key enabler in making Automated Vehicles (AVs) collaborate by sharing information, which complements on-board sensor information and facilitates precise vehicle control. T ... [+]
Vehicular communication is a key enabler in making Automated Vehicles (AVs) collaborate by sharing information, which complements on-board sensor information and facilitates precise vehicle control. This paper presents a tailored measurement campaign aimed at analyzing the performance of two vehicular communication technologies, namely IEEE 802.11p and LTE-V2X. Our study focuses on key metrics for cooperating AVs, such as end-to-end latency and packet delivery ratios. Additionally, we investigate the feasibility of channel coexistence, assessing the challenges associated with concurrent channel access. The results derived from field tests are correlated with simulations conducted on PLEXE and OpenCV2X, i.e., platforms used for simulating IEEE 802.11p and LTE-V2X, respectively. This combined methodology, comprising field tests and simulations, enables the attainment of replicable conclusions, which in turn enables better design choices. [-]
xmlui.dri2xhtml.METS-1.0.item-oaire-funderName
Gobierno VascoGobierno Vasco
Comisión Europea
xmlui.dri2xhtml.METS-1.0.item-oaire-fundingStream
Ikertalde 2022-2023Elkartek 2023
H2020-ECSEL
xmlui.dri2xhtml.METS-1.0.item-oaire-awardNumber
IT1451-22KK-2023-00019
101007350
xmlui.dri2xhtml.METS-1.0.item-oaire-awardURI
Sin informaciónSin información
https://doi.org/10.3030/101007350
xmlui.dri2xhtml.METS-1.0.item-oaire-awardTitle
Sin informaciónMovilidad Autónoma Confiable mediante Tecnologías de Explicabilidad y Evaluación de Inteligencia Artificial (AUTOTRUST)
AI-augmented automation for efficient DevOps, a model-based framework for continuous development At RunTime in cyber-physical systems (AIDOaRT)