@inproceedings{47eafdd00b1541e289fd08c4ec2d1fa5,
title = "Deep learning-based v2v channel estimations using VNETs",
abstract = "The development of cooperative intelligent transportation systems brings new challenges to wireless communication technologies, where the channel estimation becomes more and more important. In this paper, a novel data-driven channel estimation method based on deep learning framework is adopted. Based on the feedforward neural network, the VNET neural network based on the convolutional neural network is proposed. The simulations and practical measurements are also provided to verify the performance advantages. The results show the achieved performance advantages of the proposed VNET-based method, which is shown to be an effective solution.",
keywords = "CNN, Channel estimation, Deep learning, Neural network, OFDM",
author = "Qi Song and Tian Lan and Xuanxuan Tian and Tingting Zhang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; International Conference on Communications, Signal Processing, and Systems, CSPS 2018 ; Conference date: 14-07-2018 Through 16-07-2018",
year = "2020",
doi = "10.1007/978-981-13-6508-9_24",
language = "English",
isbn = "9789811365072",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "184--192",
editor = "Qilian Liang and Xin Liu and Zhenyu Na and Wei Wang and Jiasong Mu and Baoju Zhang",
booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2018 CSPS Volume 3",
address = "Germany",
}