Neuroevolution-based adaptive antenna array beamforming scheme to improve the V2V communication performance at intersections

Kang Kim, Hojin; Becerra, Raimundo; Bolufé, Sandy; Azurdia-Meza, Cesar A.; Montejo-Sánchez, Samuel; Zabala-Blanco, David

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dc.contributor.author	Kang Kim, Hojin
dc.contributor.author	Becerra, Raimundo
dc.contributor.author	Bolufé, Sandy
dc.contributor.author	Azurdia-Meza, Cesar A.
dc.contributor.author	Montejo-Sánchez, Samuel
dc.contributor.author	Zabala-Blanco, David
dc.date.accessioned	2021-12-30T14:52:48Z
dc.date.available	2021-12-30T14:52:48Z
dc.date.issued	2021
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3684
dc.description.abstract	The opportunistic exchange of information between vehicles can significantly contribute to reducing the occurrence of accidents and mitigating their damages. However, in urban environments, especially at intersection scenarios, obstacles such as buildings and walls block the line of sight between the transmitter and receiver, reducing the vehicular communication range and thus harming the performance of road safety applications. Furthermore, the sizes of the surrounding vehicles and weather conditions may affect the communication. This makes communications in urban V2V communication scenarios extremely difficult. Since the late notification of vehicles or incidents can lead to the loss of human lives, this paper focuses on improving urban vehicle-to-vehicle (V2V) communications at intersections by using a transmission scheme able of adapting to the surrounding environment. Therefore, we proposed a neuroevolution of augmenting topologies-based adaptive beamforming scheme to control the radiation pattern of an antenna array and thus mitigate the effects generated by shadowing in urban V2V communication at intersection scenarios. This work considered the IEEE 802.11p standard for the physical layer of the vehicular communication link. The results show that our proposal outperformed the isotropic antenna in terms of the communication range and response time, as well as other traditional machine learning approaches, such as genetic algorithms and mutation strategy-based particle swarm optimization.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Sensors, 21(9), 2956	es_CL
dc.subject	Antenna array	es_CL
dc.subject	Genetic algorithm	es_CL
dc.subject	Intelligent transport systems	es_CL
dc.subject	Neuroevolution of augmenting topologies	es_CL
dc.title	Neuroevolution-based adaptive antenna array beamforming scheme to improve the V2V communication performance at intersections	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	www.mdpi.com/1424-8220/21/9/2956	es_CL
dc.ucm.doi	doi.org/10.3390/s21092956	es_CL