@inproceedings{0f78f937df7e4a03a9e02bba644b3981,
title = "Knowledge-Driven Deep Learning Based Channel Estimation for Terahertz Extra-Large MIMO Systems",
abstract = "Accurate channel state information (CSI) is critical in wideband terahertz (THz) extra-large multiple-input multiple-output (XL-MIMO) systems. However, the existing channel estimation methods suffer from the beam squint effect. In this paper, we propose a knowledge-driven channel estimation network called generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) to jointly estimate the channels, whereby the approximately same physical angles shared by different subcarriers can be exploited. In particular, we propose a frequency-dependent discrete Fourier transform (DFT) wideband redundant dictionary (WRD), so that the support shift caused by the beam squint effect is eliminated. Simulation results demonstrate that the proposed approach can effectively achieve channel estimation with only several layers at the cost of limited pilots.",
keywords = "Knowledge-driven, beam squint effect, channel estimation, extra-large multiple-input multiple-output (XL-MIMO), orthogonal frequency division multiplexing (OFDM)",
author = "Kuiyu Wang and Zhen Gao and Yifei Zhang and Tong Qin and Rui Na and Minghui Wu and Zhongxiang Li",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Communication Technology, ICCT 2023 ; Conference date: 20-10-2023 Through 22-10-2023",
year = "2023",
doi = "10.1109/ICCT59356.2023.10419655",
language = "English",
series = "International Conference on Communication Technology Proceedings, ICCT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "869--874",
booktitle = "2023 IEEE 23rd International Conference on Communication Technology",
address = "United States",
}