Semi-supervised extreme learning machine channel estimator and equalizer for vehicle to vehicle communications

Salazar, E.; Azurdia-Meza, Cesar A.; Zabala-Blanco, David; Bolufé, Sandy; Soto, Ismael

Mostrar el registro sencillo de la publicación

dc.contributor.author	Salazar, E.
dc.contributor.author	Azurdia-Meza, Cesar A.
dc.contributor.author	Zabala-Blanco, David
dc.contributor.author	Bolufé, Sandy
dc.contributor.author	Soto, Ismael
dc.date.accessioned	2021-12-27T11:54:09Z
dc.date.available	2021-12-27T11:54:09Z
dc.date.issued	2021
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/3661
dc.description.abstract	Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications.	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	Electronics, 10(8), 968	es_CL
dc.subject	Channel estimation and equalizer	es_CL
dc.subject	Extreme learning machines	es_CL
dc.subject	Semi-supervised learning	es_CL
dc.subject	Vehicular communications	es_CL
dc.title	Semi-supervised extreme learning machine channel estimator and equalizer for vehicle to vehicle communications	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/2079-9292/10/8/968	es_CL
dc.ucm.doi	doi.org/10.3390/electronics10080968	es_CL