@inproceedings{8699c58dddf94f5887b471b954e25099,
title = "Estimation of Pilot-assisted OFDM Channel Based on Multi-Resolution Deep Neural Networks",
abstract = "To realize the reliable communication of Unmanned Aerial Vehicle (UAV) in high-speed mobile environment, this paper proposed a pilot assisted Orthogonal Frequency Division Multiplexing (OFDM) channel estimation method based on multi-resolution depth neural networks. The model adapted all the full connection layers into dense convolution layers to improve the computational efficiency and prediction performance of channel estimation. Simulation results show that compared with traditional channel estimation methods, the normalized mean square error (NMSE) of multi-resolution depth neural networks is reduced by 78.84\%. In addition, the method reduces the symbol error rate (SER) and the bit error rate (BER) by about 66.93\%.",
keywords = "OFDM system, UAV, channel estimation, high-speed mobile environment, multi-resolution deep neural networks",
author = "Sumin Chen and Chunhui Liu and Lizhen Huang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Unmanned Systems, ICUS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",
year = "2022",
doi = "10.1109/ICUS55513.2022.9986670",
language = "英语",
series = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "764--769",
editor = "Rong Song",
booktitle = "Proceedings of 2022 IEEE International Conference on Unmanned Systems, ICUS 2022",
address = "美国",
}