@inproceedings{19a6c8de991745fdb0e00140288d8637,
title = "Super-Resolution Channel Estimation Based on Deep Sampling Feedback Structure",
abstract = "A novel pilot-assisted channel estimation model, Matrix-DenseNet, is introduced, which has a unique matrix-like structure consisting of five rows and six columns. Dense connectivity is incorporated within each row to enhance feature propagation and reduce parameter count. Additionally, deep sampling paths and feature feedback paths are set up across columns, creating a deep sampling feedback structure that further improves the extraction of multi-resolution features from the initial CSI tensor. Simulation results demonstrate that the proposed Matrix-DenseNet significantly improves the normalized mean square error (NMSE) and bit error rate (BER) performance of OFDM systems in high-speed environments.",
keywords = "Channel estimation, Deep learning, OFDM, Super resolution",
author = "Jinwei Ji and Chunhui Liu",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 4th International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024 ; Conference date: 22-06-2024 Through 25-06-2024",
year = "2025",
doi = "10.1007/978-981-96-5314-0\_37",
language = "英语",
isbn = "9789819653133",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "394--401",
editor = "Weijian Liu and Qi Wang and Jinchao Feng and Wenli Zhang",
booktitle = "Proceedings of the 4th International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2024 - Volume I",
address = "德国",
}