@inproceedings{654c70af7f1745278566fb526e1b4dce,
title = "Joint Sparse Channel Estimation and Tracking for SC-FDE Based V2X Communication Systems",
abstract = "Vehicle to everything (V2X) communication is created for safer and more efficient transprotation. Efficient and accurate channel estimation is crucial for better transmission performance of V2X communication systems. Due to the sparsity of the vehicular channels, we proposed to use sparse Bayesian learning (SBL) algorithm to improve the estimation accuracy as well as reduce the pilot overhead. Moreover, we use extended Kalman filter (EKF) algorithm to track the V2X channel variations and further reduce estimation error. An efficient frame structure with training blocks and pure data blocks is also designed. Simulation results verify that the proposed algorithm can provide accurate estimation and tracking of the channel response with a low pilot overhead.",
keywords = "V2X communication, channel estimation, channel tracking, extended Kalman filter, sparse Bayesian learning",
author = "Yufan Chen and Hua Wang and Dewei Yang and Xinyue Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE International Conference on Communication Technology, ICCT 2021 ; Conference date: 13-10-2021 Through 16-10-2021",
year = "2021",
doi = "10.1109/ICCT52962.2021.9657951",
language = "English",
series = "International Conference on Communication Technology Proceedings, ICCT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "700--704",
booktitle = "2021 IEEE 21st International Conference on Communication Technology, ICCT 2021",
address = "United States",
}