@article{96faef0c423d45118db02dcfd2557c26,
title = "A New Data Pilot-Aided Channel Estimation Scheme for Fast Time-Varying Channels in IEEE 802.11p Systems",
abstract = "The channel estimation in the IEEE 802.11p vehicle-to-everything (V2X) communication systems is generally a challenging task due to high mobility and insufficient number of pilots. Conventional data pilot-aided channel estimation schemes are difficult to apply in practice due to high complexity and the error propagation problem. In this correspondence paper, we propose a new data pilot-aided channel estimation scheme to overcome both issues. To this end, we develop a state feedback decision algorithm that enables us to extract reliable data pilots within a few received symbols. As a result, we can minimize both the error propagation effect and the computational complexity. We verify the complexity gain of the proposed scheme through the complexity analysis. Finally, we demonstrate the accuracy of the proposed channel estimation scheme in terms of the packet error rate performance.",
keywords = "Channel estimation, IEEE 802.11p, V2X, WAVE, fast time-varying channels",
author = "Seunghwan Baek and Inkyu Lee and Changick Song",
note = "Funding Information: This work was supported in part by the National Research Foundation (NRF) through the Ministry of Science, ICT and Future Planning, Korean Government, under Grant 2017R1A2B3012316 and in part by the NRF through the Ministry of Education, Korean government, under Grant 2018R1D1A1B07049824 (Development and implementation of deep learning based V2X channel estimation technique). The review of this paper was coordinated by Prof. T. Kurner. Funding Information: Manuscript received August 25, 2018; revised January 17, 2019; accepted March 12, 2019. Date of publication March 20, 2019; date of current version May 28, 2019. This work was supported in part by the National Research Foundation (NRF) through the Ministry of Science, ICT and Future Planning, Korean Government, under Grant 2017R1A2B3012316 and in part by the NRF through the Ministry of Education, Korean government, under Grant 2018R1D1A1B07049824 (Development and implementation of deep learning based V2X channel estimation technique). The review of this paper was coordinated by Prof. T. Kurner. (Corresponding author: Changick Song.) S. Baek and I. Lee are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail:, s_baek@korea.ac.kr; inkyu@korea.ac.kr). Publisher Copyright: {\textcopyright} 2019 IEEE.",
year = "2019",
month = may,
doi = "10.1109/TVT.2019.2906358",
language = "English",
volume = "68",
pages = "5169--5172",
journal = "IEEE Transactions on Vehicular Technology",
issn = "0018-9545",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "5",
}