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Open AccessArticle

Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications

1
Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile
2
Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca 3466706, Chile
3
Department of Computer Science and Industry, Faculty of Engineering Science, Universidad Católica del Maule, Talca 3480112, Chile
4
Department of Electrical Engineering, University of Santiago de Chile, Santiago 91701234, Chile
*
Author to whom correspondence should be addressed.
Academic Editors: Michele Segata, Shankar Kathiresan, Deepak Gupta, Gyanendra Prasad Joshi, Chi-Hua Chen and Vicente García-Díaz
Electronics 2021, 10(8), 968; https://doi.org/10.3390/electronics10080968
Received: 12 February 2021 / Revised: 3 April 2021 / Accepted: 9 April 2021 / Published: 19 April 2021
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. View Full-Text
Keywords: channel estimation and equalizer; extreme learning machine; IEEE 802.11p amendment; semi-supervised learning; vehicular communications channel estimation and equalizer; extreme learning machine; IEEE 802.11p amendment; semi-supervised learning; vehicular communications
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MDPI and ACS Style

Salazar, E.; Azurdia-Meza, C.A.; Zabala-Blanco, D.; Bolufé, S.; Soto, I. Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications. Electronics 2021, 10, 968. https://doi.org/10.3390/electronics10080968

AMA Style

Salazar E, Azurdia-Meza CA, Zabala-Blanco D, Bolufé S, Soto I. Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications. Electronics. 2021; 10(8):968. https://doi.org/10.3390/electronics10080968

Chicago/Turabian Style

Salazar, Eduardo; Azurdia-Meza, Cesar A.; Zabala-Blanco, David; Bolufé, Sandy; Soto, Ismael. 2021. "Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications" Electronics 10, no. 8: 968. https://doi.org/10.3390/electronics10080968

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