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CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications

Abstract : IEEE 802.11p standard defines wireless technology protocols that enable vehicular transportation and manage traffic efficiency. A major challenge in the development of this technology is ensuring communication reliability in highly dynamic vehicular environments, where the wireless communication channels are doubly selective, thus making channel estimation and tracking a relevant problem to investigate. In this paper, a novel deep learning (DL)-based weighted interpolation estimator is proposed to accurately estimate vehicular channels especially in high mobility scenarios. The proposed estimator is based on modifying the pilot allocation of the IEEE 802.11p standard so that more transmission data rates are achieved. Extensive numerical experiments demonstrate that the developed estimator significantly outperforms the recently proposed DL-based frameby-frame estimators in different vehicular scenarios, while substantially reducing the overall computational complexity.
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https://hal.archives-ouvertes.fr/hal-03381681
Contributor : Abdul Karim Gizzini Connect in order to contact the contributor
Submitted on : Sunday, October 17, 2021 - 6:25:18 PM
Last modification on : Friday, April 1, 2022 - 3:55:58 AM
Long-term archiving on: : Tuesday, January 18, 2022 - 6:21:20 PM

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Abdul Karim Gizzini, Marwa Chafii, Ahmad Nimr, Raed M Shubair, Gerhard Fettweis. CNN aided Weighted Interpolation for Channel Estimation in Vehicular Communications. IEEE Transactions on Vehicular Technology, Institute of Electrical and Electronics Engineers, 2021, pp.1-1. ⟨10.1109/TVT.2021.3120267⟩. ⟨hal-03381681⟩

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